Automated measurement of penile curvature using deep learning-based novel quantification method.

ABSTRACT:

Objective

Develop a reliable, automated deep learning-based method for accurate measurement of penile curvature (PC) using 2-dimensional images.

Materials and methods

A set of nine 3D-printed models was used to generate a batch of 913 images of penile curvature (PC) with varying configurations (curvature range 18° to 86°). The penile region was initially localized and cropped using a YOLOv5 model, after which the shaft area was extracted using a UNet-based segmentation model. The penile shaft was then divided into three distinct predefined regions: the distal zone, curvature zone, and proximal zone. To measure PC, we identified four distinct locations on the shaft that reflected the mid-axes of proximal and distal segments, then trained an HRNet model to predict these landma

SUBMITTER: Baray SB 

PROVIDER: S-EPMC10150132 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature