Machine Learning Creates a Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett’s Oesophagus amongst Non-expert Endoscopists
Gastroenterology Research and Practice2018Vol. 2018, pp. 1–9
Citations Over TimeTop 10% of 2018 papers
Vinay Sehgal, Avi Rosenfeld, David Graham, Gideon Lipman, Raf Bisschops, Krish Ragunath, Manuel Rodriguez‐Justo, Marco Novelli, Matthew Banks, Rehan Haidry, Laurence Lovat
Abstract
ML is able to define rules learnt from expert opinion. These generate a simple algorithm to accurately predict dysplasia. Once taught to non-experts, the algorithm significantly improves their rate of dysplasia detection. This opens the door to standardised training and assessment of competence for those who perform endoscopy in BE. It may shorten the learning curve and might also be used to compare competence of trainees with recognised experts as part of their accreditation process.
Related Papers
- → Eradication of Dysplastic Barrett’s Oesophagus Using Photodynamic Therapy: Long-Term Follow-Up(2003)70 cited
- → Radiofrequency ablation is associated with decreased neoplastic progression in patients with Barrett's oesophagus and confirmed low-grade dysplasia(2015)
- → PTU-155 Progression Of Low Grade Dysplasia To High Grade Dysplasia In Barrett’s Oesophagus In A Single Centre(2014)
- Addressing Controversies in the Management of Barrett's Oesophagus with Low Grade Dysplasia(2017)
- → The Clinical associations with dysplasia in Barrett’s oesophagus in an Irish Endoscopy Population(2024)