Machine learning in deep brain stimulation: A systematic review

Peralta, Maxime; Jannin, Pierre; Baxter, Jamie

doi:10.1016/j.artmed.2021.102198

Cited by 16 publications

(8 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead, this paper shows the themes that researchers have investigated and emphasizes opportunities for future research. Excellent reviews exist on implementing machine learning models in deep brain stimulation research [ 22 ], and the studies reported provide examples of future research projects that could incorporate explainable methods.…”

Section: Discussionmentioning

confidence: 99%

“…The aim of this paper is to synthesize recent literature on explainable artificial intelligence approaches to deep brain stimulation [ 21 ]. There is evidence that machine learning models can predict treatment outcomes and identify treatment targets [ 22 ]. It has also been reported that in the domain of closed-loop brain stimulation, explainable artificial intelligence can improve treatment outcomes and advance fundamental knowledge about brain-stimulation relationships [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence

Allen

2023

Biomedicines

View full text Add to dashboard Cite

Deep brain stimulation is a treatment that controls symptoms by changing brain activity. The complexity of how to best treat brain dysfunction with deep brain stimulation has spawned research into artificial intelligence approaches. Machine learning is a subset of artificial intelligence that uses computers to learn patterns in data and has many healthcare applications, such as an aid in diagnosis, personalized medicine, and clinical decision support. Yet, how machine learning models make decisions is often opaque. The spirit of explainable artificial intelligence is to use machine learning models that produce interpretable solutions. Here, we use topic modeling to synthesize recent literature on explainable artificial intelligence approaches to extracting domain knowledge from machine learning models relevant to deep brain stimulation. The results show that patient classification (i.e., diagnostic models, precision medicine) is the most common problem in deep brain stimulation studies that employ explainable artificial intelligence. Other topics concern attempts to optimize stimulation strategies and the importance of explainable methods. Overall, this review supports the potential for artificial intelligence to revolutionize deep brain stimulation by personalizing stimulation protocols and adapting stimulation in real time.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence

Allen

2023

Biomedicines

View full text Add to dashboard Cite

show abstract

“…Recent review shows that modern ML algorithms such as DNN, convolutional neural networks, recurrent neural networks, long-short term memory neural networks alongside the conventional algorithms such as LR, LDA, SVM, are increasingly being used to analyze imaging, local field potentials, EEG, microelectrode recordings (MER) data in DBS research. However, none of 73 studies employed radiomic features ( Peralta et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Radiomic features of amygdala nuclei and hippocampus subfields help to predict subthalamic deep brain stimulation motor outcomes for Parkinson‘s disease patients

et al. 2022

View full text Add to dashboard Cite

Background and purposeThe aim of the study is to predict the subthalamic nucleus (STN) deep brain stimulation (DBS) outcomes for Parkinson’s disease (PD) patients using the radiomic features extracted from pre-operative magnetic resonance images (MRI).MethodsThe study included 34 PD patients who underwent DBS implantation in the STN. Five patients (15%) showed poor DBS motor outcome. All together 9 amygdalar nuclei and 12 hippocampus subfields were segmented using Freesurfer 7.0 pipeline from pre-operative MRI images. Furthermore, PyRadiomics platform was used to extract 120 radiomic features for each nuclei and subfield resulting in 5,040 features. Minimum Redundancy Maximum Relevance (mRMR) feature selection method was employed to reduce the number of features to 20, and 8 machine learning methods (regularized binary logistic regression (LR), decision tree classifier (DT), linear discriminant analysis (LDA), naive Bayes classifier (NB), kernel support vector machine (SVM), deep feed-forward neural network (DNN), one-class support vector machine (OC-SVM), feed-forward neural network-based autoencoder for anomaly detection (DNN-A)) were applied to build the models for poor vs. good and very good STN-DBS motor outcome prediction.ResultsThe highest mean prediction accuracy was obtained using regularized LR (96.65 ± 7.24%, AUC 0.98 ± 0.06) and DNN (87.25 ± 14.80%, AUC 0.87 ± 0.18).ConclusionThe results show the potential power of the radiomic features extracted from hippocampus and amygdala MRI in the prediction of STN-DBS motor outcomes for PD patients.

show abstract

“…This paper consolidates recent literature and offers a comprehensive synthesis of how to apply explainable artificial-intelligence methods to the utilization of digital health data in precision medicine. Machine learning is an effective approach to identifying treatment targets and accurately predicting treatment outcomes [ 21 ]. For example, there is evidence for using an artificial-intelligence-based system to select patients for intervention using the electrocardiograph signal to predict atrial fibrillation [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review

Allen

2024

JPM

View full text Add to dashboard Cite

This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. As healthcare increasingly tailors treatments to individual characteristics, the integration of artificial intelligence with digital health data becomes crucial. Leveraging a topic-modeling approach, this paper distills the key themes of 27 journal articles. We included peer-reviewed journal articles written in English, with no time constraints on the search. A Google Scholar search, conducted up to 19 September 2023, yielded 27 journal articles. Through a topic-modeling approach, the identified topics encompassed optimizing patient healthcare through data-driven medicine, predictive modeling with data and algorithms, predicting diseases with deep learning of biomedical data, and machine learning in medicine. This review delves into specific applications of explainable artificial intelligence, emphasizing its role in fostering transparency, accountability, and trust within the healthcare domain. Our review highlights the necessity for further development and validation of explanation methods to advance precision healthcare delivery.

show abstract

Machine learning in deep brain stimulation: A systematic review

Cited by 16 publications

References 79 publications

Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence

Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence

Radiomic features of amygdala nuclei and hippocampus subfields help to predict subthalamic deep brain stimulation motor outcomes for Parkinson‘s disease patients

The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review

Contact Info

Product

Resources

About