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Wahbah M, Zitouni MS, Al Sakaji R, Funamoto K, Widatalla N, Krishnan A, Kimura Y, Khandoker AH. A deep learning framework for noninvasive fetal ECG signal extraction. Front Physiol 2024; 15:1329313. [PMID: 38711954 PMCID: PMC11073781 DOI: 10.3389/fphys.2024.1329313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 03/22/2024] [Indexed: 05/08/2024] Open
Abstract
Introduction: The availability of proactive techniques for health monitoring is essential to reducing fetal mortality and avoiding complications in fetal wellbeing. In harsh circumstances such as pandemics, earthquakes, and low-resource settings, the incompetence of many healthcare systems worldwide in providing essential services, especially for pregnant women, is critical. Being able to continuously monitor the fetus in hospitals and homes in a direct and fast manner is very important in such conditions. Methods: Monitoring the health of the baby can potentially be accomplished through the computation of vital bio-signal measures using a clear fetal electrocardiogram (ECG) signal. The aim of this study is to develop a framework to detect and identify the R-peaks of the fetal ECG directly from a 12 channel abdominal composite signal. Thus, signals were recorded noninvasively from 70 pregnant (healthy and with health conditions) women with no records of fetal abnormalities. The proposed model employs a recurrent neural network architecture to robustly detect the fetal ECG R-peaks. Results: To test the proposed framework, we performed both subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) tests. The proposed framework achieved average accuracy values of 94.2% and 88.8%, respectively. More specifically, the leave-one-subject-out test accuracy was 86.7% during the challenging period of vernix caseosa layer formation. Furthermore, we computed the fetal heart rate from the detected R-peaks, and the demonstrated results highlight the robustness of the proposed framework. Discussion: This work has the potential to cater to the critical industry of maternal and fetal healthcare as well as advance related applications.
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Affiliation(s)
- Maisam Wahbah
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
- Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - M. Sami Zitouni
- College of Engineering and Information Technology, University of Dubai, Dubai, United Arab Emirates
| | - Raghad Al Sakaji
- Department of Industrial and Systems Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | | | - Namareq Widatalla
- Health Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Anita Krishnan
- Children’s National Hospital, Washington, DC, United States
| | | | - Ahsan H. Khandoker
- Health Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
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Watanabe K. Current status of the position on labor progress prediction for contemporary pregnant women using Friedman curves: An updated review. J Obstet Gynaecol Res 2024; 50:313-321. [PMID: 38037733 DOI: 10.1111/jog.15842] [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: 09/03/2023] [Accepted: 11/20/2023] [Indexed: 12/02/2023]
Abstract
AIM Prediction 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. METHODS We 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. RESULTS Traditional 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. CONCLUSION There 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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Affiliation(s)
- Kaori Watanabe
- National Center for Global Health and Medicine, National College of Nursing, Tokyo, Japan
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3
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Kopanitsa G, Metsker O, Kovalchuk S. Machine Learning Methods for Pregnancy and Childbirth Risk Management. J Pers Med 2023; 13:975. [PMID: 37373964 DOI: 10.3390/jpm13060975] [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: 04/23/2023] [Revised: 06/04/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Machine learning methods enable medical systems to automatically generate data-driven decision support models using real-world data inputs, eliminating the need for explicit rule design. In this research, we investigated the application of machine learning methods in healthcare, specifically focusing on pregnancy and childbirth risks. The timely identification of risk factors during early pregnancy, along with risk management, mitigation, prevention, and adherence management, can significantly reduce adverse perinatal outcomes and complications for both mother and child. Given the existing burden on medical professionals, clinical decision support systems (CDSSs) can play a role in risk management. However, these systems require high-quality decision support models based on validated medical data that are also clinically interpretable. To develop models for predicting childbirth risks and due dates, we conducted a retrospective analysis of electronic health records from the perinatal Center of the Almazov Specialized Medical Center in Saint-Petersburg, Russia. The dataset, which was exported from the medical information system, consisted of structured and semi-structured data, encompassing a total of 73,115 lines for 12,989 female patients. Our proposed approach, which includes a detailed analysis of predictive model performance and interpretability, offers numerous opportunities for decision support in perinatal care provision. The high predictive performance achieved by our models ensures precise support for both individual patient care and overall health organization management.
