Machine learning and disease prediction in obstetrics

Arain, Zara; Iliodromiti, Stamatina; Slabaugh, Gregory; David, Anna L.; Chowdhury, Tina T.

doi:10.1016/j.crphys.2023.100099

Cited by 8 publications

(4 citation statements)

References 70 publications

(97 reference statements)

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“…In the second phase of our study, we applied an artificial intelligence model for PB prediction using machine learning (ML) by incorporating quantification of cytokines because these are the crucial mediators of the inflammatory process observed in preterm labor. The main areas that may benefit from ML techniques in the medical field are diagnosis and outcome prediction; ML can transform how medicine works [ 19 , 44 ]. The use of AI methods in medical care could facilitate personalized pregnancy management and improve public health, especially in low- and middle-income countries.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, artificial intelligence has been used in different healthcare fields to predict, prevent, diagnose, and monitor different pathologies, even in obstetrics [ 18 ]. Models using machine learning seem more accurate than risk calculators as these are used to analyze massive data and, through algorithms, identify patterns for making predictions [ 19 ]; furthermore, it has also been proposed that machine learning models can be helpful in personalized pregnancy management, especially in low- and middle-income countries [ 18 ]. The goal of identifying women at high risk for developing PB is to individualize the clinical follow-up and offer medical preventive strategies (e.g., progesterone) that reduce by up to 90% of the risk of preterm birth in women with a history of this outcome and by 42% in pregnant women with short cervix detected in second-trimester screening [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth

Borboa-Olivares,

Rodríguez-Sibaja,

Espejel-Nuñez

et al. 2023

IJMS

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Preterm birth (PB) is a leading cause of perinatal morbidity and mortality. PB prediction is performed by measuring cervical length, with a detection rate of around 70%. Although it is known that a cytokine-mediated inflammatory process is involved in the pathophysiology of PB, none screening method implemented in clinical practice includes cytokine levels as a predictor variable. Here, we quantified cytokines in cervical-vaginal mucus of pregnant women (18–23.6 weeks of gestation) with high or low risk for PB determined by cervical length, also collecting relevant obstetric information. IL-2, IL-6, IFN-γ, IL-4, and IL-10 were significantly higher in the high-risk group, while IL-1ra was lower. Two different models for PB prediction were created using the Random Forest machine-learning algorithm: a full model with 12 clinical variables and cytokine values and the adjusted model, including the most relevant variables-maternal age, IL-2, and cervical length- (detection rate 66 vs. 87%, false positive rate 12 vs. 3.33%, false negative rate 28 vs. 6.66%, and area under the curve 0.722 vs. 0.875, respectively). The adjusted model that incorporate cytokines showed a detection rate eight points higher than the gold standard calculator, which may allow us to identify the risk PB risk more accurately and implement strategies for preventive interventions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth

Borboa-Olivares,

Rodríguez-Sibaja,

Espejel-Nuñez

et al. 2023

IJMS

View full text Add to dashboard Cite

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“…It is extensively employed during pregnancy, and the obtained images can be used to train DL models like CNN to automate and enhance the identification of abnormalities [93]. An echocardiogram consists of a detailed US test of the fetal heart, performed prenatally; utilizing AI for analyzing echocardiograms holds promise in advancing prenatal diagnosis and improving heart defect screening [94]. In this context, Gong et al conducted a study wherein they developed an innovative GAN model.…”

Section: Congenital Heart Diseasesmentioning

confidence: 99%

Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches

Yousefpour Shahrivar,

Karami,

Karami

2023

Biomimetics

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Fetal development is a critical phase in prenatal care, demanding the timely identification of anomalies in ultrasound images to safeguard the well-being of both the unborn child and the mother. Medical imaging has played a pivotal role in detecting fetal abnormalities and malformations. However, despite significant advances in ultrasound technology, the accurate identification of irregularities in prenatal images continues to pose considerable challenges, often necessitating substantial time and expertise from medical professionals. In this review, we go through recent developments in machine learning (ML) methods applied to fetal ultrasound images. Specifically, we focus on a range of ML algorithms employed in the context of fetal ultrasound, encompassing tasks such as image classification, object recognition, and segmentation. We highlight how these innovative approaches can enhance ultrasound-based fetal anomaly detection and provide insights for future research and clinical implementations. Furthermore, we emphasize the need for further research in this domain where future investigations can contribute to more effective ultrasound-based fetal anomaly detection.

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“…Despite landmark efforts undertaken to improve global standardization in this field, the degree of inter-pathologist variability in examination findings remains high, particularly in low resource settings where specialized training and expertise are lacking [7]. Considering these challenges, placental pathology is a key clinical domain that may benefit most from the implementation of computer-aided diagnosis (CAD) through the development of machine learning algorithms capable of identifying distinct visual patterns in placental histopathology specimens [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Maternal Vascular Malperfusion Lesions of the Placenta using Machine Learning

Patnaik,

Khodee,

Vasam

et al. 2023

Preprint

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Introduction: Hypertensive disorders of pregnancy (HDP) and fetal growth restriction (FGR) are common obstetrical complications, often with pathological features of maternal vascular malperfusion (MVM) in the placenta. Currently, clinical placental pathology methods involve a manual visual examination of histology sections, a practice that can be resource-intensive and demonstrate moderate-to-poor inter-pathologist agreement on diagnostic outcomes, dependant on the degree of pathologist sub-specialty training. Methods: This study aims to apply machine learning (ML) feature extraction methods to classify digital images of placental histopathology specimens, collected from cases of HDP [gestational hypertension (GH), preeclampsia (PE), PE + FGR], normotensive FGR, and healthy pregnancies, according to the presence or absence of MVM lesions. 159 digital images were captured from histological placental specimens, manually scored for MVM lesions (MVM- or MVM+) and used to develop a support vector machine (SVM) classifier model, using features extracted from pretrained ResNet18. The model was trained with and without data augmentation, and with and without data shuffling, and the performance of the classifiers assessed and compared through measurements of accuracy, precision, and recall using confusion matrices. Results: The SVM model demonstrated accuracies between 7-78% for WSI-level MVM classification, with poorest performance observed on images with borderline MVM presence, as determined through post hoc observation. Conclusion: The results are promising for the integration of ML methods into the placental histopathological examination process. Using this study as a proof-of-concept will lead our group and others to carry ML models further in placental histopathology.

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Machine learning and disease prediction in obstetrics

Cited by 8 publications

References 70 publications

A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth

A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth

Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches

Automated Detection of Maternal Vascular Malperfusion Lesions of the Placenta using Machine Learning

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