Monte Carlo Algorithm-Based Multimodal Magnetic Resonance Imaging Prognosis Prediction in Analysis of Rehabilitation Effect of Exercise Learning on Stroke Patients and Influencing Factors of Memory Function
Notice bibliographique
Résumé
Based on Monte Carlo algorithm and multimodal MRI diagnosis, the effect of motor learning on motor memory function recovery in stroke patients was investigated in this research. A total of 26 stroke patients with hemiplegia treated in hospital in the past three years were recruited. Patients were rolled into routine group (13 cases) and experimental group (13 cases) according to different follow-up rehabilitation methods. All patients were treated with intravenous thrombolysis. After treatment, the conventional group received conventional rehabilitation therapy and the experimental group received restraint induced exercise therapy (CIMT). Then, T1-weighted imaging, T2-weighted imaging, 3D anatomical imaging, and resting state examinations were performed on the patients before and after treatment. All image data and image processing were performed by the Monte Carlo algorithm. Before treatment and six weeks after rehabilitation treatment, the patients’ mental state and memory function were tested using Addenbrooke’s Cognitive Examination (ACE-III) and Montreal Cognitive Assessment (MoCA). In addition, the Fugl-Meyer motor assessment, the simple test for evaluating hand function, and the modified Barthel index were used to evaluate the patient’s ability of daily living. After processing, the quality of multimode MRI image was improved obviously, and the lesion was more prominent. The fractional amplitude of low frequency fluctuation of supplement motor area in stroke patients increased after treatment combined with exercise rehabilitation ( P < 0.05 ) and ReHo decreased compared with that before treatment. The connection function of the left and right hippocampus was enhanced. The difference in ACE-III (experimental group: 16 versus 21; control group: 17.1 versus 19) scores between the two groups after treatment and before treatment was remarkable ( P < 0.05 ), but the score of patients in experimental group improved better. The MoCA (experimental group: 24.38 versus 26.47; control group: 23.13 versus 23.37) scores of the two groups of patients changed greatly from those before treatment ( P < 0.05 ), and the MoCA score ratio between the two groups was also statistically different (26.47 versus 23.37; P < 0.05 ). There was a statistical difference in the living ability of the two groups of patients before and after treatment ( P < 0.05 ). The Monte Carlo algorithm had a good processing effect on multimodal MRI images. The recovery of the experimental group was evidently better, and the difference between the two groups was substantial ( P < 0.05 ). CIMT had a good effect on the recovery of exercise rehabilitation and memory function of patients with ischemic stroke.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».