Automated quantification of penile curvature using artificial intelligence

• DOI: 10.3389/frai.2022.954497
• Publication Date: 2022-08-30T05:05:51Z
• Authors: Tariq O. Abbas; Mohamed AbdelMoniem; Muhammad E. H. Ch...
• Keywords: machine learning; Artificial Intelligence; Electronic computers. Computer science; chordee; 006; QA75.5-76.95; hypospadias; artificial intelligence; penile curvature

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°) 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.