A Roadmap for Automatic Surgical Site Infection Detection and Evaluation Using User-Generated Incision Images

Area of Science:

• Digital Health
• Medical Imaging
• Machine Learning in Healthcare

Background:

• Emerging technologies like smartphones and wearable sensors are driving patient-centered healthcare.
• Mobile health (mHealth) apps represent a significant advancement in healthcare delivery.
• Patient-taken incision images offer a novel approach for remote healthcare monitoring.

Purpose of the Study:

• To review challenges and solutions for surgical site infection (SSI) detection using patient-taken incision images.
• To investigate image quality, feature extraction, and analysis challenges in mHealth monitoring.
• To explore opportunities and challenges in applying image analysis and machine learning for SSI prediction.

Main Methods:

• Review of recent image analysis techniques for extracting meaningful image markers.
• Exploration of machine learning solutions for surgical site image analysis.
• Discussion of interactive image acquisition strategies for improved data quality.

Main Results:

• Identified key challenges in image quality, feature extraction, and data analysis for mHealth incision monitoring.
• Reviewed current image analysis and machine learning methods applicable to SSI detection.
• Highlighted the potential of these methods for accurate SSI prediction.

Conclusions:

• Sustainable mHealth apps require interactive image acquisition and customized analysis.
• Machine learning and advanced image analysis are crucial for effective out-of-clinic patient care.
• Patient-taken incision images can lead to substantially reduced healthcare costs.