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'KAIZEN' method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals

التفاصيل البيبلوغرافية
العنوان: 'KAIZEN' method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals
المؤلفون: Naoki Okada, Yutaka Umemura, Shoi Shi, Shusuke Inoue, Shun Honda, Yohsuke Matsuzawa, Yuichiro Hirano, Ayano Kikuyama, Miho Yamakawa, Tomoko Gyobu, Naohiro Hosomi, Kensuke Minami, Natsushiro Morita, Atsushi Watanabe, Hiroyuki Yamasaki, Kiyomitsu Fukaguchi, Hiroki Maeyama, Kaori Ito, Ken Okamoto, Kouhei Harano, Naohito Meguro, Ryo Unita, Shinichi Koshiba, Takuro Endo, Tomonori Yamamoto, Tomoya Yamashita, Toshikazu Shinba, Satoshi Fujimi
المصدر: Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
بيانات النشر: Nature Portfolio, 2024.
سنة النشر: 2024
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: Abstract Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primarily owing to methodological defects and the lack of implementation considerations for inference. In this study, all development processes of the deep-learning models are performed based on strict criteria of the “KAIZEN checklist”, which is proposed based on previous AI development guidelines to overcome the deficiencies mentioned above. We develop and evaluate two binary-classification deep-learning models to triage COVID-19: a slice model examining a Computed Tomography (CT) slice to find COVID-19 lesions; a series model examining a series of CT images to find an infected patient. We collected 2,400,200 CT slices from twelve emergency centers in Japan. Area Under Curve (AUC) and accuracy were calculated for classification performance. The inference time of the system that includes these two models were measured. For validation data, the slice and series models recognized COVID-19 with AUCs and accuracies of 0.989 and 0.982, 95.9% and 93.0% respectively. For test data, the models’ AUCs and accuracies were 0.958 and 0.953, 90.0% and 91.4% respectively. The average inference time per case was 2.83 s. Our deep-learning system realizes accuracy and inference speed high enough for practical use. The systems have already been implemented in four hospitals and eight are under progression. We released an application software and implementation code for free in a highly usable state to allow its use in Japan and globally.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-024-52135-y
URL الوصول: https://doaj.org/article/3a9763ca04cb4d99b957cda61fb9b90d
رقم الأكسشن: edsdoj.3a9763ca04cb4d99b957cda61fb9b90d
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20452322
DOI:10.1038/s41598-024-52135-y