ArticleScientific reports2023
An AI based classifier model for lateral pillar classification of Legg-Calve-Perthes.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Deep learning in the diagnosis, staging, and prognosis of osteonecrosis of the femoral head: a systematic review.BMC musculoskeletal disorders · 2026Pooled it
- EDRNet-based adolescent idiopathic scoliosis screening method using bare-back images.BMC medical informatics and decision making · 2026Article
- Determination of Modified Waldenström Staging in Legg-Calvé-Perthes Disease Using Deep Learning.Journal of the Pediatric Orthopaedic Society of North America · 2026Article
- Femoral head vascular status in early-stage Legg-Calvé-Perthes disease assessed by contrast-enhanced magnetic resonance imaging: comparison with the contralateral side.Orphanet journal of rare diseases · 2025Article
- Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network.Journal of orthopaedic surgery and research · 2025Article
- Ethical guidance for reporting and evaluating claims of AI outperforming human doctors.NPJ digital medicine · 2024Article
- Artificial intelligence-based orthopaedic perpetual design.Journal of clinical orthopaedics and trauma · 2024Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We intended to compare the doctors with a convolutional neural network (CNN) that we had trained using our own unique method for the Lateral Pillar Classification (LPC) of Legg-Calve-Perthes Disease (LCPD). Thousands of training data sets are frequently required for artificial intelligence (AI) applications in medicine. Since we did not have enough real patient radiographs to train a CNN, we devised a novel method to obtain them. We trained the CNN model with the data we created by modifying the normal hip radiographs. No real patient radiographs were ever used during the training phase. We tested the CNN model on 81 hips with LCPD. Firstly, we detected the interobserver reliability of the whole system and then the reliability of CNN alone. Second, the consensus list was used to compare the results of 11 doctors and the CNN model. Percentage agreement and interobserver analysis revealed that CNN had good reliability (ICC = 0.868). CNN has achieved a 76.54% classification performance and outperformed 9 out of 11 doctors. The CNN, which we trained with the aforementioned method, can now provide better results than doctors. In the future, as training data evolves and improves, we anticipate that AI will perform significantly better than physicians.
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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.