Translational Medical Science

Abstract

Anemia, characterized by a deficiency in healthy blood, poses a significant health challenge globally. While historical diagnosis relied on visual cues like paleness and fatigue, contemporary approaches integrate state-of-the-art technology with traditional knowledge. In addressing this issue, our initiative employs advanced image analysis and machine learning techniques to swiftly identify anemia from nail photos, preserving conventional diagnostic methods while enhancing accuracy. While others focus on traditional diagnostic methods, our approach harnesses the power of image analysis and machine learning to detect anemia early through nail color and shape analysis. Prioritizing ethical considerations, particularly data privacy, guides our methodology. Additionally, we engage the public to illuminate ethical concerns surrounding medical image analysis. Preliminary results demonstrate promising outcomes, with our machine learning model achieving an accuracy rate of 90% in identifying anemia from nail photos. Early detection holds significant benefits, including cost savings, improved quality of life, and timely treatment, ultimately enhancing vitality, productivity, and well-being across demographics and geographic locations. To comprehensively address anemia, a multifaceted strategy integrating public awareness campaigns, healthcare interventions, and continued research is imperative. In conclusion, our initiative not only pays homage to historical diagnostic practices but also paves the way for an ethically driven future where technology enhances healthcare outcomes.

Abstract

The diagnosis and treatment of skin diseases in dogs—such as fungal infection, bacterial dermatosis, and hypersensitivity dermatitis—are challenging tasks in veterinary dermatology. Traditional methods are laborious and rely on subjective measurements that can lead to human error and delay in determining the true cause. This paper proposes a deep-learning approach with a Convolutional Neural Network (CNN) architecture aimed at automated detection and classification of canine skin conditions into four categories: fungal infections, bacterial dermatosis, hypersensitivity dermatitis, and healthy skin. The methodology trains and validates on a high-resolution labeled image dataset, applying data augmentation (e.g., rotation, scale, and flip) for stronger generalization. Results indicate high sensitivity, specificity, and accuracy relative to conventional diagnostics. Beyond clinical efficiency and timely interventions, the approach could be integrated into an intuitive application for broader veterinary use. The study further describes a streamlined preprocessing pipeline and optimization suited to resource-constrained environments; trained on a publicly available dermatological dataset of labeled dog images, the model achieved approximately 95% accuracy with robust performance across classes.

Abstract

Gait analysis is the assessment of walking patterns through coordination and balance of muscles in the body. It is essential in the diagnosis of neurological disorders and monitoring of patient progress for rehabilitation. Conventional gait analysis is heavily reliant on force plates to measure the Ground Reaction Forces (GRF) that a person exerts. However, these systems are constrained by high costs, constant maintenance, and repeated foot strikes to ensure accurate data. This study establishes an alternative novel in-shoe device that utilizes strain gauges to measure the GRF of a person. It addresses the key limitations of force plates while maintaining the accuracy and precision of the measurements. The system explores four 3D-printed strain gauge mounts, positioned within the sole of the shoe. This is simulation-based and replicates the functionality of force plates by capturing real-time GRF data while walking. Adjustments to the strain gauge positioning allowed for optimized force distribution. The system demonstrates an accuracy of approximately 95%, which has been supported by quantitative metrics like high correlation coefficient and low error rates. Beyond its empirical accuracy, the participant’s comfort while using the shoe was a critical consideration while designing the device.