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increasing numbers patients novel drugs distinct causes systolic diastolic heart failure automated assessment cardiac function important aimed provide noninvasive method predict diagnosis patients undergoing cardiac mri cmri obtain left ventricular enddiastolic pressure lvedp modelling study patients undergone cardiac catheterisation university hospital heidelberg heidelberg germany july 15 2004 march 16 2023 identified individual left ventricular pressure measurements used existing patient data routine cardiac diagnostics initial group extracted patients diagnosed ischaemic cardiomyopathy dilated cardiomyopathy hypertrophic cardiomyopathy amyloidosis well control individuals structural phenotype data pseudonymised processed within university hospitals ai infrastructure used data build different models predict either demographic ie aiage aisex diagnostic ie aicoronary artery disease aicardiomyopathy aicmp functional parameters ie ailvedp randomly divided datasets via computer training validation test datasets aicmp compared models validated prospective setting benchmarking also done 66 936 patients undergone cardiac catheterisation university hospital heidelberg identified 183 772 individual left ventricular pressure measurements extracted 4390 patients initial group 1131 258 diagnosed ischaemic cardiomyopathy 1064 242 diagnosed dilated cardiomyopathy 816 186 diagnosed hypertrophic cardiomyopathy 202 46 diagnosed amyloidosis 1177 267 control individuals structural phenotype core cohort included patients cardiac catherisation cmri within 30 days emergency cases excluded aisex able predict patient sex areas receiver operating characteristic curves aucs 078 95 ci 077078 aiage able predict patient age mean absolute error 786 years 777795 pearson correlation 057 95 ci 056057 aucs classification tasks ranged 082 95 ci 079084 ischaemic cardiomyopathy 092 091094 hypertrophic cardiomyopathy ai models could easily integrated clinical practice provide added value information content cmri allowing disease classification prediction diastolic function informatics life initiative klaustschira foundation german center cardiovascular research ecardiology section german cardiac society ai health innovation cluster heidelberg