A Comprehensive Guide to Artificial Intelligence in Endoscopic Ultrasound

Abstract

Background: Endoscopic Ultrasound (EUS) is widely used for the diagnosis of bilio-pancreatic and gastrointestinal (GI) tract diseases, for the evaluation of subepithelial lesions, and for sampling of lymph nodes and solid masses located next to the GI tract. The role of Artificial Intelligence in healthcare in growing. This review aimed to provide an overview of the current state of AI in EUS from imaging to pathological diagnosis and training.

Methods: AI algorithms can assist in lesion detection and characterization in EUS by analyzing EUS images and identifying suspicious areas that may require further clinical evaluation or biopsy sampling. Deep learning techniques, such as convolutional neural networks (CNNs), have shown great potential for tumor identification and subepithelial lesion (SEL) evaluation by extracting important features from EUS images and using them to classify or segment the images.

Results: AI models with new features can increase the accuracy of diagnoses, provide faster diagnoses, identify subtle differences in disease presentation that may be missed by human eyes, and provide more information and insights into disease pathology.

Conclusions: The integration of AI in EUS images and biopsies has the potential to improve the diagnostic accuracy, leading to better patient outcomes and to a reduction in repeated procedures in case of non-diagnostic biopsies.

Keywords: artificial intelligence; biopsy; endoscopic ultrasound; pathological diagnosis.

Figure 1

By combining recognized EUS-image features for pancreatic lesion diagnosis with measurements of non-Euclidean anatomical features, significant progress can be made in distinguishing diverse sub-types that have varying outcomes, A, B, and C. The utilization of fractal geometry, specifically the surface fractal dimension, as a measure of the space-filling property of an irregularly shaped structure, can be effectively merged as a feature within an AI-based neuronal network classification system, to achieve a more precise anatomical classifier system.