Feasibility of a Deep‐Learning Model for Objective Visualization‐Quality Assessment During Cholangioscopy‐Guided Lithotripsy: A Proof‐of‐Concept Study

During peroral cholangioscopy‐guided electrohydraulic lithotripsy (EHL), stone fragmentation obscures the visual field (the “snowstorm effect”), and visualization quality has been assessed only subjectively. This proof‐of‐concept study explored the feasibility of a deep‐learning method for objectively quantifying visualization quality during EHL and, as an exploratory application, compared viscous gel immersion with saline irrigation. A ResNet‐18 model was trained on expert‐annotated frames (5‐point scale) from three development cases. For independent validation, 120 frames from 12 previously unseen comparison cases were scored by two blinded endoscopists (inter‐reader weighted κ = 0.98). The model showed only moderate agreement with their consensus (weighted κ = 0.60; accuracy 70.8%); because the validation sample was enriched for predicted‐usable frames, metrics re‐weighted to the natural frame distribution gave sensitivity 29.8% and specificity 93.5%, indicating systematic under‐detection of usable views. Agreement did not differ systematically between gel and saline frames. The High‐Quality View Rate (HQVR) was the proportion of EHL‐phase frames scored Good/Excellent. In a patient‐level comparison, median HQVR was 22.7% for gel versus 8.4% for saline (exact Mann–Whitney U, p = 0.15, not significant); in post hoc exploratory analyses, a threshold‐independent mean predicted score differed (1.65 vs. 1.35, p = 0.005) and the longest continuous usable interval favored gel (median 24.4 vs. 5.2 s). Mild cholangitis occurred in 1/5 gel and 4/7 saline patients. Training such a model is feasible, but it is not yet a validated measurement tool, and the gel‐versus‐saline signal is hypothesis‐generating only. Larger multi‐case training and prospective validation are required. Trial Registration : N/A