ObjectiveMedical images have been increased rapidly in digital medicine era, presenting an opportunity for the intervention of artificial intelligence (AI). In order to explore the value of convolutional neural network (CNN) algorithms in endoscopic images, we developed an AI-assisted comprehensive analysis system for endoscopic images and explored its performance in clinical real scenarios.MethodsA total of 6,270 white light endoscopic images from 516 cases were used to train 14 different CNN models. The images were divided into training set, validation set and test set according to 7:1:2 for exploring the possibility of discrimination of gastric cancer (GC) and benign lesions (nGC), gastric ulcer (GU) and ulcerated cancer (UCa), early gastric cancer (EGC) and nGC, infection of Helicobacter pylori (Hp) and no infection of Hp (noHp), as well as metastasis and no-metastasis at perigastric lymph nodes.ResultsAmong the 14 CNN models, EfficientNetB7 revealed the best performance on two-category of GC and nGC [accuracy: 96.40% and area under the curve (AUC)=0.9959], GU and UCa (accuracy: 90.84% and AUC=0.8155), EGC and nGC (accuracy: 97.88% and AUC=0.9943), and Hp and noHp (accuracy: 83.33% and AUC=0.9096). Whereas, InceptionV3 model showed better performance on predicting metastasis and no-metastasis of perigastric lymph nodes for EGC (accuracy: 79.44% and AUC=0.7181). In addition, the integrated analysis of endoscopic images and gross images of gastrectomy specimens was performed on 95 cases by EfficientNetB7 and RFB-SSD object detection model, resulting in 100% of predictive accuracy in EGC.ConclusionsTaken together, this study integrated image sources from endoscopic examination and gastrectomy of gastric tumors and incorporated the advantages of different CNN models. The AI-assisted diagnostic system will play an important role in the therapeutic decision-making of EGC.
