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AI-based computer-aided diagnosis (AI-CAD): the latest review to read first

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Abstract

The third artificial intelligence (AI) boom is coming, and there is an inkling that the speed of its evolution is quickly increasing. In games like chess, shogi, and go, AI has already defeated human champions, and the fact that it is able to achieve autonomous driving is also being realized. Under these circumstances, AI has evolved and diversified at a remarkable pace in medical diagnosis, especially in diagnostic imaging. Therefore, this commentary focuses on AI in medical diagnostic imaging and explains the recent development trends and practical applications of computer-aided detection/diagnosis using artificial intelligence, especially deep learning technology, as well as some topics surrounding it.

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taken from the homepage of former R2 Technology, Inc. Los Altos CA, USA, now Hologic)

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Notes

  1. On December 6, 2018, approval for the software to determine if it is a neoplastic polyp or not with possibility shown as a number in the super-magnifying endoscope was freshly obtained, based on the Pharmaceuticals and Medical Devices Law in Japan. Support vector machine (SVM) type machine learning instead of deep learning is used in this system.

  2. The American College of Radiology (ACR) Data Science Institute (DSI) has created a new resource for radiology researchers. A complete list of AI algorithms cleared by the FDA related to medical imaging, published on the ACR DSI website, is said to be updated regularly. https://www.acrdsi.org/DSI-Services/FDA-Cleared-AI-Algorithms

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Funding

This research was supported in part by a Grant-in-Aid for Scientific Research (B) (No. 19H03599) and (C) (No. 19K10347), by the Japan Society for the Promotion of Science, and also in part by a Collaborative Research Grant from EyeTech Co., Ltd., Japan.

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  1. Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1, Yanagido, Gifu City, Gifu, 501-1194, Japan

    Hiroshi Fujita

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Correspondence to Hiroshi Fujita.

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Fujita, H. AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiol Phys Technol 13, 6–19 (2020). https://doi.org/10.1007/s12194-019-00552-4

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