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Everything you need to know about Computed Tomography (CT) & CT Scanning

Deep Learning: Clinical Applications (general) Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Clinical Applications (General)

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  • “It is time to move beyond studies showing that AI can detect opacities at CT or chest radiography—this is now well established. Instead, there is a great need for AI systems, based on a combination of imaging, laboratory, and clinical information, that provide actionable predictions otherwise unavailable or less accurate without AI.”
    Artificial Intelligence of COVID-19 Imaging: A Hammer in Search of a Nail  
    Ronald M. Summers
    Radiology 2020; 00:1–3 
  • “More observer performance experiments are necessary to determine whether AI improves clinical interpretation according to reader experience level and reading paradigm (first, concurrent, or second reader). Prospective outcome studies are necessary to determine whether the use of AI leads to changes in patient care, shortened hospitalizations, and reduced morbidity and mortality. Nonradiology clinical information will need to be routinely incorporated into AI models. Assessment of risk and progression of the chronic sequela of COVID-19 infection is necessary. A prospective randomized controlled trial would be exemplary.”
    Artificial Intelligence of COVID-19 Imaging: A Hammer in Search of a Nail  
    Ronald M. Summers
    Radiology 2020; 00:1–3 
  • “How does one put this deluge of articles into context? It seems unlikely that an AI system would detect many patients with COVID-19 who had a negative reverse transcription polymerase chain reaction test. Anecdotes will occur. But from a general perspective, this is unlikely to propel dissemination of the AI technology. What about distinguishing COVID-19 from other viral pneumonias? It seems unlikely that clinical decision mak- ing would depend on the recommendations of AI, given more definitive laboratory tests are available. Could AI lead to a fully automated interpretation? This has not been the focus of COVID-19 imaging AI to date. Multitask approaches that identify multiple abnormalities at chest imaging besides opacities will be needed, such as universal lesion detection. What about mortality prediction? Hazard ratios on the order of 2 to 3, as found in the article by Mushtaq et al, are generally insufficient for clinical decision making. While it is possible that prediction of an adverse outcome could lead to more aggressive treatment, it could also lead to unnecessary costs and adverse effects.”
    Artificial Intelligence of COVID-19 Imaging: A Hammer in Search of a Nail  
    Ronald M. Summers
    Radiology 2020; 00:1–3 
  • "What are the current needs of AI systems for COVID-19 and CT and chest radiography? Public challenges or competitions pitting different AI systems against one another would enable “apples-to-apples” comparisons of performance. More observer performance experiments are necessary to determine whether AI improves clinical interpretation according to reader experience level and reading paradigm (first, concurrent, or second reader). Prospective outcome studies are necessary to determine whether the use of AI leads to changes in patient care, shortened hospitalizations, and reduced morbidity and mortality. Nonradiology clinical information will need to be routinely incorporated into AI models. Assessment of risk and progression of the chronic sequela of COVID-19 infection is necessary.”
    Artificial Intelligence of COVID-19 Imaging: A Hammer in Search of a Nail  
    Ronald M. Summers
    Radiology 2020; 00:1–3 
  • “Why aren’t better data available? One of our patients, a veteran, once remarked in frustration after trying to obtain his prior medical records:, “Doc, why is it that we can see a specific car in a moving convoy on the other side of the world, but we can’t see my CT scan from the hospital across the street?” Sharing data in medicine is hard enough for a single patient, never mind the hundreds or thousands of cases needed to reliably train machine learning algorithms. Whether in treating patients or building AI tools, data in medicine are locked in little silos everywhere.”
    Health Care AI Systems Are Biased
    Amit Kaushal, Russ Altman, Curt Langlotz
    Scientific American November 2021
  • "Medical data sharing should be more commonplace. But the sanctity of medical data and the strength of relevant privacy laws provide strong incentives to protect data, and severe consequences for any error in data sharing. Data are sometimes sequestered for economic reasons; one study found hospitals that shared data were more likely to lose patients to local competitors. And even when the will to share data exists, lack of interoperability between medical records systems remains a formidable technical barrier. The backlash from big tech’s use of personal data over the past two decades has also cast a long shadow over medical data sharing. The public has become deeply skeptical of any attempt to aggregate personal data, even for a worthy purpose.”
    Health Care AI Systems Are Biased
    Amit Kaushal, Russ Altman, Curt Langlotz
    Scientific American November 2021
  • “AI has reached health care, and radiology in particular,and it is here to stay. Careful evaluation and adoption of AI-based tools will allow radiologists to pioneer the transition toward AI- enabled, patient-centric health care delivery. In collaboration, radiology researchers, health care providers, industry partners, and policymakers have the potential to realize the promise of AI to provide equal access to high-quality care, overcome the challenge of ongoing performance monitoring, and achieve the development of socially beneficial AI solutions.”
    Artificial Intelligence in Radiology: The Computer’s Helping Hand Needs Guidance
    Evis Sala, Stephan Ursprung
    Radiology: Artificial Intelligence 2020; 2(6):e200207
  • “Solutions that improve the quality of radiologic diagnosis without generating immediate financial benefit are less likely to permeate the mass market rapidly. Nonetheless, their potential to promote health equity through increasing diagnostic quality and consistency in non-subspecialized settings could profoundly improve the overall performance of a health care system. Therefore, noncommer- cial stakeholders should pursue research and publication of socially desirable AI solutions.”
