Design and Application of Automated Algorithms for Diagnosis and Treatment Optimization in Neurodegenerative Diseases.
Neuroinformatics
; 20(3): 765-775, 2022 07.
Article
en En
| MEDLINE
| ID: mdl-35262881
ABSTRACT
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".Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Enfermedad de Parkinson
/
Enfermedades Neurodegenerativas
/
Estimulación Encefálica Profunda
/
Enfermedad de Alzheimer
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Aged
/
Humans
Idioma:
En
Revista:
Neuroinformatics
Asunto de la revista:
INFORMATICA MEDICA
/
NEUROLOGIA
Año:
2022
Tipo del documento:
Article
País de afiliación:
España
Pais de publicación:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
/
UNITED STATES OF AMERICA
/
US
/
USA