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

Table 1 Results from the two validation cohorts, mean (minimum-maximum)

Dataset	Size (mm)	Mean absolute percentage deviation (%)
	Gr.Trth	AplDepth Pro	UniK3D	DphAnyV2	DphAnyV2 3DVCol	Expert	Trainee
Endoscopic submucosal dissection dataset	31.4 (7.1-73.3)	68.3	75.3	77.0	36.0	27.3	37.5
External dataset	5.2 (2.8-11.7)	102.4	55.0	37.6	33.9

The first column displays the mean ground truth size, with the minimum and maximum sizes. The second to fifth rows show the mean absolute percentage errors from the three models, Apple Depth Pro, UniK3D, and Depth Anything V2 with no fine-tuning, as well as the fine-tuned Depth Anything V2 model (DphAnyV2_3DVCol). The last two rows show the expert and trainee results for the endoscopic submucosal dissection dataset.

Table 2 Results from the two validation cohorts

Dataset	Mean correctly classified binned Sizes (%)
	AplDepth Pro	UniK3D	DphAnyV2	DphAnyV2 3DVCol	Expert	Trainee
Endoscopic submucosal dissection dataset	53.5	50.3	37.0	59.8	61.9	39.9
External dataset	42.9	52.4	54.8	59.5

Displayed is the overall correctly classified binned sizes, i.e. if the models or humans have correctly predicted the polyp size within the bins: (1) 0-5 mm; (2) 6-9 mm; (3) 10-19 mm; (4) 20-39 mm; and (5) > 39 mm.