AI-based model for automatic identification of multiple sclerosis based on enhanced sea-horse optimizer and MRI scans.

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.

Circulating tumor cells clusters and their role in Breast cancer metastasis; a review of literature.

Breast cancer is a significant and deadly threat to women globally. Moreover, Breast cancer metastasis is a complicated process involving multiple biological stages, which is considered a substantial cause of death, where cancer cells spread from the original tumor to other organs in the body-representing the primary mortality factor. Circulating tumor cells (CTCs) are cancer cells detached from the primary or metastatic tumor and enter the bloodstream, allowing them to establish new metastatic sites. CTCs can travel alone or in groups called CTC clusters. Studies have shown that CTC clusters have more potential for metastasis and a poorer prognosis than individual CTCs in breast cancer patients. However, our understanding of CTC clusters' formation, structure, function, and detection is still limited. This review summarizes the current knowledge of CTC clusters' biological properties, isolation, and prognostic significance in breast cancer. It also highlights the challenges and future directions for research and clinical application of CTC clusters.