Deep Learning based Automatic Quantification of Urethral Plate Quality using the Plate Objective Scoring Tool (POST)

TL;DR

This study develops a deep learning model to automatically assess urethral plate quality in hypospadias repair using 2D images, aiming to improve objectivity and reproducibility of the evaluation process.

Contribution

The paper introduces a novel deep learning framework for landmark detection and urethral plate assessment, demonstrating high accuracy and robustness in a large dataset.

Findings

Achieved 99.5% mean average precision in glans localization

Predicted landmarks with a normalized mean error of 0.07152

Model shows high robustness and potential for clinical application

Abstract

Objectives: To explore the capacity of deep learning algorithm to further streamline and optimize urethral plate (UP) quality appraisal on 2D images using the plate objective scoring tool (POST), aiming to increase the objectivity and reproducibility of UP appraisal in hypospadias repair. Methods: The five key POST landmarks were marked by specialists in a 691-image dataset of prepubertal boys undergoing primary hypospadias repair. This dataset was then used to develop and validate a deep learning-based landmark detection model. The proposed framework begins with glans localization and detection, where the input image is cropped using the predicted bounding box. Next, a deep convolutional neural network (CNN) architecture is used to predict the coordinates of the five POST landmarks. These predicted landmarks are then used to assess UP quality in distal hypospadias. Results: The proposed model accurately localized the glans area, with a mean average precision (mAP) of 99.5% and an overall sensitivity of 99.1%. A normalized mean error (NME) of 0.07152 was achieved in predicting the coordinates of the landmarks, with a mean squared error (MSE) of 0.001 and a 20.2% failure rate at a threshold of 0.1 NME. Conclusions: This deep learning application shows robustness and high precision in using POST to appraise UP quality. Further assessment using international multi-centre image-based databases is ongoing. External validation could benefit deep learning algorithms and lead to better assessments, decision-making and predictions for surgical outcomes.