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Related Experiment Video

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Space-time-regulated imaging analyzer for smart coagulation diagnosis.

Longfei Chen1, Le Yu1, Yantong Liu1

  • 1Key Laboratory of Artificial Micro- and Nano- Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China; Renmin Hospital, Wuhan University, Wuhan 430060, China; Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China.

Cell Reports. Medicine
|October 7, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered method using optical clotting biophysics for rapid, automated blood coagulation diagnoses. The system achieves high accuracy, offering a convenient and low-cost diagnostic solution for clinical settings.

Keywords:
blood coagulationclotting biophysical propertiesintelligent diagnosisoptofluidicspoint-of-care testsmart medical device

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Area of Science:

  • Biophysics
  • Medical Diagnostics
  • Artificial Intelligence

Background:

  • Current blood coagulation diagnostics face challenges with large, time-sensitive clinical caseloads.
  • There is a need for efficient and automated diagnostic methods.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-assisted method for intelligent blood coagulation diagnoses.
  • To enable efficient, automated, and user-friendly blood function analysis.

Main Methods:

  • Utilizing artificial intelligence (AI)-assisted optical clotting biophysics (OCB) properties identification.
  • Employing image differential calculation for precise OCB property acquisition.
  • Implementing space-time regulation for on-demand OCB identification and diverse blood function diagnoses.
  • Integrating smartphones and cloud computing for automated analysis.

Main Results:

  • Prospective assays on 41 clinical cases demonstrated high diagnostic accuracy.
  • Achieved 97.6% accuracy for coagulation factors.
  • Achieved 95.1% accuracy for fibrinogen function.
  • Achieved 100% accuracy for comprehensive blood coagulation diagnoses.

Conclusions:

  • The developed AI-assisted OCB method provides accurate and convenient blood coagulation diagnoses.
  • This approach enables low-cost diagnostics and offers a pathway for discovering new diagnostic markers.
  • The system supports efficient analysis for time-sensitive clinical needs.