Inclusive and Accurate Clinical Diagnostics Using Intelligent Computation and Smartphone Imaging.

Area of Science:

• Biomedical Engineering
• Clinical Diagnostics
• Optical Imaging

Background:

• Smartphone colorimetry faces challenges with ambient light interference and device variability, limiting clinical applications.
• Existing methods struggle with accuracy due to environmental factors and manufacturer differences, hindering reliable clinical decision-making.

Purpose of the Study:

• To systematically characterize smartphone imaging interferences and develop a robust method for clinical analysis.
• To transform smartphone cameras into versatile analytical instruments for real-time diagnostics.
• To enable accurate skin oxygen measurements for cardiovascular disease and anemia monitoring, accounting for skin tone variations.

Main Methods:

• Systematic characterization of smartphone imaging interferences.
• Derivation of a strongly correlated parameter for sample quantification.
• Development of a real-time imaging solution for mobile phone cameras.
• Clinical validation through skin imaging for cyanosis diagnosis and oxygen concentration measurement across the Fitzpatrick scale.

Main Results:

• A novel method enables smartphone cameras to function as analytical instruments, overcoming model, software, and OS differences.
• Demonstrated clinical applicability in diagnosing cyanosis and measuring local oxygen concentration in patients' skin.
• The solution accurately accounts for individual skin tones (Fitzpatrick scale), improving upon current optical oximetry.

Conclusions:

• Smartphone-based colorimetry can be reliably used for clinical analysis, overcoming previous limitations.
• The developed method provides a pathway for accurate, convenient, and accessible cardiovascular disease and anemia monitoring.
• This innovation unlocks the potential of mobile phone cameras as powerful diagnostic tools in diverse clinical settings.