Artificial Intelligence Integration with Advanced MRI Biomarkers for Early and Differential Diagnosis of HSV Encephalitis

ანოტაცია

Herpes simplex virus of the brain (HSE) is a life-threatening neurological emergency which is characterized by high morbidity and mortality unless early diagnosed and treated. Recognition promptly is essential, because the mortality rate in untreated cases is about 70 percent whereas antiviral therapy decreases the death rate to 10-20 per cent in populations/patients treated and prevents long term neurological-cognitive sequelae. Developed magnetic resonance imaging (MRI) has been the key in the early detection of the HSE and the use of sequences like diffusion-weighted imaging (DWI), fluid-attenuated inversion recovery (FLAIR), and T2-weighted imaging has been found to be of high sensitivity and specificity. The lesions tend to be restricted to medial temporal lobes and limbic structures although extratemporal and bihemispheric damages have been reported. These imaging biomarkers can distinguish other pathologies, like neoplasm and cerebro vascular event, and can be employed to distinguish between HSE and autoimmune encephalitis (AE), which can both share similar clinical and radiologic phenotypes. The most recent advances in artificial intelligence (AI), which include classical machine learning (CML) and deep learning (DL) have shown significant potential to increase diagnostic accuracy. The presence of AI in combination with multi-sequence MRI allows automatic lesion detection, increased pattern recognition, and better classification between HSE, AE, and gliomas. Imaging with clinical and laboratory data such as cerebral things of analysis, serological marker and patient demographics have shown a better performance in diagnosis compared to the diagnostic performance of experienced radiologists and could predict functional outcome and cognitive recovery. However, a few issues restrict the use of multicenter imaging, such as inconsistent image schemes, small sample sizes, and technical demands of sophisticated imaging techniques like pseudo continuous arterial spin labeling (pCASL). Standardization of imaging workflows, larger multicenter and prospective validation studies must determine the reproducibility, generalization and clinical utility of imaging workflows. Synergistic use of high-tech MRI biomarkers with AI-related analytics is a perspective of early and differentiated diagnosis of HSE. These combined methods can help significantly improve patient outcomes and decrease the HSE-related burden of neurological morbidity because of timely antiviral intervention and individualized long-term care.