Editorial: Machine intelligence and technology: clinical applications in neurology

Abstract

Machine intelligence (MI) has emerged as a powerful catalyst reshaping numerous facets of modern society. Key breakthroughs in object detection, content generation, chatbots, robotics, and medical applications have revolutionized how we interact with our surroundings, exchange information, automate tasks, and deliver healthcare services. This transformative success owes much to pivotal enabling technologies such as high-quality sensors and computational hardware capable of simulating complex biological models and training advanced MI algorithms with large amounts of data.Integrating MI into medical devices and decision support tools holds promising prospects for clinical neurology, especially with wearable sensors and neuroimaging techniques (Functional Magnetic Resonance Imaging (fMRI), Functional Near-Infrared Spectroscopy (fNIRS), Electroencephalography (EEG), Magnetoencephalography (MEG), Computed Tomography (CT), and Positron Emission Tomography (PET)), for diagnosing and treating neurological disorders. However, widespread adoption faces challenges in clinical practice due to the complexity of MI systems, resulting in opaque machine decisions. Issues like uncertainty, bias, reliability, and privacy in MI models and data hinder adoption. Moreover, portability, cost, and energy efficiency barriers in current MI and neuroimaging systems limit clinical accessibility, especially for underserved communities. Addressing these challenges is crucial to fully harness MI's potential in enhancing brain well-being and healthcare outcomes, ensuring equitable access to advanced medical technologies.Several neurological challenges can be reframed as machine learning problems, presenting opportunities to leverage patient data for data-driven insights. By employing advanced MI algorithms and computational techniques, we can harness the wealth of information embedded within patient records to enhance diagnostic accuracy, tailor treatment approaches, and uncover novel patterns and correlations that may have previously gone unnoticed. This transformative approach holds the potential to revolutionize how neurological disorders are understood and managed. Liu Guofang et al. conducted an analysis of the correlation between consciousness states and primary brainstem hemorrhage (PBH). Their study revealed that two common CT scan indicators, hemorrhage volume and involvement of the ventricular system, were associated with the patients' consciousness status. Xiongpeng He et al. proposed the application of machine learning for diagnosing pediatric autism. Utilizing Tract-Based Spatial Statistics derived from diffusion kurtosis imaging, they observed significant alterations in brain microstructure among children with autism compared to standard MRI scans. Additionally, their findings suggested potential neuroimaging biomarkers for pediatric autism diagnosis, including Kurtosis fractional anisotropy (FAK), mean kurtosis (MK), axial kurtosis (KA), and Lateralization index (LI). Dongang Wang et al. investigated the effectiveness of artificial intelligence-based CT intracranial hemorrhage detection developed by VeriScout. Their study, conducted amidst influences, such as artifacts and post-operative scans, revealed that the tool surpassed the sensitivity of general radiologists with only a minor trade-off in specificity.Hongjiang Cheng et al. employed univariable or multivariable linear regression analyses with an intracranial artery feature extraction technique to explore the association of distal arterial morphologic features-including artery length, density, and average tortuosity, measured from 3D Time-of-Flight Magnetic Resonance Angiography (3D-TOF MRA)-with different brain structures such as gray matter volume (GMV), white matter volume (WMV), and cerebrospinal fluid volume (CSFV). Additionally, they investigated correlations between these cerebrovascular characteristics and GMV across different brain regions. Their study enhanced the methodology