Digital pattern recognition for the identification and classification of hypospadias using artificial intelligence vs. experienced pediatric urologist.

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Abstract

To improve hypospadias classification system, we hereby, show the use of machine learning/image recognition to increase objectivity of hypospadias recognition and classification. Hypospadias anatomical variables such as meatal location, quality of urethral plate, glans size and ventral curvature have been identified as predictors for post-operative outcomes but there is still significant subjectivity between evaluators.
A hypospadias image database with 1169 anonymized images (837 distal and 332 proximal) was used. Images were standardized (ventral aspect of the penis including the glans, shaft and scrotum) and classified into distal or proximal and uploaded for training with TensorFlow®. Data from the training was outputted to TensorBoard, to assess for the loss function. The model was then run on a set of 29 “Test” images randomly selected. Same set of images were distributed amongst expert clinicians in pediatric urology. Inter and intrarater analysis were performed using Fleiss Kappa statistical analysis using the same 29 images shown to the algorithm.
After training with 627 images, detection accuracy was 60%. With1169 images, accuracy increased to 90%. Inter-rater analysis amongst expert pediatric urologists was k= 0.86 and intra-rater 0.74. Image recognition model emulates the almost perfect inter-rater agreement between experts.
Our model emulates expert human classification of patients with distal/proximal hypospadias. Future applicability will be on standardizing the use of these technologies and their clinical applicability. The ability of using variables different than only anatomical will feed deep learning algorithms and possibly better assessments and predictions for surgical outcomes.
Copyright © 2020. Published by Elsevier Inc.