Design and Application of Automated Algorithms for Diagnosis and Treatment Optimization in Neurodegenerative Diseases

Neurodegenerative diseases represent a growing healthcare problem, mainly related to an aging population worldwide and thus their increasing prevalence. In particular, Alzheimer’s disease (AD) and Parkinson’s disease (PD) are leading neurodegenerative diseases. To aid their diagnosis and optimize tr...

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Vydáno v:Neuroinformatics (Totowa, N.J.) Ročník 20; číslo 3; s. 765 - 775
Hlavní autoři: Estella, Francisco, Suarez, Esther, Lozano, Beatriz, Santamarta, Elena, Saiz, Antonio, Rojas, Fernando, Rojas, Ignacio, Blazquez, Marta, Nader, Lydia, Sol, Javier, Seijo, Fernando
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.07.2022
Springer Nature B.V
Témata:
Aged
Aging
Algorithms
Alzheimer Disease - diagnostic imaging
Alzheimer Disease - therapy
Alzheimer's disease
Bioinformatics
Biomedical and Life Sciences
Biomedicine
Classification
Cognitive ability
Computational Biology/Bioinformatics
Computed tomography
Computer Appl. in Life Sciences
Deep brain stimulation
Deep Brain Stimulation - methods
Diagnosis
Electrodes, Implanted
Humans
Magnetic Resonance Imaging
Movement disorders
Neurodegenerative diseases
Neurodegenerative Diseases - diagnostic imaging
Neurodegenerative Diseases - therapy
Neurology
Neurosciences
Original Article
Parkinson Disease - diagnostic imaging
Parkinson Disease - therapy
Parkinson's disease
Patients
Parkinson disease
Brain
Decision trees
Alzheimer disease
Deep brain stimulation
Classification
ISSN:1539-2791, 1559-0089, 1559-0089
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Shrnutí:Neurodegenerative diseases represent a growing healthcare problem, mainly related to an aging population worldwide and thus their increasing prevalence. In particular, Alzheimer’s disease (AD) and Parkinson’s disease (PD) are leading neurodegenerative diseases. To aid their diagnosis and optimize treatment, we have developed a classification algorithm for AD to manipulate magnetic resonance images (MRI) stored in a large database of patients, containing 1,200 images. The algorithm can predict whether a patient is healthy, has mild cognitive impairment, or already has AD. We then applied this classification algorithm to therapeutic outcomes in PD after treatment with deep brain stimulation (DBS), to assess which stereotactic variables were the most important to consider when performing surgery in this indication. Here, we describe the stereotactic system used for DBS procedures, and compare different planning methods with the gold standard normally used (i.e., neurophysiological coordinates recorded intraoperatively). We used information collected from database of 72 DBS electrodes implanted in PD patients, and assessed the potentially most beneficial ranges of deviation within planning and neurophysiological coordinates from the operating room, to provide neurosurgeons with additional landmarks that may help to optimize outcomes: we observed that x coordinate deviation within CT scan and gold standard intra-operative neurophysiological coordinates is a robust matric to pre-assess positive therapy outcomes- “good therapy” prediction if deviation is higher than 2.5 mm. When being less than 2.5 mm, adding directly calculated variables deviation (on Y and Z axis) would lead to specific assessment of “very good therapy”.
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ISSN:1539-2791
1559-0089
1559-0089
DOI:10.1007/s12021-022-09578-3