    Artificial Intelligence in Radiology: The Computer’s Helping Hand Needs Guidance
    Evis Sala, Stephan Ursprung
    Radiology: Artificial Intelligence 2020; 2(6):e200207
  • AI and Measuring Its Value
    1. How will the solution integrate into the current clinical pathway?
    a) Rule-in/rule-out/triage of patients b) First reader/second reader c) Equality of use case and approved use
    Artificial Intelligence in Radiology: The Computer’s Helping Hand Needs Guidance
    Evis Sala, Stephan Ursprung
    Radiology: Artificial Intelligence 2020; 2(6):e200207
  • AI and Measuring Its Value
    2. How well will the algorithm generalize?
    a)  Comparability of patient demographics (age, sex, ethnicity) between testing dataset and intended patient cohort
    b)  Comparability of the clinical setting (screening, diagnostic, investigative, or therapeutic setting) between testing dataset and intended use case
    c)  Evidence from monitoring programs and ongoing trials
    Artificial Intelligence in Radiology: The Computer’s Helping Hand Needs Guidance
    Evis Sala, Stephan Ursprung
    Radiology: Artificial Intelligence 2020; 2(6):e200207
  • AI and Measuring Its Value
    3. Does the solution have a clinical and/or cost-benefit in real use?
    a)  Equivalent or improved diagnostic accuracy: sensitivity, specificity...
    b)  Overall cost-benefit: clinician time, reducing or increasing additional tests, overdiagnosis and overtreatment of indolent findings
    Artificial Intelligence in Radiology: The Computer’s Helping Hand Needs Guidance
    Evis Sala, Stephan Ursprung
    Radiology: Artificial Intelligence 2020; 2(6):e200207
  • Results: A total of 119 software offerings from 55 companies were identified. There were 46 algorithms that currently have Food and Drug Administration and/or Conformité Européenne approval (as of November 2019). Of the 119 offerings, distribution of software targets was 34 of 70 (49%), 21 of 70 (30%), 14 of 70 (20%), and one of 70 (1%) for diagnostic, quantitative, repetitive, and explorative tasks, respectively. A plurality of companies are focused on nodule detection at chest CT and two-dimensional mammography. There is very little activity in certain subspecialties, including pediatrics and nuclear medicine. A comprehensive table is available on the website hitilab.org/pages/ai-companies.
    Conclusion: The radiology AI marketplace is rapidly maturing, with an increase in product offerings. Radiologists and practice administrators should educate themselves on current product offerings and important factors to consider before purchase and implementation.
    The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings
    Yasasvi Tadavarthi et al.
    Radiology: Artificial Intelligence 2020; 2(6):e200004
  • “In many cases, AI radiology software represents uncharted ter- ritory. The vast majority of AI radiology companies are nascent and working on their initial clinical offerings. Because of this, many companies seek partnerships with institutions for clinical feedback or even codevelopment. Questions about ownership of data and intellectual property often arise during codevelopment, which can be difficult to address. An institution commonly provides data and medical expertise, whereas the industry partner provides technical expertise, engineering support, and productization strategies that may incorporate several years of prior effort and investment. Clear delineation of ownership of any resultant algorithm, software, or datasets is important to address in advance and, if applicable, the route for approval through the institution’s technology transfer or licensing office.”
    The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings
    Yasasvi Tadavarthi et al.
    Radiology: Artificial Intelligence 2020; 2(6):e200004
  • "Unlike quality improvements, efficiency improvement is easier to demonstrate as an ROI. For example, practices can measure the difference in study read times with and without the AI software. Time savings can be directly translated into person- hours and subsequent cost savings. In the nascent stages of radiology AI software, saved person-hours may be the quickest and most efficacious method to demonstrate value. As an added benefit, AI software geared toward increasing efficiency for repetitive or mundane tasks could be used to attract radiologists to join the practice in a competitive market.”
    The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings
    Yasasvi Tadavarthi et al.
    Radiology: Artificial Intelligence 2020; 2(6):e200004
  • "Serving the needs of radiologists requires maintaining a balance between providing additional useful information while minimizing false-positive findings, unnecessary clicks, and mouse mileage. Length of time required for educating users on using new software must also be considered, as many radiology AI software packages may function differently than what the clinician is accustomed to. Launching a separate application window, which is a frequently used implementation for many breast and prostate MRI workflows, is generally viewed as inconvenient.”
    The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings
    Yasasvi Tadavarthi et al.
    Radiology: Artificial Intelligence 2020; 2(6):e200004
  • "There are relatively few applications of AI in the abdomen and pelvis. Detection of free air, hem- orrhage, and aortic dissection and/or aneurysm are among the few in CT analysis. Other appli- cations include detection and characterization of hepatic tumors at MRI and automated process- ing and detection of lesions at CT colonography. Currently, none are FDA or CE approved.”
    The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings
    Yasasvi Tadavarthi et al.
    Radiology: Artificial Intelligence 2020; 2(6):e200004
  • “The promise and potential benefits of radiology AI software continue to grow, and radiologists, practice administrators, and IT staff must continue to educate themselves on the potential ben- efits, drawbacks, and costs of implementation. We encourage the reader to consider using the guidelines in the Table when evalu- ating companies to ensure all aspects of a purchasing decision are considered. Deployed correctly, these software can be a boon to both patients and providers in an ever-evolving health care setting with increasing imaging volumes and complexity.”
    The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings
    Yasasvi Tadavarthi et al.
    Radiology: Artificial Intelligence 2020; 2(6):e200004

