A Comprehensive Guide to Artificial Intelligence in Endoscopic Ultrasound

Kareem Khalaf; Maria Terrin; Manol Jovani; Tommy Rizkala; Marco Spadaccini; Katarzyna M. Pawlak; Matteo Colombo; Marta Andreozzi; Alessandro Fugazza; Antonio Facciorusso; Fabio Grizzi; Cesare Hassan; Alessandro Repici; Silvia Carrara

doi:10.20944/preprints202305.0063.v1

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preprints.org > medicine and pharmacology > gastroenterology and hepatology > doi: 10.20944/preprints202305.0063.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

A Comprehensive Guide to Artificial Intelligence in Endoscopic Ultrasound

Version 1 : Received: 1 May 2023 / Approved: 2 May 2023 / Online: 2 May 2023 (06:05:02 CEST)

A peer-reviewed article of this Preprint also exists.

Khalaf, K.; Terrin, M.; Jovani, M.; Rizkala, T.; Spadaccini, M.; Pawlak, K.M.; Colombo, M.; Andreozzi, M.; Fugazza, A.; Facciorusso, A.; Grizzi, F.; Hassan, C.; Repici, A.; Carrara, S. A Comprehensive Guide to Artificial Intelligence in Endoscopic Ultrasound. J. Clin. Med. 2023, 12, 3757. Khalaf, K.; Terrin, M.; Jovani, M.; Rizkala, T.; Spadaccini, M.; Pawlak, K.M.; Colombo, M.; Andreozzi, M.; Fugazza, A.; Facciorusso, A.; Grizzi, F.; Hassan, C.; Repici, A.; Carrara, S. A Comprehensive Guide to Artificial Intelligence in Endoscopic Ultrasound. J. Clin. Med. 2023, 12, 3757.

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 lo-cated 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 analysing EUS images and identifying suspicious areas that may require further clinical evaluation or biopsy sampling. Deep learning tech-niques, such as convolutional neural networks (CNNs), have shown great potential for tumour 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 in-sights into disease pathology. Conclusions: The integration of AI in EUS images and biopsies has the potential to improve the diagnostic accura-cy, leading to better patient outcomes and to a reduction of repeated procedures in case of non-diagnostic biopsies.

Keywords

Endoscopic Ultrasound; Artificial Intelligence; Biopsy; Pathological Diagnosis.

Subject

Medicine and Pharmacology, Gastroenterology and Hepatology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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