A Mobile AI-enhanced Platform for Standardized Wound Assessment and Clinical Decision Support
Journal:
medRxiv
Published Date:
Jan 23, 2026
Abstract
Chronic wounds affect over 1.2 million Canadians and incur healthcare costs exceeding $13 billion annually, with global expenditures approaching $149 billion. Current clinical practice relies on manual measurements and subjective visual evaluations, which overestimate wound area by up to 40% and demonstrate poor-to-moderate inter-rater reliability. This variability complicates longitudinal monitoring and evidence-based treatment selection. We developed and evaluated an integrated mobile platform combining deep learning-based wound assessment with clinical decision support. A curated dataset of 1,648 de-identified clinical wound photographs was assembled from wound care clinics, representing diverse aetiologies (arterial, venous, diabetic foot ulcers, pressure injuries) and skin tones (32% Monk Skin Tone 7-10). Three convolutional neural networks were trained: (1) an EfficientNet-B7-based classifier for wound etiology, (2) a gated pressure injury staging network, and (3) a DeepLabv3 encoder-decoder architecture with ResNet backbone for multi-class tissue segmentation (epithelialization, granulation, slough, eschar). Fiducial marker-based calibration enabled automated wound size quantification. A rule-based recommendation engine mapped assessment outputs to evidence-based dressing selections. The system was deployed as a cross-platform mobile application with cloud-native backend infrastructure. The wound classification model achieved 91.75% mean accuracy across four wound categories. Pressure injury staging accuracy ranged from 67% (Stage III) to 92% (Stage I). Tissue segmentation yielded a mean Dice similarity coefficient of 0.64 +/- 0.06 and pixel-level accuracy of 98%. Automated size estimation demonstrated strong correlation with manual measurements (r = 0.73, n=53), with mean absolute error of 3.7 +/- 2.1 mm; 84.2% of measurements fell within the +/- 5 mm clinical equivalence margin. Fiducial marker detection succeeded in 93% of test images. Performance remained stable across skin tone categories and imaging conditions. This integrated platform demonstrates technical feasibility for standardized, objective wound assessment addressing documented limitations of manual practices. The system provides interpretable segmentation overlays and actionable treatment recommendations while maintaining clinician oversight. These findings support progression to prospective validation studies evaluating real-world clinical utility and patient outcomes. Keywords: wound assessment, machine learning, deep learning, tissue segmentation, clinical decision support, mobile health, telemedicine