  • The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings
    Yasasvi Tadavarthi et al.
    Radiology: Artificial Intelligence 2020; 2(6):e200004
  • OBJECTIVE To assess the performance of artificial intelligence(AI) algorithms in realistic radiology workflows by performing an objective comparative evaluation of the preliminary reads of anteroposterior (AP) frontal chest radiographs performed by an AI algorithm and radiology residents.
    CONCLUSIONS AND RELEVANCE These findings suggest that it is possible to build AI algorithms that reach and exceed the mean level of performance of third-year radiology residents for full- fledged preliminary read of AP frontal chest radiographs. This diagnostic study also found that while the more complex findings would still benefit from expert overreads, the performance of AI algorithms was associated with the amount of data available for training rather than the level of difficulty of interpretation of the finding. Integrating such AI systems in radiology workflows for preliminary interpretations has the potential to expedite existing radiology workflows and address resource scarcity while improving overall accuracy and reducing the cost of care.
    Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents
    Joy T. Wu et al.
    JAMA Network Open. 2020;3(10):e2022779. doi:10.1001/jamanetworkopen.2020.22779
  • Question: How does an artificial intelligence (AI) algorithm compare with radiology residents in full-fledged preliminary reads of anteroposterior (AP) frontal chest radiographs?
    Findings: This diagnostic study was conducted among 5 third-year radiology residents and an AI algorithm using a study data set of 1998 AP frontal chest radiographs assembled through a triple consensus with adjudication ground truth process covering more than 72 chest radiograph findings. There was no statistically significant difference in sensitivity between the AI algorithm and the radiology residents, but the specificity and positive predictive value were statistically higher for AI algorithm.
    Meaning: These findings suggest that well-trained AI algorithms can reach performance levels similar to radiology residents in covering the breadth of findings in AP frontal chest radiographs, which suggests there is the potential for the use of AI algorithms for preliminary interpretations of chest radiographs in radiology workflows to expedite radiology reads, address resource scarcity, improve overall accuracy, and reduce the cost of care.
    Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents
    Joy T. Wu et al.
    JAMA Network Open. 2020;3(10):e2022779. doi:10.1001/jamanetworkopen.2020.22779
  • “Overall, this study points to the potential use AI systems in future radiology workflows for preliminary interpretations that target the most prevalent findings, leaving the final reads performed by the attending physician to still catch any potential misses from the less-prevalent fine-grained findings. Having attending physicians quickly correct the automatically produced reads, we can expect to significantly expedite current dictation-driven radiology workflows, improve accuracy, and ultimately reduce the overall cost of care.”
    Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents
    Joy T. Wu et al.
    JAMA Network Open. 2020;3(10):e2022779. doi:10.1001/jamanetworkopen.2020.22779
  • IMPORTANCE Machine learning (ML) algorithms can identify patients with cancer at risk of short-term mortality to inform treatment and advance care planning. However, no ML mortality risk prediction algorithm has been prospectively validated in oncology or compared with routinely used prognostic indices.
    OBJECTIVE To validate an electronic health record–embedded ML algorithm that generated real-time predictions of 180-day mortality risk in a general oncology cohort
    CONCLUSIONS AND RELEVANCE In this prognostic study, an ML algorithm was feasibly integrated into the electronic health record to generate real-time, accurate predictions of short-term mortality for patients with cancer and outperformed routinely used prognostic indices. This algorithm may be used to inform behavioral interventions and prompt earlier conversations about goals of care and end-of-life preferences among patients with cancer.
    Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer
    Christopher R. Manz et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.4331 Published online September 24, 2020.
  • OBJECTIVE To validate an electronic health record–embedded ML algorithm that generated real-time predictions of 180-day mortality risk in a general oncology cohort
    CONCLUSIONS AND RELEVANCE In this prognostic study, an ML algorithm was feasibly integrated into the electronic health record to generate real-time, accurate predictions of short-term mortality for patients with cancer and outperformed routinely used prognostic indices. This algorithm may be used to inform behavioral interventions and prompt earlier conversations about goals of care and end-of-life preferences among patients with cancer.
    Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer
    Christopher R. Manz et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.4331 online September 24, 2020.
  • OBJECTIVE To validate an electronic health record–embedded ML algorithm that generated real-time predictions of 180-day mortality risk in a general oncology cohort.
    DESIGN, SETTING, AND PARTICIPANTS This prognostic study comprised a prospective cohort of patients with outpatient oncology encounters between March 1, 2019, and April 30, 2019. An ML algorithm, trained on retrospective data from a subset of practices, predicted 180-day mortality risk between 4 and 8 days before a patient’s encounter. Patient encounters took place in 18 medical or gynecologic oncology practices, including 1 tertiary practice and 17 general oncology practices, within a large US academic health care system. Patients aged 18 years or older with outpatient oncology or hematology and oncology encounters were included in the analysis. Patients were excluded if their appointment was scheduled after weekly predictions were generated and if they were only evaluated in benign hematology, palliative care, or rehabilitation practices.
    Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer
    Christopher R. Manz et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.4331 online September 24, 2020.
  • CONCLUSIONS AND RELEVANCE In this prognostic study, an ML algorithm was feasibly integrated into the electronic health record to generate real-time, accurate predictions of short-term mortality for patients with cancer and outperformed routinely used prognostic indices. This algorithm may be used to inform behavioral interventions and prompt earlier conversations about goals of care and end-of-life preferences among patients with cancer.
    Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer
    Christopher R. Manz et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.4331 online September 24, 2020.
  • Key Points
    Question: Can a machine learning algorithm prospectively identify patients with cancer at risk of 180-day mortality?
    Findings: In this prognostic cohort study of 24582 patients seen in oncology practices within a large health care system, a machine learning algorithm integrated into the electronic health record accurately identified the risk of 180-day mortality with good discrimination and positive predictive value of 45.2%. When added to performance status– and comorbidity-based classifiers, the algorithm favorably reclassified patients.
    Meaning: An integrated machine learning algorithm demonstrated good prospective performance compared with traditional prognostic classifiers and may inform clinician and patient decision-making in oncology.
    Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer
    Christopher R. Manz et al.
    JAMA Oncol. doi:10.1001/jamaoncol.2020.4331 online September 24, 2020.
  • “In summary, research on AI-powered technologies in the medical domain was at early stage in the 1970s. However, associated deep learning algorithms significantly attracted and revolutionized the scientific community with subsequent evolution of research and exponential growth of multidisciplinary publications since that time. Work in this field has impacted radiology as an area of predominant interest and has been led by institutions in the United States, Spain, France, China, and England. The bibliometric study reported herein can provide a broad overview and valuable guidance to help medical researchers gain insights into key points and trace the global trends regarding the status of AI research in medicine, particularly in radiology and other relevant multispecialty areas.”
    Artificial Intelligence in Various Medical Fields With Emphasis on Radiology: Statistical Evaluation of the Literature
    Emre Pakdemirli, Urszula Wegner
    Cureus 12(10): e10961. DOI 10.7759/cureus.10961
  • "Academic literature on AI in medicine had little room for optimism through the late 1970s. However, AI- driven software soon influenced and inspired qualified staff across the globe with subsequent increase of numerous associated publications in the 1990s. Importantly, this positive research trend demonstrates continuous dramatic growth pattern over the recent years.”
    Artificial Intelligence in Various Medical Fields With Emphasis on Radiology: Statistical Evaluation of the Literature
    Emre Pakdemirli, Urszula Wegner
    Cureus 12(10): e10961. DOI 10.7759/cureus.10961