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Affiliation(s)
- Georgy Kopanitsa
- Faculty of Digital Transformations, ITMO University, 4 Birzhevaya Liniya, 199034 Saint-Petersburg, Russia
- Almazov National Medical Research Centre, Ulitsa Akkuratova, 2, 197341 Saint-Petersburg, Russia
| | - Oleg Metsker
- Almazov National Medical Research Centre, Ulitsa Akkuratova, 2, 197341 Saint-Petersburg, Russia
| | - Sergey Kovalchuk
- Faculty of Digital Transformations, ITMO University, 4 Birzhevaya Liniya, 199034 Saint-Petersburg, Russia
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Arain Z, Iliodromiti S, Slabaugh G, David AL, Chowdhury TT. Machine learning and disease prediction in obstetrics. Curr Res Physiol 2023; 6:100099. [PMID: 37324652 PMCID: PMC10265477 DOI: 10.1016/j.crphys.2023.100099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 05/09/2023] [Indexed: 06/17/2023] Open
Abstract
Machine learning technologies and translation of artificial intelligence tools to enhance the patient experience are changing obstetric and maternity care. An increasing number of predictive tools have been developed with data sourced from electronic health records, diagnostic imaging and digital devices. In this review, we explore the latest tools of machine learning, the algorithms to establish prediction models and the challenges to assess fetal well-being, predict and diagnose obstetric diseases such as gestational diabetes, pre-eclampsia, preterm birth and fetal growth restriction. We discuss the rapid growth of machine learning approaches and intelligent tools for automated diagnostic imaging of fetal anomalies and to asses fetoplacental and cervix function using ultrasound and magnetic resonance imaging. In prenatal diagnosis, we discuss intelligent tools for magnetic resonance imaging sequencing of the fetus, placenta and cervix to reduce the risk of preterm birth. Finally, the use of machine learning to improve safety standards in intrapartum care and early detection of complications will be discussed. The demand for technologies to enhance diagnosis and treatment in obstetrics and maternity should improve frameworks for patient safety and enhance clinical practice.
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Affiliation(s)
- Zara Arain
- Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - Stamatina Iliodromiti
- Women's Health Research Unit, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, UK
| | - Gregory Slabaugh
- Digital Environment Research Institute, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 1HH, UK
| | - Anna L. David
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, Medical School Building, Huntley Street, London, WC1E 6AU, UK
| | - Tina T. Chowdhury
- Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
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5
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Cartus AR. Machine learning to study placental pathology: Risk of reification and other considerations. Paediatr Perinat Epidemiol 2023; 37:362-364. [PMID: 36792534 DOI: 10.1111/ppe.12961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 02/17/2023]
Affiliation(s)
- Abigail R Cartus
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, USA
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6
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MacNell N, Feinstein L, Wilkerson J, Salo PM, Molsberry SA, Fessler MB, Thorne PS, Motsinger-Reif AA, Zeldin DC. Implementing machine learning methods with complex survey data: Lessons learned on the impacts of accounting sampling weights in gradient boosting. PLoS One 2023; 18:e0280387. [PMID: 36638125 PMCID: PMC9838837 DOI: 10.1371/journal.pone.0280387] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 12/28/2022] [Indexed: 01/14/2023] Open
Abstract
Despite the prominent use of complex survey data and the growing popularity of machine learning methods in epidemiologic research, few machine learning software implementations offer options for handling complex samples. A major challenge impeding the broader incorporation of machine learning into epidemiologic research is incomplete guidance for analyzing complex survey data, including the importance of sampling weights for valid prediction in target populations. Using data from 15, 820 participants in the 1988-1994 National Health and Nutrition Examination Survey cohort, we determined whether ignoring weights in gradient boosting models of all-cause mortality affected prediction, as measured by the F1 score and corresponding 95% confidence intervals. In simulations, we additionally assessed the impact of sample size, weight variability, predictor strength, and model dimensionality. In the National Health and Nutrition Examination Survey data, unweighted model performance was inflated compared to the weighted model (F1 score 81.9% [95% confidence interval: 81.2%, 82.7%] vs 77.4% [95% confidence interval: 76.1%, 78.6%]). However, the error was mitigated if the F1 score was subsequently recalculated with observed outcomes from the weighted dataset (F1: 77.0%; 95% confidence interval: 75.7%, 78.4%). In simulations, this finding held in the largest sample size (N = 10,000) under all analytic conditions assessed. For sample sizes <5,000, sampling weights had little impact in simulations that more closely resembled a simple random sample (low weight variability) or in models with strong predictors, but findings were inconsistent under other analytic scenarios. Failing to account for sampling weights in gradient boosting models may limit generalizability for data from complex surveys, dependent on sample size and other analytic properties. In the absence of software for configuring weighted algorithms, post-hoc re-calculations of unweighted model performance using weighted observed outcomes may more accurately reflect model prediction in target populations than ignoring weights entirely.
