Trial reportNeurobiology of aging2010
Boosting power for clinical trials using classifiers based on multiple biomarkers.
Trial report in Neurobiology of aging, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
102 citing papers in PubMed, 2 syntheses or guidelines pooled it, 189 citations in OpenAlex.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Advancing Alzheimer's research: A review of big data promises.International journal of medical informatics · 2017Pooled it
- Simulating effects of biomarker enrichment on Alzheimer's disease prevention trials: conceptual framework and example.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2014Trial
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- Application of artificial intelligence in Alzheimer's disease: a bibliometric analysis.Frontiers in neuroscience · 2025Review
- Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.Reviews on recent clinical trials · 2025Review
- Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.Journal of medical Internet research · 2024Article
- A Review on the Use of Modern Computational Methods in Alzheimer's Disease-Detection and Prediction.Current Alzheimer research · 2024Review
- Alzheimer's disease detection using data fusion with a deep supervised encoder.Frontiers in dementia · 2024Article
- Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay.Scientific reports · 2023Article
- Social complexity, life-history and lineage influence the molecular basis of castes in vespid wasps.Nature communications · 2023Article
- Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- A review of brain imaging biomarker genomics in Alzheimer's disease: implementation and perspectives.Translational neurodegeneration · 2022Review
- Mental Health Intent Recognition for Arabic-Speaking Patients Using the Mini International Neuropsychiatric Interview (MINI) and BERT Model.Sensors (Basel, Switzerland) · 2022Article
- Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages.Medical image analysis · 2022Article
- Multimodal neuroimage data fusion based on multikernel learning in personalized medicine.Frontiers in pharmacology · 2022Article
- Artificial Intelligence for Alzheimer's Disease: Promise or Challenge?Diagnostics (Basel, Switzerland) · 2021Review
- Spatially-Constrained Fisher Representation for Brain Disease Identification With Incomplete Multi-Modal Neuroimages.IEEE transactions on medical imaging · 2020Article
- Application of Generalized Split Linearized Bregman Iteration algorithm for Alzheimer's disease prediction.Aging · 2020Article
- Individual identification for different age groups using functional connectivity strength.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2020Article
42 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Machine learning methods pool diverse information to perform computer-assisted diagnosis and predict future clinical decline. We introduce a machine learning method to boost power in clinical trials. We created a Support Vector Machine algorithm that combines brain imaging and other biomarkers to classify 737 Alzheimer's disease Neuroimaging initiative (ADNI) subjects as having Alzheimer's disease (AD), mild cognitive impairment (MCI), or normal controls. We trained our classifiers based on example data including: MRI measures of hippocampal, ventricular, and temporal lobe volumes, a PET-FDG numerical summary, CSF biomarkers (t-tau, p-tau, and Abeta(42)), ApoE genotype, age, sex, and body mass index. MRI measures contributed most to Alzheimer's disease (AD) classification; PET-FDG and CSF biomarkers, particularly Abeta(42), contributed more to MCI classification. Using all biomarkers jointly, we used our classifier to select the one-third of the subjects most likely to decline. In this subsample, fewer than 40 AD and MCI subjects would be needed to detect a 25% slowing in temporal lobe atrophy rates with 80% power--a substantial boosting of power relative to standard imaging measures.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.