Evaluation of an assistant diagnosis system for gastric neoplastic lesions under white light endoscopy based on artificial intelligence / 中华消化内镜杂志

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.

Outcomes of simultaneous endoscopic submucosal dissection for synchronous multiple gastric neoplastic lesions: a retrospective comparative study.

BACKGROUND AND AIMS: Simultaneous endoscopic submucosal dissection (ESD) is occasionally used in synchronous multiple gastric neoplastic lesions (SMGL). Therefore, we aim to evaluate the safety and efficacy of simultaneous ESD for SMGL compared with ESD for single lesions. METHODS: A total of 1058 patients who received ESD from November 2006 to September 2019 were retrospectively evaluated in this study, including 997 single gastric epithelial lesions treated by single ESD (unifocal group) and 125 SMGL from 61 patients treated by simultaneous ESD (multifocal group). RESULTS: The mean procedure time was 49.2 ± 41.30 min and 89.5 ± 66.33 min in unifocal group and multifocal group, respectively (p < 0.001). There was no significant difference in postoperative stenosis rate (1.0% vs. 0.0%, p = 1.000), intraoperative bleeding (endoscopic resection bleeding-c3 grade) rate (0.5% vs. 1.6%, p = 0.696), postoperative bleeding rate (1.3% vs. 0.0%, p = 0.461), and perforation rate (0.9% vs. 1.6%, p = 0.449) between the two groups. In addition, en block resection rate (p = 0.825), complete resection rate (p = 0.856) and curative resection rate (p = 0.709) were comparable between the two groups. During the follow-up, the local recurrence rate per patient: p = 0.363; per lesion: p = 0.235) was not significantly different between the two groups, however, the cumulative incidence of metachronous lesions after treatment was significantly higher in the multifocal group than the other group (10.0% vs. 3.2%, p = 0.004). CONCLUSIONS: Simultaneous ESD is safe and effective in the treatment of SMGL. However, separate ESD is recommended for SMGL with longer procedure time. Besides, the metachronous gastric neoplastic lesions should be paid attention to during follow-up.

Diagnosis and segmentation effect of the ME-NBI-based deep learning model on gastric neoplasms in patients with suspected superficial lesions - a multicenter study.

Background: Endoscopically visible gastric neoplastic lesions (GNLs), including early gastric cancer and intraepithelial neoplasia, should be accurately diagnosed and promptly treated. However, a high rate of missed diagnosis of GNLs contributes to the potential risk of the progression of gastric cancer. The aim of this study was to develop a deep learning-based computer-aided diagnosis (CAD) system for the diagnosis and segmentation of GNLs under magnifying endoscopy with narrow-band imaging (ME-NBI) in patients with suspected superficial lesions. Methods: ME-NBI images of patients with GNLs in two centers were retrospectively analysed. Two convolutional neural network (CNN) modules were developed and trained on these images. CNN1 was trained to diagnose GNLs, and CNN2 was trained for segmentation. An additional internal test set and an external test set from another center were used to evaluate the diagnosis and segmentation performance. Results: CNN1 showed a diagnostic performance with an accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 90.8%, 92.5%, 89.0%, 89.4% and 92.2%, respectively, and an area under the curve (AUC) of 0.928 in the internal test set. With CNN1 assistance, all endoscopists had a higher accuracy than for an independent diagnosis. The average intersection over union (IOU) between CNN2 and the ground truth was 0.5837, with a precision, recall and the Dice coefficient of 0.776, 0.983 and 0.867, respectively. Conclusions: This CAD system can be used as an auxiliary tool to diagnose and segment GNLs, assisting endoscopists in more accurately diagnosing GNLs and delineating their extent to improve the positive rate of lesion biopsy and ensure the integrity of endoscopic resection.