Domain specific training with three-dimensional virtual colonoscopy images improves artificial intelligence based automated size prediction for large lesions

Figure 4 Percentage of correctly binned size predictions per size category for four artificial intelligence models (AplDepthPro, UniK3D, DphAnyV2, DphAnyV2_3DVCol). A: Percentage of correctly binned size predictions per size category for four artificial intelligence (AI) models (AplDphPro, UniK3D, DphAnyV2, DphAnyV2_3DVCol), pooled experts, and pooled trainees. Measurements were classified into five bins (≤ 5 mm, 6-9 mm, 10-19 mm, 20-39 mm, > 39 mm). Across the three well-populated bins, experts showed the most consistent accuracy (49%-68%), while trainees performed well for smaller sizes (Bin 3: 64%) but markedly declined for larger measurements (Bin 5: 10%). Among AI models, DphAnyV2_3DVCol achieved the highest accuracy in the larger size ranges; B: Percentage of correctly binned size predictions per size category for four AI models (AplDepthPro, UniK3D, DphAnyV2, DphAnyV2_3DVCol) in a dataset restricted to smaller measurements. All models performed best in Bin 3, with three of four reaching 75% accuracy. Performance was notably weaker in Bin 2 across all models, with AplDepthPro achieving the lowest accuracy (17%).