  • Artificial Intelligence in Various Medical Fields With Emphasis on Radiology: Statistical Evaluation of the Literature
    Emre Pakdemirli, Urszula Wegner
    Cureus 12(10): e10961. DOI 10.7759/cureus.10961
  • “The decision of what imaging test is most appropriate in each situation is influenced by many factors, some of which are highly subjective. The issue of over- and under-utilization of imaging resources is something that every clinician and radiologist struggles with. The desire to not miss acute pathology is balanced with the potential detriment of excessive radiation dose to susceptible populations. There likely exists a combination of historical and objective factors which can predict outcomes with sufficient sensitivity and specificity to guide the ordering pattern of most physicians.”
    Applications of artificial intelligence in the emergency department
    Supratik K. Moulik, Nina Kotter, Elliot K. Fishman
    Emergency Radiology (2020) 27:355–358
  • "In the future, predictive models will be trained on clinical, treatment, laboratory, and genetic data of individuals to facilitate personalized treatments. Machine learning systems are uniquely equipped for finding groups and subgroups that require more aggressive management. The goal of incorporating viral and host genetic data will require significant advances in computing and genetic sequencing.”
    Applications of artificial intelligence in the emergency department
    Supratik K. Moulik, Nina Kotter, Elliot K. Fishman
    Emergency Radiology (2020) 27:355–358
  • "It is important to keep the evolution of the AI/ML technology in context so as not to become overly enthusiastic about the current capabilities and simultaneously not to become overly pessimistic about future developments. Though the promised delivery date of fully self-driving cars has continuously been pushed back for the past decade, it is undeniable that drivers in semiautonomous vehicles are safer than unassisted drivers. Similarly, there are tangible patient care and cost benefits to be obtained through staged development of AL/ML systems even if fully autonomous MD systems are not on the horizon.”
    Applications of artificial intelligence in the emergency department
    Supratik K. Moulik, Nina Kotter, Elliot K. Fishman
    Emergency Radiology (2020) 27:355–358
  • “The FDA approval process to date has focused on applications (apps) that affect patient triage and not necessarily apps that have the computer serve as the only or final reader. We have chosen a select group of apps to provide the reader with a sense of the current state of AI use in the ER setting. Because adoption of new technology and FDA approval are always works in progress, it is not our intention here to be comprehensive. For a more thorough review of approved AI applications, please see the American College of Radiology record available here (https:// www.acrdsi.org/DSI-Services/FDA-Cleared-AI-Algorithms).”
    The first use of artificial intelligence (AI) in the ER: triage not diagnosis
    Edmund M. Weisberg, Linda C. Chu, Elliot K. Fishman
    Emergency Radiology (2020) 27:361–366
  • “Digital technology, including its omnipresent connectedness and its powerful artificial intelligence, is the most recent long wave of humanity’s socioeconomic evolution. The first technological revolutions go all the way back to the Stone, Bronze, and Iron Ages, when the transformation of material was the driving force in the Schumpeterian process of creative destruction. A second metaparadigm of societal modernization was dedicated to the transformation of energy (aka the “industrial revolutions”), including water, steam, electric, and combustion power. The current metaparadigm focuses on the transformation of information. Less than 1% of the world's technologically stored information was in digital format in the late 1980s, surpassing more than 99% by 2012. Every 2.5 to 3 years, humanity is able to store more information than since the beginning of civilization. The current age focuses on algorithms that automate the conversion of data into actionable knowledge.”
    Digital technology and social change: the digital transformation of society from a historical perspective
    Martin Hilbert
    DIALOGUES IN CLINICAL NEUROSCIENCE • Vol 22 • No. 2 • 2020
  • “The current metaparadigm focuses on the transformation of information. Less than 1% of the world's technologically stored information was in digital format in the late 1980s, surpassing more than 99% by 2012. Every 2.5 to 3 years, humanity is able to store more information than since the beginning of civilization. The current age focuses on algorithms that automate the conversion of data into actionable knowledge.”
    Digital technology and social change: the digital transformation of society from a historical perspective
    Martin Hilbert
    DIALOGUES IN CLINICAL NEUROSCIENCE • Vol 22 • No. 2 • 2020
  • “Each technological revolution, originally received as a bright new set of opportunities, is soon recognized as a threat to the established way of doing things in firms, institutions, and society at large. The new techno-economic paradigm gradually takes shape as a different “common sense” for effective action in any area of endeavor. But while competitive forces, profit seeking, and survival pressures help diffuse the changes in the economy, the wider social and institutional spheres — where change is also needed — are held back by strong inertia stemming from routine, ideology, and vested interests. It is this difference in rhythm of change, between the techno-economic and the socio-institutional spheres, that would explain the turbulent period.”
    Digital technology and social change: the digital transformation of society from a historical perspective
    Martin Hilbert
    DIALOGUES IN CLINICAL NEUROSCIENCE • Vol 22 • No. 2 • 2020
  • “The first focused on the transformation of material, including stone, bronze, and iron. The second, often referred to as industrial revolutions, was dedicated to the transformation of energy, including water, steam, electric, and combustion power. Finally, the most recent metaparadigm aims at transforming information. It started out with the proliferation of communication and stored data and has now entered the age of algorithms, which aims at creating automated processes to convert the existing information into actionable knowledge.”
    Digital technology and social change: the digital transformation of society from a historical perspective
    Martin Hilbert
    DIALOGUES IN CLINICAL NEUROSCIENCE • Vol 22 • No. 2 • 2020
  • Use of AI in Radiology beyond Reading Scans
    - IMAGE PRODUCTION AND QUALITY CONTROL
    - IMPROVING RADIOLOGY WORKFLOW
    - BUSINESS APPLICATIONS
    - BILLING AND COLLECTIONS
    - RESEARCH APPLICATIONS
  • “The ultimate goal of AI in medical imaging is to improve patient outcomes. In this review, we have summarized some of the many ways in which noninterpretive AI is relevant to radiologists and their patients. At this time, only a few of these techniques are ready to translate into clinical practice. Regardless of which of these techniques are ultimately adopted, we hope that this review will provoke thought in the wider community of academic radiologists, and to help lead us to even newer and more intriguing applications.”
    Noninterpretive Uses of Artificial Intelligence in Radiology
    Michael L. Richardson et al.
    Acad Radiol 2020 (in press)
  • “Deep learning approaches can also assist radiologists by assigning higher priorities to cases on the worklist that may contain emergent abnormalities. Such prioritization has been proposed in the setting of triage or screening systems to detect abnormalities on chest radiographs, abdominal CT, or head CT. In these paradigms, there is an image interpretation component to the AI’s tasks, but the role of the AI is not to primarily render an interpretation but to alert radiologists to potential critical findings and improve turnaround time for reporting of potentially actionable abnormalities.”
    Noninterpretive Uses of Artificial Intelligence in Radiology
    Michael L. Richardson et al.
    Acad Radiol 2020 (in press)
  • "AI models have been used to successfully localize and annotate organs such as the kidney, segmental anatomy such as lobes of the liver or lung, and automated detection and labeling of vertebral bodies. This is extremely useful when volumetric assessment of a lesion or organ is needed. Examples include automated estimate of renal volume in a potential donor, liver volumes in patients with potential seg- mental or lobar resection and volumetric assessment in tumor treatment response.”
