Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver · 2023
Analysis of ultrasonographic images using a deep learning-based model as ancillary diagnostic tool for diagnosing gallbladder polyps.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Choi Jin Ho, Lee Jaesung, Lee Sang Hyub, Lee Sanghyuk, Moon A-Seong, Cho Sung-Hyun, Kim Joo Seong, Cho In Rae, Paik Woo Hyun, Ryu Ji Kon, Kim Yong-Tae
Аннотация
BACKGROUND: Accurately diagnosing gallbladder polyps (GBPs) is important to avoid misdiagnosis and overtreatment.
AIMS: To evaluate the efficacy of a deep learning model and the accuracy of a computer-aided diagnosis by physicians for diagnosing GBPs.
METHODS: This retrospective cohort study was conducted from January 2006 to September 2021, and 3,754 images from 263 patients were analyzed. The outcome of this study was the efficacy of the developed deep learning model in discriminating neoplastic GBPs (NGBPs) from non-NGBPs and to evaluate the accuracy of a computer-aided diagnosis with that made by physicians.
RESULTS: The efficacy of discriminating NGBPs from non- NGBPs using deep learning was 0.944 (accuracy, 0.858; sensitivity, 0.856; specificity, 0.861). The accuracy of an unassisted diagnosis of GBP was 0.634, and that of a computer-aided diagnosis was 0.785 (p<0.001). There were no significant differences in the accuracy of a computer-aided diagnosis between experienced (0.835) and inexperienced (0.772) physicians (p = 0.251). A computer-aided diagnosis significantly assisted inexperienced physicians (0.772 vs. 0.614; p < 0.001) but not experienced physicians.
CONCLUSIONS: Deep learning-based models discriminate NGBPs from non- NGBPs with excellent accuracy. As ancillary diagnostic tools, they may assist inexperienced physicians in improving their diagnostic accuracy.
AIMS: To evaluate the efficacy of a deep learning model and the accuracy of a computer-aided diagnosis by physicians for diagnosing GBPs.
METHODS: This retrospective cohort study was conducted from January 2006 to September 2021, and 3,754 images from 263 patients were analyzed. The outcome of this study was the efficacy of the developed deep learning model in discriminating neoplastic GBPs (NGBPs) from non-NGBPs and to evaluate the accuracy of a computer-aided diagnosis with that made by physicians.
RESULTS: The efficacy of discriminating NGBPs from non- NGBPs using deep learning was 0.944 (accuracy, 0.858; sensitivity, 0.856; specificity, 0.861). The accuracy of an unassisted diagnosis of GBP was 0.634, and that of a computer-aided diagnosis was 0.785 (p<0.001). There were no significant differences in the accuracy of a computer-aided diagnosis between experienced (0.835) and inexperienced (0.772) physicians (p = 0.251). A computer-aided diagnosis significantly assisted inexperienced physicians (0.772 vs. 0.614; p < 0.001) but not experienced physicians.
CONCLUSIONS: Deep learning-based models discriminate NGBPs from non- NGBPs with excellent accuracy. As ancillary diagnostic tools, they may assist inexperienced physicians in improving their diagnostic accuracy.