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Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology

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Abstract

An ultrasound (US) examination is a common noninvasive technique widely applied for diagnosis of a variety of diseases. Based on the rapid development of US equipment, many US images have been accumulated and are now available and ready for the preparation of a database for the development of computer-aided US diagnosis with deep learning technology. On the contrary, because of the unique characteristics of the US image, there could be some issues that need to be resolved for the establishment of computer-aided diagnosis (CAD) system in this field. For example, compared to the other modalities, the quality of a US image is, currently, highly operator dependent; the conditions of examination should also directly affect the quality of US images. So far, these factors have hampered the application of deep learning-based technology in the field of US diagnosis. However, the development of CAD and US technologies will contribute to an increase in diagnostic quality, facilitate the development of remote medicine, and reduce the costs in the national health care through the early diagnosis of diseases. From this point of view, it may have a large enough potential to induce a paradigm shift in the field of US imaging and diagnosis of liver diseases.

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Acknowledgements

This work was supported by Japan Agency for Medical Research and Development under the Grant number 18lk1010030h0001 (M. Kudo, T. Shiina, N. Nishida), and partially supported by Grant-in-Aid for Scientific Research (KAKENHI: 16K09382) from the Japanese Society for the Promotion of Science (N. Nishida) and a grant from the Smoking Research Foundation (N. Nishida).

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Authors and Affiliations

  1. Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, 337-2 Ohno-higashi, Osaka-sayama, Osaka, 589-8511, Japan

    Naoshi Nishida & Masatoshi Kudo

  2. Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Makoto Yamakawa & Tsuyoshi Shiina

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  1. Naoshi Nishida
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  2. Makoto Yamakawa
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  3. Tsuyoshi Shiina
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  4. Masatoshi Kudo
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Correspondence to Naoshi Nishida.

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Nishida, N., Yamakawa, M., Shiina, T. et al. Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology. Hepatol Int 13, 416–421 (2019). https://doi.org/10.1007/s12072-019-09937-4

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  • DOI: https://doi.org/10.1007/s12072-019-09937-4

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