Evidence map›Paper›PMID 42870151›Full record

ArticleJournal of imaging informatics in medicine2026

AI-based Hip Fracture Classification from Pelvic X-ray Images Using a Hybrid PEFT-ViT and XGBoost Model.

Ziyad M Mohaidat, Mohammad Amin, Ruba Khasawneh, Ahmad Manasrah, Rabia Emhamed Al Mamlook, Mahmoud Atatreh, Bara'a Al-Smadi, Khaled Freihat, Ali M Sharadga, Mohammad Z Mohaidat and 3 more

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Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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5 · Who and what money

Authors and funding

13 authors.

Ziyad M MohaidatOrthopedic Division, Special Surgery Department, Faculty of Medicine, Jordan University of Science and Technology, King Abdullah University Hospital, Irbid, 22110, Jordan. zmmohaidat@just.edu.jo.
Mohammad AminComputer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, 21163, Jordan.
Ruba KhasawnehDepartment of Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, King Abdullah University Hospital, Irbid, 22110, Jordan.
Ahmad ManasrahOrthopedic Division, Special Surgery Department, Faculty of Medicine, Jordan University of Science and Technology, King Abdullah University Hospital, Irbid, 22110, Jordan.
Rabia Emhamed Al MamlookDepartment of Business Administration, Trine University, Angola, IN, USA.
Mahmoud AtatrehOrthopedic Division, Special Surgery Department, Faculty of Medicine, Jordan University of Science and Technology, King Abdullah University Hospital, Irbid, 22110, Jordan.
Bara'a Al-SmadiComputer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, 21163, Jordan.
Khaled FreihatFaculty of Medicine, Jordan University of Science and Technology, Irbid, 22110, Jordan.
Ali M SharadgaDepartment of Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, King Abdullah University Hospital, Irbid, 22110, Jordan.
Mohammad Z MohaidatFaculty of Medicine, Jordan University of Science and Technology, Irbid, 22110, Jordan.
Mahmoud A ObeidatFaculty of Medicine, Jordan University of Science and Technology, Irbid, 22110, Jordan.
Amal AlishwaitComputer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, 21163, Jordan.
Salem AlhatamlehComputer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, 21163, Jordan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate radiographic classification of hip fractures into femoral neck (intracapsular) versus intertrochanteric (extracapsular) types is essential for surgical planning, yet most AI studies focus on binary detection using large datasets from high-resource regions, with no prior work from the Middle East. This study proposes a Hybrid PEFT-ViT and XGBoost framework for three-class classification of hip radiographs (Normal, Intertrochanteric fracture, Neck of femur fracture), using 768 X-ray images collected from King Abdullah University Hospital (KAUH) in Northern Jordan. A LoRA-fine-tuned ViT-Base/16 backbone extracted fused class-token and patch-token features, which were classified using XGBoost. On an independent test set of 77 radiographs, the proposed model achieved an accuracy of 88.31% (95% CI 79.3-93.7%), a sensitivity of 87.75%, a specificity of 94.28%, and an F1-score of 87.75%, the highest among the evaluated CNN-based (MobileNetV2, DenseNet121) and transformer-based (ViT, DeiT, Swin) baselines. These results show that clinically meaningful hip fracture classification can be achieved using a single-center dataset, providing a practical framework for resource-limited settings, although external validation is still required.

Indexed as

Deep learningHip fractureLow-Rank AdaptationRadiograph classificationVision TransformerXGBoost

Identifiers

PMID42870151

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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.