We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




Machine-Learning Algorithm Analyzes Images to Identify Schizophrenia Patients

By MedImaging International staff writers
Posted on 31 Jul 2018
Image: Regions of the brain that showed a statistically significant difference between patients with schizophrenia and patients without it (Photo courtesy of the University of Alberta).
Image: Regions of the brain that showed a statistically significant difference between patients with schizophrenia and patients without it (Photo courtesy of the University of Alberta).
A team of researchers from the University of Alberta (Edmonton, Alberta, Canada) has developed a machine-learning algorithm to examine functional magnetic resonance imaging (fMRI) images for identifying patients suffering from schizophrenia and ascertaining if they would respond to treatment. In a study, the algorithm examined MRI images of both newly diagnosed, previously untreated schizophrenia patients and healthy subjects and successfully identified patients with schizophrenia at 78% accuracy. It also predicted whether or not a patient would respond positively to a specific antipsychotic treatment named risperidone with 82% accuracy.

Schizophrenia, a severe and disabling psychiatric disorder that comes with delusions, hallucinations and cognitive impairments, affects approximately one in 100 people at some point in their lives. Early diagnosis of schizophrenia and other mental disorders still remains a challenge, while clinicians face the difficult task of devising a personalized treatment strategy during the first visit with a patient. Currently, the treatment of schizophrenia is usually determined using a trial-and-error style. However, if a drug does not work properly, the patient can suffer prolonged symptoms and side effects, and miss the best time window to control and treat the disease.

The initial trial results of the machine-learning algorithm for the diagnosis and treatment of schizophrenia have been encouraging. However, further validations on large samples will be required and more refinement is needed to improve its accuracy before using it in a clinical environment. The researchers also expect to expand their work to include other mental illness such as major depressive and bipolar disorders.

"This is the first step, but ultimately we hope to find reliable biomarkers that can predict schizophrenia before the symptoms show up," said Bo Cao, an assistant professor of psychiatry at the University of Alberta, who led the research. "We also want to use machine learning to optimize a patient's treatment plan. It wouldn't replace the doctor. In the future, with the help of machine learning, if the doctor can select the best medicine or procedure for a specific patient at the first visit, it would be a good step forward."

Related Links:
University of Alberta

New
Breast Localization System
MAMMOREP LOOP
Silver Member
X-Ray QA Meter
T3 AD Pro
New
Diagnostic Ultrasound System
DC-80A
Medical Radiographic X-Ray Machine
TR30N HF

Channels

Ultrasound

view channel
Image: The new implantable device for chronic pain management is small and flexible (Photo courtesy of The Zhou Lab at USC)

Wireless Chronic Pain Management Device to Reduce Need for Painkillers and Surgery

Chronic pain affects millions of people globally, often leading to long-term disability and dependence on opioid medications, which carry significant risks of side effects and addiction.... Read more

Nuclear Medicine

view channel
Image: The diagnostic tool could improve diagnosis and treatment decisions for patients with chronic lung infections (Photo courtesy of SNMMI)

Novel Bacteria-Specific PET Imaging Approach Detects Hard-To-Diagnose Lung Infections

Mycobacteroides abscessus is a rapidly growing mycobacteria that primarily affects immunocompromised patients and those with underlying lung diseases, such as cystic fibrosis or chronic obstructive pulmonary... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.