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Purpose: Early and accurate assessment of distal radius fractures (DRFs) is crucial for optimal prognosis. Identifying fractures likely to lose threshold alignment (instability) in a cast is vital for treatment decisions, yet prediction tools' accuracy and reliability remain challenging. Artificial intelligence (AI), particularly Convolutional Neural Networks (CNNs), can evaluate radiographic images with high performance. This systematic review aims to summarize studies utilizing CNNs to detect, classify, or predict loss of threshold alignment of DRFs., Methods: A literature search was performed according to the PRISMA. Studies were eligible when the use of AI for the detection, classification, or prediction of loss of threshold alignment was analyzed. Quality assessment was done with a modified version of the methodologic index for non-randomized studies (MINORS)., Results: Of the 576 identified studies, 15 were included. On fracture detection, studies reported sensitivity and specificity ranging from 80 to 99% and 73-100%, respectively; the AUC ranged from 0.87 to 0.99; the accuracy varied from 82 to 99%. The accuracy of fracture classification ranged from 60 to 81% and the AUC from 0.59 to 0.84. No studies focused on predicting loss of thresholds alignement of DRFs., Conclusion: AI models for DRF detection show promising performance, indicating the potential of algorithms to assist clinicians in the assessment of radiographs. In addition, AI models showed similar performance compared to clinicians. No algorithms for predicting the loss of threshold alignment were identified in our literature search despite the clinical relevance of such algorithms., Competing Interests: Declarations. Ethical approval: This study was performed at the Trauma Research Unit Department of Surgery, Erasmus University Medical Center Rotterdam, the Netherlands and at the Orthopedic Department, the University of Groningen, University Medical Center Groningen, the Netherlands. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Introduction: This study compares computed tomography (CT) with plain radiography in its ability to assess distal radius fracture (DRF) malalignment after closed reduction and cast immobilization., Methods: Malalignment is defined as radiographic fracture alignment beyond threshold values according to the Dutch guideline encompassing angulation, inclination, positive ulnar variance and intra-articular step-off or gap. After identifying 96 patients with correct alignment on initial post-reduction radiographs, we re-assessed alignment on post-reduction CT scans., Results: Significant discrepancies were found between radiographs and CT scans in all measurement parameters. Notably, intra-articular step-off and gap variations on CT scans led to the reclassification of the majority of cases from correct alignment to malalignment. CT scans showed malalignment in 53% of cases, of which 73% underwent surgery., Conclusion: When there is doubt about post-reduction alignment based on radiograph imaging, additional CT scanning often reveals malalignment, primarily due to intra-articular incongruency., Competing Interests: Declarations. Ethical approval: Ethical approval to report this case/these cases was obtained from the Medical Ethics Committee of the Erasmus University Medical Center, Rotterdam, the Netherlands (MEC-2020–0258). Informed consent: Written informed consent was obtained from all subjects before the study. Conflict of interest: The authors declare no competing interests., (© 2024. The Author(s).)

Purpose: This study aimed to evaluate the incidence of, and factors associated with, reoperation after distal radius nonunion repair., Methods: We conducted a retrospective cohort study at a multicenter academic institution and identified adult patients who underwent open reduction and internal fixation for distal radius nonunion between January 2005 and August 2021. Thirty-three patients were included in this study. The cohort consisted of 13 males (13/33) and had a median age of 56 years (interquartile ranges: 49-64). Median follow-up was 59 months (interquartile ranges: 23-126)., Results: Unplanned reoperations occurred in eight of 33 patients. The most common reasons for reoperation were irrigation and debridement for infection, revision surgery for persistent nonunion, and unplanned hardware removal. In total, 10 complications occurred in nine patients. The most common complications were infection and persistent nonunion; both occurred in three cases., Conclusions: Complications after distal radius nonunion repair are common. Reoperation after distal radius nonunion repair is required in approximately one of four cases., Type of Study/level of Evidence: Prognosis IV., Competing Interests: Conflicts of Interest No benefits in any form have been received or will be received related directly to this article., (Copyright © 2024 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.)

Purpose: The use of computed tomography (CT) in fractures is time consuming, challenging and suffers from poor inter-surgeon reliability. Convolutional neural networks (CNNs), a subset of artificial intelligence (AI), may overcome shortcomings and reduce clinical burdens to detect and classify fractures. The aim of this review was to summarize literature on CNNs for the detection and classification of fractures on CT scans, focusing on its accuracy and to evaluate the beneficial role in daily practice., Methods: Literature search was performed according to the PRISMA statement, and Embase, Medline ALL, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar databases were searched. Studies were eligible when the use of AI for the detection of fractures on CT scans was described. Quality assessment was done with a modified version of the methodologic index for nonrandomized studies (MINORS), with a seven-item checklist. Performance of AI was defined as accuracy, F1-score and area under the curve (AUC)., Results: Of the 1140 identified studies, 17 were included. Accuracy ranged from 69 to 99%, the F1-score ranged from 0.35 to 0.94 and the AUC, ranging from 0.77 to 0.95. Based on ten studies, CNN showed a similar or improved diagnostic accuracy in addition to clinical evaluation only., Conclusions: CNNs are applicable for the detection and classification fractures on CT scans. This can improve automated and clinician-aided diagnostics. Further research should focus on the additional value of CNN used for CT scans in daily clinics., (© 2022. The Author(s).)