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Affiliation(s)
- Nathaniel MacNell
- Social & Scientific Systems, a DLH Holdings Company, Durham, North Carolina, United States of America
| | - Lydia Feinstein
- Social & Scientific Systems, a DLH Holdings Company, Durham, North Carolina, United States of America
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Jesse Wilkerson
- Social & Scientific Systems, a DLH Holdings Company, Durham, North Carolina, United States of America
| | - Pӓivi M. Salo
- Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, North Carolina, United States of America
| | - Samantha A. Molsberry
- Social & Scientific Systems, a DLH Holdings Company, Durham, North Carolina, United States of America
| | - Michael B. Fessler
- Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, North Carolina, United States of America
| | - Peter S. Thorne
- Department of Occupational and Environmental Health, University of Iowa, College of Public Health, Iowa City, Iowa, United States of America
| | - Alison A. Motsinger-Reif
- Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, North Carolina, United States of America
| | - Darryl C. Zeldin
- Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, North Carolina, United States of America
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Umamaheswaran S., John R, Nagarajan S., Karthick Raghunath K. M., Arvind K. S.. Predictive Assessment of Fetus Features Using Scanned Image Segmentation Techniques and Deep Learning Strategy. INTERNATIONAL JOURNAL OF E-COLLABORATION 2022. [DOI: 10.4018/ijec.307130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Fetus weight at various stages of pregnancy is a critical component in determining the health of the baby. Abnormalities arising early in the pregnancy may be prevented by preventive measures. A variety of techniques suggested to predict foetus weight. Computer vision is a capability that can estimate the weight of a baby based on ultra-sonograms taken at various stages of pregnancy. Using the scanned data, one may train an advanced convolutional neural network that helps in accurately forecasting the fetus's size, weight, and overall health. The research utilizes computer vision techniques with image clustering methods for preprocessing, to predict the foetus's health, training datasets defective foetus datasets and healthy foetus datasets. Developing an integrated computer vision and a deep neural network is the hour which decrease the cost of operations and manual processes This study estimate the fetus's weight with optimal accuracy range at varying gestation age.
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Active Management of Labor Process under Smart Medical Model Improves Vaginal Delivery Outcomes of Pregnant Women with Preeclampsia. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:8926335. [PMID: 35432840 PMCID: PMC9010162 DOI: 10.1155/2022/8926335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 03/09/2022] [Accepted: 03/26/2022] [Indexed: 11/18/2022]
Abstract
Background In a global environment of increasing cesarean delivery rate, promoting vaginal delivery, reducing the rate of first cesarean section, and the incidence of vaginal delivery complications are the objectives of obstetric medical quality and safety in China. As a common obstetric complication, preeclampsia affects the safety of many pregnant women. It is the obstetrician's great responsibility to promote vaginal delivery and improve delivery outcomes in preeclampsia. To this end, we explored the roles of active labor management under the smart medical model in improving the outcomes of vaginal delivery for pregnant women with preeclampsia. Methods The clinical data of 219 cases of preeclampsia pregnant women who delivered vaginally in our hospital from January 2017 to December 2020 were retrospectively analyzed. According to different labor process management, they were divided into study group (active labor process management group) and control group (normal labor process management group). Active labor process management methods included intrapartum ultrasound, central fetal heart rate monitoring, Doula delivery, labor analgesia, and quality of life care. The differences in delivery process, delivery outcome, bleeding causes, and hemostatic measures were compared between the two groups. Results (1) The incidence of preeclampsia in our hospital showed an increasing trend in recent four years; (2) in smart hospitals, the active management of labor process reduced the probability of transferring to the cesarean section in preeclampsia pregnant women with vaginal trial failure; and (3) active labor process management reduced the rate of lateral episiotomy, decreased the postpartum hemorrhage volume within two hours, and improved the vaginal delivery outcome of preeclampsia pregnant women. Conclusions In the era of the rapid development of the Internet, vigorously promoting the construction of smart hospitals and actively managing the delivery process can reduce the failure rate of vaginal trial delivery and improve the outcomes of vaginal delivery in preeclampsia women.
