The MTDDH dataset for quality evaluation of pelvic X-ray and diagnosis of developmental dysplasia of the hip

Developmental Dysplasia of the Hip (DDH) stands as one of the preeminent hip disorders prevalent in pediatric orthopedics. Automated diagnostic instruments, driven by artificial intelligence methodologies, are capable of providing substantial assistance to clinicians in the diagnosis of DDH. We have developed a dataset designated as Multitasking DDH (MTDDH), which is composed of two sub-datasets. Dataset 1 encompasses 1,250 pelvic X-ray images, with annotations demarcating four discrete regions for the evaluation of pelvic X-ray quality, in tandem with eight pivotal points serving as support for DDH diagnosis. Dataset 2 contains 906 pelvic X-ray images, and each image has been annotated with eight key points for assisting in the diagnosis of DDH. Notably, MTDDH represents the pioneering dataset engineered for the comprehensive evaluation of pelvic X-ray quality while concurrently offering the most exhaustive set of eight key points to bolster DDH diagnosis, thus fulfilling the exigency for enhanced diagnostic precision. Ultimately, we presented the elaborate process of constructing the MTDDH and furnished a concise introduction regarding its application.

发育性髋关节发育不良（Developmental Dysplasia of the Hip, DDH）是小儿骨科领域最为常见的髋关节疾病之一。依托人工智能技术的自动化诊断工具，能够为临床医师开展DDH诊断提供重要辅助支持。本研究构建了一款命名为多任务发育性髋关节发育不良数据集（Multitasking DDH, MTDDH）的数据集，其包含两个子数据集：子数据集1包含1250张骨盆X线影像，标注内容涵盖用于评估骨盆X线影像质量的4个独立区域，以及用于辅助DDH诊断的8个关键解剖点位；子数据集2包含906张骨盆X线影像，每张影像均标注有8个用于辅助DDH诊断的关键点位。值得注意的是，MTDDH是首个兼具骨盆X线影像质量综合评估能力与目前最为完备的8个关键解剖点位标注的DDH专用数据集，可满足临床对提升诊断精度的迫切需求。最后，本文详细阐述了MTDDH的构建流程，并对其应用场景进行了简要介绍。

搜集汇总

背景与挑战

背景概述

MTDDH数据集是一个专门用于骨盆X光质量评估和发育性髋关节发育不良（DDH）诊断的医疗影像数据集。它包含两个子数据集：Dataset 1有1,250张图像，标注了四个质量评估区域和八个诊断关键点；Dataset 2有906张图像，每个图像标注了八个关键点。该数据集是首个旨在全面评估骨盆X光质量并提供详尽关键点以提升DDH诊断精度的创新资源，适用于人工智能驱动的自动化诊断工具开发。