Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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The Journal of the American Academy of Orthopaedic Surgeons
Clinical Outcomes and Safety Profile of Open and Arthroscopic Subtalar Joint Arthrodesis: A Systematic Review
BACKGROUND: Open and arthroscopic subtalar arthrodesis are safe and effective surgeries for patients with notable arthritis; however, not an established superiority among them has been found. This systematic review aims to present all of the available literature on studies directly comparing both surgeries. METHODS: Two independent authors completed a systematic literature search using the following databases: PubMed, Embase, and the Cochrane library. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocol and the Cochrane Handbook guidelines were followed. In addition, the MINORS score was used to evaluate the quality and bias of the nonrandomized controlled trials. Our search criteria included only studies that included both open and arthroscopic subtalar arthrodesis. RESULTS: A total of four studies including 125 open and 130 arthroscopic surgeries met the inclusion criteria for the current systematic review. The mean age and body mass index of patients in the open and arthroscopic groups were 50.0 ± 2.3 and 47.9 ± 1.4 years and 29.0 ± 3.7 and 29.5 ± 2.6 kg/m 2 , respectively. Patients in the open cohort and arthroscopic cohort were followed for a mean of 16.9 ± 6.5 and 15.8 ± 4.7 months, respectively. Union rate was 95.4% ± 4.6% vs. 93.6 ± 4.0%, and the time to fusion was 14 ± 1.6 vs. 10.5 ± 1.6 weeks. The overall complication rate among patients who underwent open arthrodesis was 44.0%, whereas the complication rate was 39% among patients who underwent arthroscopic arthrodesis. Patients stayed in the hospital for 2.2 ± 0.9 days following an open procedure versus 1.1 ± 0.5 days following minimally invasive procedures. CONCLUSION: The current systematic review found a high rate of union among both group of patients. Although trends suggested higher union rates and fewer complications in the arthroscopic cohort, no notable differences were found. Although limited, we hope that this systematic review may help to guide evidence-based decisions when planning the surgical management of patients with severe subtalar arthritis.
17 July 2026
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High diagnostic accuracy of a resnet50-based deep learning model for osteochondral lesions of the talus on magnetic resonance imaging
OBJECTIVES: This study aims to evaluate the diagnostic performance of a ResNet50-based convolutional neural network (CNN) in detecting osteochondral lesions of the talus (OLTs) on magnetic resonance imaging (MRI) and to compare its efficacy between T1- and T2- weighted sequences. MATERIALS AND METHODS: A total of 219 ankle MRI scans were reviewed retrospectively, including 60 with confirmed OLTs and 159 without lesions. From each study, coronal and sagittal T1- and T2-weighted images were extracted and standardized to 224 × 224 pixels. Augmentation techniques were applied to strengthen model training. Data were divided into training, validation, and test sets in a 60:20:20 split. A ResNet50 model initialized with ImageNet weights was fine-tuned using crossentropy loss with class weighting. Diagnostic performance was summarized with accuracy, precision, recall, and F1-scores. RESULTS: The model performed better on T1 sequences, achieving an accuracy of 94.1% (95% confidence interval [CI] 88.3-97.1%) and an area under the curve [AUC] of 0.93 (95% CI 0.87-0.97), with patient cases classified at 0.92 precision and 0.82 recall. Healthy controls in the T1 group were recognized with 0.95 precision and 0.98 recall. In contrast, T2 sequences were less reliable, showing an accuracy of 87.2% (95% CI 80.5-91.9%) and an AUC of 0.91 (95% CI 0.85-0.95). Precision for patient cases in the T2 group was notably lower (0.65) despite a recall of 0.81. Misclassifications were more frequent in the T2 dataset, as evidenced by the confusion matrices. CONCLUSION: Even with a relatively modest dataset, the ResNet50 model delivered strong results for T1-weighted MRI. While T2 images proved more challenging, suggesting that deep learning can add value to routine assessment of OLTs.
5 May 2026
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