Researchers created an AI tool that predicts recurrence of Barrett’s esophagus following endoscopic eradication therapies with greater than 90% accuracy.
Key Details
- 1AI model predicts recurrence of Barrett’s esophagus and related dysplasia or cancer after EET.
- 2Model achieved over 90% accuracy using data from more than 2,500 patients.
- 3Key risk factors: longer affected tissue length, higher body weight, older age, more treatment sessions, higher-grade dysplasia.
- 4Recurrence occurred in about 30% of patients, with average return two years after therapy.
- 5Validated on internal and external datasets, showing robust performance across groups.
- 6International collaborations underway to further validate AI tool.
Why It Matters

Source
EurekAlert
Related News

Hybrid AI Model Enhances Early Lung Cancer Detection on CT Scans
Researchers developed a hybrid AI model that significantly improves early lung cancer detection from CT scans.

NIH Invests Additional $12.6M in USC-Led Imaging AI for Alzheimer's
NIH has renewed and expanded its support for a USC-led consortium developing AI to decode and treat Alzheimer's using imaging and genomic data.

USC Unveils Joint Biomedical Engineering Department Bridging Medicine, Engineering, and Imaging
USC's medical and engineering schools launch a joint biomedical engineering department to accelerate interdisciplinary research and innovation, including imaging and AI.