Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicentre retrospective diagnostic study

Abstract

Background We aimed to develop and validate a real-time deep convolutional neural networks (DCNNs) system for detecting early gastric cancer (EGC). Methods All 45,240 endoscopic images from 1364 patients were divided into a training dataset (35823 images from 1085 patients) and a validation dataset (9417 images from 279 patients). Another 1514 images from three other hospitals were used as external validation. We compared the diagnostic performance of the DCNN system with endoscopists, and then evaluated the performance of endoscopists with or without referring to the system. Thereafter, we evaluated the diagnostic ability of the DCNN system in video streams. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value and Cohen's kappa coefficient were measured to assess the detection performance. Finding The DCNN system showed good performance in EGC detection in validation datasets, with accuracy (85.1%–91.2%), sensitivity (85.9%–95.5%), specificity (81.7%–90.3%), and AUC (0.887–0.940). The DCNN system showed better diagnostic performance than endoscopists and improved the performance of endoscopists. The DCNN system was able to process oesophagogastroduodenoscopy (OGD) video streams to detect EGC lesions in real time. Interpretation We developed a real-time DCNN system for EGC detection with high accuracy and stability. Multicentre prospective validation is needed to acquire high-level evidence for its clinical application. Funding This work was supported by the National Natural Science Foundation of China (grant nos. 81672935 and 81871947), Jiangsu Clinical Medical Center of Digestive System Diseases and Gastrointestinal Cancer (grant no. YXZXB2016002), and Nanjing Science and Technology Development Foundation (grant no. 2017sb332019).

Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network

“…Based on the full-text review, we excluded eight more studies because they were not in adherence with our inclusion criteria. Finally, 15 studies met all inclusion criteria [ 6 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. The flow diagram of the systematic search is presented in Figure 2 .…”

“…Our study findings demonstrate that the CNN model can improve the detection performance of EGC, which is higher than that of endoscopists. Tang et al [ 35 ] reported that the detection performance of EGC is even higher when endoscopists use the CNN model ( Table 4 ). Obtaining high-quality images to detect EGC is difficult, especially for inexperienced endoscopists.…”

A Novel Model Based on Deep Convolutional Neural Network Improves Diagnostic Accuracy of Intramucosal Gastric Cancer (With Video)

“…Fourth, we only included images with histologically proven malignancy, indicating the system could not be used to differentiate malignant lesions from non-cancer mucosa. We have established an AI system in detecting early gastric cancer from non-cancer mucosa in our previous report (30). The two systems can be used together to detect early gastric cancer lesions from non-cancer mucosa first and then differentiate intramucosal GC from advanced lesions, thus may facilitate the endoscopic treatment of GC.…”

Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases

“…However, the majority of included studies were graded as low quality, mainly due to the high risk of selection bias in the training or validation sets and for the lack of clinical real-time validation. Therefore, Tang et al [ 43 ] developed and validated a real-time DL-CNN system for detecting EGC that confirmed high accuracy rates of AI with sensitivity, specificity, and the AUC ranging from 85.9% to 95.5%, 81.7–90.3%, and 0.887–0.940, respectively. Finally, Wu et al [ 44 ] carried out a multicenter, randomized, controlled trial using a deep-learning CNN and deep reinforcement-learning system named “ENDOANGEL”.…”