Objective:To develop an artificial intelligence model based on multi-modal endoscopic images for identifying gastric neoplasms and to compare its diagnostic efficacy with traditional models and endoscopists.Methods:A total of 3 267 images of gastric neoplasms and non-neoplastic lesions under white light (WL) endoscopy and weak magnification (WM) endoscopy from 463 patients at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from March 2018 to December 2019 were utilized. Two single-modal models (WL model and WM model) were constructed based on WL and WM images separately. WL and WM images of corresponding lesions were combined into image pairs for creating a multi-modal (MM) characteristics integration model. A test set consisting of 696 images of 102 lesions from 97 patients from March 2020 to March 2021 was used to compare the diagnostic efficacy of the single-modal models and a multi-modal model for gastric neoplastic lesions at both the image and the lesion levels. Additionally, video clips of 80 lesions from 80 patients from January 2022 to June 2022 were employed to compare diagnostic efficacy of the WM model, the MM model and 7 endoscopists at the lesion level for gastric neoplasms.Results:In the image test set, the sensitivity and accuracy of MM model were 84.96% (576/678), and 86.89% (1 220/1 289), respectively, for diagnosing gastric neoplasms at the image level, which were superior to 63.13% (113/179) and 80.59% (353/438) of WM model ( χ2=42.81, P<0.001; χ2=10.33, P=0.001), and also better than those of WL model [70.47% (74/105), χ2=13.52, P<0.001; 67.82% (175/258), χ2=57.27, P<0.001]. The MM model showed a sensitivity of 87.50% (28/32), a specificity of 88.57% (62/70), and an accuracy of 88.24% (90/102) at the lesion level. The specificity ( χ2=22.99, P<0.001) and accuracy ( χ2=19.06, P<0.001) were significantly higher than those of WL model; however, there was no significant difference compared with those of the WM model ( P>0.05). In the video test, the sensitivity, specificity and accuracy of the MM model at the lesion level were 95.00% (19/20), 93.33% (56/60) and 93.75% (75/80). These results were significantly better than those of endoscopists, who had a sensitivity of 77.14% (108/140), a specificity of 79.29% (333/420), and an accuracy of 78.75% (441/560), with significant differences ( χ2=18.62, P<0.001; χ2=35.07, P<0.001; χ2=53.12, P<0.001), and was higher than the sensitivity of advanced endoscopists [83.33% (50/60)] with significant difference ( χ2=4.23, P=0.040). Conclusion:The artificial intelligence model based on multi-modal endoscopic images for the diagnosis of gastric neoplasms shows high efficacy in both image and video test sets, outperforming the average diagnostic performance of endoscopists in the video test.

Objective:To assess the diagnostic efficacy of upper gastrointestinal endoscopic image assisted diagnosis system (ENDOANGEL-LD) based on artificial intelligence (AI) for detecting gastric lesions and neoplastic lesions under white light endoscopy.Methods:The diagnostic efficacy of ENDOANGEL-LD was tested using image testing dataset and video testing dataset, respectively. The image testing dataset included 300 images of gastric neoplastic lesions, 505 images of non-neoplastic lesions and 990 images of normal stomach of 191 patients in Renmin Hospital of Wuhan University from June 2019 to September 2019. Video testing dataset was from 83 videos (38 gastric neoplastic lesions and 45 non-neoplastic lesions) of 78 patients in Renmin Hospital of Wuhan University from November 2020 to April 2021. The accuracy, the sensitivity and the specificity of ENDOANGEL-LD for image testing dataset were calculated. The accuracy, the sensitivity and the specificity of ENDOANGEL-LD in video testing dataset for gastric neoplastic lesions were compared with those of four senior endoscopists.Results:In the image testing dataset, the accuracy, the sensitivity, the specificity of ENDOANGEL-LD for gastric lesions were 93.9% (1 685/1 795), 98.0% (789/805) and 90.5% (896/990) respectively; while the accuracy, the sensitivity and the specificity of ENDOANGEL-LD for gastric neoplastic lesions were 88.7% (714/805), 91.0% (273/300) and 87.3% (441/505) respectively. In the video testing dataset, the sensitivity [100.0% (38/38) VS 85.5% (130/152), χ2=6.220, P=0.013] of ENDOANGEL-LD was higher than that of four senior endoscopists. The accuracy [81.9% (68/83) VS 72.0% (239/332), χ2=3.408, P=0.065] and the specificity [ 66.7% (30/45) VS 60.6% (109/180), χ2=0.569, P=0.451] of ENDOANGEL-LD were comparable with those of four senior endoscopists. Conclusion:The ENDOANGEL-LD can accurately detect gastric lesions and further diagnose neoplastic lesions to help endoscopists in clinical work.

Objective:To construct a real-time artificial intelligence (AI)-assisted endoscepic diagnosis system based on YOLO v3 algorithm, and to evaluate its ability of detecting focal gastric lesions in gastroscopy.Methods:A total of 5 488 white light gastroscopic images (2 733 images with gastric focal lesions and 2 755 images without gastric focal lesions) from June to November 2019 and videos of 92 cases (288 168 clear stomach frames) from May to June 2020 at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University were retrospectively collected for AI System test. A total of 3 997 prospective consecutive patients undergoing gastroscopy at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from July 6, 2020 to November 27, 2020 and May 6, 2021 to August 2, 2021 were enrolled to assess the clinical applicability of AI System. When AI System recognized an abnormal lesion, it marked the lesion with a blue box as a warning. The ability to identify focal gastric lesions and the frequency and causes of false positives and false negatives of AI System were statistically analyzed.Results:In the image test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 92.3% (5 064/5 488), 95.0% (2 597/2 733), 89.5% (2 467/ 2 755), 90.0% (2 597/2 885) and 94.8% (2 467/2 603), respectively. In the video test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 95.4% (274 792/288 168), 95.2% (109 727/115 287), 95.5% (165 065/172 881), 93.4% (109 727/117 543) and 96.7% (165 065/170 625), respectively. In clinical application, the detection rate of local gastric lesions by AI System was 93.0% (6 830/7 344). A total of 514 focal gastric lesions were missed by AI System. The main reasons were punctate erosions (48.8%, 251/514), diminutive xanthomas (22.8%, 117/514) and diminutive polyps (21.4%, 110/514). The mean number of false positives per gastroscopy was 2 (1, 4), most of which were due to normal mucosa folds (50.2%, 5 635/11 225), bubbles and mucus (35.0%, 3 928/11 225), and liquid deposited in the fundus (9.1%, 1 021/11 225).Conclusion:The application of AI System can increase the detection rate of focal gastric lesions.