google ads
Search

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

Deep Learning: Human Interface Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Human Interface

-- OR --

  • “To prevent patient privacy compromise while promoting scientific research on large datasets that aims to improve patient care, the implementation of technical solutions to simultaneously address the demands for data protection and utilization is mandatory. Here we present an overview of current and next-generation methods for federated, secure and privacy-preserving artificial intelligence with a focus on medical imaging applications, alongside potential attack vectors and future prospects in medical imaging and beyond.”
    Secure, privacy-preserving and federated machine learning in medical imaging
    Georgios A. Kaissis et al.
    Nat Mach Intell 2, 305–311 (2020)

  • Secure, privacy-preserving and federated machine learning in medical imaging
    Georgios A. Kaissis et al.
    Nat Mach Intell 2, 305–311 (2020)
  • Key Points
    • Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians.
    • Implementation of AI in radiology is facilitated by the presence of a local champion.
    • Evidence on the clinical added value of AI in radiology is needed for successful implementation.
    Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors
    Lea Strohm et al.
    Eur Radiol (2020). https://doi.org/10.1007/s00330-020-06946-y
  • “Considering the great attention AI applications are receiving in radiology and other medical disciplines like pathology, un- derstanding the barriers of and facilitators for the implemen- tation of AI is important. One of the important facilitating factors is the presence of a “local champion,” an individual with a strong personal interest in AI applications who most often initiates and actively advances AI implementation in the organization.”
    Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors
    Lea Strohm et al.
    Eur Radiol (2020). https://doi.org/10.1007/s00330-020-06946-y
  • "Among the most prominent hindering factors is the uncertain added value for clinical practice, which causes low acceptance of AI applications among adopters and complicates the mobilization of funds to acquire AI applications. Furthermore, the failure to include all relevant stakeholders in the planning, execution, and monitoring phase of the implementation of AI applications was found to be a major hindering factor.”
    Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors
    Lea Strohm et al.
    Eur Radiol (2020). https://doi.org/10.1007/s00330-020-06946-y
  • “To increase the acceptance among adopters, more evidence of the added benefit of their AI applications in the clinical setting is needed. Also, all involved stakeholders (most notably radiologists and referring clinicians) should be included in the decisions for and the design of implementation processes of AI applications.”
    Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors
    Lea Strohm et al.
    Eur Radiol (2020). https://doi.org/10.1007/s00330-020-06946-y
  • “In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symp- toms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT–PCR assay and next-generation sequencing RT–PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT–PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.”
    Artificial intelligence–enabled rapid diagnosis of patients with COVID-19
    Xueyan Mei et al.
    Nat Med (2020). https://doi.org/10.1038/s41591-020-0931-3 
  • “It is essential to develop technology that empowers doctors so that they can get back to doing what they trained for and love. It is equally important that we return to patients their doctors’ undivided attention. Accordingly, healthcare IT development should begin with a deep understanding of how clinicians need and want to work, then implement AI capabilities with the explicit goal of adapting to and supporting how they deliver care. Ambient clinical intelligence (ACI) is one promising approach.”
    How AI in the Exam Room Could Reduce Physician Burnout
    Michael Ash, Joe Petro, Shafiq Rab
    Harvard Business Review
    November 2019
  • “As the name indicates, ACI is less a device than a set of capabilities as unobtrusively present and available as the light and sound in the exam room. The best way to picture ACI is to think of a typical exam room with a flat-screen display on the wall showing requested information. An inconspicuous array of microphones captures the patient interaction accurately regardless of speakers’ movements or positions. A computer isn’t needed in the exam room, because the computing and data entry takes place behind the scenes in back-end and cloud- based systems. ACI builds on the familiar speech recognition technology that doctors have used for the past 20 years. It also uses voice biometrics — in short, a way to identify individuals by voice — to authenticate clinical users, and other technologies to distinguish between the clinician, patient, and anyone else in the exam room. It also integrates conversational AI, machine learning, speech synthesis, natural language understanding, and cloud computing to provide diagnostic guidance and clinical intelligence.”
    How AI in the Exam Room Could Reduce Physician Burnout
    Michael Ash, Joe Petro, Shafiq Rab
    Harvard Business Review
    November 2019
  • “Radiologists will remain ultimately responsible for patient care and will need to acquire new skills to do their best for patients in the new AI ecosystem. The radiology community needs an ethical framework to help steer technological development, influence how different stakeholders respond to and use AI, and implement these tools to make best decisions and actions for, and increasingly with, patients.”
    Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement
    J. Raymond Geis et al.
    Insights into Imaging (2019) 10:101
  • “When you ask a medical doctor why he diagnosed this or this, he’s going to give you some rea- sons,” he says. “But how come it takes 20 years to make a good doctor? Because the information is just not in books.” 


