Colonoscopycal polyp prediction-categorysation, space independent virtual reality workspace for medical consiliums

The aim of the present study is to develop and evaluate a computer-based methods for automated and improved detection and classification of different colorectal lesions, especially polyps. For this purpose first, pit pattern and vascularization features of up to 1000 polyps with a size of 10 mm or smaller will be detected and stored in our web based picture database made by a zoom BLI colonoscopy. These polyps are going to be imaged and subsequently removed for histological analysis. The polyp images are analyzed by a newly developed deep learning computer algorithm. The results of the deep learning automatic classification (sensitivity, specificity, negative predictive value, positive predictive value and accuracy) are compared to those of human observers, who were blinded to the histological gold standard.

The database and the examination data will be available for the doctors via a space independent workspace, developed with the Apertusvr programmers library, where the medical personell will have the opportunity for further discussions the results of the examinations.

https://clinicaltrials.gov/ct2/show/NCT03234725?term=NCT03234725&rank=1