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      Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

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          Dermatologist-level classification of skin cancer with deep neural networks

          Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
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            Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

            Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
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              Mastering the game of Go with deep neural networks and tree search.

              The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'value networks' to evaluate board positions and 'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Journal of Evidence-Based Medicine
                J Evid Based Med.
                Wiley
                1756-5383
                1756-5391
                November 2020
                November 13 2020
                November 2020
                : 13
                : 4
                : 301-312
                Affiliations
                [1 ]Department of Emergency Medicine, Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China
                [2 ]Interventional Cardiology and Cardiovascular Medicine Research, Department of Cardiology and Internal Medicine Nicolaus Copernicus University Bydgoszcz Poland
                [3 ]Faculty of Medicine University of Alberta Edmonton Canada
                [4 ]Department of Surgery, 2D, Walter C Mackenzie Health Sciences Centre University of Alberta Edmonton Alberta Canada
                [5 ]Department of Surgery State University of New York Upstate Medical University Syracuse New York
                [6 ]Programme in Health Services and Systems Research Duke‐NUS Medical School Singapore
                [7 ]Department of Respiratory Care, Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China
                [8 ]College of Information Engineering Zhejiang University of Technology Hangzhou China
                [9 ]Department of Critical Care Medicine, Ren Ji Hospital, School of Medicine Shanghai Jiao Tong University Shanghai China
                [10 ]Department of biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital Sichuan University Chengdu China
                Article
                10.1111/jebm.12418
                33185950
                53867147-ff4c-4ba9-a5e2-ade5347f217d
                © 2020

                http://onlinelibrary.wiley.com/termsAndConditions#vor

                http://doi.wiley.com/10.1002/tdm_license_1.1

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