A dataset for quality evaluation of pelvic X-ray and diagnosis of developmental dysplasia of the hip.
Authors
Affiliations (11)
Affiliations (11)
- Department of Data and Information, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China.
- National Clinical Research Center for Child Health, Hangzhou, China.
- Information Management Department, Hebei Children's Hospital, Shijiazhuang, China.
- Department of Orthopedics, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
- National Clinical Research Center for Child Health, Hangzhou, China. [email protected].
- Department of Orthopedics, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China. [email protected].
- Department of Data and Information, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China. [email protected].
- Sino-Finland Joint AI Laboratory for Child Health of Zhejiang Province, Hangzhou, China. [email protected].
- National Clinical Research Center for Child Health, Hangzhou, China. [email protected].
Abstract
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.