A Brain Network Disorders study showcases the use of machine learning in improving early diagnosis of neurological disorders

The increase in human life expectancy is one of the notable achievements of modern society. Though population aging is significant, not all individuals age in a healthy manner. A significant portion of the elderly population is affected by neurological disorders. These disorders can have a serious impact on an individual’s daily life by affecting cognitive, motor, sensory, and memory functioning. According to the World Health Organization, more than one in three people are affected by neurological disorders. These disorders impact the patient’s and caregiver’s quality of life and substantially pose as a global health burden.

Neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy, present complex diagnostic challenges due to overlapping symptoms and variable progression. Conventional diagnostic techniques involve radiological data, electrophysiological studies, and molecular biology techniques, combined with patient history, symptoms, and physical examination. Though these methods provide core findings on the patient’s status, establishing an early, precise, and accurate diagnosis has been a challenge.

The development of artificial intelligence and machine learning (ML) has been a game changer in the healthcare domain. ML-based integration of diagnostic techniques can aid in realizing early, accurate, and efficient diagnosis of neurological disorders. Against this backdrop, a research team led by Dr. Yiyin Zhang and Dr. Hailu Wang from the Breast Center, Peking University, China, together with colleagues Dr. Yuru Li, Dr. Xiaowei Chang, Dr. Jianlin Wu, and Dr. Yuchen Liu, reviewed how ML can enhance early diagnosis of these disorders. Their findings were made available online on July 08, 2025, and published in Volume 1, Issue 3, of the journal Brain Network Disorders on September 01, 2025.

“ML provides an unprecedented opportunity to refine neurological diagnostics, identifying at-risk individuals before significant symptoms arise. Also, ML can aid in providing more precise diagnosis, tailored to individual patients,” says Dr. Zhang.

The researchers reviewed how supervised, unsupervised, and deep learning algorithms, particularly convolutional and recurrent neural networks, can process complex datasets—ranging from neuroimaging and electroencephalography (EEG) signals to genomic and clinical records—to detect subtle patterns missed by conventional diagnostic approaches. Integrating multimodal data allows the identification of early biomarkers, accurate disease classification, and prediction of progression trajectories.

In Alzheimer’s disease, ML models have successfully combined data from magnetic resonance imaging, positron emission tomography, EEG, and genetic data to differentiate mild cognitive impairment from normal aging and predict disease progression. Similar approaches in Parkinson’s disease have utilized EEG data, voice signals, and polygenic risk scores to improve early diagnosis and monitor motor and cognitive decline. Epilepsy management benefits from wearable sensors paired with ML algorithms, which detect seizure events and forecast recurrence, enabling proactive interventions and personalized treatment planning.

This study highlights that ML’s strength lies in processing multimodal and temporally complex data—a necessity for neurological disorders, which differ from systemic diseases in their spatial and temporal intricacy. By leveraging these computational tools, clinicians can gain actionable insights into disease mechanisms and risk profiles, enhancing both early detection and individualized care.

“Our findings suggest that integrating AI-driven analysis with clinical expertise can transform patient outcomes in neurological care,” notes Dr. Wang. “We noticed that the current studies focus only on the performance of ML models, rather than their real-time practicality, when used with clinician’s findings.” Further studies should focus on merging ML models with clinicians, scientists, and ethicists to ensure this technology is accessible to all patients.

This research demonstrates the potential for ML to reduce misdiagnosis, shorten the time to intervention, and optimize healthcare resources. Furthermore, developing interpretable models and multimodal frameworks ensures that these technologies can be translated safely and effectively into real-world clinical practice. As datasets expand and algorithms evolve, such ML-based tools could revolutionize the detection, monitoring, and management of neurological disorders, ultimately improving the quality of life of patients worldwide.

Reference

DOI: https://doi.org/10.1016/j.bnd.2025.04.001