One of the most promising areas in precision medicine and healthcare is the use of artificial intelligence (AI) machine learning as a tool to help clinicians predict patient outcomes and provide personalized treatment. A new study from Amsterdam UMC and Radoboudumuk shows how an AI algorithm can predict how effective antidepressants will be for depressed patients up to eight weeks early, using MRI brain scans and clinical data.
Depression, also known as major depressive disorder (MDD), is a common mood disorder that affects approximately 280 million people worldwide, according to the World Institute for Health Metrics and Evaluation. According to the National Institute of Mental Health (NIMH), major depression is one of the most common mental illnesses in the United States, and antidepressants are a commonly used treatment.
According to the NIMH, people with depression may need to try multiple types of antidepressants to find the right fit for the individual, and it usually takes one or two antidepressants to be effective. It is said to take a month. Therefore, if clinicians had a way to more quickly predict a patient’s response to antidepressants, it could accelerate a patient’s path to health recovery and potentially save lives. People with depression are at increased risk of developing physical illness. For example, it has been reported that people with depression have a 40% higher risk of metabolic and cardiovascular disease than the general population. The Lancet Commission on Psychiatry: A blueprint for protecting the physical health of people with mental illness.
Antidepressants approved by the U.S. Food and Drug Administration (FDA) include N-methyl D-aspartate (NMDA) antagonists (esketamine), monoamine oxidase inhibitors (MAOIs), and the neuroactive steroid gamma-aminobutyric acid (GABA). There are various types such as. – Receptor positive regulators (brexanolone), tricyclic and tetracyclic antidepressants, serotonin and norepinephrine reuptake inhibitors (SNRIs), atypical antidepressants (trazodone, nefazodone), and selective serotonin reuptake inhibitors drugs (SSRIs).
According to It is included. Medication for depression was announced on american family doctor In 2023.
Researchers from Amsterdam UMC and Radboudamk focused their study on a type of SSRI called sertraline, an antidepressant that increases the amount of the mood-regulating hormone serotonin in the brain. Sertraline is a commonly prescribed selective serotonin reuptake inhibitor, and Zoloft is the trade name.
Serotonin is a neurotransmitter that transmits signals between neurons. Selective serotonin reuptake inhibitors increase available serotonin by blocking serotonin reuptake by neurons, which helps improve signal transmission between brain nerve cells. In addition to sertraline, other SSRI drugs include fluoxetine, escitalopram, citalopram, paroxetine, vortioxetine, and vilazodone, according to the FDA.
The brain MRI scans and clinical data used in this study were part of a previous study called Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC), a U.S. university-led study on depression. Results from 229 patients diagnosed with major depressive disorder in a clinical trial. Texas (Utah) Southwestern Medical Center is collaborating with principal investigators affiliated with Columbia University and Massachusetts General Hospital.
For this new study, researchers in Amsterdam created an AI algorithm to predict the effects of sertraline. Researchers conducted assessments at baseline and after one week of treatment with sertraline and placebo. Liesbeth Lenemann, MD, professor of neuroradiology at Amsterdam UMC, is the corresponding author of the study, and co-authors include Erik (Henriks) Rouhe, MD, and Henk-Jan Muzarz, MD, PhD. D., Ivan Maximov, Ph.D., Inge Groote, MD, Ph.D., Atle Bjørnerud, Ph.D., Henk Marquering, Ph.D., Matthan Caan, Ph.D., Maarten Poirot, MS
The researchers found that the accuracy of the AI algorithm was better for multimodal models than for unimodal models. Multimodal models in artificial intelligence are deep learning models that relate data from different sources. In this case, the researchers combined multimodal MRI data with clinical data, and perfusion imaging was key.
The AI showed that increased blood circulation in the anterior cingulate cortex, the brain’s emotion-regulating region, was an indicator of the drug’s effectiveness, Radoboudmuk psychiatrist Rouhe said in an article in Amsterdam UMC. .
Researchers have created a useful tool that leverages AI machine learning, clinical data, and MRI brain scanning technology to provide precision medicine for depression. This proof of concept provides clinicians with a way to provide more personalized treatment in a timely manner, which may lead to better outcomes for patients with depression in the future.
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