    Noninterpretive Uses of Artificial Intelligence in Radiology
    Michael L. Richardson et al.
    Acad Radiol 2020 (in press)
  • “In radiology, image-based search engines can provide valuable opportunities for education as well as research. Large volumes of medical imaging are accumulating in shared and public databases, and image-based search engines connected to these databases may allow easy discovery and comparison of visually similar cases. As opposed to text searches, which are likely to find cases with similar diagnoses, image searches may also find visually similar cases with different diagnoses. Correlation of visual and textual features of images found using image-based search engines may also provide interesting research opportunities.”
    Noninterpretive Uses of Artificial Intelligence in Radiology
    Michael L. Richardson et al.
    Acad Radiol 2020 (in press)
  • “With rapidly advancing progress in the development of algorithms for detecting and classifying imaging findings, more attention has turned towards limitations of these algorithms and particularly to vulnerabilities in these algorithms. To date, adversarial algorithms have been developed that can systematically deceive a trained AI model or a human radiologist. Notable examples include one algorithm that tricked an AI model into misclassifying pneumothorax on chest radio- graphs and another that misled human radiologists by adding fake pulmonary nodules and removing real pulmonary nodules from chest CT exams.”
    Noninterpretive Uses of Artificial Intelligence in Radiology
    Michael L. Richardson et al.
    Acad Radiol 2020 (in press)
  • “AI tools might also help in tracking radiology resident performance and evaluate competency. AI tools are being devel- oped and implemented across medical specialties to evaluate physician competence, and radiology training should be particularly amenable given the highly digitized nature of radiology practice. Metrics used for evaluation of resident competency, such as the ACGME/American Board of Radiology milestone project, could incorporate AI-based assessments in the future.”
    Noninterpretive Uses of Artificial Intelligence in Radiology
    Michael L. Richardson et al.
    Acad Radiol 2020 (in press)
  • “The ultimate goal of AI in medical imaging is to improve patient outcomes. In this review, we have summarized some of the many ways in which noninterpretive AI is relevant to radiologists and their patients. At this time, only a few of these techniques are ready to translate into clinical practice. Regardless of which of these techniques are ultimately adopted, we hope that this review will provoke thought in the wider community of academic radiologists, and to help lead us to even newer and more intriguing applications.”
    Noninterpretive Uses of Artificial Intelligence in Radiology
    Michael L. Richardson et al.
    Acad Radiol 2020 (in press)
  • “With machine learning, the input is based on hand-engineered features, while unsupervised deep learning is able to learn these features itself directly from data. Multiple research groups are working on applying AI to improve the reconstruction of CT images. One application is image-space-based reconstructions in which convolutional neural networks are trained with low-dose CT images to recon- struct routine-dose CT images. Another approach is to optimize IR algorithms.”
    The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence
    Willemink MJ, Noël PB
    European Radiology (2019) 29:2185–2195
  • “Generally, IR algorithms are based on manually designed prior functions resulting in low-noise images without loss of structures. Deep learning methods allow for implementing more complex functions, which have the potential to enable lower-dose CT and sparse-sampling CT. These AI techniques have the potential to reduce CT radiation doses while speeding up reconstruction times. Also, deep learning can be used to optimize image quality without reducing the radiation dose, e.g., by more advanced DECT monochromatic image reconstruction and metal artifact reduction.”
    The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence
    Willemink MJ, Noël PB
    European Radiology (2019) 29:2185–2195
  • “These methods are not yet ready for clinical implementation; however, it is expected that AI will play, in the near future, a major role in CT image reconstruction and restoration. We expect that AI will fit in current clinical CT imaging workflow by enhancing current reconstruction methods, for example by significantly accelerating the reconstruction process since application of a trained network can be instantaneously.”
    The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence
    Willemink MJ, Noël PB
    European Radiology (2019) 29:2185–2195
  • “In this context, artificial-intelligence tools have been designed to support radiologists in the identification of lung nodules since when chest radiography was the diagnostic imaging modality of choice to detect lung cancer. With the advent of low-dose CT and in particular with its implementation in screening trials, many computer-aided detection (CAD) systems for lung nodule identification have been developed . The CAD potential in improving radiologists’ performance has been deeply investigated, highlighting that the CAD can successfully be used as a second reader.”
    The potential contribution of artificial intelligence to dose reduction in diagnostic imaging of lung cancer. 
    Retico A, Fantacci M
    Journal of Medical Artificial Intelligence, North America, 2, mar. 2019
  • “The research in lung cancer diagnosis is now advancing in two distinct fields: the improvement in the image acquisition instrumentation and reconstruction techniques based on iterative processes is allowing to obtain high-quality CT images even at low and ultra-low dose (i.e., a dose amount very similar to that of a chest radiography), whereas the recent acceleration in the implementation of deep-learning methods in the medical imaging field is leading to an enhancement of the performance of CAD systems across different imaging modalities, in both detection and diagnosis tasks.”
    The potential contribution of artificial intelligence to dose reduction in diagnostic imaging of lung cancer. 
    Retico A, Fantacci M
    Journal of Medical Artificial Intelligence, North America, 2, mar. 2019
  • “As AI continues to evolve,health care as we know it will dramatically change. Radiologists have always served at the forefront in adapting new technologies in medicine, and it should be no different with the advent of the AI revolution. I will not replace radiologists; instead those radiologists who take advantage of AI may ultimately replace those who refuse to accept it .It is crucial we build an ecosystem of key players in technology, research, radiology, and the regulatory bodies who will work together to effectively and safely integrate AI into clinical practice. As a of this technology will expand our efficiency and decision making capabilities, leading to earlier and better detection of disease and improve outcomes for our patients.”
    Artificial intelligence in radiology: the ecosystem essential to improving patient care
    Sogani J, Allen B Jr, K Dreyer, McGintgy GY
    Clinical Imaging (in press)
  • Competency, Motive, Transparency
  • Competency reflects both the extent to which physicians are perceived to have clinical mastery and patients’ knowledge and self-efficacy of their own health. Because much of AI is and will be used to augment the abilities of physicians, there is potential to increase physician competency and enable patient-physician trust. This includes not only AI-assisted clinical decision support (eg, by suggesting possible diagnoses to consider) but also the use of AI for physician training and quality improvement (eg, by providing automated feedback to physicians about their diagnostic performance). AI can also serve an important role in empowering patients to better understand their health and self-manage their conditions.
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “ On the other hand, trust will be compromised by AI that is inaccurate, biased, or reflective of poor-quality practices as well as AI that lacks explainability and inappropriately conflicts with physician judgment and patient autonomy.”
