PT - JOURNAL ARTICLE AU - Kaveh Vejdani AU - Thomas Liebmann AU - Nicolas Pannetier AU - Elham Khosravi AU - Mohsen Yousefnezhad AU - Pavan Krishnamurthy AU - Nima Farzan AU - Cameron Bahar AU - Ahmad Salehi AU - Hesaam Esfandyarpour AU - Padideh Kamali-Zare TI - A novel technology for objective, accurate and non-invasive early diagnosis and monitoring of Alzheimer’s disease in clinics and clinical trials AID - 10.1101/790469 DP - 2019 Jan 01 TA - bioRxiv PG - 790469 4099 - http://biorxiv.org/content/early/2019/10/02/790469.short 4100 - http://biorxiv.org/content/early/2019/10/02/790469.full AB - We have developed a novel medical image processing technology for objective, accurate, and non-invasive diagnosis, prognosis, and monitoring of Alzheimer’s disease using standard clinical brain MRI (magnetic resonance imaging) and basic clinical cognitive assessment. The technology is a robust, highly accurate, fully automated, high-throughput, cloud-based platform, which operates in a fully integrated and controlled environment. In diagnosis mode, our technology performs with 91% balanced accuracy on blind testing to diagnose the current neurocognitive status, and in prognosis mode or early detection at mild cognitive impairment (MCI) stage with 88% balanced accuracy on blind testing to predict progression from MCI to Alzheimer’s dementia (AD) within 5 years. Such prognostic capability is currently non-existent, even in specialty clinics and hospitals, a major factor in Alzheimer’s clinical trial failures. The algorithm’s diagnostic certainty precisely mirrors the diagnostic confidence of an expert cognitive neurologist for both MCI (Spearman’s Rho = 1) and AD (Rho = 1). In addition to widespread clinical applications, this novel technology can enable correct patient selection and therapeutic effect monitoring in clinical trials of Alzheimer’s disease, the crucial elements to finding a cure.CNCognitively NormalMCIMild Cognitive ImpairmentADAlzheimer’s DementiaMMSEMini Mental State ExamCDRClinical Dementia RatingFAQFunctional Assessment QuestionnaireADASAlzheimer’s Disease Assessment ScaleMRIMagnetic Resonance ImagingPETPositron Emission TomographyCTComputed TomographyCSFCerebro-Spinal FluidMLMachine LearningAIArtificial IntelligenceADNIAlzheimer’s Disease Neuroimaging InitiativeOASISOpen Access Series of Imaging StudiesAIBLAustralian Imaging, Biomarkers and Lifestyle study