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Abstract
Diagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations. We investigated the ability of AI to detect superficial esophageal squamous cell carcinoma (ESCC) from esophagogastroduodenoscopy (EGD) videos. We retrospectively collected 8428 EGD images of esophageal cancer to develop a convolutional neural network through deep learning. We evaluated the detection accuracy of the AI diagnosing system compared with that of 18 endoscopists. We used 144 EGD videos for the two validation sets. First, we used 64 EGD observation videos of ESCCs using both white light imaging (WLI) and narrow-band imaging (NBI). We then evaluated the system using 80 EGD videos from 40 patients (20 with superficial ESCC and 20 with non-ESCC). In the first set, the AI system correctly diagnosed 100% ESCCs. In the second set, it correctly detected 85% (17/20) ESCCs. Of these, 75% (15/20) and 55% (11/22) were detected by WLI and NBI, respectively, and the positive predictive value was 36.7%. The endoscopists correctly detected 45% (25–70%) ESCCs. With AI real-time assistance, the sensitivities of the endoscopists were significantly improved without AI assistance (p < 0.05). AI can detect superficial ESCCs from EGD videos with high sensitivity and the sensitivity of the endoscopist was improved with AI real-time support.
Highlights
Diagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations
esophageal squamous cell carcinoma (ESCC) diagnosed in advanced stages often requires invasive treatment and has a poor prognosis; early detection is important for optimal prognosis[4]
We evaluated the ability of AI to detect ESCC from esophagogastroduodenoscopy (EGD) videos
Summary
Diagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations. We investigated the ability of AI to detect superficial esophageal squamous cell carcinoma (ESCC) from esophagogastroduodenoscopy (EGD) videos. We used 64 EGD observation videos of ESCCs using both white light imaging (WLI) and narrow-band imaging (NBI). AI can detect superficial ESCCs from EGD videos with high sensitivity and the sensitivity of the endoscopist was improved with AI real-time support. Iodine staining can improve ESCC detection with high sensitivity and specificity. It can cause severe discomfort and increases the procedure time[5,6,7]. Computer-aided diagnosis using artificial intelligence (AI) with deep learning methods could be a useful adjunct to endoscopic examination that could improve detection of early cancers[12,13,14]. Analysis of AI diagnosis using videos will aid in realizing the real-time support of AI diagnosing systems for endoscopic examination
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