ArticleJoint diseases and related surgery2023
The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods.
Article in Joint diseases and related surgery, 2023. 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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Who cites it
11 citing papers in PubMed, 9 citations in OpenAlex.
- Intelligent extraction of CT image landmarks for improving cam-type femoroacetabular impingement assessment.European radiology · 2026Article
- Evaluation of the reliability of novel pelvic X-ray assessment software: CalculOrther.Acta orthopaedica et traumatologica turcica · 2026Article
- Diagnostic performance of multimodal imaging integrating X-ray, CT, and MRI in femoroacetabular impingement syndrome.American journal of translational research · 2026Article
- How Common Is Femoroacetabular Impingement Morphology in Asymptomatic Adults? A 3D CT-Based Insight into Hidden Risk.Journal of clinical medicine · 2025Article
- Detection and classification of femoral neck fractures from plain pelvic X-rays using deep learning and machine learning methods.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2025Article
- Assessing the performance of ChatGPT-4o on the Turkish Orthopedics and Traumatology Board Examination.Joint diseases and related surgery · 2025Article
- Artificial intelligence in medical and biological research: promise and perils of ChatGPT and DeepSeek in advancing healthcare.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Characterizing hip joint morphology using a multitask deep learning model.Journal of hip preservation surgery · 2025Review
- CORR Insights®: Is Quantitative Radiographic Measurement of Acetabular Version Reliable in Anteverted and Retroverted Hips?Clinical orthopaedics and related research · 2024Article
- Outcomes and complications of hip arthroscopy for femoroacetabular impingement syndrome: A narrative review.Journal of clinical orthopaedics and trauma · 2024Review
- Artificial intelligence: Who must have autonomy the machine or the human?Joint diseases and related surgery · 2024Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aim of this study was to evaluate diagnostic ability of deep learning models, particularly convolutional neural network models used for image classification, for femoroacetabular impingement (FAI) using hip radiographs. MATERIALS AND
methodsBetween January 2010 and December 2020, pelvic radiographs of a total of 516 patients (270 males, 246 females; mean age: 39.1±3.8 years; range, 20 to 78 years) with hip pain were retrospectively analyzed. Based on inclusion and exclusion criteria, a total of 888 hip radiographs (308 diagnosed with FAI and 508 considered normal) were evaluated using deep learning methods. Pre-trained VGG-16, ResNet-101, MobileNetV2, and Inceptionv3 models were used for transfer learning.
resultsAs assessed by performance measures such as accuracy, sensitivity, specificity, precision, F-1 score, and area under the curve (AUC), the VGG-16 model outperformed other pre-trained networks in diagnosing FAI. With the pre-trained VGG-16 model, the results showed 86.6% accuracy, 82.5% sensitivity, 89.6% specificity, 85.5% precision, 83.9% F1 score, and 0.92 AUC.
conclusionIn patients with suspected FAI, pelvic radiography is the first imaging method to be applied, and deep learning methods can help in the diagnosis of this syndrome.
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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.