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August 2, 2026BiomimeticsOpen Access

Advances in Machine Learning-Assisted Optical Sensing Arrays for Disease Diagnosis

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Authors

XSXuetong SunHSHao SunBWBeibei Wang

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Overview

Review evaluates machine learning integration in optical sensing arrays for improved disease diagnosis, highlighting implications.

Key Points

  • The aim is to review how machine learning enhances the functionality of optical sensing arrays for disease diagnosis.
  • Systematic overview of optical sensing arrays with ML integration.
  • Focus on colorimetric, fluorescent, and SERS sensor modalities.
  • Analysis of unsupervised, supervised, and deep learning algorithms for performance enhancement.
  • ML algorithms improve diagnostic accuracy for cancers and infectious diseases.
  • Challenges include data standardization and clinical application.
  • Future directions are suggested for point-of-care and personalized diagnostics.

Cite This Study

Sun et al. (2026) studied this question.

synapsesocial.com/papers/6a6eeae61b0468a7eeab384ehttps://doi.org/10.3390/biomimetics11080531
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