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Related Concept Videos

Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
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Related Experiment Video

Updated: Sep 12, 2025

Questionnaire Survey and User Requirement Analysis for Designing Innovative Cell Wounding Tools
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Developing an AI-powered wound assessment tool: a methodological approach to data collection and model optimization.

Alessio Stefanelli1, Sofia Zahia2, Guillaume Chanel3,4

  • 1Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts, Western Switzerland, Avenue Champel 47, Geneva, CH-1206, Switzerland.

BMC Medical Informatics and Decision Making
|August 9, 2025
PubMed
Summary

This study developed an AI-powered mobile tool for chronic wound assessment, improving diagnosis and care. The technology aids healthcare professionals in managing complex wounds more effectively.

Keywords:
Medical imagingTissue segmentationWound assessmentWound monitoringWound segmentation

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Area of Science:

  • Digital health
  • Artificial intelligence in medicine
  • Wound care technology

Background:

  • Chronic wounds (CWs) pose a significant healthcare challenge due to prolonged healing and high costs.
  • Inadequate wound assessment by healthcare professionals (HCPs) leads to suboptimal treatment and complications.
  • Limited training and high clinical workload contribute to assessment challenges.

Purpose of the Study:

  • To develop an artificial intelligence (AI)-powered wound assessment tool.
  • To integrate the AI tool into a mobile application for HCP support.
  • To enhance diagnosis, monitoring, and clinical decision-making in wound care.

Main Methods:

  • A multicenter observational study was conducted across three Swiss healthcare institutions.
  • A hybrid dataset of ~4,000 wound images was compiled (retrospective and prospective).
  • Deep learning models were trained and validated using labeled images for segmentation and tissue classification.

Main Results:

  • AI wound segmentation achieved a DICE score of 92% and IOU of 85%.
  • Tissue classification yielded a preliminary DICE score of 78%, with variations across tissue types.
  • Optimized models achieved real-time mobile inference (0.3s processing time) with minimal performance reduction.

Conclusions:

  • An AI-driven digital tool can assist clinical wound assessment and education.
  • Integration of AI models shows potential to improve diagnostic precision and personalized care.
  • AI holds promise for transforming wound care and advancing clinical training, despite classification challenges.