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Machine Learning-Based Approach towards Identification of Pharmaceutical Suspensions Exploiting Speckle Pattern

Valentina Bello1, Luca Coghe1, Alessia Gerbasi1

  • 1Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.

Sensors (Basel, Switzerland)
|October 26, 2024
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Summary
This summary is machine-generated.

Accurate identification of parenteral artificial nutrition (PAN) drugs is vital. This study combines speckle pattern imaging and AI to precisely classify these critical medical suspensions, preventing potentially fatal errors.

Keywords:
artificial nutritionimaging statisticslight scatteringmachine learningoptical sensingspeckle pattern imagingturbid suspension drugs

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Area of Science:

  • Biomedical Engineering
  • Medical Diagnostics
  • Pharmaceutical Sciences

Background:

  • Parenteral artificial nutrition (PAN) is essential for patient care but incorrect drug administration poses severe health risks.
  • Distinguishing between similar-looking PAN drug suspensions using basic optical methods is challenging.
  • Accurate, real-time identification of PAN drugs before injection is critical to patient safety.

Purpose of the Study:

  • To develop and validate a novel method for precise classification of parenteral artificial nutrition (PAN) drug suspensions.
  • To leverage speckle pattern (SP) imaging combined with artificial intelligence (AI) for pharmaceutical analysis.
  • To establish a new optical sensing platform for identifying critical medical treatments.

Main Methods:

  • Acquisition of speckle pattern (SP) images from various commercial pharmaceutical suspensions used for PAN.
  • Extraction of statistical parameters from the acquired SP images.
  • Training and evaluation of machine learning algorithms (Random Forest and Multi-Layer Perceptron Network) for drug classification.

Main Results:

  • The combined approach of SP imaging and AI achieved accurate classification of PAN drug suspensions.
  • Machine learning models demonstrated high performance in identifying different pharmaceutical formulations.
  • The developed method offers a reliable solution for distinguishing between visually similar turbid liquid drugs.

Conclusions:

  • Speckle pattern imaging coupled with artificial intelligence provides a powerful tool for identifying parenteral artificial nutrition (PAN) drugs.
  • This novel optical sensing platform enhances patient safety by enabling accurate drug verification.
  • The study presents the first application of this combined technique for the specific identification of PAN drugs.