Search
Close this search box.
Search

AI based algorithms help identify instances of schizophrenia

AI and deep/machine learning platforms such as IBM Watson are used more frequently every day to support scientific research and diagnostics. Now, IBM scientists and the University of Alberta in Edmonton, Canada, have demonstrated that AI and machine learning algorithms can help to predict instances of schizophrenia with 74 percent accuracy.

This retrospective analysis also showed the technology predicted the severity of specific symptoms in schizophrenia patients with significant correlation, based on correlations between activity observed across different regions of the brain. This pioneering research could furthermore help scientists identify more reliable objective neuroimaging biomarkers that could be used to predict schizophrenia and its severity. The findings were published in Nature’s partner journal, Schizophrenia Research.

Schizophrenia is a chronic and debilitating neurological disorder that affects 7 or 8 out of every 1,000 people. People with schizophrenia can experience hallucinations, delusions or thought disorders, along with cognitive impairments, such as an inability to pay attention and physical impairments, such as movement disorders.

Innovative multidisciplinary approach

Dr. Serdar Dursun, a Professor of Psychiatry & Neuroscience with the University of Alberta, believes the research together with IBM offers a unique, innovative multidisciplinary approach. It opens new insights and advances our understanding of the neurobiology of schizophrenia, which may help to improve the treatment and management of the disease.

 “We’ve discovered a number of significant abnormal connections in the brain that can be explored in future studies, and AI-created models bring us one step closer to finding objective neuroimaging-based patterns that are diagnostic and prognostic markers of schizophrenia.”

In their paper, the researchers analyzed de-identified brain functional Magnetic Resonance Imaging (fMRI) data from the open data set, Function Biomedical Informatics Research Network (fBIRN) for patients with schizophrenia and schizoaffective disorders, as well as a healthy control group.

MRI measures brain activity through blood flow changes in particular areas of the brain. Specifically, the fBIRN data set reflects research done on brain networks at different levels of resolution, from data gathered while study participants conducted a common auditory test. Examining scans from 95 participants, researchers used machine learning techniques to develop a model of schizophrenia that identifies the connections in the brain most associated with the illness.

Machine learning algorithms 74% accurate

The results of the IBM and University of Alberta research demonstrated that, even on more challenging neuroimaging data collected from multiple sites (different machines, across different groups of subjects etc.) the machine learning algorithm was able to discriminate between patients with schizophrenia and the control group with 74% accuracy using the correlations in activity across different areas of the brain.

Additionally, the research showed that functional network connectivity could also help determine the severity of several symptoms after they have manifested in the patient, such as inattentiveness, bizarre behavior and formal thought disorder, as well as alogia, (poverty of speech) and lack of motivation.

According to IBM, the prediction of symptom severity could lead to a more quantitative, measurement-based characterization of schizophrenia; viewing the disease on a spectrum, as opposed to a binary label of diagnosis or non-diagnosis. This objective, data-driven approach to severity analysis could eventually help clinicians identify treatment plans that are customized to the individual.

Data-driven measures

“The ultimate goal of this research effort is to identify and develop objective, data-driven measures for characterizing mental states, and apply them to psychiatric and neurological disorders” said Ajay Royyuru, Vice President of Healthcare & Life Sciences, IBM Research. “We also hope to offer new insights into how AI and machine learning can be used to analyze psychiatric and neurological disorders to aid psychiatrists in their assessment and treatment of patients.”

As part of the ongoing partnership, researchers will continue to investigate areas and connections in the brain that hold significant links to schizophrenia. Work will continue on improving the algorithms by conducting machine learning analysis on larger datasets, and by exploring ways to extend these techniques to other psychiatric disorders such as depression or post-traumatic stress disorder.

Whixx

ICT&health World Conference 2024

Experience the future of healthcare at the ICT&health World Conference from May 14th to 16th, 2024!
Secure your ticket now and immerse yourself in groundbreaking technologies and innovative solutions.
Engage with fellow experts and explore the power of global collaborations.

Share this article!

Read also
Doximity_edited
Innovation Adoption: How to Traverse The Valleys of Death
Pioneering Cardiac Arrest Detection for Enhanced Survival.
CardioWatch Revolutionizes Cardiac Arrest Detection
Dr. Oscar Díaz-Cambronero, Head of Perioperative Medicine Department at La Fe Hospital, spearheads innovative telemonitoring initiatives revolutionizing patient care
Smartwatches Saving Lives Inside and Outside the Hospital
EIT 2024
EIT Awards 2024. Two European startups are revolutionizing the treatment of cardiovascular diseases
Bertrand Piccard, Swiss explorer and founder of the Solar Impulse Foundation
EIT Summit 2024. What are the trigger points that drive or inhibit innovation?
MMC pioneers wireless monitoring for premature infants with the innovative Bambi Belt, revolutionizing care with improved comfort and mobility.
Wireless Monitoring of Vital Signs in Premature Infants at Máxima MC
Data protection-critical incidents resulting from human error are often rooted in stress, routine, negative attitudes toward IT, and deficits in employees' identification with the healthcare facility.
How cyberpsychology helps prevent human errors leading to data leaks
What technologies will enter our homes in a few months? ICT&health checked it out at the CES 2024.
CES 2024: Meet the exciting innovations for health and well-being
An article on a new study on e-health assessment tools
eHealth success lies at the intersection of technology, people, and organization
Unlocking the Future: Professor Sylvia Thun, a trailblazer in healthcare interoperability, discusses the crucial role of seamless data exchange in revolutionizing medicine and empowering individuals with comprehensive access to their health data.
Seamless data exchange will unlock the long-awaited benefits of digitalization
Follow us