1
|
Giaxi P, Vivilaki V, Sarella A, Harizopoulou V, Gourounti K. Artificial Intelligence and Machine Learning: An Updated Systematic Review of Their Role in Obstetrics and Midwifery. Cureus 2025; 17:e80394. [PMID: 40070886 PMCID: PMC11895402 DOI: 10.7759/cureus.80394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/11/2025] [Indexed: 03/14/2025] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) are rapidly evolving technologies with significant implications in obstetrics and midwifery. This systematic review aims to evaluate the latest advancements in AI and ML applications in obstetrics and midwifery. A search was conducted in three electronic databases (PubMed, Scopus, and Web of Science) for studies published between January 1, 2022, and February 20, 2025, using keywords related to AI, ML, obstetrics, and midwifery. The review adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for updated systematic reviews. Studies were selected based on their focus on AI/ML applications in obstetrics and midwifery, while non-English publications and review studies were excluded. The review included 64 studies, highlighting significant advancements in AI and ML applications across various domains in obstetrics and midwifery. Findings indicate that AI and ML models and systems achieved high accuracy in areas, such as assisted reproduction, diagnosis (e.g., 3D/4D ultrasound and MRI), pregnancy risk assessment (e.g., preeclampsia, gestational diabetes, preterm birth), fetal monitoring, mode of birth, and perinatal outcomes (e.g., mortality rates, postpartum hemorrhage, hypertensive disorders, neonatal respiratory distress). AI and ML have significant potential in transforming obstetric and midwifery care. The great number of studies reporting significant improvements suggests that the widespread adoption of AI and ML in these fields is imminent. Interdisciplinary collaboration between clinicians, data scientists, and policymakers will be crucial in shaping the future of maternal and neonatal healthcare.
Collapse
Affiliation(s)
- Paraskevi Giaxi
- Department of Midwifery, Faculty of Health and Caring Sciences, University of West Attica, Athens, GRC
| | - Victoria Vivilaki
- Department of Midwifery, Faculty of Health and Caring Sciences, University of West Attica, Athens, GRC
| | - Angeliki Sarella
- Department of Midwifery, Faculty of Health and Caring Sciences, University of West Attica, Athens, GRC
| | - Vikentia Harizopoulou
- Department of Midwifery, Faculty of Health and Caring Sciences, University of West Attica, Athens, GRC
| | - Kleanthi Gourounti
- Department of Midwifery, Faculty of Health and Caring Sciences, University of West Attica, Athens, GRC
| |
Collapse
|
2
|
Malik V, Agrawal N, Prasad S, Talwar S, Khatuja R, Jain S, Sehgal NP, Malik N, Khatuja J, Madan N. Prediction of Preeclampsia Using Machine Learning: A Systematic Review. Cureus 2024; 16:e76095. [PMID: 39834976 PMCID: PMC11743919 DOI: 10.7759/cureus.76095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2024] [Indexed: 01/22/2025] Open
Abstract
Preeclampsia is one of the leading causes of maternal and perinatal morbidity and mortality. Early prediction is the need of the hour so that interventions like aspirin prophylaxis can be started. Nowadays, machine learning (ML) is increasingly being used to predict the disease and its prognosis. This review explores the methodologies, predictors, and performance of ML models for preeclampsia prediction, emphasizing their comparative advantages, challenges, and clinical applicability. We conducted a systematic search of databases including PubMed, Cochrane, and Scopus for studies published in the last 10 years using terms such as "preeclampsia", "risk factors", "machine learning", "artificial intelligence", and "deep learning". Words and phrases were selected using MeSH, a controlled vocabulary. Appropriate articles were selected using Boolean operators "OR" and "AND". The database search yielded 325 records. After removing duplicates and non-English articles, and completing a title and abstract search 55 research articles were assessed for eligibility of which 11 were included in this review. The risk of bias was found to be high in three of the studies and low in the rest. Clinicodemographic characteristics, laboratory reports, Doppler ultrasound, and some innovative ones like genotypic data and fundal images were predictors used to train ML models. More than ten different ML models were used in the 11 studies from diverse countries like the United States, the United Kingdom, China, and Korea. The area under the curve varied from 0.76 to 0.97. ML algorithms such as extreme gradient boosting (XGBoost), random forest, and neural networks consistently demonstrated superior predictive accuracy Non-interpretable or black box ML models may not find clinical application on ethical grounds. The future of preeclampsia prediction using ML lies in balancing model performance with interpretability. Human oversight remains indispensable in implementing and interpreting these models to achieve better maternal outcomes. Further research and validation across diverse populations are critical to establishing the universal applicability of these promising ML-based approaches.
