AI-driven diagnostic enhancements for neurological diseases using multimodal biosignals, Volume III

Diagnosing neurological disorders presents a significant challenge for health services and health policies, due to the intricate nature of the nervous system and the overlapping symptoms of various diseases. Among the nearly 700 identified neurological disorders, the most recognized include Alzheimer’s and Parkinson’s diseases, ischemic stroke, epilepsy, multiple sclerosis, neuroinfectious and neuromuscular diseases. Diagnostic tools such as magnetic resonance imaging (MRI), functional near-infrared spectroscopy (fNIRS), cerebrospinal fluid (CSF) biomarkers, electroencephalographic signals (EEG), magnetoencephalographic signals (MEG), positron emission tomography (PET) scans, single-photon emission computed tomography (SPECT) images, genetic testing, and neurophysiological tests are instrumental in identifying these conditions. Additionally, the human microbiome is being explored as another diagnosis biomarker, given its potential role in influencing neurological health and disease states. Due to the generally low accuracy rates in diagnosing neurological diseases, there is a pressing need for more research to identify distinctive pathological indicators. By harnessing the ongoing accumulation of microbiome and biosignal data, we can extract pathological insights. These insights, when integrated with statistical and machine learning methods, enable us to construct models that quantify the cognitive load linked to disease progression. This area of research concentrates on the latest developments and technologies that utilize artificial intelligence systems, including machine learning and deep learning, to diagnose neurological diseases through bio-signals or human microbiome analysis.The scope of this research includes, but is not limited to, the following areas:• Classical machine learning methods, such as decision trees, support-vector machines, and k-nearest neighbors.• Deep learning techniques, including Convolutional Neural Networks, Long Short-Term Memory networks, and autoencoders.• Analysis of microbiome data and/or of various bio-signals: EEG, EMG, ECG, MEG, fNIRS, speech, and Evoked Potentials.• Integration of multimodal biosignal data for comprehensive diagnostic models.• Development of real-time diagnostic tools and systems.• Case studies and clinical trials demonstrating the efficacy of AI-based diagnostic methods.• Ethical considerations and challenges in implementing AI for neurological disease diagnosis. Topic editors: Pedro Miguel Rodrigues, Patrícia Batista, João Paulo Teixeira, Nauber Gois, Ph.D. Source: https://www.frontiersin.org/research-topics/86213/ai-driven-diagnostic-enhancements-for-neurological-diseases-using-multimodal-biosignals-volume-iii