High performance in risk stratification of intraductal papillary mucinous neoplasms by confocal laser endomicroscopy image analysis with convolutional neural networks (with video)

Jorge D Machicado, Wei-Lun Chao, David E Carlyn, Tai-Yu Daniel Pan, Sarah Poland, Victoria L Alexander, Tassiana G Maloof, Kelly Dubay, Olivia Ueltschi, Dana M Middendorf, Muhammed O Jajeh, Aadit B Vishwanath, Kyle Porter, Phil A Hart, Georgios I Papachristou, Zobeida Cruz-Monserrate, Darwin L Conwell, Somashekar G Krishna
July, 2021
PDF Cite

Abstract

EUS-guided needle-based confocal laser endomicroscopy (EUS-nCLE) can differentiate high-grade dysplasia/adenocarcinoma (HGD-Ca) in intraductal papillary mucinous neoplasms (IPMNs) but requires manual interpretation. We sought to derive predictive computer-aided diagnosis (CAD) and artificial intelligence (AI) algorithms to facilitate accurate diagnosis and risk stratification of IPMNs.

Type
2
Publication
Gastrointestinal endoscopy
Tai-Yu Daniel Pan
Tai-Yu Daniel Pan
Ph.D. Candidate

PhD candidate working on machine learning and computer vision at The Ohio State University