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Related Experiment Video

Updated: Jun 25, 2025

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Automated Analysis of Nuclear Parameters in Oral Exfoliative Cytology Using Machine Learning.

Shubhangi Mhaske1,2, Karthikeyan Ramalingam1, Preeti Nair3

  • 1Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND.

Cureus
|May 23, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) aids oral cancer diagnosis by analyzing nuclear parameters in exfoliative cytology. Convolutional neural networks (CNN) and support vector machine (SVM) models show promise for early detection and improved patient outcomes.

Keywords:
ai and machine learningartificial intelligenceconvoluted neural networkscytopathology techniquesdetector comparisondiagnostic accuracyexfoliative cytologymachine learningoral cancersupport vector machine

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Cytopathology

Background:

  • Oral cancer is a significant global health issue requiring advanced diagnostic tools.
  • Non-invasive methods like exfoliative cytology are gaining interest, especially with AI integration.

Purpose of the Study:

  • To utilize machine learning for automated analysis of nuclear parameters in oral exfoliative cytology.
  • To compare the diagnostic accuracy of Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) AI systems.

Main Methods:

  • A comparative diagnostic study involving 60 patients (30 control, 30 with suspicious oral malignancy).
  • Exfoliative cytology smears were collected, stained, and analyzed using AI.
  • Image preprocessing, feature extraction, and model evaluation were performed using SPSS software.

Main Results:

  • Significant differences in nuclear size, shape, and chromatin distribution were observed between groups (p<0.05 to p<0.001).
  • The Convolutional Neural Network (CNN) model achieved a higher accuracy (0.7790) compared to the Support Vector Machine (SVM) model (0.6472).

Conclusions:

  • AI, particularly deep learning architectures, is increasingly vital in oncology research.
  • The diagnostic accuracy of CNN and SVM models suggests potential for improved early oral cancer detection.
  • Enhanced early detection can lead to better patient outcomes and healthcare practices.