Purpose: To develop and comparatively evaluate a reproducible Deep Learning (DL) framework for automated multiclass retinal disease classification from Optical Coherence Tomography (OCT) images in a retrospective comparative diagnostic accuracy study. Method: A publicly available OCT dataset containing 2064 images from 820 patients was used to classify seven diagnostic categories: Age-Related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), normal retina, Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO) and Vitreomacular Interface Disease (VID). To avoid data leakage, the dataset was partitioned at the patient level into training (1481 images; 592 patients), validation (281 images; 105 patients) and test (302 images; 123 patients) sets. Eight Convolutional Neural Network (CNN) architectures (InceptionV3, VGG16, MobileNetV2, AlexNet, EfficientNetV2B2, ResNet50, DenseNet121 and Xception) were trained within the same experimental pipeline using pretrained weights, weighted cross-entropy loss, Adaptive Moment Estimation with decoupled weight decay (AdamW) optimisation, early stopping and checkpointing. Performance was assessed using overall accuracy, balanced accuracy, macro Area Under the Receiver Operating Characteristic Curve (AUROC), per-class precision, recall and F1-score, confusion matrices and Expected Calibration Error (ECE), with temperature scaling applied for probability calibration. Results: All evaluated models demonstrated strong diagnostic performance on the patient-disjoint test set. Xception achieved the best overall classification results, with 93.05% accuracy and 87.39% balanced accuracy. ResNet50 and DenseNet121 also performed strongly, achieving accuracies of 91.72% and 91.39%, respectively, with macro AUROC values above 0.98. ResNet50 produced the highest macro AUROC (0.99021) and the lowest calibrated ECE (0.02171), indicating strong discrimination and reliable probability estimates. Per-class analysis showed better performance for dominant categories such as AMD and normal retina, whereas minority classes remained more challenging. Conclusions: A reproducible DL framework for OCT-based retinal disease classification was developed and shown to perform effectively across seven retinal diagnostic categories. Among the evaluated architectures, Xception provided the best overall classification performance, while ResNet50 offered the strongest discrimination and calibration. These findings support the potential of DL as a reliable tool for automated retinal disease detection from OCT images. Clinical trial number: Not applicable.

The obliquity of the first metatarsocuneiform joint has long been proposed as a contributing factor in the development of hallux valgus (HV). Several studies have evaluated the medial slope of the distal articular surface of the medial cuneiform relative to its longitudinal axis, commonly described as the distal medial cuneiform angle (DMCA). An increased DMCA has been hypothesized to predispose individuals to HV by altering first-ray biomechanics.

Background: The incidence of secondary medial arch instability (talonavicular and first ray instability) associated with medial malleolus fractures has not been totally quantified, and to date, its contribution to poor foot function is unknown. Methods: Twenty-five patients with various mechanisms of ankle injuries associated with medial malleolus fractures who underwent surgical treatment were assessed for patient demographics, mechanism of injury, type of fracture and quantity of medial malleolus displacement in X-rays prior to surgery, type of fixation, and level of midfoot instability, assessed by determining and quantifying talonavicular laxity and first ray instability. Results: In rotational ankle fractures with the medial malleolus involved, increased midfoot laxity and first ray instability were present in injured ankles compared to the uninjured (p0.05). There was no significant difference in the spring ligament thickness between injured and uninjured feet (p>0.05), and the displacement of the medial malleolus was not a predictor for the development of increased talonavicular laxity (p>0.05). Conclusion: Ankle fractures with medial malleolus fractures have a significantly higher incidence of medial arch instability. Rotational ankle injuries, arising from supination external rotation and pronation injuries, can significantly affect medial arch stability. We advocate that surgical restoration of superficial deltoid-spring/capsular-ligamentous integrity and/or early post-operative orthotics after medial malleolar fractures may protect the first ray destabilization and preserve future foot function.