Researchers at Ondokuz Mayıs University (OMU) are developing digital twin technology that could enable personalized diagnosis and treatment for neurological disorders. The project combines artificial intelligence with patients' clinical data to create virtual patient models capable of predicting disease progression and supporting individualized treatment planning.

Led by Prof. Dr. Murat Terzi, Head of the Department of Neurology at the OMU Faculty of Medicine and President of the Turkish Neurological Society, the multidisciplinary research team is training artificial intelligence using patients' magnetic resonance imaging (MRI) scans, electroencephalography (EEG) recordings, voice samples, and gait analysis data to build comprehensive digital representations of individual patients.

The technology is expected to improve the diagnosis and management of neurological diseases such as multiple sclerosis (MS), Alzheimer's disease, Parkinson's disease, and epilepsy by enabling clinicians to anticipate disease progression at an earlier stage and optimize treatment strategies while minimizing potential harm to patients.

Creating Virtual Patients for Personalized Medicine

Speaking about the project, Prof. Dr. Murat Terzi emphasized that early diagnosis plays a crucial role in determining the clinical course of neurological diseases and noted that digital technologies and artificial intelligence are becoming increasingly valuable in clinical decision-making.

"Within our Departments of Neurology and Neuroscience, we place great importance on artificial intelligence and digital technologies. We are conducting several projects in this area. One of them involves what we call a 'digital twin,' where we train artificial intelligence using patients' existing clinical data, including MRI and EEG findings. The system evaluates each patient's age, sex, symptoms, clinical findings, brain imaging, EEG results, blood tests, and other available data to create a virtual patient model. We then use these models to support early diagnosis and plan treatments based on the predicted course of the disease."

Integrating Multiple Clinical Data Sources

Dr. Terzi explained that the project brings together a wide range of patient data to improve diagnostic accuracy and treatment planning.

"At OMU, we are conducting numerous studies focusing on epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease, and cerebrovascular disorders. We record patients' voice data and analyze videos of their walking patterns. We combine these findings with brain imaging, computed tomography (CT) scans, MRI scans, EEG recordings, and electromyography (EMG) examinations in a comprehensive database. By integrating these data, we aim to predict how each disease is likely to progress and identify potential developments at an early stage, allowing us to provide the most accurate diagnosis and the most appropriate treatment."

Preparing AI for the Future of Healthcare

Dr. Terzi noted that the research has received support from both Turkish funding organizations and international partners.

He emphasized that the team has trained artificial intelligence using the clinical MRI, EEG, and EMG data of hundreds of patients, highlighting the growing importance of machine learning as healthcare increasingly adopts robotics and remote medical technologies.

"Machine learning is essential because the future of healthcare will involve greater use of robotics and remote interventions. We want to ensure that emerging robotic systems are trained with the most accurate medical information so they can assist healthcare professionals, especially physicians. Just as MRI, ultrasound, EEG, and EMG have become indispensable diagnostic tools today, we envision AI-powered technologies becoming valuable clinical decision-support systems that help us achieve more accurate diagnoses."

Dr. Terzi added that the research team has been developing the digital twin technology for the past three years and continues to refine and expand the system.