Collapse
Affiliation(s)
- Vinayak Malik
- Computer Science, University of Wisconsin, Madison, USA
| | - Neha Agrawal
- Obstetrics and Gynecology, Dr. Baba Saheb Ambedkar Hospital and Medical College, New Delhi, IND
| | - Sonal Prasad
- Obstetrics and Gynecology, Dr. Baba Saheb Ambedkar Hospital and Medical College, New Delhi, IND
| | - Sukriti Talwar
- Computer Science, Delhi Technological University, New Delhi, IND
| | - Ritu Khatuja
- Obstetrics and Gynecology, Dr. Baba Saheb Ambedkar Hospital and Medical College, New Delhi, IND
| | - Sandhya Jain
- Obstetrics and Gynecology, Dr. Baba Saheb Ambedkar Hospital and Medical College, New Delhi, IND
| | - Nidhi Prabha Sehgal
- Anesthesiology and Critical Care, Dr. Baba Saheb Ambedkar Hospital and Medical College, New Delhi, IND
| | - Neeru Malik
- Obstetrics and Gynecology, Dr. Baba Saheb Ambedkar Medical College and Hospital, New Delhi, IND
| | - Jeewant Khatuja
- Automation and Robotics, University School of Automation and Robotics, Guru Gobind Singh Indraprastha University, New Delhi, IND
| | - Nikita Madan
- Obstetrics and Gynecology, ESI Hospital and Postgraduate Institute of Medical Sciences and Research (PGIMER) Basaidarapur, New Delhi, IND
| |
Collapse
|
3
|
Feng W, Luo Y. Preeclampsia and its prediction: traditional versus contemporary predictive methods. J Matern Fetal Neonatal Med 2024; 37:2388171. [PMID: 39107137 DOI: 10.1080/14767058.2024.2388171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 07/29/2024] [Accepted: 07/30/2024] [Indexed: 08/09/2024]
Abstract
OBJECTIVE Preeclampsia (PE) poses a significant threat to maternal and perinatal health, so its early prediction, prevention, and management are of paramount importance to mitigate adverse pregnancy outcomes. This article provides a brief review spanning epidemiology, etiology, pathophysiology, and risk factors associated with PE, mainly discussing the emerging role of Artificial Intelligence (AI) deep learning (DL) technology in predicting PE, to advance the understanding of PE and foster the clinical application of early prediction methods. METHODS Our narrative review comprehensively examines the PE epidemiology, etiology, pathophysiology, risk factors and predictive approaches, including traditional models and AI deep learning technology. RESULTS Preeclampsia involves a wide range of biological and biochemical risk factors, among which poor uterine artery remodeling, excessive immune response, endothelial dysfunction, and imbalanced angiogenesis play important roles. Traditional PE prediction models exhibit significant limitations in sensitivity and specificity, particularly in predicting late-onset PE, with detection rates ranging from only 30% to 50%. AI models have exhibited a notable level of predictive accuracy and value across various populations and datasets, achieving detection rates of approximately 70%. Particularly, they have shown superior predictive capabilities for late-onset PE, thereby presenting novel opportunities for early screening and management of the condition. CONCLUSION AI DL technology holds promise in revolutionizing the prediction and management of PE. AI-based approaches offer a pathway toward more effective risk assessment methods by addressing the shortcomings of traditional prediction models. Ongoing research efforts should focus on expanding databases and validating the performance of AI in diverse populations, leading to the development of more sophisticated prediction models with improved accuracy.
