Computer Vision in Upper Limb Orthopaedics - A Scoping Review of Imaging-Based Algorithms for Fracture Detection and Radiographic Measurement

Sep 29, 2026

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Computer vision techniques are increasingly applied to medical imaging and may provide valuable assistance in upper limb orthopaedics, where radiographs, CT, MRI, and ultrasound underpin diagnosis and treatment planning. Deep learning has enabled automated interpretation that is becoming more accurate. This scoping review maps and characterises computer vision use, covering clinical problems, imaging modalities, and reported methodological approaches across the literature.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Introduction And Background

Imaging in upper limb orthopaedics

Diagnostic burden and need for automation

Pitfalls of manual interpretation

Clinical impact of misinterpretation

How to cite this article

Review began 11/17/2025 Review ended 12/13/2025 Published 12/20/2025 © Copyright 2025 Akram et al. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0. , which permits unrestricted use , distribution , and reproduction in any medium , provided the original author and source are credited. DOI: 10.7759/cureus.99729 Open Access Review Article Computer Vision in Upper Limb Orthopaedics: AScoping Review of Imaging-Based Algorithms for Fracture Detection and Radiographic Measurement Nimra Akram 1, Bilal Qaddoura 2, Donia Karimaghaei 3, Sirtaaj Mattoo 1 , Abdullah Al-Jumaili 4 , Sarkhell Radha 5 1. Trauma and Orthopaedics, Royal Berkshire NHS Foundation Trust, Reading, GBR 2. Trauma and Orthopaedics, University Hospital Southampton NHS Foundation Trust, Southampton, GBR 3. Trauma and Orthopaedics, Epsom and St Helier University Hospitals NHS Trust, London, GBR 4. Trauma and Orthopaedics, Croydon University Hospital, London, GBR 5. Orthopaedics, Croydon University Hospital, London, GBR Corresponding author: Nimra Akram, [nimra.akram2@nhs.net](nimra.akram2@nhs.net) Abstract Computer vision techniques are increasingly applied to medical imaging and may provide valuable assistance in upper limb orthopaedics, a field where radiographs, CT, MRI, and ultrasound are central to diagnosis and treatment planning. The development of deep learning has made automated interpretation of orthopaedic imaging both feasible and increasingly accurate. This review maps and characterises the current use of computer vision in upper limb orthopaedics, describing the clinical problems addressed, the imaging modalities used, and the methodological approaches reported in the literature. Ovid MEDLINE and Embase were searched from January 1995 to October 2025. Studies were eligible if they applied an automated or semi-automated computer vision technique to upper limb imaging for diagnostic or planning purposes. Conference abstracts and non-orthopaedic applications were excluded. Two reviewers independently screened titles, abstracts, and full texts. Data extraction followed the PRISMA extension for scoping reviews (PRISMA-ScR) framework. Sixteen studies met the inclusion criteria. Most focused on wrist and shoulder imaging, particularly radiograph-based fracture detection and postoperative morphometric assessment. Convolutional neural networks, detection networks (e.g., YOLO), and segmentation architectures (e.g., U-Net) were most commonly used. Performance was generally high across fracture classification and measurement tasks, although most studies were retrospective, single-centre-based, and lacked external validation. Computer vision in upper limb orthopaedics remains at an early but promising stage. Automated systems for fracture detection and radiographic measurement demonstrate encouraging accuracy, yet widespread clinical use is limited by small datasets, lack of prospective validation, and inconsistent reporting. Future research should prioritise reproducible multicentre studies, inclusion of soft-tissue modalities, and exploration of intra-operative and real-time clinical applications. Categories: Radiology, Orthopedics Keywords: artificial intelligence (ai), computer vision, msk radiology, orthopaedic surgery shoulder and elbow and upper extremity, scoping review Introduction And Background Imaging is central to the diagnosis, treatment planning, and postoperative assessment of upper limb conditions. From subtle wrist fractures to complex shoulder arthroplasties, radiographs, CT, MRI, and ultrasound support the orthopaedic decision-making process. However, interpretation of these images is often subjective and dependent on the clinician’s experience. Inter-observer variability and the increasing volume of imaging performed each day have prompted a search for automated methods that can assist clinicians with image interpretation [1] . The clinical and econ

FAQs

What imaging modalities and upper-limb clinical problems are targeted by computer vision in this review?

The review focuses on upper limb orthopaedics problems supported by radiographs, CT, MRI, and ultrasound, covering diagnostic and planning tasks. It also highlights fracture detection and radiographic measurement as major use cases.

Which study types were included or excluded in the scoping review?

Included studies applied automated or semi-automated computer vision to upper limb imaging for diagnostic or planning purposes. Conference abstracts and non-orthopaedic applications were excluded.

Why is widespread clinical adoption currently limited?

Although performance is generally high, most studies are retrospective, single-centre, and lack external validation. The review also notes small datasets, insufficient prospective validation, and inconsistent reporting.

Sep 29, 2026

Upload by jayni