PenoMeter: a machine learning and algorithmic tool to advance Peyronie's disease assessment.

Background: Peyronie's disease curvature assessment is a critical step for patient assessment; however, tools for objective, unbiased, and reproducible quantification are currently limited.

Aim: To develop an automated computational tool to identify the penis from a 2D image and to accurately and reproducibly measure the degree of angulation.

Methods: We developed PenoMeter using instance segmentation to identify penile anatomical components, key point detection to identify shaft corners, geometric calculations to locate and measure the angulation of the point of maximal curvature.