Research square · 2025
Revisiting Centiloids using AI.
СтатьяПрепринтНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
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История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Bourgeat Pierrick, Fripp Jurgen, Lebrat Leo, Xia Ying, Feizpour Azadeh, Cox Timothy, Zisis Georgios, Gillman Ashley, Goyal Manu, Tosun Duygu, Benzinger Tammie, LaMontagne Pamela, Breakspear Michael, Lupton Michelle, Short Cathy, Adam Robert, Robertson Joanne, Sperling Reisa, O'Bryant Sid, Johnson Sterling, Jack Clifford, Schwarz Christopher, Park Denise C, Barkhof Frederik, Farrar Gill, Bollack Ariane, Collij Lyduine, Landau Susan, Koeppe Robert, Morris John, Weiner Michael, Villemagne Victor, Masters Colin, Rowe Christopher, Doré Vincent
Аннотация
The Centiloid scale is the standard for Amyloid ( A β ) PET quantification, widely used in research, clinical settings, and trial stratification. However, variability between tracers and scanners remains a challenge. This study introduces DeepSUVR, a deep learning method to correct Centiloid quantification, by penalising implausible longitudinal trajectories during training. The model was trained using data from 2,098 participants (6,762 A β PET scans) in AIBL/ADNI and validated using 15,806 A β PET scans from 10,543 participants across 10 external datasets. DeepSUVR increased correlation between tracers, and reduced variability in the A β -negatives. It showed the strongest association with cognition, highest AUC against visual reads and best longitudinal consistency between studies. DeepSUVR also increased the effect size for detecting lower Centiloid increase per year in the A4 study. DeepSUVR advances A β PET quantification, outperforming standard approaches, which is particularly important for consistent decision making and to detect subtle and early changes in clinical interventions.