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Artificial Intelligence and Its Role in Identifying Esophageal Neoplasia

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Abstract

Randomized trials have demonstrated that ablation of dysplastic Barrett’s esophagus can reduce the risk of progression to cancer. Endoscopic resection for early stage esophageal adenocarcinoma and squamous cell carcinoma can significantly reduce postoperative morbidity compared to esophagectomy. Unfortunately, current endoscopic surveillance technologies (e.g., high-definition white light, electronic, and dye-based chromoendoscopy) lack sensitivity at identifying subtle areas of dysplasia and cancer. Random biopsies sample only approximately 5% of the esophageal mucosa at risk, and there is poor agreement among pathologists in identifying low-grade dysplasia. Machine-based deep learning medical image and video assessment technologies have progressed significantly in recent years, enabled in large part by advances in computer processing capabilities. In deep learning, sequential layers allow models to transform input data (e.g., pixels for imaging data) into a composite representation that allows for classification and feature identification. Several publications have attempted to use this technology to help identify dysplasia and early esophageal cancer. The aims of this reviews are as follows: (a) discussing limitations in our current strategies to identify esophageal dysplasia and cancer, (b) explaining the concepts behind deep learning and convolutional neural networks using language appropriate for clinicians without an engineering background, (c) systematically reviewing the literature for studies that have used deep learning to identify esophageal neoplasia, and (d) based on the systemic review, outlining strategies on further work necessary before these technologies are ready for “prime-time,” i.e., use in routine clinical care.

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Acknowledgments

John Cyrus (Research and Education Librarian): Assisted in literature search of following databases: Pubmed/Medline, Embase, Cochrane library and Web of Science for studies. Tilak Shah: ASGE, McGuire Research Institute. Research contract with Allergan. Sana Syed: K23DK117061-01A1 (SS) National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health.

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Authors and Affiliations

  1. Division of Gastroenterology, Virginia Commonwealth University Health System, 1200 East Marshall St, PO Box 980711, Richmond, VA, 23298, USA

    Taseen Syed & Tilak Shah

  2. Division of Gastroenterology, Hunter Holmes McGuire Veterans Affairs Medical Center, Richmond, VA, USA

    Taseen Syed & Tilak Shah

  3. University of Miami Miller School of Medicine, Miami, FL, USA

    Akash Doshi

  4. Department of Medicine, Rush University Medical Center, Chicago, IL, USA

    Shan Guleria

  5. Department of Pediatrics, Division of Gastroenterology, Hepatology and Nutrition, University of Virginia School of Medicine and UVA Child Health Research Center, Charlottesville, VA, USA

    Sana Syed

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  1. Taseen Syed
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Correspondence to Taseen Syed.

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Syed, T., Doshi, A., Guleria, S. et al. Artificial Intelligence and Its Role in Identifying Esophageal Neoplasia. Dig Dis Sci 65, 3448–3455 (2020). https://doi.org/10.1007/s10620-020-06643-2

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