论文标题
使用板客观评分工具（POST）对尿道板质量的深度自动量化（POST）
Deep Learning based Automatic Quantification of Urethral Plate Quality using the Plate Objective Scoring Tool (POST)
论文作者
论文摘要
目标：探索深度学习算法进一步简化和优化尿道板（UP）质量评估的能力，使用板客观评分工具（POST），旨在提高Hypospadias修复中提高评估的客观性和可重复性。方法：五个关键的邮政地标是由691图像数据集的专家标记的，该数据集是接受原发性杂质修复的青春期前男孩。然后，该数据集用于开发和验证基于深度学习的地标检测模型。所提出的框架始于瞥见的定位和检测，其中输入图像使用预测的边界框裁剪。接下来，使用深度卷积神经网络（CNN）结构来预测五个邮政标记的坐标。然后，这些预测的地标被用来评估远端催化性的质量。结果：所提出的模型准确地定位了龟头区域，平均平均精度（MAP）为99.5％，总体灵敏度为99.1％。在预测地标的坐标时，达到了0.07152的归一化平均误差（NME），平均平方误差（MSE）为0.001，在0.1 nme的阈值时达到20.2％的故障率。结论：此深度学习应用程序在使用帖子评估质量时表现出鲁棒性和高精度。使用国际多中心基于图像的数据库进行进一步评估。外部验证可以使深度学习算法受益，并导致更好的评估，决策和对手术结果的预测。
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.
