Fetal biometry and amniotic fluid volume assessment end-to-end automation using Deep Learning

Saad Slimani (), Salaheddine Hounka, Abdelhak Mahmoudi, Taha Rehah, Dalal Laoudiyi, Hanane Saadi, Amal Bouziyane, Amine Lamrissi, Mohamed Jalal, Said Bouhya, Mustapha Akiki, Youssef Bouyakhf, Bouabid Badaoui, Amina Radgui, Musa Mhlanga and El Houssine Bouyakhf
Additional contact information
Saad Slimani: Deepecho
Salaheddine Hounka: Telecommunications Systems Services and Networks lab (STRS Lab), INPT
Abdelhak Mahmoudi: Deepecho
Taha Rehah: Deepecho
Dalal Laoudiyi: Hassan II University
Hanane Saadi: Mohammed VI University Hospital
Amal Bouziyane: Université Mohammed VI des Sciences de la Santé, Hôpital Universitaire Cheikh Khalifa
Amine Lamrissi: Hassan II University
Mohamed Jalal: Hassan II University
Said Bouhya: Hassan II University
Mustapha Akiki: Abou Madi Radiology Clinic
Youssef Bouyakhf: Deepecho
Bouabid Badaoui: Mohammed V University in Rabat
Amina Radgui: Telecommunications Systems Services and Networks lab (STRS Lab), INPT
Musa Mhlanga: Epigenomics & Single Cell Biophysics
El Houssine Bouyakhf: Deepecho

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract Fetal biometry and amniotic fluid volume assessments are two essential yet repetitive tasks in fetal ultrasound screening scans, aiding in the detection of potentially life-threatening conditions. However, these assessment methods can occasionally yield unreliable results. Advances in deep learning have opened up new avenues for automated measurements in fetal ultrasound, demonstrating human-level performance in various fetal ultrasound tasks. Nevertheless, the majority of these studies are retrospective in silico studies, with a limited number including African patients in their datasets. In this study we developed and prospectively assessed the performance of deep learning models for end-to-end automation of fetal biometry and amniotic fluid volume measurements. These models were trained using a newly constructed database of 172,293 de-identified Moroccan fetal ultrasound images, supplemented with publicly available datasets. the models were then tested on prospectively acquired video clips from 172 pregnant people forming a consecutive series gathered at four healthcare centers in Morocco. Our results demonstrate that the 95% limits of agreement between the models and practitioners for the studied measurements were narrower than the reported intra- and inter-observer variability among expert human sonographers for all the parameters under study. This means that these models could be deployed in clinical conditions, to alleviate time-consuming, repetitive tasks, and make fetal ultrasound more accessible in limited-resource environments.

Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-42438-5 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42438-5

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-42438-5

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().