Collapse
Affiliation(s)
- Wei Feng
- Department of Gynecology, China Aerospace Science & Industry Corporation 731 Hospital, Beijing, China
| | - Ying Luo
- Department of Gynecology, China Aerospace Science & Industry Corporation 731 Hospital, Beijing, China
| |
Collapse
|
4
|
Nguyen-Hoang L, Sahota DS, Pooh RK, Duan H, Chaiyasit N, Sekizawa A, Shaw SW, Seshadri S, Choolani M, Yapan P, Sim WS, Ma R, Leung WC, Lau SL, Lee NMW, Leung HYH, Meshali T, Meiri H, Louzoun Y, Poon LC. Validation of the first-trimester machine learning model for predicting pre-eclampsia in an Asian population. Int J Gynaecol Obstet 2024; 167:350-359. [PMID: 38666305 DOI: 10.1002/ijgo.15563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/07/2024] [Accepted: 04/16/2024] [Indexed: 09/25/2024]
Abstract
OBJECTIVES To evaluate the performance of an artificial intelligence (AI) and machine learning (ML) model for first-trimester screening for pre-eclampsia in a large Asian population. METHODS This was a secondary analysis of a multicenter prospective cohort study in 10 935 participants with singleton pregnancies attending for routine pregnancy care at 11-13+6 weeks of gestation in seven regions in Asia between December 2016 and June 2018. We applied the AI+ML model for the first-trimester prediction of preterm pre-eclampsia (<37 weeks), term pre-eclampsia (≥37 weeks), and any pre-eclampsia, which was derived and tested in a cohort of pregnant participants in the UK (Model 1). This model comprises maternal factors with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor (PlGF). The model was further retrained with adjustments for analyzers used for biochemical testing (Model 2). Discrimination was assessed by area under the receiver operating characteristic curve (AUC). The Delong test was used to compare the AUC of Model 1, Model 2, and the Fetal Medicine Foundation (FMF) competing risk model. RESULTS The predictive performance of Model 1 was significantly lower than that of the FMF competing risk model in the prediction of preterm pre-eclampsia (0.82, 95% confidence interval [CI] 0.77-0.87 vs. 0.86, 95% CI 0.811-0.91, P = 0.019), term pre-eclampsia (0.75, 95% CI 0.71-0.80 vs. 0.79, 95% CI 0.75-0.83, P = 0.006), and any pre-eclampsia (0.78, 95% CI 0.74-0.81 vs. 0.82, 95% CI 0.79-0.84, P < 0.001). Following the retraining of the data with adjustments for the PlGF analyzers, the performance of Model 2 for predicting preterm pre-eclampsia, term pre-eclampsia, and any pre-eclampsia was improved with the AUC values increased to 0.84 (95% CI 0.80-0.89), 0.77 (95% CI 0.73-0.81), and 0.80 (95% CI 0.76-0.83), respectively. There were no differences in AUCs between Model 2 and the FMF competing risk model in the prediction of preterm pre-eclampsia (P = 0.135) and term pre-eclampsia (P = 0.084). However, Model 2 was inferior to the FMF competing risk model in predicting any pre-eclampsia (P = 0.024). CONCLUSION This study has demonstrated that following adjustment for the biochemical marker analyzers, the predictive performance of the AI+ML prediction model for pre-eclampsia in the first trimester was comparable to that of the FMF competing risk model in an Asian population.