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “Motive refers to a patient’s trust that the physician is acting solely in the interests of the patient. Patients are likely to perceive motive through the lens of the extent of the open dialogue they have with their physicians. Through greater automation of low-value tasks, such as clinical documentation, it is possible that AI will free up physicians to identify patients’ goals, barriers, and beliefs, and counsel them about their decisions and choices, thereby increasing trust. Conversely, AI could automate more of the physician’s workflow, but then fill freed-up time with more patients with clinical issues that are more cognitively or emotionally complex. AI could also enable greater distribution of care across a care team (both human agents and computer agents).”
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “Whether this would enhance or harm trust would depend on the degree of collaboration among team members and the information flow, and could compromise trust if robust, longitudinal relationships were impeded.”
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “Well-designed AI that allows patients to appreciate and understand that clinical decisions are based on evidence and expert consensus should enhance trust. It can also process patient data (including health care and consumer data) to provide physicians’ insight on patients’ behaviors and preferences.”
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “ Moreover, if patient data are routinely shared with external entities for AI development, patients may become less transparent about divulging their information to physicians, and physicians may be more reluctant to acknowledge their own uncertainties. AI that does not explain the source or nature of its recommendations (“black box”) may also erode trust.”
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “Where health care AI is implemented by health systems, it should be directed toward automating the transactional, business, and documentation aspects of care; doing so may provide time to physicians to engage with their patients more deeply. If AI is effective in relieving physicians from the burdens of data entry and other clerical tasks, much of the reclaimed time should be made available for patient care, shared decision-making, and counseling, which are the cornerstones of effective health care that are often compromised today.”
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “When health care AI is developed by health systems and third-party organizations using patient data, physicians should be mindful of the effect on patient-physician trust. It will be important to develop ethical approaches that allow for patient input into decisions by health systems to share data for the purposes of developing AI through some combination of individual patient consent and the involvement of patient advocacy groups.”
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “While the health system believes acquisition and use of this data are in the best interest of its patients (after all, office visits are short, and this knowledge can help guide its physicians as to a patient’s greatest risks), many patients might perceive this as an invasion of privacy and worry that the data might paint an incomplete picture of their lives and lead to unnecessary or inaccurate medical recommendations.”
    Leading your organization to responsible AI
    Roger Burkhardt, Nicolas Hohn, and Chris Wigley
    McKinsey & Company (May 2019)
  • As a result, leaders must ask data-science teams fairly granular questions to understand how they sampled the data to train their models. Do data sets reflect real-world populations? Have they included data that are relevant to minority groups? Will performance tests during model development and use uncover issues with the data set? What could we be missing?
    Leading your organization to responsible AI
    Roger Burkhardt, Nicolas Hohn, and Chris Wigley
    McKinsey & Company (May 2019)
  • Additionally, leaders must encourage their organization to move from a compliance mind-set to a co-creation mind-set in which they share their company’s market and technical acumen in the development of new regulations. Recent work in the United Kingdom between the Financial Conduct Authority (FCA), the country’s banking regulator, and the banking industry offers a model for this new partnership approach. The FCA and banking industry have teamed in creating a “regulatory sandbox” where banks can experiment with AI approaches that challenge or lie outside of current regulatory norms, such as using new data to improve fraud detection or better predict a customer’s propensity to purchase products.
    Leading your organization to responsible AI
    Roger Burkhardt, Nicolas Hohn, and Chris Wigley
    McKinsey & Company (May 2019)
  • “Today, some 80% of large companies have adopted machine learning and other forms of artificial intelligence (AI) in their core business. Five years ago, the figure was less than 10%. Nevertheless, the majority of companies still use AI tools as point solutions — discrete applications, isolated from the wider enterprise IT architecture. That’s what we found in a recent analysis of AI practices at more than 8,300 large, global companies in what we believe is one of the largest-scale studies of enterprise IT systems to date.”
    Taking a Systems Approach to Adopting AI
    by Bhaskar Ghosh, Paul R. Daugherty, H. James Wilson, and Adam Burden
    Harvard Business Review
  • “Edge computing is also breaking boundaries by moving much of the processing out to the edge of networks, where they meet with the physical world, as with smartphones, robots, drones, security cameras, and IoT. For instance, blockchain company Filament is using data-efficient AI, blockchain, and the Internet of Things (IoT) to enable secure and autonomous edge-computing transactions through a decentralized network stack — independent of underlying infrastructure.”
    Taking a Systems Approach to Adopting AI
    by Bhaskar Ghosh, Paul R. Daugherty, H. James Wilson, and Adam Burden
    Harvard Business Review
  • ”Artificial intelligence is a vital part of adaptable systems. Whether it’s virtual agents, natural language processing, machine learning, advanced analytics, or other forms of AI, companies have a host of opportunities to transform the way they do business once their architectures make AI an integral part of the transaction flow. By finding a responsible, transparent balance between human and machine intelligence, and combining it with more basic forms of robotic process automation, adaptable systems can create value in ways that were previously impossible.”
    Taking a Systems Approach to Adopting AI
    by Bhaskar Ghosh, Paul R. Daugherty, H. James Wilson, and Adam Burden
    Harvard Business Review
  • As systems evolve, so must the IT workforce. Companies will need multidisciplinary talent that can bridge infrastructure, development tools, programming languages, AI, and machine learning. They’ll also need to combine human talent with a growing army of smart machines to create entirely new kinds of hybrid IT roles. And they’ll need to develop new ways to continuously evolve their workforce, using ongoing learning and organizational transformation to adapt to the relentless pace of systemic AI advances.
    Taking a Systems Approach to Adopting AI
    by Bhaskar Ghosh, Paul R. Daugherty, H. James Wilson, and Adam Burden
    Harvard Business Review
  • “In healthcare, faxes remain the most common method that practitioners use to communicate with each other, and therefore often contain important clinical information: lab results, specialist consult notes, prescriptions and so on. Because most healthcare fax numbers are public, doctors also receive scores of pizza menus, travel specials, and other “junk faxes.” Faxes don’t contain any structured text — so it takes medical practice staff an average of two minutes and 36 seconds to review each document and input relevant data into patient records. Through a combination of machine learning and business-process outsourcing that has automated the categorizing of faxes, we’ve reduced time-per-fax for our practices to one minute and 11 seconds. As a result, last year alone we managed to eliminate over 3 million hours of work from the healthcare system.”
  • “In healthcare, faxes remain the most common method that practitioners use to communicate with each other, and therefore often contain important clinical information: lab results, specialist consult notes, prescriptions and so on. Because most healthcare fax numbers are public, doctors also receive scores of pizza menus, travel specials, and other “junk faxes.” Faxes don’t contain any structured text — so it takes medical practice staff an average of two minutes and 36 seconds to review each document and input relevant data into patient records.”


