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The author(s) declarefinancial support was received for theresearch, authorship, and/or publication of this article. This study was funded by the Bill and Melinda Gates Foundation (INV-021528) and partially funded by the grant # RYC2022-035960-I by MICIU/AEI/10.13039/501100011033, by FSE.

Analysis of institutional authors

Aguado, Ainhoa MAuthorJimenez-Perez, GuillermoAuthorPrats-Valero, JosaAuthorCrispi, FatimaAuthorBijnens, BartAuthor
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Article

AI-enabled workflow for automated classification and analysis of feto-placental Doppler images

Publicated to:Front Digit Health. 6 1455767- - 2024-10-16 6(), DOI: 10.3389/fdgth.2024.1455767

Authors: Aguado, Ainhoa M; Jimenez-Perez, Guillermo; Chowdhury, Devyani; Prats-Valero, Josa; Sanchez-Martinez, Sergio; Hoodbhoy, Zahra; Mohsin, Shazia; Castellani, Roberta; Testa, Lea; Crispi, Fatima; Bijnens, Bart; Hasan, Babar; Bernardino, Gabriel

Affiliations

Aga Khan Univ, Dept Paediat & Child Hlth, Karachi, Pakistan - Author
Cardiol Care Children, Lancaster, PA USA - Author
ICREA, Barcelona, Spain - Author
Inst Invest Biomed August Pi i Sunyer IDIBAPS, Barcelona, Spain - Author
Sindh Inst Urol & Transplantat SIUT, Karachi, Pakistan - Author
Univ Barcelona, Hosp Clin & Hosp St Joan Deu, BCNatal Barcelona Ctr Maternal Fetal & Neonatal Me, Ctr Biomed Res Rare Dis CIBER ER, Barcelona, Spain - Author
Univ Pompeu Fabra, BCN MedTech, DTIC, Barcelona, Spain - Author
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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.

Keywords
ArticleArtificial intelligenceClassificationControlled studyConvolutional neural networkConvolutional neural networksDeep learningDiagnosisElectric potentialFemaleFeto-placental doppleFeto-placental dopplerFloHeart left ventricleHigh income countryHumanImage analysisMiddle cerebral arteryMiddle income countryPlacentaPredictionUltrasoundUltrasound view classificationUltrasound waveform delineationUmbilical arteryViewsWaveformWorkflowWorkload

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Front Digit Health due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 20/44, thus managing to position itself as a Q1 (Primer Cuartil), in the category Medical Informatics.

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-05-21:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 14.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 12 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.25.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Pakistan; United States of America.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Aguado Martin, Ainhoa Marina) .