AI Analyzed MRI Scans May Predict Patient Antidepressant Response
In a study presented to the Conference in Machine Intelligence in Medical Imaging (C-MIMI), researchers found that a machine-learning algorithm applied to brain MRI scans could predict if patients would respond to antidepressants.
Ridley reports that “[R]esearchers from Stony Brook University in New York trained a machine-learning model that uses analysis of pretreatment structural MRI exams to predict remission in patients with depression. In validation, the algorithm showed high specificity and good accuracy.
‘The model has the potential to assist clinicians with a clinical decision-making tool for antidepressant treatment planning,’ said presenter Dr. Farzana Ali.”
Though aseembling a large-enough dataset to train a deep-learning neural network may not be feasible, smaller datasets can be used to train machine learning. The researchers used “eXtreme Gradient Boosting (XGBoost), a tree-boosting classifier that has been shown to be able to detect depression with greater than 97% accuracy, she said,” according to Ridley.
“‘Our future research will focus on assessing performance of the algorithm in external data sets following appropriate parameter tuning with a particular focus on improving sensitivity,”‘Ali said,” quoted Ridley.