google ads
Search

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)

-- OR --

  • “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
© 1999-2019 Elliot K. Fishman, MD, FACR. All rights reserved.