    Can we open the black box of AI?
Artificial intelligence is everywhere. But before scientists trust it, they first need to understand how machines learn.
 Davide Castelvecchi
Nature Vol 538, Issue 7623 Oct 2016
  • “Each of these tasks is amenable to automa- tion. Organs can be located by the computer using atlas- and landmark-based methods. Organ volume and shape can be assessed by finding the edges of the organs in three dimensions, a process known as segmentation. Lesions can be detected and segmented by assessing the patterns of Hounsfield unit intensities in the organs to identify anomalies. Example pat- terns include variations in intensities, texture, and shape. The quantitative measurements of these patterns are known as features.” 


    Progress in Fully Automated Abdominal CT Interpretation
Summers RM
AJR 2016; 207:67–79
  • “In the other, generic features are used and a machine-learning algorithm is taught to distinguish disease from nondisease sites by being trained on labeled cases, without the need for handcrafted features. The latter approach, which is made feasible by recent advances in computer science known collo- quially as deep learning, is increasingly being used because it markedly increases the efficiency of image analysis development.”

    Progress in Fully Automated Abdominal CT Interpretation
Summers RM
AJR 2016; 207:67–79
  • “To perform fully automated abdominal CT image interpretation at the level of a trained ra- diologist, the computer must assess all the or- gans and detect all the abnormalities present in the images. Although this is a seemingly daunting task for the software developer, the numbers of organs and potential abnormalities are finite and can be addressed methodically .” 
Progress in Fully Automated Abdominal CT Interpretation
Summers RM
AJR 2016; 207:67–79
  • “The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular "omics" features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology.”


    Image analysis and machine learning in digital pathology: Challenges and opportunities.
Madabhushi A1, Lee G2.
Med Image Anal. 2016 Oct;33:170-5
  • “The nationwide implementation of electronic medical records (EMRs) resulted in many unanticipated consequences, even as these systems enabled most of a patient’s data to be gathered in one place and made those data readily accessible to clinicians caring for that patient. The redundancy of the notes, the burden of alerts, and the overflowing inbox has led to the “4000 keystroke a day” problem and has contributed to, and perhaps even accelerated, physician reports of symptoms of burnout. Even though the EMR may serve as an efficient administrative business and billing tool, and even as a powerful research warehouse for clinical data, most EMRs serve their front-line users quite poorly. The unanticipated consequences include the loss of important social rituals (between physicians and between physicians and nurses and other health care workers) around the chart rack and in the radiology suite, where all specialties converged to discuss patients.”


    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
  • “The lessons learned with the EMR should serve as a guide as artificial intelligence and machine learning are developed to help process and creatively use the vast amounts of data being generated in the health care system. Outside of medicine, the use of artificial intelligence in predictive policing, bail decisions, and credit scoring has shown that artificial intelligence can actually exaggerate racial and other bias.”


    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
  • “Similar concerns around artificial intelligence predictive models in health care have been discussed: clearly, in the 3-step process of selecting a dataset, creating an appropriate predictive model, and evaluating and refining the model, there is nothing more critical than the data. Bad data (such as from the EMR) can be amplified into worse models. For example, a model might classify patients with a history of asthma who present with pneumonia as having a lower risk of mortality than those with pneumonia alone, not registering the context that this is an artifact of clinicians admitting and treating such patients earlier and more aggressively. Since machine learning presents no human interface and cannot be interrogated, even if its predictions are extraordinarily accurate, some clinicians are likely to view the “black box” with suspicion.”


    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
  • “In the care of the sick, there is a key function played by physicians, referred to by Tinsley Harrison as the “priestly function of the physician.” Human intelligence working with artificial intelligence—a well-informed, empathetic clinician armed with good predictive tools and unburdened from clerical drudgery—can bring physicians closer to fulfilling Peabody’s maxim that the secret of care is in “caring for the patient.”


    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
  • “It is likely that machine learning applications will soon transform some sectors of health care in ways that may be valuable but may have unintended consequences. Use of ML-DSS could create problems in contemporary medicine and lead to misuse. The quality of any ML-DSS and subsequent regulatory decisions about its adoption should not be grounded only in performance metrics, but rather should be subject to proof of clinically important improvements in relevant outcomes compared with usual care, along with the satisfaction of patients and physicians.”


    Unintended Consequences of Machine Learning in Medicine.
Cabitza F, Rasoini R, Gensini GF
JAMA. 2017 Aug 8;318(6):517-518
© 1999-2020 Elliot K. Fishman, MD, FACR. All rights reserved.