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.
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.
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.
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.