Deep Learning Model for Histologic Diagnosis of Dysplastic Barrett's Esophagus: Multisite Cohort External Validation.
Year: 2026
Type: article
Abstract: INTRODUCTION: The risk of progression to esophageal adenocarcinoma (EAC) in Barrett's esophagus (BE) increases with advancing degrees of dysplasia. There is a critical need to improve the diagnosis of BE dysplasia, given substantial interobserver variability and overcalls of dysplasia during manual community pathologist reads. We aimed to externally validate a previously cross-validated BE dysplasia diagnosis deep learning model (BEDDLM) that predicts dysplasia grade on whole slide images (WSIs). METHODS: We digitized non-dysplastic BE (NDBE), low-grade (LGD), and high-grade dysplasia (HGD) histology slides from three external academic centers. A consensus read by two expert study pathologists was used as the criterion standard. Slide stain characteristics were normalized using cycle-generative adversarial networks (cGANs). Whole slide images were assessed by BEDDLM using an ensemble approach, combining a "You Only Look Once" (YOLO) model followed by a ResNet101 classifier model. RESULTS: We included 489 WSIs. Consensus histopathology revealed 232 NDBE, 117 LGD, and 140 HGD WSIs. The mean age (SD) was 66·9 (11·4); 413 (84·7%) were males. Using the BEDDLM ensemble model, sensitivity and specificity for NDBE were 73.3% (95% CI: 67.09-78.85%); 93.4% (95% CI: 89.62-96.10%); for LGD, 84.6% (95% CI: 76.78-90.62%), and 80.6% (95% CI: 76.26-84.54%); and for HGD, 80.7% (95% CI: 73.19-86.89%) and 94.8% (95% CI: 91.97-96.91%), respectively. The F1 score was 0.81, 0 .69, and 0.83 for NDBE, LGD, and HGD, respectively. DISCUSSION: Our externally validated deep learning model demonstrates substantial accuracy for the diagnosis of BE dysplasia grade on WSIs.
Source: UNC Libraries
DOI: 10.17615/e50w-q914
Authors: Iyer, Prasad G, Otaki, Fouad, Wong, Mary, Lewis, Jason, Moassefi, Mana, Vogelsang, David, Leggett, Cadman L, Erickson, Bradley J, Peter, Shajan, Faghani, Shahriar, Katzka, David A, Codipilly, D Chamil, Garg, Nikita, Patil, Pallavi, Konda, Vani, Wani, Sachin, Brinkerhoff, Brian, Shaheen, Nicholas, Hagen, Catherine, Al Diffalha, Sameer
Institution: University of North Carolina at Chapel Hill
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7155564620