AI’s Potential to Anticipate Antidepressant Effects in Children and Teens

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AI’s Potential to Anticipate Antidepressant Effects in Children and Teens

The Mayo Clinic has published a study where researchers have made the first move toward using Artificial Intelligence to anticipate early results of antidepressant use in children and teens diagnosed with major depressive disorder. 

The study showed that there are variations in six symptoms related to depression. Including low self-esteem, irritability, social withdrawal, excess fatigue, depressed feelings, and difficulty having fun.  

More than 50% of mental health disorders are identified and diagnosed during youth before patients turn 18 years old. For this reason, it is essential to have predictive approaches in place to gauge treatment results in children and teens with depression. 

This is the view of Mayo Clinic’s researcher and lead author of the study, Arjun P. Athreya, PH. D, M.S. It’s also the reason why this study is focused on a global public health issue that’s commonly undertreated; pediatric depression. 

The Details of the Study

Researchers relied on the study Children’s Depression Rating Scaled, Revised to evaluate the six symptoms mentioned earlier and predict the results of antidepressant pharmacotherapy for 10 to 12 weeks. Fluoxetine testing databanks predicted the symptoms’ 10 to 12-week outcomes with an average precision of 73% at 4 to 6 weeks. 

Duloxetine testing datasets, on the other hand, predicted the symptoms’ 10 to 12-week outcomes with an average precision of 76% at 4 to 6 weeks. Anticipating the precision of response and remission in patients who received placebo treatments had a lower average of 67%. 

Paul Croarkin, D.O., senior author and psychiatrist at Mayo Clinic, states that this precursory work suggests that Artificial Intelligence shows a lot of potential for aiding clinical decisions by arming physicians with information on the selection, use, and dosing of antidepressants for youth. The study produced better treatment results in samples of children and teens treated with two kinds of antidepressants. 

The Meaning of the Study’s Findings

What these findings show is that an Artificial Intelligence platform that leverages machine learning can foresee the results of antidepressant treatment in children and teens early in therapy. 

This is an incredible benefit for the future because it can help busy physicians plan for better treatment and avoid treatment options that may not be effective for patients. 

Additionally, the study shows that AI and patient data offer great potential to make sure children and teens receive treatment with the highest possible benefits while reducing the risks of side effects, as reported by Dr. Athreya. 

He also stated that the algorithm was created to imitate the clinician’s logic of treatment management based on their educated guess on whether the patient will benefit or not from antidepressant pharmacotherapy at the current dosage. As a computer engineer, it was essential for Dr. Athreya to understand patient needs and how AI can be useful to clinicians. 

What the integration does is use changes in clinical symptoms during the early stages of treatment. Patients are interviewed as usual, but this platform provides accurate predictions regarding treatment results. 

The study’s outcomes provide a foundation for further research that incorporates brain-based measures, pharmacogenomic information, and physiological data to measure the accuracy of medical approaches for treating youth with depression. 

Conclusion

This will refine the care of children and teens who battle depression while also assisting physicians in ensuring that antidepressants will benefit patients as much as possible. 

Being able to predict antidepressant results in youngsters who are treated for depression will help manage a disease that could be a lifelong burden. Though it’s still in its early stages, the research is quite promising and future research that includes bio-data will allow the improvement of treatment planning and diagnostic precision.

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