Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging
| Autor: | Syuzo Kaneko, Ryuji Hamamoto, Ken Asada, Kanto Shozu, Masaaki Komatsu, Ai Dozen, Suguru Yasutomi, Akira Sakai, Hidenori Machino |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Image quality QH301-705.5 detection Medicine (miscellaneous) Review General Biochemistry Genetics and Molecular Biology 030218 nuclear medicine & medical imaging Food and drug administration 03 medical and health sciences ultrasound imaging 0302 clinical medicine Informed consent explainability Medical imaging Biology (General) preprocessing Modalities business.industry segmentation deep learning Medical research artificial intelligence Imaging analysis machine learning classification 030220 oncology & carcinogenesis Ultrasound imaging Artificial intelligence business |
| Zdroj: | Biomedicines, Vol 9, Iss 720, p 720 (2021) Biomedicines |
| ISSN: | 2227-9059 |
| Popis: | Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies. |
| Databáze: | OpenAIRE |
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