In Plastic and reconstructive surgery ; h5-index 62.0
BACKGROUND : Recent evidence has shown that patient drawings of pain can predict poor outcomes in headache surgery. Given that interpretation of pain drawings requires some clinical experience, the authors developed a machine learning framework capable of automatically interpreting pain drawings to predict surgical outcomes. This platform will allow surgeons with less clinical experience, neurologists, primary care practitioners, and even patients to better understand candidacy for headache surgery.
METHODS : A random forest machine learning algorithm was trained on 131 pain drawings provided prospectively by headache surgery patients before undergoing trigger-site deactivation surgery. Twenty-four features were used to describe the anatomical distribution of pain on each drawing for interpretation by the machine learning algorithm. Surgical outcome was measured by calculating percentage improvement in Migraine Headache Index at least 3 months after surgery. Artificial intelligence predictions were compared with clinician predictions of surgical outcome to determine artificial intelligence performance.
RESULTS : Evaluation of the data test set demonstrated that the algorithm was consistently more accurate (94%) than trained clinical evaluators. Artificial intelligence weighted diffuse pain, facial pain, and pain at the vertex as strong predictors of poor surgical outcome.
CONCLUSIONS : This study indicates that structured algorithmic analysis is able to correlate pain patterns drawn by patients to Migraine Headache Index percentage improvement with good accuracy (94%). Further studies on larger data sets and inclusion of other significant clinical screening variables are required to improve outcome predictions in headache surgery and apply this tool to clinical practice.
Chartier Christian, Gfrerer Lisa, Knoedler Leonard, Austen William G
2023-Feb-01
