Artificial Intelligence in the Detection of Barrett's Esophagus: A Systematic Review

Akash Patel; Gagandeep Singh Arora; Mona Roknsharifi; Parneet Kaur; Hamna Javed

doi:10.7759/cureus.47755

Artificial Intelligence in the Detection of Barrett's Esophagus: A Systematic Review

Cureus. 2023 Oct 26;15(10):e47755. doi: 10.7759/cureus.47755. eCollection 2023 Oct.

Authors

Akash Patel¹, Gagandeep Singh Arora^{2

3}, Mona Roknsharifi³, Parneet Kaur^{4

5}, Hamna Javed⁶

Affiliations

¹ Internal Medicine, Eisenhower Health, Rancho Mirage, USA.
² Hepatobiliary Pancreatic Surgery and Liver Transplant, BLK-Max Super Speciality Hospital, New Delhi, IND.
³ Internal Medicine, University of California, Riverside, San Bernardino, USA.
⁴ Emergency, Civil Hospital, Mukerian, IND.
⁵ Internal Medicine, Suburban Community Hospital, Philadelphia, USA.
⁶ Internal Medicine, Saint Agnes Medical Center, Fresno, USA.

Abstract

Barrett's esophagus (BE) remains a significant precursor to esophageal adenocarcinoma, requiring accurate and efficient diagnosis and management. The increasing application of machine learning (ML) technologies presents a transformative opportunity for diagnosing and treating BE. This systematic review evaluates the effectiveness and accuracy of machine learning technologies in BE diagnosis and management by conducting a comprehensive search across PubMed, Scopus, and Web of Science databases up to the year 2023. The studies were organized into five categories: computer-aided systems, natural language processing and text-based systems, deep learning on histology and biopsy images, real-time and video analysis, and miscellaneous studies. Results indicate high sensitivity and specificity across machine learning applications. Specifically, computer-aided systems showed sensitivities ranging from 84% to 100% and specificities from 64% to 90.7%. Natural language processing and text-based systems achieved an accuracy as high as 98.7%. Deep learning techniques applied to histology and biopsy images displayed sensitivities up to greater than 90% and a specificity of 100%. Furthermore, real-time and video analysis technologies demonstrated high performance with assessment speeds of up to 48 frames per second (fps) and a mean average precision of 75.3%. Overall, the reviewed literature underscores the growing capability and efficiency of machine learning technologies in diagnosing and managing Barrett's esophagus, often outperforming traditional diagnostic methods. These findings highlight the promising future role of machine learning in enhancing clinical practice and improving patient care for Barrett's esophagus.

Keywords: ai and machine learning; artificial intelligence in gastroenterology; artificial intelligence in healthcare; artificial intelligence in medicine; barrett's esophagus; barrett's esophagus (be); clinical gastroenterology; deep learning artificial intelligence; general internal medicine; internal medicine.

Publication types

Review