Deep Learning and fMRI-Based Pipeline for Optimization of Deep Brain Stimulation During Parkinson’s Disease Treatment: Toward Rapid Semi-Automated Stimulation Optimization

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Jianwei Qiu;Afis Ajala;John Karigiannis;Jürgen Germann;Brendan Santyr;Aaron Loh;Luca Marinelli;Thomas Foo;Radhika Madhavan;Desmond Yeo;Alexandre Boutet;Andres Lozano
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引用次数: 0

Abstract

Objective: 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.Methods and procedures: 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.Results: 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.Conclusion: 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.
基于深度学习和 fMRI 的帕金森病治疗期间脑深部刺激优化管道:实现快速半自动刺激优化
目的:优化脑深部刺激(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(旨在最大限度地提高患者的临床疗效并减少不良反应)需要多次临床访问,这大大增加了每位患者的优化时间(TPP)和患者的成本负担,并限制了接受 DBS 治疗的患者人数。对于具有刺激方向性的电极或临床反馈有延迟的疾病,TPP 会进一步延长。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,有可能将单次临床就诊的 TPP 从 ~1 年缩短到几小时:我们开发了一种基于无监督自动编码器(AE)的模型,从先前获得的122个血氧饱和度依赖性(BOLD)fMRI数据集中提取有意义的特征,这些数据集来自39名接受DBS治疗的先验临床优化的帕金森病患者。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:结果:最佳和非最佳 DBS 的 AE 提取特征被区分开来。AE-MLP 分类模型的准确度、精确度、召回率、F1 分数和综合 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-yz 接触位置时,精确度分别为 0.79 ± 0.04、0.85 ± 0.04、0.82 ± 0.05、0.83 ± 0.05 和 0.70 ± 0.07:结论:所提出的 AE-MLP 模型在基于 fMRI 的 DBS 参数分类和预测方面取得了很好的结果,有可能促进半自动化 DBS 参数的快速优化。临床与转化影响声明--本文介绍了基于深度学习的半自动化 DBS 参数优化管道,它有可能显著缩短每位患者的优化时间,减轻患者的经济负担,同时提高患者吞吐量。
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来源期刊
CiteScore
7.40
自引率
2.90%
发文量
65
审稿时长
27 weeks
期刊介绍: The IEEE Journal of Translational Engineering in Health and Medicine is an open access product that bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. The journal provides a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. A unique aspect of the journal is its ability to foster a collaboration between physicians and engineers for presenting broad and compelling real world technological and engineering solutions that can be implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The journal provides an active forum for clinical research and relevant state-of the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies. The scope of the journal includes, but is not limited, to topics on: Medical devices, healthcare delivery systems, global healthcare initiatives, and ICT based services; Technological relevance to healthcare cost reduction; Technology affecting healthcare management, decision-making, and policy; Advanced technical work that is applied to solving specific clinical needs.
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