Automated quantification of penile curvature using artificial intelligence

ObjectiveTo develop and validate an artificial intelligence (AI)-based algorithm for capturing automated measurements of Penile curvature (PC) based on 2-dimensional images. Materials and methodsNine 3D-printed penile models with differing curvature angles (ranging from 18 to 88 degrees) were used to compile a 900-image dataset featuring multiple camera positions, inclination angles, and background/lighting conditions. The proposed framework of PC angle estimation consisted of three stages: automatic penile area localization, shaft segmentation, and curvature angle estimation. The penile model images were captured using a smartphone camera and used to train and test a Yolov5 model that automatically cropped the penile area from each image. Next, an Unet-based segmentation model was trained, validated, and tested to segment the penile shaft, before a custom Hough-Transform-based angle estimation technique was used to evaluate degree of PC. ResultsThe proposed framework displayed robust performance in cropping the penile area [mean average precision (mAP) 99.4%] and segmenting the shaft [Dice Similarity Coefficient (DSC) 98.4%]. Curvature angle estimation technique generally demonstrated excellent performance, with a mean absolute error (MAE) of just 8.5 when compared with ground truth curvature angles. ConclusionsConsidering current intra- and inter-surgeon variability of PC assessments, the framework reported here could significantly improve precision of PC measurements by surgeons and hypospadiology researchers.

Automated summary

This study developed and validated an AI-based algorithm for automated measurement of penile curvature. The proposed framework showed robust performance in capturing and segmenting the penile area, as well as estimating the degree of curvature. It has the potential to significantly improve the precision of penile curvature measurements by surgeons and researchers in hypospadiology.