ArticleInternational journal for numerical methods in biomedical engineering2022
Magnetic resonance imaging image analysis of the therapeutic effect and neuroprotective effect of deep brain stimulation in Parkinson's disease based on a deep learning algorithm.
Article in International journal for numerical methods in biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Computational Insights Into Smart Bioelectronics in Digital Health Care (2020-2024): Topic Modeling Study.JMIR medical informatics · 2026Review
- Magnetic resonance imaging image analysis of the therapeutic effect and neuroprotective effect of deep brain stimulation in Parkinson's disease based on a deep learning algorithm.International journal for numerical methods in biomedical engineering · 2022Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to study the therapeutic neuroprotective effect of deep brain stimulation (DBS) in Parkinson's disease (PD), based on the deep learning algorithm, this study combines with magnetic resonance imaging (MRI) image analysis technology to study the clinical efficacy of DBS in the surgical treatment of PD and the neuroprotective and neurological recovery effects after surgery. Establish a deep learning algorithm model based on MRI image analysis technology, comparison of UPDRS motor status assessment and the improvement of daily life ability before and after DBS surgery, evaluate the accuracy rate and the detection speed of the model. The models constructed in this study have an accuracy rate of more than 90% in the PD detection test, and the detection speed of the algorithm model under the condition of big data is between 60 and 200 ms. DBS significantly improve a series of clinical symptoms in patients with PD. The deep learning algorithm model based on MRI image analysis technology in this paper has a certain effect. DBS operation can improve the symptoms of PD, and has the effect of neuroprotection and neurological recovery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.