ReviewKorean journal of radiology2021
Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment.
Review in Korean journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed.
- Hybrid approach with convolutional neural network and regression models for age estimation in panoramic radiographs.International journal of legal medicine · 2026Observational
- Current applications and challenges of artificial intelligence applied to diagnostics in pediatric musculoskeletal imaging.Pediatric radiology · 2026Review
- Deep learning-based automatic measurement of the femoral head ossification center in healthy Korean children: development of a novel radiographic growth chart.European radiology · 2026Article
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- [Artificial intelligence algorithm for bone age estimation].Revista medica del Instituto Mexicano del Seguro Social · 2026Observational
- Transfer Learning From Hand-Trained Deep Learning Models to Estimate Bone Age From Knee Radiographs.Orthopaedic journal of sports medicine · 2026Article
- [Artificial intelligence in musculoskeletal radiology : Current advances and emerging frontiers].Orthopadie (Heidelberg, Germany) · 2026Article
- digiBONE: an automated tool for segmental Greulich-Pyle bone age assessment of Indian children and adolescents.Frontiers in endocrinology · 2026Article
- Comparative analysis of the bone age of wrist bones in Chinese children with different causes of short stature and central precocious puberty.Quantitative imaging in medicine and surgery · 2025Article
- Automatic bone age assessment: a Turkish population study.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Objective Assessment of Bone Age in Orthodontics: A Scoping Review.Journal of pharmacy & bioallied sciences · 2025Review
- The Impact of Artificial Intelligence on Radiologists' Reading Time in Bone Age Radiograph Assessment: A Preliminary Retrospective Observational Study.Journal of imaging informatics in medicine · 2025Observational
- External Validation of an Upgraded AI Model for Screening Ileocolic Intussusception Using Pediatric Abdominal Radiographs: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- Advancing Spine Fracture Detection: The Role of Artificial Intelligence in Clinical Practice.Korean journal of neurotrauma · 2025Review
- Bibliometric analysis of the application of artificial intelligence in orthopedic imaging.Quantitative imaging in medicine and surgery · 2025Article
- Retrospective Clinical Trial to Evaluate the Effectiveness of a New Tanner-Whitehouse-Based Bone Age Assessment Algorithm Trained with a Deep Neural Network System.Diagnostics (Basel, Switzerland) · 2025Article
- Charting the growth through intelligence: A SWOC analysis on AI-assisted radiologic bone age estimation.International journal of legal medicine · 2025Review
- Bone Age Assessment Using Various Medical Imaging Techniques Enhanced by Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Review
- Inter-rater variability and repeatability in the assessment of the Tanner-Whitehouse classification of hand radiographs for the estimation of bone age.Skeletal radiology · 2024Article
- Doctor simulator: Delta-Age-Sex-AdaIn enhancing bone age assessment through AdaIn style transfer.Pediatric radiology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained challenging. The rapidly evolving deep learning technology has shown promising results in automated bone age assessment. In this review article, we will provide information regarding the history of automated bone age assessments, discuss the current status, and present a literature review, as well as the future directions of artificial intelligence-based bone age assessments.
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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.