Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt.
Technology of Radiology and Medical Imaging Program, Faculty of Applied Health Sciences Technology, Galala University, Suez, 435611, Egypt.
Sci Rep. 2024 May 27;14(1):12104. doi: 10.1038/s41598-024-61876-9.
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.
本研究旨在开发一种人工智能增强方法，以加快和准确诊断多发性硬化症（MS），这是一种影响中枢神经系统的慢性疾病，导致进行性损伤。传统的诊断方法速度较慢，需要大量的专业知识，因此需要创新的解决方案。我们的方法包括两个阶段：首先，使用一阶直方图、灰度共生矩阵和局部二值模式从脑 MRI 图像中提取特征。然后，采用一种结合正弦余弦算法和海马优化器的独特特征选择技术来识别最重要的特征。利用包含 38 名 MS 患者（平均年龄 34.1 ± 10.5 岁；男性 17 名，女性 21 名）和匹配的健康对照组图像的 eHealth 实验室数据集，我们的模型使用 K-最近邻分类器实现了惊人的 97.97%的检测准确率。在包含 262 例 MS 病例（199 名女性，63 名男性；平均年龄 31.26 ± 10.34 岁）和 163 名健康个体（109 名女性，54 名男性；平均年龄 32.35 ± 10.30 岁）的更大数据集上进行的进一步验证表明，对于 FLAIR 图像，随机森林分类器的准确率为 92.94%，对于 T2 加权图像，准确率为 91.25%，优于现有的 MS 检测方法。这些结果突出了该技术作为一种临床决策工具，用于早期识别和管理 MS 的潜力。
