AI Article Synopsis

  • The study focuses on creating an AI-based method to quickly and accurately diagnose Multiple Sclerosis (MS), which is a chronic condition impacting the central nervous system.
  • The approach includes extracting features from brain MRI images using various techniques and employing a unique feature selection method to pinpoint the most vital characteristics for diagnosis.
  • The model achieved high detection accuracy rates (97.97% with k-nearest neighbors and around 91-93% with Random Forest) using datasets of MS patients and healthy controls, demonstrating its potential to improve early identification and treatment in clinical settings.

Article Abstract

This study aims to develop an AI-enhanced methodology for the expedited and accurate diagnosis of Multiple Sclerosis (MS), a chronic disease affecting the central nervous system leading to progressive impairment. Traditional diagnostic methods are slow and require substantial expertise, underscoring the need for innovative solutions. Our approach involves two phases: initially, extracting features from brain MRI images using first-order histograms, the gray level co-occurrence matrix, and local binary patterns. A unique feature selection technique combining the Sine Cosine Algorithm with the Sea-horse Optimizer is then employed to identify the most significant features. Utilizing the eHealth lab dataset, which includes images from 38 MS patients (mean age 34.1 ± 10.5 years; 17 males, 21 females) and matched healthy controls, our model achieved a remarkable 97.97% detection accuracy using the k-nearest neighbors classifier. Further validation on a larger dataset containing 262 MS cases (199 females, 63 males; mean age 31.26 ± 10.34 years) and 163 healthy individuals (109 females, 54 males; mean age 32.35 ± 10.30 years) demonstrated a 92.94% accuracy for FLAIR images and 91.25% for T2-weighted images with the Random Forest classifier, outperforming existing MS detection methods. These results highlight the potential of the proposed technique as a clinical decision-making tool for the early identification and management of MS.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11637031PMC
http://dx.doi.org/10.1038/s41598-024-61876-9DOI Listing

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