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Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers
Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, such as the position of the fetus, the habitus of...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10589237/ https://www.ncbi.nlm.nih.gov/pubmed/37863944 http://dx.doi.org/10.1038/s41598-023-44689-0 |
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author | Ghabri, Haifa Alqahtani, Mohammed S. Ben Othman, Soufiene Al-Rasheed, Amal Abbas, Mohamed Almubarak, Hassan Ali Sakli, Hedi Abdelkarim, Mohamed Naceur |
author_facet | Ghabri, Haifa Alqahtani, Mohammed S. Ben Othman, Soufiene Al-Rasheed, Amal Abbas, Mohamed Almubarak, Hassan Ali Sakli, Hedi Abdelkarim, Mohamed Naceur |
author_sort | Ghabri, Haifa |
collection | PubMed |
description | Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, such as the position of the fetus, the habitus of the mother, and the imaging technique. In addition, image interpretation must be performed by a trained healthcare professional who can take into account all relevant clinical factors. Artificial intelligence is playing an increasingly important role in medical imaging and can help solve many of the challenges associated with fetal organ classification. In this paper, we propose a deep-learning model for automating fetal organ classification from ultrasound images. We trained and tested the model on a dataset of fetal ultrasound images, including two datasets from different regions, and recorded them with different machines to ensure the effective detection of fetal organs. We performed a training process on a labeled dataset with annotations for fetal organs such as the brain, abdomen, femur, and thorax, as well as the maternal cervical part. The model was trained to detect these organs from fetal ultrasound images using a deep convolutional neural network architecture. Following the training process, the model, DenseNet169, was assessed on a separate test dataset. The results were promising, with an accuracy of 99.84%, which is an impressive result. The F1 score was 99.84% and the AUC was 98.95%. Our study showed that the proposed model outperformed traditional methods that relied on the manual interpretation of ultrasound images by experienced clinicians. In addition, it also outperformed other deep learning-based methods that used different network architectures and training strategies. This study may contribute to the development of more accessible and effective maternal health services around the world and improve the health status of mothers and their newborns worldwide. |
format | Online Article Text |
id | pubmed-10589237 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105892372023-10-22 Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers Ghabri, Haifa Alqahtani, Mohammed S. Ben Othman, Soufiene Al-Rasheed, Amal Abbas, Mohamed Almubarak, Hassan Ali Sakli, Hedi Abdelkarim, Mohamed Naceur Sci Rep Article Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, such as the position of the fetus, the habitus of the mother, and the imaging technique. In addition, image interpretation must be performed by a trained healthcare professional who can take into account all relevant clinical factors. Artificial intelligence is playing an increasingly important role in medical imaging and can help solve many of the challenges associated with fetal organ classification. In this paper, we propose a deep-learning model for automating fetal organ classification from ultrasound images. We trained and tested the model on a dataset of fetal ultrasound images, including two datasets from different regions, and recorded them with different machines to ensure the effective detection of fetal organs. We performed a training process on a labeled dataset with annotations for fetal organs such as the brain, abdomen, femur, and thorax, as well as the maternal cervical part. The model was trained to detect these organs from fetal ultrasound images using a deep convolutional neural network architecture. Following the training process, the model, DenseNet169, was assessed on a separate test dataset. The results were promising, with an accuracy of 99.84%, which is an impressive result. The F1 score was 99.84% and the AUC was 98.95%. Our study showed that the proposed model outperformed traditional methods that relied on the manual interpretation of ultrasound images by experienced clinicians. In addition, it also outperformed other deep learning-based methods that used different network architectures and training strategies. This study may contribute to the development of more accessible and effective maternal health services around the world and improve the health status of mothers and their newborns worldwide. Nature Publishing Group UK 2023-10-20 /pmc/articles/PMC10589237/ /pubmed/37863944 http://dx.doi.org/10.1038/s41598-023-44689-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ghabri, Haifa Alqahtani, Mohammed S. Ben Othman, Soufiene Al-Rasheed, Amal Abbas, Mohamed Almubarak, Hassan Ali Sakli, Hedi Abdelkarim, Mohamed Naceur Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers |
title | Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers |
title_full | Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers |
title_fullStr | Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers |
title_full_unstemmed | Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers |
title_short | Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers |
title_sort | transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10589237/ https://www.ncbi.nlm.nih.gov/pubmed/37863944 http://dx.doi.org/10.1038/s41598-023-44689-0 |
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