Clinical application of 3D reconstruction and accurate volume measurement of white matter in patients with cognitive dysfunction
Notice bibliographique
Résumé
To quantitatively measure the volume of white matter hyperintensities (WMHs) in different parts of the brain in patients with different types of cognitive function and analyze the relationship between WMH volume and cognitive function to obtain a threshold WMH volume for the early detection and clinical assessment of cognitive dysfunction. The clinical data and magnetic resonance imaging (MRI) data of patients with WMHs indicated by cranial MR in the Department of General Medicine of Shandong Provincial Third Hospital were collected. The FLAIR sequence images of the patients were subsequently analyzed with computer automated detection technology. Through deep learning-based 3D reconstruction, the specific volumes of the patients' WMHs were obtained. Patients were divided into three groups according to the Fazekas scale score: Fazekas score 1, Fazekas score 2, and Fazekas score 3. The WMH volumes within each group were subsequently compared, and the correlations between the WMH volumes of the patients in each group and their Montreal Cognitive Assessment (MoCA) scores, Trail Making Test A (TMT-A) scores, Trail Making Test B (TMT-B) scores, age, duration of hypertension, duration of diabetes, basic information, etc., were analyzed. The patients were subsequently divided into a normal group (MoCA > 25) and a mild cognitive impairment group (18 < MoCA ≤ 25) on the basis of their MoCA scores. The WMH volumes in each group were then calculated separately. The cutoff values of the WMH volume for differentiating between the normal group and mild cognitive impairment group were obtained through receiver operating characteristic (ROC) curve analysis. The MoCA scores significantly differed among the three Fazekas score groups (r = - 0.5716, P < 0.0001). There were also statistically significant differences in the total volume of WMHs among the three groups (r = 0.7527, P < 0.0001). WMH volume was positively correlated with the TMT-A and TMT-B scores (r = 0.2345, P< 0.05; r = 0.2404, P < 0.05) but negatively correlated with the MoCA score (r = - 0.4789, P < 0.0001). Moreover, WMH volume was positively associated with the duration of hypertension (F = 4.743, P < 0.05) but not with the duration of diabetes (F = 1.431, P = 0.2456). The cutoff value of WMH volume between the normal group and mild cognitive impairment group was 15.474900; at this value, the sensitivity of the WMH volume in discriminating the two groups was 0.808, and the specificity was 0.556. Automated detection technology can successfully be used to obtain the volume of WMHs in different parts of patients' brains. Since WMH volume is correlated with cognitive function scores, we can use MRI to identify and assess individuals who show potential early signs of cognitive dysfunction and administer early interventions. These findings provide potential preventive and therapeutic targets for the clinical diagnosis and treatment of cognitive dysfunction.
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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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».