Deep Learning and fMRI-Based Pipeline for Optimization of Deep Brain Stimulation During Parkinson's Disease Treatment: Toward Rapid Semi-Automated Stimulation Optimization.
Jianwei QiuAfis AjalaJohn KarigiannisJurgen GermannBrendan SantyrAaron LohLuca MarinelliThomas FooRadhika MadhavanDesmond YeoAlexandre BoutetAndres LozanoPublished in: IEEE journal of translational engineering in health and medicine (2024)
The proposed AE-MLP models yielded promising results for fMRI-based DBS parameter classification and prediction, potentially facilitating rapid semi-automated DBS parameter optimization. Clinical and Translational Impact Statement-A deep learning-based pipeline for semi-automated DBS parameter optimization is presented, with the potential to significantly decrease the optimization duration per patient and patients' financial burden while increasing patient throughput.
Keyphrases
- deep learning
- deep brain stimulation
- machine learning
- parkinson disease
- artificial intelligence
- obsessive compulsive disorder
- convolutional neural network
- end stage renal disease
- chronic kidney disease
- high throughput
- case report
- ejection fraction
- newly diagnosed
- young adults
- climate change
- patient reported outcomes
- human health
- health insurance
- combination therapy