Collapse
Affiliation(s)
- Long Nguyen-Hoang
- Department of Obstetrics and Gynecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR
| | - Daljit S Sahota
- Department of Obstetrics and Gynecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR
| | | | | | | | | | | | | | | | | | - Wen Shan Sim
- Maternal Fetal Medicine, KK Women's and Children's Hospital, Singapore
| | - Runmei Ma
- First Affiliated Hospital of Kunming Medical University, Kunming, China
| | | | - So Ling Lau
- Department of Obstetrics and Gynecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR
| | - Nikki May Wing Lee
- Department of Obstetrics and Gynecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR
| | - Hiu Yu Hillary Leung
- Department of Obstetrics and Gynecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR
| | - Tal Meshali
- Department of Mathematics, Bar Ilan University, Ramat Gan, Israel
| | - Hamutal Meiri
- The ASPRE Consortium and TeleMarpe, Tel Aviv, Israel
| | - Yoram Louzoun
- Department of Mathematics, Bar Ilan University, Ramat Gan, Israel
| | - Liona C Poon
- Department of Obstetrics and Gynecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR
| |
Collapse
|
5
|
Ricci CA, Crysup B, Phillips NR, Ray WC, Santillan MK, Trask AJ, Woerner AE, Goulopoulou S. Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research. Am J Physiol Heart Circ Physiol 2024; 327:H417-H432. [PMID: 38847756 PMCID: PMC11442027 DOI: 10.1152/ajpheart.00149.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 05/31/2024] [Accepted: 05/31/2024] [Indexed: 06/10/2024]
Abstract
The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thus, pregnancy imposes physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality. Despite advances in cardiovascular physiology research, there is still no comprehensive understanding of maternal cardiovascular adaptations in healthy pregnancies. Furthermore, current approaches for the prognosis of cardiovascular complications during pregnancy are limited. Machine learning (ML) offers new and effective tools for investigating mechanisms involved in pregnancy-related cardiovascular complications as well as the development of potential therapies. The main goal of this review is to summarize existing research that uses ML to understand mechanisms of cardiovascular physiology during pregnancy and develop prediction models for clinical application in pregnant patients. We also provide an overview of ML platforms that can be used to comprehensively understand cardiovascular adaptations to pregnancy and discuss the interpretability of ML outcomes, the consequences of model bias, and the importance of ethical consideration in ML use.
Collapse
Affiliation(s)
- Contessa A Ricci
- College of Nursing, Washington State University, Spokane, Washington, United States
- IREACH: Institute for Research and Education to Advance Community Health, Washington State University, Seattle, Washington, United States
- Elson S. Floyd College of Medicine, Washington State University, Spokane, Washington, United States
| | - Benjamin Crysup
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science, Fort Worth, Texas, United States
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, Texas, United States
| | - Nicole R Phillips
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science, Fort Worth, Texas, United States
| | - William C Ray
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, United States
| | - Mark K Santillan
- Department of Obstetrics and Gynecology, University of Iowa Carver College of Medicine, Iowa City, Iowa, United States
| | - Aaron J Trask
- Center for Cardiovascular Research, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, Ohio, United States
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, United States
| | - August E Woerner
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science, Fort Worth, Texas, United States
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, Texas, United States
| | - Styliani Goulopoulou
- Lawrence D. Longo Center for Perinatal Biology, Departments of Basic Sciences, Gynecology and Obstetrics, Loma Linda University, Loma Linda, California, United States
| |
Collapse
|
6
|
Drukker L. The Holy Grail of obstetric ultrasound: can artificial intelligence detect hard-to-identify fetal cardiac anomalies? ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2024; 64:5-9. [PMID: 38949769 DOI: 10.1002/uog.27703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/18/2024] [Indexed: 07/02/2024]
Abstract
Linked article: This Editorial comments on articles by Day et al. and Taksøe‐Vester et al.
