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Predicting ergonomic risk among laboratory technicians using a Cheetah Optimizer-Integrated Deep Convolutional Neural

Abdulmajeed Azyabi1, Abdulrahman Khamaj1, Abdulelah M Ali1

  • 1Industrial Engineering Department, College of Engineering & Computer Sciences, Jazan University, Jazan, Saudi Arabia.

Computers in Biology and Medicine
|November 6, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new AI method to predict ergonomic risks for medical laboratory technicians using deep learning and an optimization algorithm. The CHObDCNN model achieved high accuracy, improving technician safety.

Keywords:
Assessment of risks and interactionsDeep convolutional neural networkEnvironmental analyses and monitoringErgonomic risk evaluation

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Area of Science:

  • Medical Laboratory Science
  • Occupational Health and Safety
  • Artificial Intelligence in Healthcare

Background:

  • Medical laboratory technicians face significant ergonomic risks due to repetitive tasks and prolonged static postures.
  • Accurate prediction of these risks is crucial for implementing effective preventive measures and ensuring technician well-being.

Purpose of the Study:

  • To develop and validate a novel hybrid artificial intelligence framework for predicting ergonomic risks in medical laboratory technicians.
  • To enhance the accuracy and efficiency of ergonomic risk assessment in clinical laboratory settings.

Main Methods:

  • A hybrid strategy integrating Cheetah Optimizer (CHO) with Deep Convolutional Neural Network (DCNN) was proposed, termed CHObDCNN.
  • The framework utilizes image data of technicians' postures and motions, with CHO optimizing DCNN parameters for improved classification.
  • Image data underwent pre-processing to remove noise and enhance feature extraction for accurate risk prediction.

Main Results:

  • The CHObDCNN model demonstrated superior performance with an accuracy of 98.74% and precision of 98.56%.
  • The proposed method achieved a reduced computational time of 2.45 ms, indicating high efficiency.
  • Comparative analysis confirmed the effectiveness of the CHObDCNN framework over existing techniques.

Conclusions:

  • The developed CHObDCNN framework offers a highly accurate and efficient solution for predicting ergonomic risks in medical laboratory technicians.
  • This AI-driven approach has the potential to significantly improve occupational safety and health within clinical laboratory environments.
  • The study highlights the successful integration of advanced AI techniques for addressing specific workplace hazards.