{"title":"基于人工智能的新型内镜超声诊断系统，用于诊断早期胃癌的侵犯深度。","authors":"Ryotaro Uema, Yoshito Hayashi, Takashi Kizu, Takumi Igura, Hideharu Ogiyama, Takuya Yamada, Risato Takeda, Kengo Nagai, Takuya Inoue, Masashi Yamamoto, Shinjiro Yamaguchi, Takashi Kanesaka, Takeo Yoshihara, Minoru Kato, Shunsuke Yoshii, Yoshiki Tsujii, Shinichiro Shinzaki, Tetsuo Takehara","doi":"10.1007/s00535-024-02102-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system.</p><p><strong>Methods: </strong>A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases).</p><p><strong>Results: </strong>The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796-0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743-0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable.</p><p><strong>Conclusions: </strong>Our AI model demonstrated a diagnostic performance equivalent to that of experts.</p>","PeriodicalId":16059,"journal":{"name":"Journal of Gastroenterology","volume":" ","pages":"543-555"},"PeriodicalIF":6.9000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11217111/pdf/","citationCount":"0","resultStr":"{\"title\":\"A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer.\",\"authors\":\"Ryotaro Uema, Yoshito Hayashi, Takashi Kizu, Takumi Igura, Hideharu Ogiyama, Takuya Yamada, Risato Takeda, Kengo Nagai, Takuya Inoue, Masashi Yamamoto, Shinjiro Yamaguchi, Takashi Kanesaka, Takeo Yoshihara, Minoru Kato, Shunsuke Yoshii, Yoshiki Tsujii, Shinichiro Shinzaki, Tetsuo Takehara\",\"doi\":\"10.1007/s00535-024-02102-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system.</p><p><strong>Methods: </strong>A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases).</p><p><strong>Results: </strong>The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796-0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743-0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable.</p><p><strong>Conclusions: </strong>Our AI model demonstrated a diagnostic performance equivalent to that of experts.</p>\",\"PeriodicalId\":16059,\"journal\":{\"name\":\"Journal of Gastroenterology\",\"volume\":\" \",\"pages\":\"543-555\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11217111/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Gastroenterology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00535-024-02102-1\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/7 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Gastroenterology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00535-024-02102-1","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/7 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer.
Background: We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system.
Methods: A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases).
Results: The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796-0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743-0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable.
Conclusions: Our AI model demonstrated a diagnostic performance equivalent to that of experts.
期刊介绍：
The Journal of Gastroenterology, which is the official publication of the Japanese Society of Gastroenterology, publishes Original Articles (Alimentary Tract/Liver, Pancreas, and Biliary Tract), Review Articles, Letters to the Editors and other articles on all aspects of the field of gastroenterology. Significant contributions relating to basic research, theory, and practice are welcomed. These publications are designed to disseminate knowledge in this field to a worldwide audience, and accordingly, its editorial board has an international membership.
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