Cardiovascularandneurologicaldiseasesincludingtheirinteractionsaregettingtheattentionofresearchersandphysicians.Bothdiseasesoftensharecommonbiomarkers,riskfactors,andbiological pathways.Bynow,researchershaveconfirmedthatproblemsrelatedtocardiovascularleadtoneurologicalbadoutcomesandviceversa. Inaddition,researchershavestartedtousemachine/deeplearning algorithmsforbetterdiagnosis.Bynow,fewexamplesarepublishedonlittledatasetsconsistingofcomputedtomographyimages,electrocardiograms,electroencephalograms,andsoon,butmostofthework isnotdonebyartificialintelligence(AI).Inthiswork,wereviewedanumberofstudiesthathaveeither usedAIormanualcomputationwithconventionaltechniquesondifferentimagingmodalities.Fromall studies,itisfoundthatimagingmodalitiescansupportphysiciansinbetterdiagnosisofneurological outcomesfollowingcardiaceventsand/ordiseasesandviceversa.Moreover,AIdriventechnologies, like machinelearninganddeeplearning,couldbeusefultodelineateaccuratemodelsofdiseasesrelatedto neuro-cardiacpathologiesforpredictionsofconsequentbadoutcomesrelatedtothedifferentstages

Medical imaging and artificial intelligence to investigate neuro-cardiac pathologies and discover hidden relationships – a state of the art review / Shah, Syed Taimoor Hussain; Calati, Veronika; Bizzarri, Alessandra; Deriu, Marco Agostino. - ELETTRONICO. - (2022). (Intervento presentato al convegno Neurodevelopmental Impairments in Preterm Children - Computational Advancements (DETERMINED 2022)) [10.5281/zenodo.8009881].

Medical imaging and artificial intelligence to investigate neuro-cardiac pathologies and discover hidden relationships – a state of the art review

Shah, Syed Taimoor Hussain;Bizzarri, Alessandra;Deriu, Marco Agostino
2022

Abstract

Cardiovascularandneurologicaldiseasesincludingtheirinteractionsaregettingtheattentionofresearchersandphysicians.Bothdiseasesoftensharecommonbiomarkers,riskfactors,andbiological pathways.Bynow,researchershaveconfirmedthatproblemsrelatedtocardiovascularleadtoneurologicalbadoutcomesandviceversa. Inaddition,researchershavestartedtousemachine/deeplearning algorithmsforbetterdiagnosis.Bynow,fewexamplesarepublishedonlittledatasetsconsistingofcomputedtomographyimages,electrocardiograms,electroencephalograms,andsoon,butmostofthework isnotdonebyartificialintelligence(AI).Inthiswork,wereviewedanumberofstudiesthathaveeither usedAIormanualcomputationwithconventionaltechniquesondifferentimagingmodalities.Fromall studies,itisfoundthatimagingmodalitiescansupportphysiciansinbetterdiagnosisofneurological outcomesfollowingcardiaceventsand/ordiseasesandviceversa.Moreover,AIdriventechnologies, like machinelearninganddeeplearning,couldbeusefultodelineateaccuratemodelsofdiseasesrelatedto neuro-cardiacpathologiesforpredictionsofconsequentbadoutcomesrelatedtothedifferentstages
File in questo prodotto:
File Dimensione Formato  
Medical imaging neuro-cardiac.pdf

accesso aperto

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Creative commons
Dimensione 971.32 kB
Formato Adobe PDF
971.32 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2984360