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

Deep Learning: Ai and Healthcare (overview) Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ AI and Healthcare (overview)

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  • Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection
    FangLiu et al.
    Radiology 2018 (in press)


  • "By now, it’s almost old news: big data will transform medicine. It’s essential to remember, however, that data by themselves are useless. To be useful, data must be analyzed, interpreted, and acted on. Thus, it is algorithms —
    not data sets — that will prove transformative."
    Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
    Obermeyer Z, Emanuel EJ
    N Engl J Med 375;13 September 29, 2016
  • “But where machine learning shines is in handling enormous numbers of predictors — sometimes, remarkably, more predictors than observations — and combining them in nonlinear and highly interactive ways.This capacity al- lows us to use new kinds of data, whose sheer volume or complexity would previously have made analyzing them unimaginable.”


    Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
  • “Another key issue is the quantity and quality of input data. Machine learning algorithms are highly data hungry, often re- quiring millions of observations to reach acceptable performance levels.In addition, biases in data collection can substantially affect both performance and generalizability. Lactate might be a good predictor of the risk of death, for example, but only a small, nonrepresentative sample of patients have their lactate levels checked.”

    
Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
  • “Machine learning has become ubiquitous and indispensable for solving complex problems in most sciences. In astronomy, algorithms sift through millions of images from telescope surveys to classify galaxies and find supernovas.”


    Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
  • “Increasingly, the ability to transform data into knowledge will disrupt at least three areas of medicine. First, machine learning will dramatically improve the ability of health professionals to es- tablish a prognosis. Current prognostic models (e.g., the Acute Physiology and Chronic Health Evaluation [APACHE] score and the Sequential Organ Failure Assessment [SOFA] score) are restricted to only a handful of vari- ables, because humans must enter and tally the scores. But data could instead be drawn directly from EHRs or claims databases, allow- ing models to use thousands of rich predictor variables.”


    Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016


  • Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135


  • Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135
  • “One of the first and most significant hurdles to getting a CPT code is the need for peer-reviewed research in the United States that demonstrates both the efficacy and safety of the procedure. The second hurdle is the need for the procedure to be widely performed by a large number of physicians in the United States. These two requirements will prevent many AI software programs from achieving a CPT code. But, let us presume that at least one AI tool makes the cut and gets a CPT code. It will then have to be valued by the Relative Value Scale Update Committee (RUC) to get assigned RVUs.”


    Artificial Intelligence: Who Pays and How?
Schoppe, Kurt
Journal of the American College of Radiology (in press)
  • “The RUC values the professional component of a medical procedure based upon the work of a physician. The primary components of physi- cian work include the time it takes to perform the service, the level of technical skill required, and the mental effort and judgment necessary. For most AI tools I have seen, there is minimal to no physician work. Some AI processes run in the background and “prioritize” CT scans based on characteristics that may indicate an emergent finding. There is no physician work in this. Some AI processes may highlight specific imaging findings for the radiologist. This type of operation would be considered similar to computer-aided detection, and so would be valued similarly to prior CPT codes for computer-aided detection used in chest radiographs or mammography, though much of this work is either unreimbursed or bundled into the actual diagnostic procedure (eg, mammography and breast MRI).”

    
Artificial Intelligence: Who Pays and How?
Schoppe, Kurt
Journal of the American College of Radiology (in press)
  • “My opinion is that neither the government nor private payers will reimburse physicians and hospitals for using AI-driven software products. I believe that we will all purchase AI tools and treat them as an unreimbursed business expense. We will invest in AI software to ensure we are delivering high- quality work, to increase our efficiency, and to simplify clerical type tasks. In this way, paying for AI tools will merely be a cost of doing business like other operational expenses we incur.”


    Artificial Intelligence: Who Pays and How?
Schoppe, Kurt
Journal of the American College of Radiology (in press)
  • ”Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology’s contribution to patient care and population health, and will revolutionize radiologists’ workflows.”

    
Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135
  • “In conclusion, with the current fast pace in development of machine learning techniques, and deep learning in particular, there is prospect for a more widespread clinical adoption of machine learning in radiology practice. Machine learning and artificial intelligence are not expected to replace the radiologists in the foreseeable future. ese techniques can potentially facilitate radiology work ow, increase radiologist productivity, improve detection and interpretation of findings, reduce the chance of error, and enhance patient care and satisfaction.”


    Current Applications and Future Impact of Machine Learning in Radiology 
Garry Choy et al.
 Radiology 2018; 00:1–11
  • “Radiomics is a process designed to extract a large number of quantitative features from radiology images . Radiomics is an emerging field for machine learning that allows for conversion of radiologic images into mineable high-dimensional data. For instance, Zhang et al evaluated over 970 radiomics features extracted from MR images by using machine learning methods and correlated with features to predict local and distant treatment failure of advanced nasopharyngeal carcinoma.”


    Current Applications and Future Impact of Machine Learning in Radiology 
Garry Choy et al.
 Radiology 2018; 00:1–11
  • “Machine learning approaches to the interrogation of a wide spectrum of such data (sociodemographic, imaging, clinical, laboratory, and genetic) has the potential to further personalize health care, far beyond what would be possible through imaging applications alone. Precision medicine require the use of novel computational techniques to harness the vast amounts of data required to discover individualized disease factors and treatment decisions.”


    Current Applications and Future Impact of Machine Learning in Radiology 
Garry Choy et al.
 Radiology 2018; 00:1–11
  • AI in Healthcare

  • AI in Healthcare

  • “Deep learning is a type of representation learning in which the algorithm learns a composition of features that re ect a hierarchy of structures in the data. Complex representations are expressed in terms of simpler representations.”
Deep Learning: A Primer for Radiologists.”

    
Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
  • “Although neural networks have been used for decades, in re- cent years three key factors have enabled the training of large neural networks: (a) the availability of large quantities of la- beled data, (b) inexpensive and powerful parallel computing hardware, and (c) improvements in training techniques and architectures.”
Deep Learning: A Primer for Radiologists.”


    Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
  • “Deep CNNs exploit the compositional structure of natural images so that shifts and deformations of objects in the images do not significantly affect the overall performance of the network.”
Deep Learning: A Primer for Radiologists.”


    Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
  • “The creation of these large databases of labeled medical images and many associated challenges will be fundamental to foster future research in deep learning applied to medical images.”
Deep Learning: A Primer for Radiologists.”


    Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
© 1999-2018 Elliot K. Fishman, MD, FACR. All rights reserved.