The use of Artificial Intelligence (AI) for personalized medicine has recently guided dramatic improvements in the diagnostic pathway of several diseases. The European Project GENOMED4ALL, aims at using European level data of patients affected by Multiple Myeloma, Myelodysplastic Syndromes and Sickle Cell Disease (SCD) to find correlation between genomics – and other omics data – with phenotypic manifestations and seize the opportunity of improving diagnostics through AI. Silent Cerebral Infarcts (SCIs) are a significant determinant of morbidity since childhood in SCD. One of the aims of the SCD clinical case in GENOMED4ALL is the use of radiomics – quantitative method for the evaluation and interpretation of medical images- and AI firstly to develop an automatic and uniform identification and characterization of SCI through the analysis of cerebral MRI, secondly, to correlate imaging data with other types of omics data in order to predict risk of recurrence.
O-02: RADIOMICS AND ARTIFICIAL INTELLIGENCE FOR IDENTIFICATION AND MONITORING OF SILENT CEREBRAL INFARCTS IN SICKLE CELL DISEASE: FIRST ANALYSIS FROM THE GENOMED4ALL EUROPEAN PROJECT
R. , BIONDIPrimo
Methodology
;G. , CASTELLANIMembro del Collaboration Group
;
2022
Abstract
The use of Artificial Intelligence (AI) for personalized medicine has recently guided dramatic improvements in the diagnostic pathway of several diseases. The European Project GENOMED4ALL, aims at using European level data of patients affected by Multiple Myeloma, Myelodysplastic Syndromes and Sickle Cell Disease (SCD) to find correlation between genomics – and other omics data – with phenotypic manifestations and seize the opportunity of improving diagnostics through AI. Silent Cerebral Infarcts (SCIs) are a significant determinant of morbidity since childhood in SCD. One of the aims of the SCD clinical case in GENOMED4ALL is the use of radiomics – quantitative method for the evaluation and interpretation of medical images- and AI firstly to develop an automatic and uniform identification and characterization of SCI through the analysis of cerebral MRI, secondly, to correlate imaging data with other types of omics data in order to predict risk of recurrence.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
