Background and study aim: We aimed to develop a convolutional neural network (CNN) model for detecting neoplastic lesions during real-time digital single-operator cholangioscopy (DSOC) and clinically validated the model through comparisons with DSOC experts and nonexpert endoscopists.
Patients and methods: In this two-stage study, we first developed and validated CNN1. Then, we performed a multicenter diagnostic trial to compare four DSOC experts and nonexperts against an improved model (CNN2). They classified the lesions in neoplastic and nonneoplastic in accordance with Carlos Robles-Medranda (CRM) and Mendoza disaggregated criteria. The final diagnosis of neoplasia was based on histopathology and twelve-month follow-up outcomes.
Results: In stage I, CNN2 achieved a mAP of 0.88, an IoU of 83.24%, and a total loss of 0.0975. For clinical validation, a total of 170 videos from newly included patients were analyzed with the CNN2. 50% of cases had neoplastic lesions. This model achieved significant accuracy values for neoplastic diagnosis cases, with a 90.5% sensitivity, 68.2% specificity, and 74.0% and 87.8% positive and negative predictive values, respectively. CNN2 model outperformed nonexpert #2 ([ROC-CRM: 0.657 vs ROC-CNN2: 0.794, P<0.05 and ROC-Mendoza: 0.657 vs ROC-CNN2: 0.794, P<0.05]), nonexpert #4 (ROC-CRM: 0.683 vs ROC-CNN2: 0.791, P<0.05), and expert #4 ([ROC-CRM: 0.755 vs ROC-CNN2: 0.848, P<0,05 and ROC-Mendoza: 0.753 vs ROC-CNN2: 0.848, P<0,05]).
Conclusions: The proposed CNN models distinguish neoplastic bile duct lesions with good accuracy and outperformed two nonexperts and one expert endoscopists.
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