Efficacy of endoscopic ultrasound with artificial intelligence for the diagnosis of gastrointestinal stromal tumors

Abstract

Background: Although endoscopic ultrasound (EUS) is reported to be suitable for determining the layer from which subepithelial lesions (SELs) originate, it is difficult to distinguish gastrointestinal stromal tumor (GIST) from non-GIST using only EUS images. If artificial intelligence (AI) can be used for the diagnosis of SELs, it should provide several benefits, including objectivity, simplicity, and quickness. In this pilot study, we propose an AI diagnostic system for SELs and evaluate its efficacy.

Methods: Thirty sets each of EUS images with SELs ≥ 20 mm or < 20 mm were prepared for diagnosis by an EUS diagnostic system with AI (EUS-AI) and three EUS experts. The EUS-AI and EUS experts diagnosed the SELs using solely the EUS images. The concordance rates of the EUS-AI and EUS experts' diagnoses were compared with the pathological findings of the SELs.

Results: The accuracy, sensitivity, and specificity for SELs < 20 mm were 86.3, 86.3, and 62.5%, respectively for the EUS-AI, and 73.3, 68.2, and 87.5%, respectively, for the EUS experts. In contrast, accuracy, sensitivity, and specificity for SELs ≥ 20 mm were 90.0, 91.7, and 83.3%, respectively, for the EUS-AI, and 53.3, 50.0, and 83.3%, respectively, for the EUS experts. The area under the curve for the diagnostic yield of the EUS-AI for SELs ≥ 20 mm (0.965) was significantly higher than that (0.684) of the EUS experts (P = 0.007).

Conclusion: EUS-AI had a good diagnostic yield for SELs ≥ 20 mm. EUS-AI has potential as a good option for the diagnosis of SELs.

Keywords: Artificial intelligence; Convolutional neural network; Deep learning; Endoscopic ultrasound; Gastrointestinal tumors; Subepithelial lesion.