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In Gastrointestinal endoscopy ; h5-index 72.0

BACKGROUND AND AIMS : 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.

METHODS : A post-hoc analysis of a single-center prospective study evaluating EUS-nCLE (2015-2019; INDEX study) was conducted using 15,027 video frames from 35 consecutive subjects with histopathologically proven IPMNs (18 with HGD-Ca). We designed 2 CAD-convolutional neural network (CNN) algorithms: (1) guided segmentation-based model (SBM), where the CNN-AI system was trained to detect and measure papillary epithelial thickness and darkness (indicative of cellular and nuclear stratification), and (2) a reasonably agnostic holistic-based model (HBM) where the CNN-AI system automatically extracted nCLE features for risk stratification. For the detection of HGD-Ca in IPMNs, the diagnostic performance of the CNN-CAD algorithms was compared with that of the American Gastroenterological Association (AGA) and revised Fukuoka guidelines.

RESULTS : Compared with guidelines, both n-CLE-guided CNN-CAD algorithms yielded higher sensitivity (HBM: 83.3%, SBM: 83.3%, AGA: 55.6%, Fukuoka: 55.6%) and accuracy (SBM: 82.9%, HBM: 85.7%, AGA: 68.6%, Fukuoka: 74.3%) for diagnosing HGD-Ca, with comparable specificity (SBM: 82.4%, HBM: 88.2%, AGA: 82.4%, Fukuoka: 94.1%). Both CNN-CAD algorithms, the guided (SBM) and agnostic (HBM) models were comparable in risk stratifying IPMNs.

CONCLUSION : EUS-nCLE based CNN-CAD algorithms can accurately risk stratify IPMNs. Future multicenter validation studies and AI model improvements could enhance the accuracy and fully automatize the process for real-time interpretation.

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


artificial intelligence, confocal laser endomicroscopy, convoluted neural network, endoscopic ultrasound, intraductal papillary mucinous neoplasm, machine learning, neural network, pancreatic cancer, pancreatic cyst