  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.
