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

Abstract

BACKGROUND

Accurate measurement of the lesion size in colonoscopy is important for selecting the guideline-recommended resection methods and surveillance intervals. The commonly applied technique of visual size estimation by endoscopists can be inaccurate by up to 66%.

AIM

To develop, use, and compare artificial intelligence-based methods to improve size estimation.

METHODS

We used three state-of-the-art monocular depth estimation models. Furthermore, one model was fine-tuned by domain-specific training with 3060 white-light-rendered images and depth maps of a virtual colonoscopy through a three-dimensional colon model. We evaluated the models using single white-light images obtained from two independent cohorts: (1) One internal cohort of 133 patients who received an endoscopic submucosal dissection (ESD) (mean lesion diameter 31.4 mm); and (2) One external cohort of 42 patients with polyps (mean diameter 5.2 mm) receiving a snare-assisted polypectomy. The gold standard for size estimation was caliper measurement of the lesions after removal. We analyzed the absolute diameter and European Society of Gastrointestinal Endoscopy guideline conform predefined size bins as an outcome parameters. For comparison in the ESD cohort, three expert and three trainee endoscopists estimated the lesion size.

RESULTS

The mean absolute percentage error (MAPE) in the ESD cohort for the three models ranged from 68.3% to 77.0%. The fine-tuned model reached a MAPE of 37.0%. The experts and trainees achieved 27.3% and 37.5%, respectively. In the external cohort, the MAPE of the three models ranged from 37.6% to 102.4%, while the fine-tuned model achieved the best result with 33.9%.

CONCLUSION

While monocular depth estimation holds potential for automating polyp size prediction, bigger lesions pose an additional challenge and require rigorous domain-specific training using methods such as three-dimensional virtual colonoscopy.

Keywords: Polyp size; Lesion size; Colonoscopy; Artificial intelligence; Monocular depth estimation

Core Tip: Accurate measurement of colorectal polyp size is mandatory influencing the choice of resection technique and surveillance intervals. However, conventional visual estimation by endoscopists is prone to significant error rates up to 66%. In this work, we compare three state-of-the art monocular depth estimation artificial intelligence models for automated size measurement. When applied natively to colonoscopy, they show poor accuracy in size prediction. By domain specific training using virtual colonoscopy images we could significantly improve size prediction. The fine-tuned model achieved a mean absolute percentage error of 33.9%-37.0%. This performance was comparable to trainee endoscopists but remained inferior to experts.