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      Artificial Intelligence (AI)-aided Disease Prediction


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          Artificial intelligence (AI) has been widely used in clinical medicine, and it is witnessing increasing innovations in the fields of AI-aided image analysis, AI-aided lesion determination, AI-assisted healthcare management, and so on. This review article focuses on the emerging applications of AI-related medicine and AI-assisted visualized medicine, including novel diagnostic approaches, metadata analytical methods, and versatile AI-aided treatment applications in preclinical and clinical uses, and also looks at future perspectives of AI-aided disease prediction.

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          Most cited references36

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Collective dynamics of 'small-world' networks.

            Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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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.

                Author and article information

                BIO Integration
                Compuscript (Ireland )
                01 December 2020
                28 October 2020
                : 1
                : 3
                : 130-136
                [1] 1Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072, Tianjin, China
                [2] 2Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, 300072, Tianjin, China
                Author notes
                Correspondence to: Zhe Liu, E-mail: zheliu@ 123456tju.edu.cn
                Copyright © 2020 The Authors

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See https://bio-integration.org/copyright-and-permissions/

                : 09 June 2020
                : 05 August 2020
                : 24 September 2020
                Self URI (journal-page): https://bio-integration.org/

                Medicine,Molecular medicine,Radiology & Imaging,Biotechnology,Pharmacology & Pharmaceutical medicine,Microscopy & Imaging
                visualized medicine,Artificial intelligence,deep learning,clinical medicine,disease prediction


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