Human-in-the-Loop Approach Based on MRI and ECG for Healthcare Diagnosis

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dc.contributor.authorRadiuk, Pavlo
dc.contributor.authorKovalchuk, Oleksii
dc.contributor.authorSlobodzian, Vitalii
dc.contributor.authorManziuk, Eduard
dc.contributor.authorBarmak, Oleksander
dc.contributor.authorKrak, Iurii
dc.date.accessioned2022-12-19T10:54:14Z
dc.date.available2022-12-19T10:54:14Z
dc.date.issued2022-12-14
dc.descriptionRadiuk P., Kovalchuk O., Slobodzian V., Manziuk E., Barmak O., Krak Iu. Human-in-the-loop approach based on MRI and ECG for healthcare diagnosis. CEUR-WS, ISSN. 1613–0073. 2022. Vol. 3302. Pp. 9-20. URL: https://ceur-ws.org/Vol-3302/paper1.pdfuk_UA
dc.description.abstractThe presented study investigates a human-centric approach to implementing human-in-the-loop models for healthcare diagnostics. The following tasks were considered and addressed in this work: a) identify the features necessary for future healthcare diagnosis based on electrocardiogram signals in the human-in-the-loop model: P, T-peaks, QRS-complex, PQ and ST segments, and b) detect inflammatory processes in the heart muscle (myocardium) based on cardiac magnetic resonance imaging. As a result of our investigation, a novel approach was proposed for embedding (integrating) clinical knowledge about the nature of these phenomena into the electrocardiogram signal and magnetic resonance imaging. Domain knowledge about the sample’s nature is encoded similarly to the input information. Moreover, the convolution operation within our approach serves as an embedding mechanism. The results presented in the article are a starting point for using the models obtained by the proposed approach (human-in-the-loop models) for classification problems using deep learning and convolutional neural networks. Also, visual analysis shows the proposed approaches’ ability to solve practical clinical problems. It also ensures transparent interpretation of the obtained results as the human-in-the-loop model, which, in turn, is built according to the human-centric approach. Overall, our contribution allows the implementation of a scheme for obtaining artificial intelligence solutions based on the principles of trust in them.uk_UA
dc.identifier.citationRadiuk P., Kovalchuk O., Slobodzian V., Manziuk E., Barmak O., Krak Iu. Human-in-the-loop approach based on MRI and ECG for healthcare diagnosis. The 5th International Conference on Informatics & Data-Driven Medicine (IDDM-2022) : CEUR-Workshop Proceedings. Vol. 3302. (Lyon, France, 18-20 November 2022). Lyon, 2022. Pp. 9-20.uk_UA
dc.identifier.issn1613–0073
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/12851
dc.language.isoenuk_UA
dc.publisherCEUR-WSuk_UA
dc.subjectHuman-centric approachuk_UA
dc.subjecthuman-in-the-loopuk_UA
dc.subjecttrustworthiness in artificial intelligenceuk_UA
dc.subjecthealthcare diagnosisuk_UA
dc.subjectelectrocardiogramuk_UA
dc.subjectmagnetic resonance imaginguk_UA
dc.subjectautoencoderuk_UA
dc.titleHuman-in-the-Loop Approach Based on MRI and ECG for Healthcare Diagnosisuk_UA
dc.typeСтаттяuk_UA
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