Collapse
Affiliation(s)
- L Drukker
- Women's Ultrasound, Department of Obstetrics and Gynecology, Rabin-Beilinson Medical Center, School of Medicine, Faculty of Medical and Health Sciences, Tel-Aviv University, Tel Aviv, Israel
- Oxford Maternal & Perinatal Health Institute (OMPHI), University of Oxford, Oxford, UK
| |
Collapse
|
7
|
Li T, Xu M, Wang Y, Wang Y, Tang H, Duan H, Zhao G, Zheng M, Hu Y. Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China. Front Endocrinol (Lausanne) 2024; 15:1345573. [PMID: 38919479 PMCID: PMC11198873 DOI: 10.3389/fendo.2024.1345573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 05/27/2024] [Indexed: 06/27/2024] Open
Abstract
Introduction Preeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present, early identification of high-risk groups for preeclampsia and timely intervention with aspirin is an effective preventive method against preeclampsia. This study aims to develop a robust and effective preeclampsia prediction model with good performance by machine learning algorithms based on maternal characteristics, biophysical and biochemical markers at 11-13 + 6 weeks' gestation, providing an effective tool for early screening and prediction of preeclampsia. Methods This study included 5116 singleton pregnant women who underwent PE screening and fetal aneuploidy from a prospective cohort longitudinal study in China. Maternal characteristics (such as maternal age, height, pre-pregnancy weight), past medical history, mean arterial pressure, uterine artery pulsatility index, pregnancy-associated plasma protein A, and placental growth factor were collected as the covariates for the preeclampsia prediction model. Five classification algorithms including Logistic Regression, Extra Trees Classifier, Voting Classifier, Gaussian Process Classifier and Stacking Classifier were applied for the prediction model development. Five-fold cross-validation with an 8:2 train-test split was applied for model validation. Results We ultimately included 49 cases of preterm preeclampsia and 161 cases of term preeclampsia from the 4644 pregnant women data in the final analysis. Compared with other prediction algorithms, the AUC and detection rate at 10% FPR of the Voting Classifier algorithm showed better performance in the prediction of preterm preeclampsia (AUC=0.884, DR at 10%FPR=0.625) under all covariates included. However, its performance was similar to that of other model algorithms in all PE and term PE prediction. In the prediction of all preeclampsia, the contribution of PLGF was higher than PAPP-A (11.9% VS 8.7%), while the situation was opposite in the prediction of preterm preeclampsia (7.2% VS 16.5%). The performance for preeclampsia or preterm preeclampsia using machine learning algorithms was similar to that achieved by the fetal medicine foundation competing risk model under the same predictive factors (AUCs of 0.797 and 0.856 for PE and preterm PE, respectively). Conclusions Our models provide an accessible tool for large-scale population screening and prediction of preeclampsia, which helps reduce the disease burden and improve maternal and fetal outcomes.
Collapse
Affiliation(s)
- Taishun Li
- Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
- Medical Statistics and Analysis Center, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Mingyang Xu
- Information Management Division, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Yuan Wang
- Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Ya Wang
- Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Huirong Tang
- Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Honglei Duan
- Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Guangfeng Zhao
- Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Mingming Zheng
- Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Yali Hu
- Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| |
Collapse
|
8
|
Shara N, Mirabal-Beltran R, Talmadge B, Falah N, Ahmad M, Dempers R, Crovatt S, Eisenberg S, Anderson K. Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data. JMIR Cardio 2024; 8:e53091. [PMID: 38648629 DOI: 10.2196/53091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Cardiovascular conditions (eg, cardiac and coronary conditions, hypertensive disorders of pregnancy, and cardiomyopathies) were the leading cause of maternal mortality between 2017 and 2019. The United States has the highest maternal mortality rate of any high-income nation, disproportionately impacting those who identify as non-Hispanic Black or Hispanic. Novel clinical approaches to the detection and diagnosis of cardiovascular conditions are therefore imperative. Emerging research is demonstrating that machine learning (ML) is a promising tool for detecting patients at increased risk for hypertensive disorders during pregnancy. However, additional studies are required to determine how integrating ML and big data, such as electronic health records (EHRs), can improve the identification of obstetric patients at higher risk of cardiovascular conditions. OBJECTIVE This study aimed to evaluate the capability and timing of a proprietary ML algorithm, Healthy Outcomes for all Pregnancy Experiences-Cardiovascular-Risk Assessment Technology (HOPE-CAT), to detect maternal-related cardiovascular conditions and outcomes. METHODS Retrospective data from the EHRs of a large health care system were investigated by HOPE-CAT in a virtual server environment. Deidentification of EHR data and standardization enabled HOPE-CAT to analyze data without pre-existing biases. The ML algorithm assessed risk factors selected by clinical experts in cardio-obstetrics, and the algorithm was iteratively trained using relevant literature and current standards of risk identification. After refinement of the algorithm's learned risk factors, risk profiles were generated for every patient including a designation of standard versus high risk. The profiles were individually paired with clinical outcomes pertaining to cardiovascular pregnancy conditions and complications, wherein a delta was calculated between the date of the risk profile and the actual diagnosis or intervention in the EHR. RESULTS In total, 604 pregnancies resulting in birth had records or diagnoses that could be compared against the risk profile; the majority of patients identified as Black (n=482, 79.8%) and aged between 21 and 34 years (n=509, 84.4%). Preeclampsia (n=547, 90.6%) was the most common condition, followed by thromboembolism (n=16, 2.7%) and acute kidney disease or failure (n=13, 2.2%). The average delta was 56.8 (SD 69.7) days between the identification of risk factors by HOPE-CAT and the first date of diagnosis or intervention of a related condition reported in the EHR. HOPE-CAT showed the strongest performance in early risk detection of myocardial infarction at a delta of 65.7 (SD 81.4) days. CONCLUSIONS This study provides additional evidence to support ML in obstetrical patients to enhance the early detection of cardiovascular conditions during pregnancy. ML can synthesize multiday patient presentations to enhance provider decision-making and potentially reduce maternal health disparities.
