Current methods of diagnosing neurological and psychological disorders often rely on the subjective assessment of a patient’s symptoms by a clinical psychiatrist. These assessments can differ between psychiatrists, leading to different recommendations for a treatment plan. We aim to provide these psychologists with tools that provide objective criteria for diagnosis and the assessment of symptom severity in order to provide psychiatrists data-driven methodologies for assessing patients.
How will this help someone / an industry?
Our goal with this project is to use machine learning techniques to produce clinical tools that could assist medical doctors in providing faster, more effective treatment for neurological and psychiatric illnesses. We are exploring the use of brain imaging to diagnose mental disorders earlier and more accurately, to predict symptoms and their severity, and to predict which combination of drug therapies will work best for a given patient.
(Amii’s Russ Greiner is part of a team of researchers from the University of Alberta and IBM who are using machine learning to help predict schizophrenia .)
Pioneering research in “computational psychiatry” uses AI to explore disease prediction and assessment
IBM (NYSE: IBM) scientists and the University of Alberta in Edmonton, Canada, have published new data in Nature‘s partner journal, Schizophrenia, demonstrating that AI and machine learning algorithms helped predict instances of schizophrenia with 74% 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 also help scientists identify more reliable objective neuroimaging biomarkers that could be used to predict schizophrenia and its severity.
Schizophrenia is a chronic and debilitating neurological disorder that affects 7 or 8 out of every 1,000 people. Those 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.
“This unique, innovative multidisciplinary approach opens new insights and advances our understanding of the neurobiology of schizophrenia, which may help to improve the treatment and management of the disease,” says Dr. Serdar Dursun, a Professor of Psychiatry & Neuroscience with the University of Alberta. “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 the paper, 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. fMRI 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.
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, including inattentiveness, bizarre behavior and formal thought disorder, as well as alogia, (poverty of speech) and lack of motivation. 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.
“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.”
The Research Domain Criteria (RDoC) initiative of NIMH emphasizes the importance of objective measurements in psychiatry. This field, often referred to as “computational psychiatry”, aims to use modern technology and data driven approaches to improve evidence-based medical decision making in psychiatry, a field that often relies upon subjective evaluation approaches.
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.
Existing at the intersection of machine learning and artificial intelligence, machine intelligence is advanced computing that enables a machine to interact with its environment in an intelligent way.
Amii specializes in the research and development of machine learning technologies, including their application in artificial intelligence.
What is Artificial Intelligence?
Artificial intelligence (AI) is a set of algorithms, processes and methodologies that allow a computer system to perform tasks that would normally require human-level intelligence. AI can appear as a component in a larger system or in the form of a computer application, digital agent or autonomous machine.
What is Machine Learning?
Machine learning is a field of computing science focused around developing algorithms that enable a computer system to independently learn from, and continuously adapt to, data without being explicitly programmed for that data. Machine learning is a crucial component in many artificial intelligence systems.
Where is Machine Learning Used?
Recommender systems (e.g. Netflix or Amazon)
Contextual web searches (e.g. Google)
Intelligent digital assistants (e.g. Cortana or Siri)
Game-playing AI (e.g. AlphaGo or Cepheus)
Email spam filters
Why is Machine Intelligence Important?
Recently, machine intelligence technologies have experienced a global resurgence due to growing volumes and varieties of data, the utility of this data in training smart systems and an increased awareness of the value of data in providing a competitive edge in business.
Machine intelligence is expected to form the basis for most technological and business advancements for years to come. According to a report issued by McKinsey & Company, technologies that employ machine intelligence will have created over $50 trillion in economic impact by the year 2025.
How Can Machine Intelligence Enhance My Business?
Machine intelligence allows organizations to operate more efficiently and effectively, using data to predict the future and manage the present.
Computer systems with machine intelligence can perform a variety of tasks: