Prediction of preterm birth using artificial intelligence: a systematic review

Akazawa, Munetoshi; Hashimoto, Kazunori

doi:10.1080/01443615.2022.2056828

Cited by 17 publications

(10 citation statements)

References 41 publications

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“…The validation on independent data is a critical step to guard against overfitting and hence optimistically biased accuracy estimates . In the past several decades, applications of machine learning approaches to various types of clinical, molecular, and other data have been explored to predict complications of pregnancy including preterm birth [18][19][20][21][22][23] . The results of these works to date demonstrate that the prediction of PTB from varied data types including metabolites in amniotic fluid and maternal blood and urine, ultrasound images, and electronic health records, appears to be feasible to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research

Golob

Oskotsky

Tang

et al. 2023

Preprint

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Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi. We validated the crowdsourced models on novel datasets representing 331 samples from 148 pregnant individuals. From 318 DREAM challenge participants we received 148 and 121 submissions for our two separate prediction sub-challenges with top-ranking submissions achieving bootstrapped AUROC scores of 0.69 and 0.87, respectively. Alpha diversity, VALENCIA community state types, and composition (via phylotype relative abundance) were important features in the top performing models, most of which were tree based methods. This work serves as the foundation for subsequent efforts to translate predictive tests into clinical practice, and to better understand and prevent preterm birth.

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Section: Introductionmentioning

confidence: 99%

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research

Golob

Oskotsky

Tang

et al. 2023

Preprint

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show abstract

“…Studies have assessed the accuracy of AI in predicting preterm births. 33 , 34 Furthermore, in the evaluation of hypertensive-related disorders such as preeclampsia or gestational diabetes, AI has demonstrated an ability to accurately predict the onset of these conditions using predictive models like obstetric ultrasounds or knowledge base methods like metabolic panels or maternal blood profiles. 35 These evaluations aim to mitigate neonatal loss and enhance overall birth outcomes.…”

Section: Ai In Feto-maternal Healthmentioning

confidence: 99%

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

Yaseen,

Rather

2024

IJWH

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The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in fetomaternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.

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“…Hashimoto 34 Prediction of premature birth AI-based models, potentially predicting preterm birth, are valuable tools for prenatal care and management.…”

Section: Akazawa Andmentioning

confidence: 99%

Current status of the position on labor progress prediction for contemporary pregnant women using Friedman curves: An updated review

Watanabe

2023

J of Obstet and Gynaecol

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AimPrediction of labor progression is important for maternal and fetal health, as improved accuracy can lead to more timely intervention and improved outcomes. This review aims to outline the importance of predicting the progression of spontaneous parturition, detail the various methods employed to enhance this prediction and provide recommendations for future research.MethodsWe searched articles relating to labor progression and systematic review articles on Artificial Inteligence (AI) in childbirth management using PubMed. To supplement, Google Scholar was used to find recent guidelines and related documents.ResultsTraditional methods like vaginal examinations, criticized for subjectivity and inaccuracy, are gradually being replaced by ultrasound, considered a more objective and accurate approach. Further advancements have been observed with machine learning and artificial intelligence techniques, which promise to surpass the accuracies of conventional methods. The Friedman curve, developed in 1954, is the standard for assessing labor progress, but its application to Asian women, in particular, remains controversial, and various studies have reported that the actual rate of labor was slower than that indicated by the Friedman curve.ConclusionThere is a need to innovate methodologies for predicting delivery tailored to modern pregnant women, especially when they have different genetic and cultural backgrounds than their Western counterparts, such as Asians. Future research should develop predictive models of labor progression that aim to enhance medical intervention and improve the safety and well‐being of both mother and child.

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Prediction of preterm birth using artificial intelligence: a systematic review

Cited by 17 publications

References 41 publications

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

Current status of the position on labor progress prediction for contemporary pregnant women using Friedman curves: An updated review

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