AI-enabled workflow for automated classification and analysis of feto-placental Doppler images
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- dc.contributor.author Aguado, Ainhoa M.
- dc.contributor.author Jimenez-Perez, Guillermo
- dc.contributor.author Chowdhury, Devyani
- dc.contributor.author Prats-Valero, Josa
- dc.contributor.author Sanchez-Martinez, Sergio
- dc.contributor.author Hoodbhoy, Zahra
- dc.contributor.author Mohsin, Shazia
- dc.contributor.author Castellani, Roberta
- dc.contributor.author Testa, Lea
- dc.contributor.author Crispi Brillas, Fàtima
- dc.contributor.author Bijnens, Bart
- dc.contributor.author Hasan, Babar
- dc.contributor.author Bernardino Perez, Gabriel
- dc.date.accessioned 2025-10-20T06:17:02Z
- dc.date.available 2025-10-20T06:17:02Z
- dc.date.issued 2024
- dc.description.abstract Introduction: Extraction of Doppler-based measurements from feto-placental Doppler images is crucial in identifying vulnerable new-borns prenatally. However, this process is time-consuming, operator dependent, and prone to errors. Methods: To address this, our study introduces an artificial intelligence (AI) enabled workflow for automating feto-placental Doppler measurements from four sites (i.e., Umbilical Artery (UA), Middle Cerebral Artery (MCA), Aortic Isthmus (AoI) and Left Ventricular Inflow and Outflow (LVIO)), involving classification and waveform delineation tasks. Derived from data from a low- and middle-income country, our approach's versatility was tested and validated using a dataset from a high-income country, showcasing its potential for standardized and accurate analysis across varied healthcare settings. Results: The classification of Doppler views was approached through three distinct blocks: (i) a Doppler velocity amplitude-based model with an accuracy of 94%, (ii) two Convolutional Neural Networks (CNN) with accuracies of 89.2% and 67.3%, and (iii) Doppler view- and dataset-dependent confidence models to detect misclassifications with an accuracy higher than 85%. The extraction of Doppler indices utilized Doppler-view dependent CNNs coupled with post-processing techniques. Results yielded a mean absolute percentage error of 6.1 ± 4.9% (n = 682), 1.8 ± 1.5% (n = 1,480), 4.7 ± 4.0% (n = 717), 3.5 ± 3.1% (n = 1,318) for the magnitude location of the systolic peak in LVIO, UA, AoI and MCA views, respectively. Conclusions: The developed models proved to be highly accurate in classifying Doppler views and extracting essential measurements from Doppler images. The integration of this AI-enabled workflow holds significant promise in reducing the manual workload and enhancing the efficiency of feto-placental Doppler image analysis, even for non-trained readers.en
- dc.format.mimetype application/pdf
- dc.identifier.citation Aguado AM, Jimenez-Perez G, Chowdhury D, Prats-Valero J, Sánchez-Martínez S, Hoodbhoy Z, et al. AI-enabled workflow for automated classification and analysis of feto-placental Doppler images. Front Digit Health. 2024 Oct 16;6:1455767. DOI: 10.3389/fdgth.2024.1455767
- dc.identifier.doi http://dx.doi.org/10.3389/fdgth.2024.1455767
- dc.identifier.issn 2673-253X
- dc.identifier.uri http://hdl.handle.net/10230/71549
- dc.language.iso eng
- dc.publisher Frontiers
- dc.relation.ispartof Frontiers in Digital Health. 2024 Oct 16;6:1455767
- dc.rights © 2024 Aguado, Jimenez-Perez, Chowdhury, Prats-Valero, Sánchez-Martínez, Hoodbhoy, Mohsin, Castellani, Testa, Crispi, Bijnens, Hasan and Bernardino. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Artificial intelligenceen
- dc.subject.keyword Convolutional neural networksen
- dc.subject.keyword Deep learningen
- dc.subject.keyword Ultrasound view classificationen
- dc.subject.keyword Ultrasound waveform delineationen
- dc.subject.keyword Feto-placental Doppleren
- dc.title AI-enabled workflow for automated classification and analysis of feto-placental Doppler imagesen
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion