The Impacts of Subthalamic Nucleus-Deep Brain Stimulation (STN-DBS) on the Neuropsychiatric Function of Patients with Parkinson’s Disease Using Image Features of Magnetic Resonance Imaging under the Artificial Intelligence Algorithms
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Résumé
This study was to explore the effect of subthalamic nucleus- (STN-) deep brain stimulation (DBS) on the neuropsychiatric function of Parkinson’s disease (PD) patients using the magnetic resonance imaging (MRI) image analysis technology and the artificial intelligence (AI) algorithm. In this study, 40 PD patients admitted to our hospital from August 2018 to March 2020 were selected as the research objects, and they were divided into a control group and an observation group according to the random number table method, with 20 cases in each group. The patients in the control group were given oral treatment with levodopa tablets; and patients in the observation group were treated with STN-DBS + levodopa tablets. In patients, MRI examinations were performed before and after the treatment, and the image optimization processing algorithm under AI was adopted to process the images. The MRI imaging results of the two groups of patients were observed, analyzed, and compared before and after treatment; and the sports, cognition, and mental states of the two groups of patients were analyzed. It was believed that the MRI image before using the AI algorithm was blurry, and the image was clear after the noise reduction optimization process, which was convenient for observation. The data analysis revealed that the signal-to-noise ratio (SNR) after denoising (32.41) and structural similarity (SSIM) (0.79) had been improved. The results of the study suggested that the space occupation and bleeding symptoms of the two groups of patients were reduced after treatment, and those in the observation group were better than those of the control group; the incidences of dyskinesia and motor symptom fluctuations in the observation group were 5% and 0%, respectively, which were lower than those in the control group (35% and 25%, respectively). After treatment, the Unified Parkinson’s Disease Rating Scale (UPDRS) score of the two groups of patients decreased, and it was lower in the observation group than in the control group; and the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Scale (MMSE) scores increased, and those in the observation group were higher in contrast to those in the control group (all <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>P</a:mi> <a:mo><</a:mo> <a:mn>0.05</a:mn> </a:math> ). STN-DBS was beneficial to improve the clinical symptoms of patients and delay the progress of the disease, and MRI based on AI algorithms can effectively observe the changes in the neuropsychiatric function of patients, which was conducive to further clinical diagnosis and treatment.
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle