Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts

Abstract

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify-CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC Redolfi et al. MIP Pilot Study identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from "slight" to "significant" in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology.

Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN–Neuroimaging Network

“…RIN–Neuroimaging Network identified three different web-based platforms developed under previous European and National calls, such as NeuGRID ( 44 ), ARIANNAFORIRCCS ( 45 ), and Medical Informatic Platform (MIP) ( 46 ) to provide complementary tools.…”

“…NeuGRID has been identified by the Re3data initiative as an official research data repository compatible with the FAIR principle. NeuGRID provides the following services to the RIN–Neuroimaging Network : (i) persistent storage and monitoring of harmonized acquisitions of phantoms and murine models with the settings of each scanner checked over time; (ii) imaging analysis using publicly available analysis software and artificial intelligence tools ( 46 ). Once data transfer from IRCCS hospitals to the central platform is complete, a QC procedure is automatically triggered for the recently uploaded data (i.e., ACR and FUNSTAR phantoms).…”

“…MIP ( https://ebrains.eu/service/medical-informatics-platform/ ), which is cross-linked with NeuGRID, is part of a large-scale European initiative, the Human Brain Project and aims at the integration of multiparametric neuroscientific data using advanced machine learning and deep learning algorithms for the investigation of the human brain in pathologies in the future development of this Network ( 46 ).…”

Linking Brain Structure, Activity, and Cognitive Function through Computation

“…A Medical Informatics Platform (MIP; https://ebrains.eu/service/medical-informaticsplatform/) enables the analysis of large volumes of patient data throughout Europe (Redolfi et al, 2020). The MIP has opened the possibility to collect data from different hospitals, while considering high standards for data safety and security.…”

Inter-Cohort Validation of SuStaIn Model for Alzheimer’s Disease

“…Interest in the application of advanced statistics and machine learning (ML) in medicine has been constantly rising during the last years and their predictive capability allowed advancements in many fields. Particularly, data-driven approaches may contribute greatly to the advancement of neurosciences ( Oxtoby et al, 2017 ; Ten Kate et al, 2018 ; Redolfi et al, 2020 ), where diseases are regularly modeled heuristically and patient care is influenced by clinicians’ expertise ( Braak and Braak, 1991 ; Jack et al, 2010 ; Jack et al, 2013 ).…”