Collapse
Affiliation(s)
- Nawar Shara
- MedStar Health Research Institute, Hyattesville, MD, United States
- Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, DC, DC, United States
| | | | | | - Noor Falah
- MedStar Health Research Institute, Hyattesville, MD, United States
| | - Maryam Ahmad
- MedStar Health Research Institute, Hyattesville, MD, United States
| | | | | | | | - Kelley Anderson
- School of Nursing, Georgetown University, Washington, DC, United States
| |
Collapse
|
9
|
Xia Y, Wang Y, Yuan S, Hu J, Zhang L, Xie J, Zhao Y, Hao J, Ren Y, Wu S. Development and validation of nomograms to predict clinical outcomes of preeclampsia. Front Endocrinol (Lausanne) 2024; 15:1292458. [PMID: 38549768 PMCID: PMC10972945 DOI: 10.3389/fendo.2024.1292458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 02/14/2024] [Indexed: 04/02/2024] Open
Abstract
Background Preeclampsia (PE) is one of the most severe pregnancy-related diseases; however, there is still a lack of reliable biomarkers. In this study, we aimed to develop models for predicting early-onset PE, severe PE, and the gestation duration of patients with PE. Methods Eligible patients with PE were enrolled and divided into a training (n = 253) and a validation (n = 108) cohort. Multivariate logistic and Cox models were used to identify factors associated with early-onset PE, severe PE, and the gestation duration of patients with PE. Based on significant factors, nomograms were developed and evaluated using the area under the curve (AUC) and a calibration curve. Results In the training cohort, multiple gravidity experience (p = 0.005), lower albumin (ALB; p < 0.001), and higher lactate dehydrogenase (LDH; p < 0.001) were significantly associated with early-onset PE. Abortion history (p = 0.017), prolonged thrombin time (TT; p < 0.001), and higher aspartate aminotransferase (p = 0.002) and LDH (p = 0.003) were significantly associated with severe PE. Abortion history (p < 0.001), gemellary pregnancy (p < 0.001), prolonged TT (p < 0.001), higher mean platelet volume (p = 0.014) and LDH (p < 0.001), and lower ALB (p < 0.001) were significantly associated with shorter gestation duration. Three nomograms were developed and validated to predict the probability of early-onset PE, severe PE, and delivery time for each patient with PE. The AUC showed good predictive performance, and the calibration curve and decision curve analysis demonstrated clinical practicability. Conclusion Based on the clinical features and peripheral blood laboratory indicators, we identified significant factors and developed models to predict early-onset PE, severe PE, and the gestation duration of pregnant women with PE, which could help clinicians assess the clinical outcomes early and design appropriate strategies for patients.