    How AI Is Taking the Scut Work Out of Health Care
Jonathan Bush
Harvard Business Review (March 2018)
  • “Here’s just one example of the immediate opportunity: Each year, some 120 million faxes still flow into the practices of the more than 100,000 providers on the network of athenahealth, the healthcare technology company where I’m CEO. That’s right: faxes. Remember those?.”


    How AI Is Taking the Scut Work Out of Health Care
Jonathan Bush
Harvard Business Review (March 2018)
  • “We have a similar opportunity in medicine now with the application of artificial intelligence and machine learning. Glamorous projects to do everything from curing cancer to helping paralyzed patients walk through AI have generated enormous expectations. But the greatest opportunity for AI in the near term may come not from headline-grabbing moonshots but from putting computers and algorithms to work on the most mundane drudgery possible. Excessive paperwork and red-tape is the sewage of modern medicine. An estimated 14% of wasted health care spending — $91 billion — is the result of inefficient administration. Let’s give AI the decidedly unsexy job of cleaning out the administrative muck that’s clogging up our medical organizations, sucking value out of our economy, and literally making doctors ill with stress.”


    How AI Is Taking the Scut Work Out of Health Care
Jonathan Bush
Harvard Business Review (March 2018)
  • “Excessive paperwork and red-tape is the sewage of modern medicine. An estimated 14% of wasted health care spending — $91 billion — is the result of inefficient administration. Let’s give AI the decidedly unsexy job of cleaning out the administrative muck that’s clogging up our medical organizations, sucking value out of our economy, and literally making doctors ill with stress.”


