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

Autor:	Aguado AM; BCN-MedTech, DTIC, Universitat Pompeu Fabra, Barcelona, Spain.; Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain., Jimenez-Perez G; BCN-MedTech, DTIC, Universitat Pompeu Fabra, Barcelona, Spain.; Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain., Chowdhury D; Cardiology Care for Children, Lancaster, PA, United States., Prats-Valero J; BCN-MedTech, DTIC, Universitat Pompeu Fabra, Barcelona, Spain.; Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain., Sánchez-Martínez S; BCN-MedTech, DTIC, Universitat Pompeu Fabra, Barcelona, Spain., Hoodbhoy Z; Department of Paediatrics and Child Health, The Aga Khan University, Karachi, Pakistan., Mohsin S; Sindh Institute of Urology and Transplantation (SIUT), Karachi, Pakistan., Castellani R; BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain., Testa L; BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain., Crispi F; Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain.; BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain., Bijnens B; BCN-MedTech, DTIC, Universitat Pompeu Fabra, Barcelona, Spain.; Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain.; ICREA, Barcelona, Spain., Hasan B; Sindh Institute of Urology and Transplantation (SIUT), Karachi, Pakistan., Bernardino G; BCN-MedTech, DTIC, Universitat Pompeu Fabra, Barcelona, Spain.
Jazyk:	angličtina
Zdroj:	Frontiers in digital health [Front Digit Health] 2024 Oct 16; Vol. 6, pp. 1455767. Date of Electronic Publication: 2024 Oct 16 (Print Publication: 2024).
DOI:	10.3389/fdgth.2024.1455767
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (© 2024 Aguado, Jimenez-Perez, Chowdhury, Prats-Valero, Sánchez-Martínez, Hoodbhoy, Mohsin, Castellani, Testa, Crispi, Bijnens, Hasan and Bernardino.)
Databáze:	MEDLINE
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