ArticleJournal of digital imaging2019
Beyond Human Perception: Sexual Dimorphism in Hand and Wrist Radiographs Is Discernible by a Deep Learning Model.
Article in Journal of digital imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of machine learning approaches to identify psoriatic arthritis: a meta-analysis.Clinical and experimental medicine · 2025Pooled it
- Sex estimation from hand-wrist radiographic images: an artificial intelligence approach.International journal of legal medicine · 2026Article
- Artificial intelligence in multidisciplinary tumor boards enhancing decision making and clinical outcomes in oncology.iScience · 2025Review
- Predicting biological sex in pediatric skeleton X-rays using artificial intelligence.Scientific reports · 2025Article
- Deep learning for gender estimation using hand radiographs: a comparative evaluation of CNN models.BMC medical imaging · 2025Article
- The promise and limitations of artificial intelligence in musculoskeletal imaging.Frontiers in radiology · 2023Review
- The Use of Artificial Intelligence in Dentistry Practices.The Eurasian journal of medicine · 2022Article
- Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology.Pediatric radiology · 2022Review
- The current and future roles of artificial intelligence in pediatric radiology.Pediatric radiology · 2022Review
- Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges.Frontiers in oncology · 2022Review
- Use of deep learning in forensic sex estimation of virtual pelvic models from the Han population.Forensic sciences research · 2022Article
- Construction of Correlation Analysis Model of College Students' Sports Performance Based on Convolutional Neural Network.Computational intelligence and neuroscience · 2022Article
- Radiology "forensics": determination of age and sex from chest radiographs using deep learning.Emergency radiology · 2021Article
- [The Latest Trends in Attention Mechanisms and Their Application in Medical Imaging].Taehan Yongsang Uihakhoe chi · 2020Review
- Current applications and future directions of deep learning in musculoskeletal radiology.Skeletal radiology · 2020Review
- Next-generation imaging of the skeletal system and its blood supply.Nature reviews. Rheumatology · 2019Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the well-established impact of sex and sex hormones on bone structure and density, there has been limited description of sexual dimorphism in the hand and wrist in the literature. We developed a deep convolutional neural network (CNN) model to predict sex based on hand radiographs of children and adults aged between 5 and 70 years. Of the 1531 radiographs tested, the algorithm predicted sex correctly in 95.9% (κ = 0.92) of the cases. Two human radiologists achieved 58% (κ = 0.15) and 46% (κ = - 0.07) accuracy. The class activation maps (CAM) showed that the model mostly focused on the 2nd and 3rd metacarpal base or thumb sesamoid in women, and distal radioulnar joint, distal radial physis and epiphysis, or 3rd metacarpophalangeal joint in men. The radiologists reviewed 70 cases (35 females and 35 males) labeled with sex along with heat maps generated by CAM, but they could not find any patterns that distinguish the two sexes. A small sample of patients (n = 44) with sexual developmental disorders or transgender identity was selected for a preliminary exploration of application of the model. The model prediction agreed with phenotypic sex in only 77.8% (κ = 0.54) of these cases. To the best of our knowledge, this is the first study that demonstrated a machine learning model to perform a task in which human experts could not fulfill.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.