Background: Ankle fractures are among the most common injuries requiring surgical intervention. Standard radiographs are typically used for postoperative assessment; however, some patients continue to experience residual symptoms despite satisfactory radiographic outcomes. Weight-bearing computed tomography (WBCT), though not yet widely integrated into clinical practice, offers potential advantages in evaluating lower-limb deformities, injuries, and arthritis. This study explores the utility of WBCT for the midterm assessment following ankle fracture fixation and compares its findings with those obtained from standard radiographs. Methods: In this retrospective case study, we analyzed the correlations between the functional outcome scores approximately one year post-surgery and parameters assessed using WBCT. Pearson's correlation coefficient was used to evaluate these correlations, and a t-test was performed to assess their statistical significance, with a threshold p-value of 0.05. Additionally, Spearman's rank correlation coefficient was calculated as a supplementary descriptive measure, without significance testing. These correlations were then compared with those obtained from standard ankle radiographic views (anteroposterior, lateral, and mortise). Results: Several correlations were identified between WBCT parameters and functional scales, with certain parameters demonstrating high statistical significance (p < 0.05). Overall, the correlations observed for WBCT were stronger than those for standard radiographs. Conclusions: Although the study cohort was limited, the findings suggest that WBCT may provide additional insights beyond conventional radiography. Further research with larger patient groups is needed to establish its clinical relevance.

Artificial intelligence (AI) and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine. Deep learning has already been utilized for automated detection of pneumonia from chest radiographs, diabetic retinopathy, breast cancer, skin carcinoma classification, and metastatic lymphadenopathy detection, with diagnostic reliability akin to medical experts. In the World Journal of Orthopedics article, the authors apply an automated and AI-assisted technique to determine the hallux valgus angle (HVA) for assessing HV foot deformity. With the U-net neural network, the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior high-resolution radiographs. The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability. The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency. Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies, this work adds to the growing evidence supporting AI as a powerful diagnostic tool.