Faridabad, March 6
: Artificial intelligence is finding new applications in a range of fields. Now
researchers from India and Canada have developed a machine learning-based tool that
can diagnose schizophrenia with high accuracy.

Although research on
major psychiatric illnesses has been going on for decades, there are still no
reliable methods to predict and diagnose many ailments. One of reasons is the
inherent variability in biological systems. Schizophrenia is a debilitating
psychotic illness where diagnosis is often difficult due to its numerous
clinical forms and considerable overlap with other psychiatric disorders.

Researchers at NIMHANS
developed a machine learning-based tool

Researchers
at the National Institute of Mental Health and Neurosciences (NIMHANS)
used functional MRI (fMRI), a method in which magnetic
field is used to map and measure brain activity. With this, they measured brain
activity in 93 healthy and 81 schizophrenia patients.

Most
previous studies had smaller groups of people which may not capture
variabilities in the symptoms. In addition, patients were already undergoing
therapy and taking anti-psychotic drugs that is known to alter brain activity.
In the new study, patients who had not been exposed to drugs were included.
This reduced possibility of errors due to effects of drugs.

Research Method

Brain information was obtained from fMRI during the resting stage. Researchers divided the whole brain into different regions or parcels. This was done in 14 different ways based on similarities in volume, surface, connectivity etc. From each method of dividing the brain, information was derived on three features based on the region and three features based on connectivity of the brain. These parameters included frequency of brain waves, correlation between brain activity of closely-placed regions, and connectivity between different brain regions. These features were chosen as previous studies show they are altered in a schizophrenic brain.

This
helped researchers collate 84 points of data (from 14 brain division schemes,
and 6 features extracted from each scheme) from each subject. Using these data points
from healthy and schizophrenic patients, the group has built a model that could
predict schizophrenia with an accuracy of 87%. The model has been named
“EMPaSchiz” or ‘Ensemble algorithm with Multiple Parcellations for
Schizophrenia prediction’.

“The
classification accuracy our model outperforms earlier machine learning models
built for diagnosing schizophrenia using resting state fMRI on large
samples,” said Ganesan Venkatasubramanian, a member of the research team, while
speaking to India Science Wire.

More
research is needed on the model before user-friendly software can be generated,
he added. He hoped that such automated and
semi-automated diagnostic tools could be developed for detecting other kinds of
mental disorders and help predict treatment strategies.

research team

The
research team included Rimjhim Agrawal, Venkataram Shivakumar, Janardhanan C.
Narayanaswamy, and Ganesan Venkatasubramanian (NIMHANS); Sunil Vasu Kalmady,
Matthew R. G. Brown, Andrew J Greenshaw, Serdar M Dursun, Russell Greiner (Alberta
Machine Intelligence Institute, University of Alberta). This study has been published
in journal Schizophrenia.

By Dr P Surat

 (India Science Wire)

Note : Artificial intelligence is becoming a progressive tool in the field of health & science. New researchers are using machine learning for diagnosis medical problems. In Coming time artificial intelligence will be proved a revolutionary technology.