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Related Experiment Video

Updated: Feb 5, 2026

Modeling Charcot-Marie-Tooth Disease In Vitro by Transfecting Mouse Primary Motoneurons
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Developing Charcot-Marie-Tooth Disease Recognition System Using Bacterial Foraging Optimization Algorithm Based

Abdulaziz Abdullah Al-Kheraif1, Mohamed Hashem2, Mohammed Sayed S Al Esawy3

  • 1Dental Biomaterials Research Chair, Dental Health Department, College of Applied Medical Sciences, King Saud University, P.O Box 10219, Riyadh, 11433, Saudi Arabia. aalkhuraif@ksu.edu.sa.

Journal of Medical Systems
|September 12, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced method for early Charcot-Marie-Tooth (CMT) disease detection using dental images. The system effectively extracts and classifies features to identify CMT, aiding in risk factor elimination.

Keywords:
Bacterial foraging optimization algorithm based spiking neural networkCharcot–Marie–tooth (CMT) diseaseModified group delay functionNormalization process

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Computational Neuroscience

Background:

  • Charcot-Marie-Tooth (CMT) disease is a genetic neuromuscular disorder affecting muscle growth.
  • Early recognition of CMT is crucial for managing risk factors.
  • Automated systems face challenges in feature extraction and classification for CMT diagnosis.

Purpose of the Study:

  • To develop an automated system for accurate early detection of Charcot-Marie-Tooth disease using dental images.
  • To address the challenges in feature extraction and classification within diagnostic systems.
  • To improve the identification of CMT through advanced computational methods.

Main Methods:

  • Dental images undergo normalization to remove noise.
  • Feature extraction using modified group delay function and Cepstral coefficients.
  • Feature selection and classification employing the Bacterial Foraging Optimization Algorithm (BFOA) and a spiking neural network (SNN).

Main Results:

  • The proposed system successfully recognizes Charcot-Marie-Tooth disease.
  • The integration of BFOA and SNN demonstrates effective feature selection and classification.
  • Experimental results validate the framework's productivity in CMT detection.

Conclusions:

  • The developed framework offers a promising approach for the early and accurate diagnosis of Charcot-Marie-Tooth disease.
  • This method enhances diagnostic capabilities by leveraging advanced image processing and machine learning techniques.
  • Further research can explore broader applications of this technique in neuromuscular disorder detection.