Just Published!
Congratulations to Drs. Adolfo Ramirez Zamora and Joshua Wong on the publication of “Automated Image Guided Programming Algorithm Supports Clinicians during DBS Programming for Parkinson’s Disease Patients,” which appears in the June 9th issue of Neurology.
Abstract
Objective
The clinical utility of an automated image-guided programming (aIGP) algorithm for optimizing deep brain stimulation (DBS) settings in patients with Parkinson’s disease is analyzed, and its performance against standard-of-care (SoC) programming is assessed.
Background
As a process that can be time-consuming and burdensome for patients, DBS programming traditionally relies on iterative, clinician-led adjustments to balance therapeutic benefit and side effects. Recent advances in imaging and computational modeling have enabled algorithms that rapidly generate anatomically-informed stimulation settings. Automated image-guided programming can use patient-specific imaging to position a stimulation field model (SFM) within seconds, in turn targeting desired regions while minimizing spread to non-target areas.
Design/Methods
To date, this prospective, multicenter, double-blinded crossover study has enrolled 15 PD patients with bilateral STN or GPi DBS systems. Participants were evaluated acutely and chronically under both SoC and aIGP-guided programming. Motor outcomes were assessed using MDS-UPDRS III (meds off/on), and chronic efficacy was evaluated via motor diaries. Clinicians could optimize aIGP-suggested settings, and changes were quantified using similarity metrics.
Results
Initial aIGP DBS-on settings improved motor scores by 39% (meds-off) and 54% (meds-on) versus DBS-off (p<0.0001), with no significant difference from optimized SoC programming (p=0.44 and p=0.33, respectively). Chronic assessments showed aIGP-guided programs yielded 25 minutes/day more “ON” time and 58 minutes/day less “ON with dyskinesias” compared to SoC, though group-level differences were not statistically significant. Clinician-optimized aIGP settings remained largely unchanged. Additional data collected in the interim will be reported.
Conclusions
Automated image-guided programming provides rapid, anatomically guided programming that achieves motor outcomes comparable to traditional methods. Its ability to streamline DBS optimization may reduce clinical burden and enhance personalization, supporting its integration into routine neuromodulation workflows.