Attention-based Pin Site Image Classification in Orthopaedic Patients with External Fixators

Assessing chronic wound infection from photographs is challenging because visual appearance varies across wound etiologies, anatomical locations, and imaging conditions. Prior image-based deep learning methods have mainly focused on classification with limited interpretability, despite the need for evidence-grounded explanations to support point-of-care decision making. We present Infection-Reasoner, a compact 4B-parameter reasoning vision-language model for chronic wound infection classification and rationale generation. To address the scarcity of expert-labeled wound images with reasoning annotations, Infection-Reasoner is trained using a two-stage pipeline: (1) reasoning distillation, in which GPT-5.1 generates chain-of-thought rationales for unlabeled wound images to initialize wound-specific reasoning in a smaller student model (Qwen3-VL-4B-Thinking), and (2) reinforcement learning post-training with Group Relative Policy Optimization on a small labeled infection dataset to refine classification reasoning. On a held-out heterogeneous wound dataset, Infection-Reasoner achieved 86.8% accuracy, 86.4% sensitivity, and 87.1% specificity, outperforming several strong baselines, including GPT-5.1. Rationale quality was further evaluated using both multimodal large language model (MLLM) judges and wound expert review. Across four MLLM judges, visual-support agreement scores ranged from 0.722 to 0.903, while expert review rated 61.8% of rationales as Correct and 32.4% as Partially Correct.

Lesion-focused image classification presents a core analytical challenge, as discriminative signals are often sparse, spatially dispersed, and easily obscured by background noise, while conventional convolutional neural networks (CNNs) process entire images uniformly and may dilute signal relevance. This study hypothesizes that adaptive fusion of global contextual information and lesion-focused local information can improve classification performance compared with using either representation independently. We propose a three-branch, attention-guided deep learning framework built on Densely Connected Convolutional Network-121 (DenseNet-121) to improve feature attribution, interpretability, and classification reliability. The architecture consists of a global branch that learns representations from full images, followed by Gradient-weighted Class Activation Mapping (Grad-CAM) to generate attention maps that highlight prediction-relevant regions and produce masked inputs, and a local branch enhanced with a Convolutional Block Attention Module (CBAM) to extract refined spatial and channel-wise features from these focused regions. An adaptive fusion branch integrates global and local representations by learning instance-specific weights, allowing dynamic prioritization between contextual and localized information. The framework is evaluated on a synthetic Spot Pattern Dataset (SSPD) and three benchmark datasets, including skin lesion, guava leaf, and grape leaf image datasets, where the fusion branch outperformed the individual global and local branches, reaching 97.75% accuracy on the skin lesion dataset and 99.64% on the guava leaf dataset. The results highlight the value of attention-guided architectures in healthcare analytics by improving model transparency, strengthening feature relevance, and supporting more reliable data-driven decision-making in medical image analysis.

Accurate wound classification (WC) and boundary segmentation are essential for guiding clinical decisions in chronic and acute wound management. However, most existing artificial intelligence (AI) models are limited, focusing on a narrow set of wound types, limited variations in wound severity, or a single task (segmentation or classification), which reduces their clinical applicability. This study presents two dedicated instance segmentation models based on You Only Look Once (YOLO)v11 that perform wound boundary segmentation (WBS) and WC across five clinically relevant wound types: burn injury (BI), pressure injury, diabetic foot ulcer, vascular ulcer, and surgical wound. A wound-type balanced dataset of 2,963 annotated images was created to train the models for both tasks, using five-fold cross-validation. Models trained on the original, non-augmented dataset performed consistently across folds, though BI detection accuracy was relatively low; augmenting the dataset with rotation, flipping, and variations in brightness, saturation, and exposure significantly improved performance, particularly for visually subtle BI cases. Among the tested variants, YOLOv11x achieved the best WBS performance (F1-score: 0.9341; mAP50: 0.9629). For WC, YOLOv11m achieved the highest mAP50 (0.9194) and mAP50-95 (0.6950), whereas YOLOv11l achieved the highest F1-score (0.8797). The lightweight YOLOv11n provided comparable accuracy at lower computational cost, making it suitable for resource-constrained deployments. Supported by confusion matrices and visual detection outputs, the results confirm robustness against complex backgrounds and high intra-class variability, demonstrating the potential of YOLOv11-based architectures for accurate, real-time wound analysis in clinical and remote care settings.