Leveraging ECG images for predicting ejection fraction using machine learning algorithms

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dc.contributor.authorSwamy, Abhyuday Kumaraen_US
dc.contributor.authorRajagopal, Viveken_US
dc.contributor.authorKrishnan, Deepaken_US
dc.contributor.authorGhorai, Paramita Auddyaen_US
dc.contributor.authorChoukhande, Anaghaen_US
dc.contributor.authorPalani, Santhosh Rathnamen_US
dc.contributor.authorPadmanabhan, Deepaken_US
dc.contributor.authorRupert, Emmanuelen_US
dc.contributor.authorShetty, Devi Prasaden_US
dc.contributor.authorNarayan, Pradeepen_US
dc.date.accessioned2025-08-13T11:15:20Z
dc.date.available2025-08-13T11:15:20Z
dc.date.issued2025-06
dc.description.abstractIntroduction: The capability to accurately predict the ejection fraction (EF) from an electrocardiogram (ECG) holds significant and valuable clinical implications. Various algorithms based on ECG images are currently being evaluated, with most methods requiring raw signal data from ECG devices. In this study, our objective was to train and validate a neural network on a readily available ECG trace image graph to determine the presence or absence of left ventricular dysfunction (LVD). Methods: 12-lead ECG trace images paired with their echocardiogram reports performed on the same day were selected. A DenseNet121 model, using ECG images as input, was trained to identify EF <50 %. and then externally validated. Results: 1,19,281 ECG-echocardiogram pairs were used for model development. The model demonstrated com- parable performance in both the internal test data and external validation data. The area under receiver oper- ating characteristic and precision–recall curves were 0.92 and 0.78, respectively, for the internal test data and 0.88 and 0.74, respectively, for the external validation data. The model accurately identified more than 85 % of cases with EF <50 % in both datasets. Conclusions: Actual images of ECGs with simple pre-processing and model architecture can be used as a reliable tool to screen for LVD. The use of images expands the reach of these algorithms to geographies with resource and technological limitations.en_US
dc.identifier.affiliationsDepartment of Advanced Analytics & AI, Indiaen_US
dc.identifier.affiliationsDepartment of Advanced Analytics & AI, Indiaen_US
dc.identifier.affiliationsDepartment of Advanced Analytics & AI, Indiaen_US
dc.identifier.affiliationsDepartment of Biostatistics, Indiaen_US
dc.identifier.affiliationsDepartment of Advanced Analytics & AI, Indiaen_US
dc.identifier.affiliationsDepartment of Advanced Analytics & AI, Indiaen_US
dc.identifier.affiliationsDepartment of Electrophysiology, Indiaen_US
dc.identifier.affiliationsDepartment of Cardiac Anesthesia, Indiaen_US
dc.identifier.affiliationsDepartment of Cardiac Surgery, Narayana Health, Indiaen_US
dc.identifier.affiliationsDepartment of Cardiac Surgery, Narayana Health, Indiaen_US
dc.identifier.citationSwamy Abhyuday Kumara, Rajagopal Vivek, Krishnan Deepak, Ghorai Paramita Auddya, Choukhande Anagha, Palani Santhosh Rathnam, Padmanabhan Deepak, Rupert Emmanuel, Shetty Devi Prasad, Narayan Pradeep. Leveraging ECG images for predicting ejection fraction using machine learning algorithms. Indian Heart Journal. 2025 Jun; 77(3): 182-187en_US
dc.identifier.issn0019-4832
dc.identifier.placeIndiaen_US
dc.identifier.urihttps://imsear.searo.who.int/handle/123456789/253337
dc.languageenen_US
dc.publisherElsevieren_US
dc.relation.issuenumber3en_US
dc.relation.volume77en_US
dc.source.urihttps://doi.org/10.1016/j.ihj.2025.03.009en_US
dc.subjectLeft ventricular dysfunctionen_US
dc.subjectMachine learningen_US
dc.subjectArtificial intelligenceen_US
dc.titleLeveraging ECG images for predicting ejection fraction using machine learning algorithmsen_US
dc.typeJournal Articleen_US
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