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Review

Fetal Hypoxia Detection Using Machine Learning: A Narrative Review

by
Nawaf Alharbi
1,*,
Mustafa Youldash
2,
Duha Alotaibi
2,
Haya Aldossary
2,*,
Reema Albrahim
2,
Reham Alzahrani
2,
Wahbia Ahmed Saleh
2,
Sunday O. Olatunji
2 and
May Issa Aldossary
3
1
Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
2
Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia
3
Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia
*
Authors to whom correspondence should be addressed.
AI 2024, 5(2), 516-532; https://doi.org/10.3390/ai5020026
Submission received: 29 February 2024 / Revised: 1 April 2024 / Accepted: 8 April 2024 / Published: 13 April 2024
(This article belongs to the Section Medical & Healthcare AI)
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Abstract

Fetal hypoxia is a condition characterized by a lack of oxygen supply in a developing fetus in the womb. It can cause potential risks, leading to abnormalities, birth defects, and even mortality. Cardiotocograph (CTG) monitoring is among the techniques that can detect any signs of fetal distress, including hypoxia. Due to the critical importance of interpreting the results of this test, it is essential to accompany these tests with the evolving available technology to classify cases of hypoxia into three cases: normal, suspicious, or pathological. Furthermore, Machine Learning (ML) is a blossoming technique constantly developing and aiding in medical studies, particularly fetal health prediction. Notwithstanding the past endeavors of health providers to detect hypoxia in fetuses, implementing ML and Deep Learning (DL) techniques ensures more timely and precise detection of fetal hypoxia by efficiently and accurately processing complex patterns in large datasets. Correspondingly, this review paper aims to explore the application of artificial intelligence models using cardiotocographic test data. The anticipated outcome of this review is to introduce guidance for future studies to enhance accuracy in detecting cases categorized within the suspicious class, an aspect that has encountered challenges in previous studies that holds significant implications for obstetricians in effectively monitoring fetal health and making informed decisions.
Keywords: fetal hypoxia; cardiotocography; machine learning; deep learning fetal hypoxia; cardiotocography; machine learning; deep learning

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MDPI and ACS Style

Alharbi, N.; Youldash, M.; Alotaibi, D.; Aldossary, H.; Albrahim, R.; Alzahrani, R.; Saleh, W.A.; Olatunji, S.O.; Aldossary, M.I. Fetal Hypoxia Detection Using Machine Learning: A Narrative Review. AI 2024, 5, 516-532. https://doi.org/10.3390/ai5020026

AMA Style

Alharbi N, Youldash M, Alotaibi D, Aldossary H, Albrahim R, Alzahrani R, Saleh WA, Olatunji SO, Aldossary MI. Fetal Hypoxia Detection Using Machine Learning: A Narrative Review. AI. 2024; 5(2):516-532. https://doi.org/10.3390/ai5020026

Chicago/Turabian Style

Alharbi, Nawaf, Mustafa Youldash, Duha Alotaibi, Haya Aldossary, Reema Albrahim, Reham Alzahrani, Wahbia Ahmed Saleh, Sunday O. Olatunji, and May Issa Aldossary. 2024. "Fetal Hypoxia Detection Using Machine Learning: A Narrative Review" AI 5, no. 2: 516-532. https://doi.org/10.3390/ai5020026

APA Style

Alharbi, N., Youldash, M., Alotaibi, D., Aldossary, H., Albrahim, R., Alzahrani, R., Saleh, W. A., Olatunji, S. O., & Aldossary, M. I. (2024). Fetal Hypoxia Detection Using Machine Learning: A Narrative Review. AI, 5(2), 516-532. https://doi.org/10.3390/ai5020026

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