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).

Keywords: Artificial intelligence; Convolutional neural network; Detection; Early gastric cancer.

Fig. 1

Workflow for the development and validation of the DCNN system for diagnosing EGC. DCNN: Deep convolutional neural networks; EGC: Early gastric cancer.

Fig. 2

Architecture and workflow of the DCNN system. DCNN: Deep convolutional neural networks. .

Fig. 3

(a) Predictive results of the DCNN system and corresponding positive pathological tissues. (b) Predictive results of the DCNN system and corresponding annotations of experts. DCNN: Deep convolutional neural networks. .

Fig. 4

Receiver operating characteristic curves illustrating the ability of the DCNN system to diagnose EGC. Sample size: 4153 cancer images and 5264 non-cancer images in NJDTH; 203 cancer images and 203 non-cancer images in WXPH; 228 cancer images and 228 non-cancer images in TZPH; 226 cancer images and 226 non-cancer images in GCPH. NJDTH: Nanjing University Medical School Affiliated Drum Tower Hospital; WXPH: Wuxi People's Hospital; TZPH: Taizhou People's Hospital; GCPH: Gaochun People's Hospital; DCNN: Deep convolutional neural networks; EGC: Early gastric cancer.