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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

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  • “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
© 1999-2018 Elliot K. Fishman, MD, FACR. All rights reserved.