Diagnosis of early esophageal neoplasia, including dysplasia and superficial cancer, is a great challenge for endoscopists. Recently, the application of artificial intelligence (AI) using deep learning in the endoscopic field has made significant advancements in diagnosing gastrointestinal cancers. In the present study, we constructed a single-shot multibox detector using a convolutional neural network for diagnosing different histological grades of esophageal neoplasms and evaluated the diagnostic accuracy of this computer-aided system. A total of 936 endoscopic images were used as training images, and these images included 498 white-light imaging (WLI) and 438 narrow-band imaging (NBI) images. The esophageal neoplasms were divided into three classifications: squamous low-grade dysplasia, squamous high-grade dysplasia, and squamous cell carcinoma, based on pathological diagnosis. This AI system analyzed 264 test images in 10 s, and the sensitivity, specificity, and diagnostic accuracy of this system in detecting esophageal neoplasms were 96.2%, 70.4%, and 90.9%, respectively. The accuracy of this AI system in differentiating the histological grade of esophageal neoplasms was 92%. Our system showed better accuracy in diagnosing NBI (95%) than WLI (89%) images. Our results showed the great potential of AI systems in identifying esophageal neoplasms as well as differentiating histological grades.
Esophageal squamous neoplasm presents a spectrum of different diatheses. A precise assessment for individualized treatment depends on the accuracy of the initial diagnosis. Detection relies on comprehensive and accurate white-light, iodine staining, and narrow-band imaging endoscopy. These methods have limitations in addition to its invasive nature and the potential risks related to the method. These limitations include difficulties in precise tumor delineation to enable complete resection, inflammation and malignancy differentiation, and stage determination. The resolution of these problems depends on the surgeon’s ability and experience with available technology for visualization and resection. We proposed a method for identifying early esophageal cancerous lesion by endoscopy and hyperspectral endoscopic imaging. Experimental result shows the characteristic spectrum of a normal esophagus, precancerous lesion, canceration, and intraepithelial papillary capillary loop can be identified through principal component score chart. The narrow-band imaging (NBI) image shows remarkable spectral characteristic distribution, and the sensitivity and specificity of the proposed method are higher than those of other methods by ~0.8 and ~0.88, respectively. The proposed method enables the accurate visualization of target organs, it may be useful to capsule endoscope and telemedicine, which requires highly precise images for diagnosis.
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