Retinal Artificial Intelligence‐based Model Identifies Non‐demented Elderly Subjects at Risk of Alzheimer's Disease
Notice bibliographique
Résumé
BACKGROUND: RetinAD is a validated deep learning model for differentiating between Alzheimer's disease (AD) dementia and cognitively unimpaired subjects based on analyzing retinal photographs. Since certain AD-related retinal changes (e.g., microvasculopathy) may start to develop years to decades before the onset of cognitive symptoms, we hypothesized that RetinAD may also identify retinal microvasculopathy among non-demented elderly subjects. We aimed to compare measures of retinal vessel network between "positive" and "negative" cases as classified by RetinAD among elderly non-demented elderly subjects. METHOD: We recruited community subjects who were participants in the BEAT AD (Brain Health Education And Tailor-made Measures for Prevention of Alzheimer's Disease) service programme in Hong Kong. This programme invites non-demented community dwelling subjects (59-80 years old) with subjective cognitive decline (SCD). It assesses their cognitive performances using Montreal Cognitive Assessment-5 minutes (MoCA-5) and on their control in the modifiable risk factors of AD. It also provides tailor-made recommendation for the subjects of how to optimize those risk factors that are not well controlled. We obtained fundus pictures using the Topcon NW500 non-mydriatic retinal camera. We classified subjects into "positive" or "negative" using RetinAD. We conducted quantitative measurements of retinal vessels using the Singapore I Vessel Assessment (SIVA) software. RESULT: Among the 187 recruited subjects with SCD, 29 (15.5%) and 158 (84.5%) subjects were classified as "positive" and "negative", respectively. Subjects who were classified as "positive" were older (mean age 71.21 versus [vs] 67.59; p = <0.01) than those who were classified as "negative". There was no significant difference in MoCA scores between "positive" (22.79) and "negative" subjects (23.74, p = 0.28). Analysis of the retinal vessel network showed that "positive" subjects had a significantly higher branching coefficient arterioles (1.64 vs 1.49) and branching coefficient venules (1.49 vs 1.32) than that of "negative" subjects. The difference remained significant (p = 0.033) for the branching coefficient venules after being adjusted to age and mean arterial pressure. CONCLUSION: RetinAD identified non-demented elderly who had worse retinal microvasculopathy and biologically older brains. Findings suggested that RetinAD may be able to identify elderly subjects who are at risk of developing AD dementia in the future.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».