Automated quantification of penile curvature using artificial intelligence - Abbas, Tariq O.; AbdelMoniem, Mohamed; Chowdhury, Muhammad E. H. (Frontiers in Artificial Intelligence, 2022)
OBJECTIVE To develop and validate an artificial intelligence (AI)-based algorithm for capturing automated measurements of Penile curvature (PC) based on 2-dimensional images. MATERIAL AND METHODS Nine 3D-printed penile models with differing curvature angles (ranging from 18-88°) 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. RESULTS The 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. CONCLUSIONS Considering 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.