Artificial intelligence model on images of functional dyspepsia  

作　　者：Hiroshi Mihara Sohachi Nanjo Iori Motoo Takayuki Ando Haruka Fujinami Ichiro Yasuda 

机构地区：[1]Center for Medical Education,Sapporo Medical University,Sapporo 060-8556,Hokkaido,Japan [2]3rd Department of Internal Medicine,Graduate School of Medicine,University of Toyama,Toyama 9300194,Japan

出　　处：《Artificial Intelligence in Gastrointestinal Endoscopy》2025年第1期1-11,共11页胃肠道内窥镜检查中的人工智能(英文)

摘　　要：BACKGROUND Recently,it has been suggested that the duodenum may be the pathological locus of functional dyspepsia(FD).Additionally,an image-based artificial intelligence(AI)model was shown to discriminate colonoscopy images of irritable bowel syndrome from healthy subjects with an area under the curve(AUC)0.95.AIM To evaluate an AI model to distinguish duodenal images of FD patients from healthy subjects.METHODS Duodenal images were collected from hospital records and labeled as"functional dyspepsia"or non-FD in electronic medical records.Helicobacter pylori(HP)infection status was obtained from the Japan Endoscopy Database.Google Cloud AutoML Vision was used to classify four groups:FD/HP current infection(n=32),FD/HP uninfected(n=35),non-FD/HP current infection(n=39),and non-FD/HP uninfected(n=33).Patients with organic diseases(e.g.,cancer,ulcer,postoperative abdomen,reflux)and narrow-band or dye-spread images were excluded.Sensitivity,specificity,and AUC were calculated.RESULTS In total,484 images were randomly selected for FD/HP current infection,FD/HP uninfected,non-FD/current infection,and non-FD/HP uninfected.The overall AUC for the four groups was 0.47.The individual AUC values were as follows:FD/HP current infection(0.20),FD/HP uninfected(0.35),non-FD/current infection(0.46),and non-FD/HP uninfected(0.74).Next,using the same images,we constructed models to determine the presence or absence of FD in the HP-infected or uninfected patients.The model exhibited a sensitivity of 58.3%,specificity of 100%,positive predictive value of 100%,negative predictive value of 77.3%,and an AUC of 0.85 in HP uninfected patients.CONCLUSION We developed an image-based AI model to distinguish duodenal images of FD from healthy subjects,showing higher accuracy in HP-uninfected patients.These findings suggest AI-assisted endoscopic diagnosis of FD may be feasible.

关 键 词：Artificial Intelligence Cloud-based DUODENUM Functional dyspepsia 

分 类 号：R57[医药卫生—消化系统]