ArticleBMC pediatrics2022
Effect of AI-assisted software on inter- and intra-observer variability for the X-ray bone age assessment of preschool children.
Article in BMC pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 12 citations in OpenAlex.
- External Validation of Deeplasia for Automated Bone Age Assessment Compared with Four Commercial AI Systems.Diagnostics (Basel, Switzerland) · 2026Article
- [Artificial intelligence algorithm for bone age estimation].Revista medica del Instituto Mexicano del Seguro Social · 2026Observational
- Intraoperative video-based artificial intelligence model exceeding surgeon accuracy for predicting severe fibrosis in minimally invasive liver surgery.Scientific reports · 2026Article
- Developing new methods for the accurate evaluation of children's bone age based on X-ray images: a study on the accuracy and consistency of artificial intelligence and manual bone age evaluation.Quantitative imaging in medicine and surgery · 2026Article
- A comparative study of the interpretation results of different artificial intelligence bone age assessment software.Journal of pediatric endocrinology & metabolism : JPEM · 2026Article
- Non-inferiority of automated deep learning-based [EJNMMI research · 2026Article
- Interobserver and Intraobserver Reliability of a Novel Classification System for Distal Radioulnar Joint Instability.Life (Basel, Switzerland) · 2026Article
- Transforming Skin Quality Evaluation With AI: From Subjective Grading to Data-Driven Precision.Journal of cosmetic dermatology · 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
- Advancements in Machine Learning for Precision Diagnostics and Surgical Interventions in Interconnected Musculoskeletal and Visual Systems.Journal of clinical medicine · 2025Review
- Artificial intelligence model system for bone age assessment of preschool children.Pediatric research · 2024Article
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Authors and funding
7 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the rapid development of deep learning algorithms and the rapid improvement of computer hardware in the past few years, AI-assisted diagnosis software for bone age has achieved good diagnostic performance. The purpose of this study was to investigate the effect of AI-assisted software on residents' inter-observer agreement and intra-observer reproducibility for the X-ray bone age assessment of preschool children.
methodsThis prospective study was approved by the Institutional Ethics Committee. Six board-certified residents interpreted 56 bone age radiographs ranging from 3 to 6 years with structured reporting by the modified TW3 method. The images were interpreted on two separate occasions, once with and once without the assistance of AI. After a washout period of 4 weeks, the radiographs were reevaluated by each resident in the same way. The reference bone age was the average bone age results of the three experts. Both TW3-RUS and TW3-Carpal were evaluated. The root mean squared error (RMSE), mean absolute difference (MAD) and bone age accuracy within 0.5 years and 1 year were used as metrics of accuracy. Interobserver agreement and intraobserver reproducibility were evaluated using intraclass correlation coefficients (ICCs).
resultsWith the assistance of bone age AI software, the accuracy of residents' results improved significantly. For interobserver agreement comparison, the ICC results with AI assistance among 6 residents were higher than the results without AI assistance on the two separate occasions. For intraobserver reproducibility comparison, the ICC results with AI assistance were higher than results without AI assistance between the 1st reading and 2nd reading for each resident.
conclusionsFor preschool children X-ray bone age assessment, in addition to improving diagnostic accuracy, bone age AI-assisted software can also increase interobserver agreement and intraobserver reproducibility. AI-assisted software can be an effective diagnostic tool for residents in actual clinical settings.
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