GE HealthCare Technology & Innovation Center Niskayuna NY 12309 USA.
GE Global Research Niskayuna NY 12309 USA.
IEEE J Transl Eng Health Med. 2024 Aug 22;12:589-599. doi: 10.1109/JTEHM.2024.3448392. eCollection 2024.
Optimized deep brain stimulation (DBS) is fast becoming a therapy of choice for the treatment of Parkinson's disease (PD). However, the post-operative optimization (aimed at maximizing patient clinical benefits and minimizing adverse effects) of all possible DBS parameter settings using the standard-of-care clinical protocol requires numerous clinical visits, which substantially increases the time to optimization per patient (TPP), patient cost burden and limit the number of patients who can undergo DBS treatment. The TPP is further elongated in electrodes with stimulation directionality or in diseases with latency in clinical feedback. In this work, we proposed a deep learning and fMRI-based pipeline for DBS optimization that can potentially reduce the TPP from ~1 year to a few hours during a single clinical visit.
We developed an unsupervised autoencoder (AE)-based model to extract meaningful features from 122 previously acquired blood oxygenated level dependent (BOLD) fMRI datasets from 39 a priori clinically optimized PD patients undergoing DBS therapy. The extracted features are then fed into multilayer perceptron (MLP)-based parameter classification and prediction models for rapid DBS parameter optimization.
The AE-extracted features of optimal and non-optimal DBS were disentangled. The AE-MLP classification model yielded accuracy, precision, recall, F1 score, and combined AUC of 0.96 ± 0.04, 0.95 ± 0.07, 0.92 ± 0.07, 0.93 ± 0.06, and 0.98 respectively. Accuracies of 0.79 ± 0.04, 0.85 ± 0.04, 0.82 ± 0.05, 0.83 ± 0.05, and 0.70 ± 0.07 were obtained in the prediction of voltage, frequency, and x-y-z contact locations, respectively.
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.
优化的深部脑刺激（DBS）正迅速成为治疗帕金森病（PD）的首选疗法。然而，使用标准护理临床方案对所有可能的 DBS 参数设置进行术后优化（旨在最大程度地提高患者的临床获益并最小化不良反应）需要多次临床就诊，这大大增加了每位患者的优化时间（TPP）、患者的经济负担，并限制了可以接受 DBS 治疗的患者数量。在具有刺激方向性的电极或在临床反馈存在潜伏期的疾病中，TPP 进一步延长。在这项工作中，我们提出了一种基于深度学习和 fMRI 的 DBS 优化管道，该管道有可能将每位患者的 TPP 从~1 年缩短至单次临床就诊的数小时。
我们开发了一种基于无监督自动编码器（AE）的模型，从 39 名接受 DBS 治疗的预先临床优化 PD 患者的 122 个先前获得的血氧水平依赖（BOLD）fMRI 数据集提取有意义的特征。然后，将提取的特征输入基于多层感知器（MLP）的参数分类和预测模型，以快速优化 DBS 参数。
优化和非优化 DBS 的 AE 提取特征得以区分。AE-MLP 分类模型的准确率、精度、召回率、F1 得分和综合 AUC 分别为 0.96±0.04、0.95±0.07、0.92±0.07、0.93±0.06 和 0.98。在预测电压、频率和 x-y-z 接触位置时，获得的准确率分别为 0.79±0.04、0.85±0.04、0.82±0.05、0.83±0.05 和 0.70±0.07。
所提出的 AE-MLP 模型在 fMRI 引导的 DBS 参数分类和预测方面取得了有希望的结果，有可能促进快速半自动 DBS 参数优化。临床和转化影响的陈述——提出了一种基于深度学习的半自动 DBS 参数优化管道，有可能显著缩短每位患者的优化时间和患者的经济负担，同时增加患者吞吐量。
