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Joint Methodology Based on Optical Densitometry and Dynamic Light Scattering for Liver Function Assessment.

Elina Karseeva1, Ilya Kolokolnikov1, Ekaterina Medvedeva1

  • 1Higher School of Applied Physics and Space Technologies, Institute of Electronics and Telecommunications, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russia.

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This study introduces a novel method combining optical densitometry and dynamic light scattering for improved liver function assessment. The new approach aids in diagnosing liver disease and predicting treatment outcomes more effectively.

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blood flowcorrelation analysisdynamic light scatteringindocyanine greenlivermicrocirculationoptical densitometryscatteringsensors

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Area of Science:

  • Biomedical Engineering
  • Medical Diagnostics
  • Optical Physics

Background:

  • Increasing prevalence of liver damage from various causes (viral hepatitis, cancer, toxins, metabolic disorders) presents a significant health challenge.
  • Current liver function assessment methods have limitations hindering prompt and accurate diagnosis, leading to high morbidity and mortality.
  • Urgent need for advanced diagnostic tools to improve patient outcomes for liver diseases.

Purpose of the Study:

  • To develop and present a novel, combined methodology for assessing liver function.
  • To enhance the efficiency of diagnosing liver conditions and predicting treatment response.
  • To integrate individual cardiovascular system and tissue metabolism data into liver function analysis.

Main Methods:

  • Development of a laboratory model for a combined sensor utilizing optical densitometry and dynamic light scattering.
  • Creation of specialized software for sensor control and data processing.
  • Calibration and validation through modeling experiments and physical medical studies.

Main Results:

  • Successful development and calibration of a combined optical densitometry and dynamic light scattering sensor system.
  • Validated software for sensor operation and data analysis.
  • Assessment of sensor resolution for dye concentration and minimum flow rate detection.

Conclusions:

  • The new joint methodology offers a more efficient approach to diagnosing liver function and predicting treatment dynamics.
  • Integration of patient-specific physiological data (cardiovascular, metabolic) enhances diagnostic accuracy.
  • The developed sensor and software system show promise for improved liver disease management.