Collapse
Affiliation(s)
- Yan Xia
- Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, China
| | - Yao Wang
- Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, China
| | - Shijin Yuan
- Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiaming Hu
- Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, China
| | - Lu Zhang
- Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, China
| | - Jiamin Xie
- Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, China
| | - Yang Zhao
- Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, China
| | - Jiahui Hao
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yanwei Ren
- Department of Gynaecology and Obstetrics, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shengjun Wu
- Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, China
| |
Collapse
|
10
|
Vasilache IA, Scripcariu IS, Doroftei B, Bernad RL, Cărăuleanu A, Socolov D, Melinte-Popescu AS, Vicoveanu P, Harabor V, Mihalceanu E, Melinte-Popescu M, Harabor A, Bernad E, Nemescu D. Prediction of Intrauterine Growth Restriction and Preeclampsia Using Machine Learning-Based Algorithms: A Prospective Study. Diagnostics (Basel) 2024; 14:453. [PMID: 38396491 PMCID: PMC10887724 DOI: 10.3390/diagnostics14040453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/10/2024] [Accepted: 02/16/2024] [Indexed: 02/25/2024] Open
Abstract
(1) Background: Prenatal care providers face a continuous challenge in screening for intrauterine growth restriction (IUGR) and preeclampsia (PE). In this study, we aimed to assess and compare the predictive accuracy of four machine learning algorithms in predicting the occurrence of PE, IUGR, and their associations in a group of singleton pregnancies; (2) Methods: This observational prospective study included 210 singleton pregnancies that underwent first trimester screenings at our institution. We computed the predictive performance of four machine learning-based methods, namely decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), by incorporating clinical and paraclinical data; (3) Results: The RF algorithm showed superior performance for the prediction of PE (accuracy: 96.3%), IUGR (accuracy: 95.9%), and its subtypes (early onset IUGR, accuracy: 96.2%, and late-onset IUGR, accuracy: 95.2%), as well as their association (accuracy: 95.1%). Both SVM and NB similarly predicted IUGR (accuracy: 95.3%), while SVM outperformed NB (accuracy: 95.8 vs. 94.7%) in predicting PE; (4) Conclusions: The integration of machine learning-based algorithms in the first-trimester screening of PE and IUGR could improve the overall detection rate of these disorders, but this hypothesis should be confirmed in larger cohorts of pregnant patients from various geographical areas.
Collapse
Affiliation(s)
- Ingrid-Andrada Vasilache
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (I.-A.V.); (A.C.); (D.S.); (P.V.); (E.M.)
| | - Ioana-Sadyie Scripcariu
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (I.-A.V.); (A.C.); (D.S.); (P.V.); (E.M.)
| | - Bogdan Doroftei
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (I.-A.V.); (A.C.); (D.S.); (P.V.); (E.M.)
| | - Robert Leonard Bernad
- Faculty of Computer Science, Politechnica University of Timisoara, 300006 Timisoara, Romania;
| | - Alexandru Cărăuleanu
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (I.-A.V.); (A.C.); (D.S.); (P.V.); (E.M.)
| | - Demetra Socolov
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (I.-A.V.); (A.C.); (D.S.); (P.V.); (E.M.)
| | - Alina-Sînziana Melinte-Popescu
- Department of Mother and Newborn Care, Faculty of Medicine and Biological Sciences, ‘Ștefan cel Mare’ University, 720229 Suceava, Romania; (A.-S.M.-P.); (V.H.)
| | - Petronela Vicoveanu
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (I.-A.V.); (A.C.); (D.S.); (P.V.); (E.M.)
| | - Valeriu Harabor
- Department of Mother and Newborn Care, Faculty of Medicine and Biological Sciences, ‘Ștefan cel Mare’ University, 720229 Suceava, Romania; (A.-S.M.-P.); (V.H.)
| | - Elena Mihalceanu
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (I.-A.V.); (A.C.); (D.S.); (P.V.); (E.M.)
| | - Marian Melinte-Popescu
- Clinical and Surgical Department, Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University, 800216 Galati, Romania;
- Department of Internal Medicine, Faculty of Medicine and Biological Sciences, ‘Ștefan cel Mare’ University, 720229 Suceava, Romania
| | - Anamaria Harabor
- Department of Mother and Newborn Care, Faculty of Medicine and Biological Sciences, ‘Ștefan cel Mare’ University, 720229 Suceava, Romania; (A.-S.M.-P.); (V.H.)
| | - Elena Bernad
- Department of Mother and Newborn Care, Faculty of Medicine and Biological Sciences, ‘Ștefan cel Mare’ University, 720229 Suceava, Romania; (A.-S.M.-P.); (V.H.)
- Department of Obstetrics-Gynecology II, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
| | - Dragos Nemescu
- Department of Mother and Child Care, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (I.-A.V.); (A.C.); (D.S.); (P.V.); (E.M.)
| |
Collapse
|