Gastroenterology and Endoscopy Unit, Azienda Ospedaliero Universitaria Policlinico di Modena, Modena 41121, Italy.
Gastroenterology and Endoscopy Unit, Presidio Ospedaliero San Giuseppe Moscati (Aversa, CE) - ASL Caserta, Caserta 81100, Italy.
World J Gastroenterol. 2024 May 14;30(18):2482-2484. doi: 10.3748/wjg.v30.i18.2482.
The present letter to the editor is related to the study with the title "Automatic detection of small bowel (SB) lesions with different bleeding risk based on deep learning models". Capsule endoscopy (CE) is the main tool to assess SB diseases but it is a time-consuming procedure with a significant error rate. The development of artificial intelligence (AI) in CE could simplify physicians' tasks. The novel deep learning model by Zhang seems to be able to identify various SB lesions and their bleeding risk, and it could pave the way to next perspective studies to better enhance the diagnostic support of AI in the detection of different types of SB lesions in clinical practice.
这封给编辑的信与题为“基于深度学习模型的自动检测具有不同出血风险的小肠(SB)病变”的研究有关。胶囊内镜(CE)是评估 SB 疾病的主要工具,但它是一个耗时且错误率很高的程序。CE 中人工智能(AI)的发展可以简化医生的任务。Zhang 等人提出的新型深度学习模型似乎能够识别各种 SB 病变及其出血风险,并为进一步的研究铺平道路,以便更好地增强 AI 在临床实践中对不同类型 SB 病变检测的诊断支持。