Intelligent Care Management for Diabetic Foot Ulcers: A Scoping Review of Computer Vision and Machine Learning...

Area of Science:

• Medical Technology
• Artificial Intelligence in Healthcare
• Diabetology

Background:

• Diabetes affects 10% of US adults, with up to a third developing diabetic foot ulcers (DFUs).
• DFUs can lead to amputation (20%) and have high mortality rates (70% within 5 years).
• Significant human suffering and economic burden associated with DFUs, disproportionately affecting minority communities.

Purpose of the Study:

• To provide an overview of computer vision (CV) and machine learning (ML) techniques applicable to diabetic foot care.
• To explore the potential of these technologies in the detection, characterization, monitoring, and prediction of DFUs.
• To highlight the benefits and future role of computational sensing systems in improving diabetic foot care.

Main Methods:

• Scoping review of existing CV and ML techniques for DFU management.
• Analysis of automated CV methods for remote wound assessment using photographs.
• Examination of predictive ML algorithms utilizing diverse data streams.

Main Results:

• CV and ML techniques show promise for automated DFU detection and characterization.
• Remote monitoring and automated classification can revolutionize wound care.
• These technologies can facilitate patient self-monitoring, remote triaging, and timely interventions.

Conclusions:

• CV and ML are poised to significantly advance diabetic foot care.
• Computational sensing systems offer novel possibilities for improving DFU outcomes.
• Further development and implementation of these technologies are crucial for better patient knowledge and management.