Endoscopic ultrasound-based artificial intelligence for gastrointestinal subepithelial lesions
Gastrointestinal subepithelial lesions (SELs), including gastrointestinal stromal tumors (GISTs), leiomyomas, and neuroendocrine tumors (NETs), are intramural lesions found beneath the mucosa of the gastrointestinal tract. These lesions can be benign, malignant, or potentially malignant. Therefore,...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | EngMedicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950489925000193 |
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| author | Huan Jiang Liansong Ye Xianglei Yuan Qi Luo Nuoya Zhou Bing Hu |
| author_facet | Huan Jiang Liansong Ye Xianglei Yuan Qi Luo Nuoya Zhou Bing Hu |
| author_sort | Huan Jiang |
| collection | DOAJ |
| description | Gastrointestinal subepithelial lesions (SELs), including gastrointestinal stromal tumors (GISTs), leiomyomas, and neuroendocrine tumors (NETs), are intramural lesions found beneath the mucosa of the gastrointestinal tract. These lesions can be benign, malignant, or potentially malignant. Therefore, accurately distinguishing between different types and assessing malignant potential is essential for establishing appropriate treatment plans and predicting patient outcomes. Endoscopic ultrasound (EUS) is recognized as the preferred method for diagnosing SELs. While certain lesions can be identified by their echogenic features alone, a histological examination is often necessary, potentially increasing patient risk. Artificial intelligence (AI) has demonstrated impressive capabilities in medical image recognition and offers significant potential for the non-invasive assessment of SELs. It achieves this by extracting key features from EUS images and applying them to classify or segment the images. This paper reviews recent advances in the application of AI to assist EUS in diagnosing SELs. |
| format | Article |
| id | doaj-art-e854886943d54c8f809cc5ef0b67c430 |
| institution | Kabale University |
| issn | 2950-4899 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EngMedicine |
| spelling | doaj-art-e854886943d54c8f809cc5ef0b67c4302025-08-20T03:49:46ZengElsevierEngMedicine2950-48992025-06-012210007310.1016/j.engmed.2025.100073Endoscopic ultrasound-based artificial intelligence for gastrointestinal subepithelial lesionsHuan Jiang0Liansong Ye1Xianglei Yuan2Qi Luo3Nuoya Zhou4Bing Hu5Department of Gastroenterology and Hepatology/Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, ChinaDepartment of Gastroenterology and Hepatology/Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, ChinaDepartment of Gastroenterology and Hepatology/Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, ChinaDepartment of Gastroenterology and Hepatology/Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, ChinaDepartment of Gastroenterology and Hepatology/Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, ChinaCorresponding author. Department of Gastroenterology and Hepatology/Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, No.37, Guo Xue Alley, Wuhou district, Chengdu City, 610041, Sichuan Province, China.; Department of Gastroenterology and Hepatology/Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, ChinaGastrointestinal subepithelial lesions (SELs), including gastrointestinal stromal tumors (GISTs), leiomyomas, and neuroendocrine tumors (NETs), are intramural lesions found beneath the mucosa of the gastrointestinal tract. These lesions can be benign, malignant, or potentially malignant. Therefore, accurately distinguishing between different types and assessing malignant potential is essential for establishing appropriate treatment plans and predicting patient outcomes. Endoscopic ultrasound (EUS) is recognized as the preferred method for diagnosing SELs. While certain lesions can be identified by their echogenic features alone, a histological examination is often necessary, potentially increasing patient risk. Artificial intelligence (AI) has demonstrated impressive capabilities in medical image recognition and offers significant potential for the non-invasive assessment of SELs. It achieves this by extracting key features from EUS images and applying them to classify or segment the images. This paper reviews recent advances in the application of AI to assist EUS in diagnosing SELs.http://www.sciencedirect.com/science/article/pii/S2950489925000193Artificial intelligenceConvolutional neural networkEndoscopic ultrasoundGastrointestinal subepithelial lesionsGastrointestinal stromal tumor |
| spellingShingle | Huan Jiang Liansong Ye Xianglei Yuan Qi Luo Nuoya Zhou Bing Hu Endoscopic ultrasound-based artificial intelligence for gastrointestinal subepithelial lesions EngMedicine Artificial intelligence Convolutional neural network Endoscopic ultrasound Gastrointestinal subepithelial lesions Gastrointestinal stromal tumor |
| title | Endoscopic ultrasound-based artificial intelligence for gastrointestinal subepithelial lesions |
| title_full | Endoscopic ultrasound-based artificial intelligence for gastrointestinal subepithelial lesions |
| title_fullStr | Endoscopic ultrasound-based artificial intelligence for gastrointestinal subepithelial lesions |
| title_full_unstemmed | Endoscopic ultrasound-based artificial intelligence for gastrointestinal subepithelial lesions |
| title_short | Endoscopic ultrasound-based artificial intelligence for gastrointestinal subepithelial lesions |
| title_sort | endoscopic ultrasound based artificial intelligence for gastrointestinal subepithelial lesions |
| topic | Artificial intelligence Convolutional neural network Endoscopic ultrasound Gastrointestinal subepithelial lesions Gastrointestinal stromal tumor |
| url | http://www.sciencedirect.com/science/article/pii/S2950489925000193 |
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