TRACE4AD™ is an AI-powered, CE-cleared medical device that predicts subject-level risk of dementia within 24 months.
With Alzheimer’s disease being the most frequent form of dementia, identifying early-stage AD subjects at high risk of progressing to AD dementia in a short time is a crucial clinical task.
TRACE4AD™ is our proprietary CE-cleared medical device that uses machine learning to deeply analyze brain MRI images of subjects at risk of AD (possibly in combination with cognitive measures).
TRACE4AD™ provides early-stage, subject-level risk (low risk or high risk) of being affected by or progressing to dementia within 24 months.
Working through data mining and machine-learning classifiers, TRACE4AD™ performs an automatic reading of the subject's brain MRI study, and it automatically identifies features of brain atrophy that begin to occur following neuronal death caused by AD along with mild cognitive impairment typical of the early stages of AD dementia.
In addition, TRACE4AD™ offers accurate volume measurements from the T1-weighted MRI study, to aid in disease categorization or tracking disease progression.
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Clinical Evidences
AI-BASED STAGING, CAUSAL HYPOTHESIS AND PROGRESSION OF SUBJECTS AT RISK OF ALZHEIMER'S DISEASE: A MULTICENTER STUDY
Aresta, S., Nemni, R., Zanardo, M., Sirabian, G., Capelli, D., Alì, M., Vitali, P., Bertoldo, E. G., Fiolo, V., Bonanno, L., Maresca, G., Battista, P., Sardanelli, F., Pizzini, F. B., Castiglioni, I., & Salvatore, C. (2025). AI-based staging, causal hypothesis and progression of subjects at risk of Alzheimer's disease: A multicenter study. Frontiers in Neurology, 16, 1568086. https://doi.org/10.3389/fneur.2025.1568086 (publication pdf)
ALZHEIMER’S DEMENTIA EARLY DIAGNOSIS, CHARACTERIZATION, PROGNOSIS AND TREATMENT DECISION VIA A SOFTWARE-AS-MEDICAL DEVICE WITH AN ARTIFICIAL INTELLIGENT AGENT
Battista, P., Nemni, R., Vitali, P., Alì, M., Zanardo, M., Salvatore, C., Sirabian, G., Capelli, D., Bet, L., Callus, E., Bertoldo, E., Fiolo, V., Minafra, B., Fundarò, C., Scotti, G., Papa, S., Sardanelli, F., & Castiglioni, I. (2024). Alzheimer’s dementia early diagnosis, characterization, prognosis and treatment decision via a Software-as-Medical Device with an Artificial Intelligent Agent. Alzheimer's & Dementia, 20(Suppl. 1), e075674. https://doi.org/10.1002/alz.075674 (publication pdf and poster)
TRACE4AD: AN AI MODEL BASED ON MACHINE LEARNING PREDICTING THE SUBJECT RISK OF ALZHEIMER'S DISEASE DEMENTIA FROM 3T MRI T1 BRAIN STUDY AND NEUROPSYCHOLOGICAL ASSESSMENT
Interlenghi, I., Salvatore, C., & Castiglioni, I. (2021). TRACE4AD: an AI model based on machine learning predicting the subject risk of Alzheimer’s disease dementia from 3T MRI-T1 brain study and neuropsychological assessment. Technical Report.
ARTIFICIAL INTELLIGENCE AND NEUROPSYCHOLOGICAL MEASURES: THE CASE OF ALZHEIMER’S DISEASE [publication pdf] [publication website]
AUTHORS: Battista, P., Salvatore, C., Berlingeri, M., Cerasa, A., & Castiglioni, I.
JOURNAL: Neuroscience & Biobehavioral Reviews
COMPARISON OF TRANSFER LEARNING AND CONVENTIONAL MACHINE LEARNING APPLIED TO STRUCTURAL BRAIN MRI FOR THE EARLY DIAGNOSIS AND PROGNOSIS OF ALZHEIMER'S DISEASE [publication pdf] [publication website]
AUTHORS: Nanni, L., Interlenghi, M., Brahnam, S., Salvatore, C., Papa, S., Nemni, R., ... & Alzheimer's Disease Neuroimaging Initiative
JOURNAL: Frontiers in neurology
TEXTURE DESCRIPTORS AND VOXELS FOR THE EARLY DIAGNOSIS OF ALZHEIMER'S DISEASE
Nanni, L., Brahnam, S., Salvatore, C., Castiglioni, I., & Alzheimer's Disease Neuroimaging Initiative. (2019). Texture descriptors and voxels for the early diagnosis of Alzheimer’s disease. Artificial Intelligence in Medicine, 97, 19–26. https://doi.org/10.1016/j.artmed.2019.05.003
MRI CHARACTERIZES THE PROGRESSIVE COURSE OF AD AND PREDICTS CONVERSION TO ALZHEIMER'S DEMENTIA 24 MONTHS BEFORE PROBABLE DIAGNOSIS
Salvatore, C., Cerasa, A., & Castiglioni, I. (2018). MRI characterizes the progressive course of AD and predicts conversion to Alzheimer’s dementia 24 months before probable diagnosis. Frontiers in Aging Neuroscience, 10, 135. https://doi.org/10.3389/fnagi.2018.00135
A WRAPPED MULTI-LABEL CLASSIFIER FOR THE AUTOMATIC DIAGNOSIS AND PROGNOSIS OF ALZHEIMER'S DISEASE
Salvatore, C., & Castiglioni, I. (2018). A wrapped multi-label classifier for the automatic diagnosis and prognosis of Alzheimer’s disease. Journal of Neuroscience Methods, 302, 14–23. https://doi.org/10.1016/j.jneumeth.2017.12.016
OPTIMIZING NEUROPSYCHOLOGICAL ASSESSMENTS FOR COGNITIVE, BEHAVIORAL, AND FUNCTIONAL IMPAIRMENT CLASSIFICATION: A MACHINE LEARNING STUDY
Battista, P., Salvatore, C., & Castiglioni, I. (2017). Optimizing neuropsychological assessments for cognitive, behavioral, and functional impairment classification: A machine learning study. Behavioural Neurology, 2017, Article ID 1850909. https://doi.org/10.1155/2017/1850909
COMBINING MULTIPLE APPROACHES FOR THE EARLY DIAGNOSIS OF ALZHEIMER'S DISEASE
Nanni, L., Salvatore, C., Cerasa, A., Castiglioni, I., & Alzheimer's Disease Neuroimaging Initiative. (2016). Combining multiple approaches for the early diagnosis of Alzheimer’s disease. Pattern Recognition Letters, 84, 259–266. https://doi.org/10.1016/j.patrec.2016.10.010
FRONTIERS FOR THE EARLY DIAGNOSIS OF AD BY MEANS OF MRI BRAIN IMAGING AND SUPPORT VECTOR MACHINES
Salvatore, C., Battista, P., & Castiglioni, I. (2016). Frontiers for the early diagnosis of AD by means of MRI brain imaging and support vector machines. Current Alzheimer Research, 13(5), 509–533. https://doi.org/10.2174/1567205013666151116141705
MAGNETIC RESONANCE IMAGING BIOMARKERS FOR THE EARLY DIAGNOSIS OF ALZHEIMER'S DISEASE: A MACHINE LEARNING APPROACH
Salvatore, C., Cerasa, A., Battista, P., Gilardi, M. C., Quattrone, A., & Castiglioni, I. (2015). Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer’s disease: A machine learning approach. Frontiers in Neuroscience, 9, 307. https://doi.org/10.3389/fnins.2015.00307
