Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video)

Abstract

Background and aims: Few artificial intelligence-based technologies have been developed to improve the efficiency of screening for esophageal squamous cell carcinoma (ESCC). Here, we developed and validated a novel system of computer-aided detection (CAD) using a deep neural network (DNN) to localize and identify early ESCC under conventional endoscopic white-light imaging.

Methods: We collected 2428 (1332 abnormal, 1096 normal) esophagoscopic images from 746 patients to set up a novel DNN-CAD system in 2 centers and prepared a validation dataset containing 187 images from 52 patients. Sixteen endoscopists (senior, mid-level, and junior) were asked to review the images of the validation set. The diagnostic results, including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared between the DNN-CAD system and endoscopists.

Results: The receiver operating characteristic curve for DNN-CAD showed that the area under the curve was >96%. For the validation dataset, DNN-CAD had a sensitivity, specificity, accuracy, PPV, and NPV of 97.8%, 85.4%, 91.4%, 86.4%, and 97.6%, respectively. The senior group achieved an average diagnostic accuracy of 88.8%, whereas the junior group had a lower value of 77.2%. After referring to the results of DNN-CAD, the average diagnostic ability of the endoscopists improved, especially in terms of sensitivity (74.2% vs 89.2%), accuracy (81.7% vs 91.1%), and NPV (79.3% vs 90.4%).

Conclusions: The novel DNN-CAD system used for screening of early ESCC has high accuracy and sensitivity, and can help endoscopists to detect lesions previously ignored under white-light imaging.