Hypospadias

The following models are related to hypospadias and associated conditions.

Abbas et al. (2024)

Validating estimation of penile curvature Article DOI https://doi.org/10.1016/j.jpurol.2023.09.008 Objective Assess the feasibility and validity of an AI-based model for the automatic measurement of severity of penile curvature AI Approach Segmentation Data Source(s) Seven 3D-printed penile models with various curvature angles; images taken of penile models with a smartphone Model Input 256 x 256 .jpeg images…

Wahyudi et al. (2025)

Penile parameters for circumcision Article DOI https://doi.org/10.1016/j.jpedsurg.2025.162358 Objective To accurately identify normal penile parameters for circumcision eligibility from digital images of the penis using a mobile application. AI Approach CNN with transfer learning Data Source(s) Single institution image series (633 smartphone photos) Model Input 224 x 224 px color JPEG images of penis (dorsal, ventral,…

He et al. (2024)

Hypospadias classification, Hypospadias. Article DOI https://doi.org/10.3389/fped.2024.1297642 Objective To identify hypospadias classification from genital anthropometric features AI Approach Random forrest (RF) and Support vector machines (SVM) Data Source(s) Single institutional series (259 patients, prospective testing +130 patients) Model Input Penoscrotal distance, anogenital distance, 2D:4D finger ratio Model Outcome Distal, midshaft, or proximal hypospadias Model Metrics Distal:…

Abbas et al. (2023)

Penile curvature, Hypospadias Article DOI https://doi.org/10.3389/fped.2023.1149318 Objective To determine accurate penile curvature without requiring arc length AI Approach CNN Data Source(s) Nine 3D-printed penile models (913 image dataset); 4 intraoperative penile images Model Input Image of hypospadias Model Outcome Penile Curvature Model Metrics Abs. mean average error < 5 degreesAI variance (for cases of 30…

Abbas et al. (2023)

Urethral plate characteristic, Hypospadias Article DOI https://doi.org/10.1016/j.jpurol.2023.03.033 Objective To classify POST score in hypospadias repair AI Approach CNN Data Source(s) Institutional hypospadias image database (691-image dataset) Model Input Image of hypospadias Model Outcome Glans Detection, POST score Model Metrics Mean average precision = 99.5%Overall sensitivity = 99.1%Normalized mean error = 0.072 Model Usability https://hypospadias-ai.netlify.app/ AI…

Abbas et al. (2022)

Penile Curvature, Hypospadias Article DOI https://doi.org/10.3389/frai.2022.954497 Objective To capture for capturing automated measurements of penile curvature based on 2-dimensional images AI Approach CNN Data Source(s) Nine 3D-printed penile models (900 image dataset) Model Input 2D image of penis Model Outcome Penis curvature Model Metrics Mean average Precision = 99.4%,Dice Similarity Coefficient = 98.4%Mean absolute error…

Fernandez et al. (2021)

Proximal vs. Distal Hypospadias, Hypospadias Article DOI https://doi.org/10.1016/j.urology.2020.09.019 Objective To classify distal versusproximal hypospadias AI Approach CNN Data Source(s) Hypospadias image database (1169 anonymized images) Model Input Image of hypospadias Model Outcome Distal vs. proximal hypospadias Model Metrics Accuracy = 90% Model Usability NA AI = Artificial Intelligence, CNN = Convolutional neural network