    How AI Is Taking the Scut Work Out of Health Care
Jonathan Bush
Harvard Business Review (March 2018)
  • Open Radiology?

  • PURPOSE: Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The objective of this study was to develop robust diagnostic technology to automate DR screening. Referral of eyes with DR to an ophthalmologist for further evaluation and treatment would aid in reducing the rate of vision loss, enabling timely and accurate diagnoses.


    CONCLUSIONS: A fully data-driven artificial intelligence-based grading algorithm can be used to screen fundus photographs obtained from diabetic patients and to identify, with high reliability, which cases should be referred to an ophthalmologist for further evaluation and treatment. The implementation of such an algorithm on a global basis could reduce drastically the rate of vision loss attributed to DR.
Automated Identification of Diabetic Retinopathy Using Deep Learning.
Gargeya R1, Leng T2.
Ophthalmology. 2017 Mar 27. pii: S0161-6420(16)31774-2

  • “In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deepconvolutional neural network (CNN) as the classifier model to which the input consists of a large image window. The multiple layers in the CNN classifier are trained to automatically extract image features relevant to MCs at different spatial scales.”


    Global detection approach for clustered microcalcifications in mammograms using a deep learning network.
Wang J, Nishikawa RM, Yang Y
J Med Imaging (Bellingham). 2017 Apr;4(2):024501
  • “In the same manner that automated blood pressure measurement and automated blood cell counts freed clinicians from some tasks, artificial intelligence could bring back meaning and purpose in the practice of medicine while providing new levels of efficiency and accuracy. Physicians must proactively guide, oversee, and monitor the adoption of artificial intelligence as a partner in patient care.”


    What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
  • “Recently, these deep learning algorithms have been applied to medical imaging in several clinical settings, such as detection of breast cancer on mammograms, segmentation of liver metastases with computed tomography (CT), brain tumor segmentation with magnetic resonance (MR) imaging, classification of interstitial lung disease with high-resolution chest CT, and generation of relevant labels pertaining to the content of medical images.”


    Deep Learning: A Primer for Radiologists
Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
  • “There may be resistance to merging 2 distinct medical specialties, each of which has unique pedagogy, tradition, accreditation, and reimbursement. However, artificial intelligence will change these diagnostic fields. The merger is a natural fusion of human talent and artificial intelligence. United, radiologists and pathologists can thrive with the rise of artificial intelligence.”


    Adapting to Artificial Intelligence 
Radiologists and Pathologists as Information Specialists 
Jha S, Topol EJ
JAMA. Published online November 29, 2016. doi:10.1001/jama.2016.17438
  • “Information specialists should train in the traditional sciences of pathology and radiology. The training should take no longer than it presently takes because the trainee will not spend time mastering the pattern recognition required to become a competent radiologist or pathologist. Visual interpretation will be restricted to perceptual tasks that artificial intelligence cannot perform as well as humans. The trainee need only master enough medical physics to improve suboptimal quality of medical images. Information special- ists should be taught Bayesian logic, statistics, and data science and be aware of other sources of information such as genomics and bio- metrics, insofar as they can integrate data from disparate sources with a patient’s clinical condition.”


    Adapting to Artificial Intelligence 
Radiologists and Pathologists as Information Specialists 
Jha S, Topol EJ
JAMA. Published online November 29, 2016. doi:10.1001/jama.2016.17438
  • “In recent years machine learning (ML) has revolutionized the fields of computer vision and medical image analysis. Yet, a number of doubts remain about the applicability of ML in clinical practice. Medical doctors may question the lack of interpretability of classifiers; Or it is argued that ML methods require huge amounts of training data. Here we discuss some of these issues and show: 
1. how decision trees (a special class of ML models) can be understood as an automatically-optimized generalization of conventional algorithms, and 
2. how the issue of collecting labelled data (e.g. images) applies to both manually-designed and learning-based algorithms.”


    Machine learning for medical images analysis
Criminisi A
Medical Image Analysis
Volume 33, October 2016, Pages 91–93
  • “Ultimately, these researchers argue, the complex answers given by machine learning have to be part of science’s toolkit because the real world is complex: for phenomena such as the weather or the stock mar- ket, a reductionist, synthetic description might not even exist.“

    There are things we cannot verbalize,” says Stéphane Mallat, an applied math- ematician at the École Polytechnique in Paris.
  • For select cancer histologies, aggressive focal therapy of oligometastatic lesions is already the clinical standard of care (i.e. colorectal cancer and sarcomas), while for other tumor types the evidence is still emerging (i.e. prostate, breast, etc.). It is increasingly important, therefore, for the radiologist interpreting oncology patients’ staging or restaging examinations to be aware of those diseases for which targeted therapy of oligometastases may be undertaken to effectively guide such management. The improved imaging resolution provided by technological advances promise to aid in the detection of subtle sites of disease to ensure the identification of patients with oligometastases amenable to targeted treatment. 


    What the Radiologist Needs to Know to Guide Patient Management 
Steven P. Rowe, MD, Hazem Hawasli, Elliot K. Fishman, MD, Pamela T. Johnson, 
Acad Radiol 2016; 23:326–328
  • “As such, some of the impetus for exploring aggressive and potentially curative treatment in patients with oligometastases can come from improvements in imaging technology and techniques. Thus, radiologists should not only understand the implications of the new paradigm of oligometastatic disease for how they interpret studies, but they should also ac- tively engage in the research necessary to optimize the selection of patients for aggressive therapy of oligometastases.“

    What the Radiologist Needs to Know to Guide Patient Management 
Steven P. Rowe, MD, Hazem Hawasli, Elliot K. Fishman, MD, Pamela T. Johnson, 
Acad Radiol 2016; 23:326–328
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