Comparative analysis of various brain imaging techniques

Deciphering Mechanisms, Prevention Strategies, Management Plans, Medications, and Research Techniques for Strokes in Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune rheumatic condition characterized by an unpredictable course and a wide spectrum of manifestations varying in severity. Individuals with SLE are at an increased risk of cerebrovascular events, particularly strokes. These strokes manifest with a diverse range of symptoms that cannot be solely attributed to conventional risk factors, underscoring their significance among the atypical risk factors in the context of SLE. This complexity complicates the identification of optimal management plans and the selection of medication combinations for individual patients. This susceptibility is further complicated by the nuances of neuropsychiatric SLE, which reveals a diverse array of neurological symptoms, particularly those associated with ischemic and hemorrhagic strokes. Given the broad range of clinical presentations and associated risks linking strokes to SLE, ongoing research and comprehensive care strategies are essential. These efforts are critical for improving patient outcomes by optimizing management strategies and discovering new medications. This review aims to elucidate the pathological connection between SLE and strokes by examining neurological manifestations, risk factors, mechanisms, prediction and prevention strategies, management plans, and available research tools and animal models. It seeks to explore this medical correlation and discover new medication options that can be tailored to individual SLE patients at risk of stroke.

Image-based state-of-the-art techniques for the identification and classification of brain diseases: a review

Detection and classification methods have a vital and important role in identifying brain diseases. Timely detection and classification of brain diseases enable an accurate identification and effective management of brain impairment. Brain disorders are commonly most spreadable diseases and the diagnosing process is time-consuming and highly expensive. There is an utmost need to develop effective and advantageous methods for brain diseases detection and characterization. Magnetic resonance imaging (MRI), computed tomography (CT), and other various brain imaging scans are used to identify different brain diseases and disorders. Brain imaging scans are the efficient tool to understand the anatomical changes in brain in fast and accurate manner. These different brain imaging scans used with segmentation techniques and along with machine learning and deep learning techniques give maximum accuracy and efficiency. This paper focuses on different conventional approaches, machine learning and deep learning techniques used for the detection, and classification of brain diseases and abnormalities. This paper also summarizes the research gap and problems in the existing techniques used for detection and classification of brain disorders. Comparison and evaluation of different machine learning and deep learning techniques in terms of efficiency and accuracy are also highlighted in this paper. Furthermore, different brain diseases like leukoariaosis, Alzheimer's, Parkinson's, and Wilson's disorder are studied in the scope of machine learning and deep learning techniques.