Predicting Motor Responsiveness to Deep Brain Stimulation with Machine Learning.

Deep brain stimulation is a complex movement disorder intervention that requires highly invasive brain surgery. Clinicians struggle to predict how patients will respond to this treatment. To address this problem, we are working toward developing a clinical tool to help neurologists predict deep brain stimulation response. We analyzed a cohort of 105 Parkinson's patients who underwent deep brain stimulation at Vanderbilt University Medical Center. We developed binary and multicategory models for predicting likelihood of motor symptom reduction after undergoing deep brain stimulation. We compared the performances of our best models to predictions made by neurologist experts in movement disorders. The strongest binary classification model achieved a 10-fold cross validation AUC of 0.90, outperforming the best neurologist predictions (0.56). These results are promising for future clinical applications, though more work is necessary to validate these findings in a larger cohort and taking into consideration broader quality of life outcome measures.

Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models.

AIMS: Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform. Here, we aim to explore how one such tool based on a convolutional neural network, can be used to build a generalizable multi-classification model capable of classifying microscopic images of human tissue samples with the ultimate goal of providing a differential diagnosis (a list of look-alikes) for each entity. METHODS: We obtained three institutional training datasets and one generalizability test dataset, each containing images of histologic tissues in 38 categories. Models were trained on data from single institutions, low quantity combinations of multiple institutions, and high quantity combinations of multiple institutions. Models were tested against withheld validation data, external institutional data, and generalizability test images obtained from Google image search. Performance was measured with macro and micro accuracy, sensitivity, specificity, and f1-score. RESULTS: In this study, we were able to show that such a model's generalizability is dependent on both the training data source variety and the total number of training images used. Models which were trained on 760 images from only a single institution performed well on withheld internal data but poorly on external data (lower generalizability). Increasing data source diversity improved generalizability, even when decreasing data quantity: models trained on 684 images, but from three sources improved generalization accuracy between 4.05% and 18.59%. Maintaining this diversity and increasing the quantity of training images to 2280 further improved generalization accuracy between 16.51% and 32.79%. CONCLUSIONS: This pilot study highlights the significance of data diversity within such studies. As expected, optimal models are those that incorporate both diversity and quantity into their platforms.s.