Effect of a single acupuncture treatment on surgical wound healing in dogs: a randomized, single blinded, controlled pilot study

<p>Abstract</p> <p>Background</p> <p>The aim of the study was to investigate the effect of acupuncture on wound healing after soft tissue or orthopaedic surgery in dogs.</p> <p>Methods</p> <p>29 dogs were submitted to soft tissue and/or orthopaedic surgeries. Five dogs had two surgical wounds each, so there were totally 34 wounds in the study. All owners received instructions for post operative care as well as antibiotic and pain treatment. The dogs were randomly assigned to treatment or control groups. Treated dogs received one dry needle acupuncture treatment right after surgery and the control group received no such treatment. A veterinary surgeon that was blinded to the treatment, evaluated the wounds at three and seven days after surgery in regard to oedema (scale 0-3), scabs (yes/no), exudate (yes/no), hematoma (yes/no), dermatitis (yes/no), and aspect of the wound (dry/humid).</p> <p>Results</p> <p>There was no significant difference between the treatment and control groups in the variables evaluated three and seven days after surgery. However, oedema reduced significantly in the group treated with acupuncture at seven days compared to three days after surgery, possibly due the fact that there was more oedema in the treatment group at day three (although this difference was nor significant between groups).</p> <p>Conclusions</p> <p>The use of a single acupuncture treatment right after surgery in dogs did not appear to have any beneficial effects in surgical wound healing.</p

Myo-Guide: A Machine Learning-Based Web Application for Neuromuscular Disease Diagnosis With MRI

\ua9 2025 The Author(s). Journal of Cachexia, Sarcopenia and Muscle published by Wiley Periodicals LLC.Background: Neuromuscular diseases (NMDs) are rare disorders characterized by progressive muscle fibre loss, leading to replacement by fibrotic and fatty tissue, muscle weakness and disability. Early diagnosis is critical for therapeutic decisions, care planning and genetic counselling. Muscle magnetic resonance imaging (MRI) has emerged as a valuable diagnostic tool by identifying characteristic patterns of muscle involvement. However, the increasing complexity of these patterns complicates their interpretation, limiting their clinical utility. Additionally, multi-study data aggregation introduces heterogeneity challenges. This study presents a novel multi-study harmonization pipeline for muscle MRI and an AI-driven diagnostic tool to assist clinicians in identifying disease-specific muscle involvement patterns. Methods: We developed a preprocessing pipeline to standardize MRI fat content across datasets, minimizing source bias. An ensemble of XGBoost models was trained to classify patients based on intramuscular fat replacement, age at MRI and sex. The SHapley Additive exPlanations (SHAP) framework was adapted to analyse model predictions and identify disease-specific muscle involvement patterns. To address class imbalance, training and evaluation were conducted using class-balanced metrics. The model\u27s performance was compared against four expert clinicians using 14 previously unseen MRI scans. Results: Using our harmonization approach, we curated a dataset of 2961 MRI samples from genetically confirmed cases of 20 paediatric and adult NMDs. The model achieved a balanced accuracy of 64.8% \ub1 3.4%, with a weighted top-3 accuracy of 84.7% \ub1 1.8% and top-5 accuracy of 90.2% \ub1 2.4%. It also identified key features relevant for differential diagnosis, aiding clinical decision-making. Compared to four expert clinicians, the model obtained the highest top-3 accuracy (75.0% \ub1 4.8%). The diagnostic tool has been implemented as a free web platform, providing global access to the medical community. Conclusions: The application of AI in muscle MRI for NMD diagnosis remains underexplored due to data scarcity. This study introduces a framework for dataset harmonization, enabling advanced computational techniques. Our findings demonstrate the potential of AI-based approaches to enhance differential diagnosis by identifying disease-specific muscle involvement patterns. The developed tool surpasses expert performance in diagnostic ranking and is accessible to clinicians worldwide via the Myo-Guide online platform