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Vitamin D Deficiency, Excessive Gestational Weight Gain, and Oxidative Stress Predict Small for Gestational Age Newborns Using an Artificial Neural Network Model. Antioxidants (Basel) 2022; 11:antiox11030574. [PMID: 35326224 PMCID: PMC8944993 DOI: 10.3390/antiox11030574] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 03/10/2022] [Accepted: 03/14/2022] [Indexed: 02/04/2023] Open
Abstract
(1) Background: Size at birth is an important early determinant of health later in life. The prevalence of small for gestational age (SGA) newborns is high worldwide and may be associated with maternal nutritional and metabolic factors. Thus, estimation of fetal growth is warranted. (2) Methods: In this work, we developed an artificial neural network (ANN) model based on first-trimester maternal body fat composition, biochemical and oxidative stress biomarkers, and gestational weight gain (GWG) to predict an SGA newborn in pregnancies with or without obesity. A sensibility analysis to classify maternal features was conducted, and a simulator based on the ANN algorithm was constructed to predict the SGA outcome. Several predictions were performed by varying the most critical maternal features attained by the model to obtain different scenarios leading to SGA. (3) Results: The ANN model showed good performance between the actual and simulated data (R2 = 0.938) and an AUROC of 0.8 on an independent dataset. The top-five maternal predictors in the first trimester were protein and lipid oxidation biomarkers (carbonylated proteins and malondialdehyde), GWG, vitamin D, and total antioxidant capacity. Finally, excessive GWG and redox imbalance predicted SGA newborns in the implemented simulator. Significantly, vitamin D deficiency also predicted simulated SGA independently of GWG or redox status. (4) Conclusions: The study provided a computational model for the early prediction of SGA, in addition to a promising simulator that facilitates hypothesis-driven constructions, to be further validated as an application.
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10
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Cartus AR, Naimi AI, Himes KP, Jarlenski M, Parisi SM, Bodnar LM. Can Ensemble Machine Learning Improve the Accuracy of Severe Maternal Morbidity Screening in a Perinatal Database? Epidemiology 2022; 33:95-104. [PMID: 34711736 DOI: 10.1097/ede.0000000000001433] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Severe maternal morbidity (SMM) is an important maternal health indicator, but existing tools to identify SMM have substantial limitations. Our objective was to retrospectively identify true SMM status using ensemble machine learning in a hospital database and to compare machine learning algorithm performance with existing tools for SMM identification. METHODS We screened all deliveries occurring at Magee-Womens Hospital, Pittsburgh, PA (2010-2011 and 2013-2017) using the Centers for Disease Control and Prevention list of diagnoses and procedures for SMM, intensive care unit admission, and/or prolonged postpartum length of stay. We performed a detailed medical record review to confirm case status. We trained ensemble machine learning (SuperLearner) algorithms, which "stack" predictions from multiple algorithms to obtain optimal predictions, on 171 SMM cases and 506 non-cases from 2010 to 2011, then evaluated the performance of these algorithms on 160 SMM cases and 337 non-cases from 2013 to 2017. RESULTS Some SuperLearner algorithms performed better than existing screening criteria in terms of positive predictive value (0.77 vs. 0.64, respectively) and balanced accuracy (0.99 vs. 0.86, respectively). However, they did not perform as well as the screening criteria in terms of true-positive detection rate (0.008 vs. 0.32, respectively) and performed similarly in terms of negative predictive value. The most important predictor variables were intensive care unit admission and prolonged postpartum length of stay. CONCLUSIONS Ensemble machine learning did not globally improve the ascertainment of true SMM cases. Our results suggest that accurate identification of SMM likely will remain a challenge in the absence of a universal definition of SMM or national obstetric surveillance systems.
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Affiliation(s)
- Abigail R Cartus
- From the Department of Epidemiology, Brown University School of Public Health, Providence, RI
| | - Ashley I Naimi
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA
| | - Katherine P Himes
- Magee-Womens Research Institute, University of Pittsburgh, Pittsburgh, PA
- Department of Obstetrics, Gynecology, and Reproductive Services, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Marian Jarlenski
- Department of Health Policy and Management, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA
| | - Sara M Parisi
- Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA
| | - Lisa M Bodnar
- Magee-Womens Research Institute, University of Pittsburgh, Pittsburgh, PA
- Department of Obstetrics, Gynecology, and Reproductive Services, University of Pittsburgh School of Medicine, Pittsburgh, PA
- Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA
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11
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Artificial intelligence in obstetrics. Obstet Gynecol Sci 2021; 65:113-124. [PMID: 34905872 PMCID: PMC8942755 DOI: 10.5468/ogs.21234] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 11/02/2021] [Indexed: 11/10/2022] Open
Abstract
This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly.
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Visweswaran S, McLay B, Cappella N, Morris M, Milnes JT, Reis SE, Silverstein JC, Becich MJ. An atomic approach to the design and implementation of a research data warehouse. J Am Med Inform Assoc 2021; 29:601-608. [PMID: 34613409 PMCID: PMC8922189 DOI: 10.1093/jamia/ocab204] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/27/2021] [Accepted: 09/10/2021] [Indexed: 11/14/2022] Open
Abstract
Objective As a long-standing Clinical and Translational Science Awards (CTSA) Program hub, the University of Pittsburgh and the University of Pittsburgh Medical Center (UPMC) developed and implemented a modern research data warehouse (RDW) to efficiently provision electronic patient data for clinical and translational research. Materials and Methods We designed and implemented an RDW named Neptune to serve the specific needs of our CTSA. Neptune uses an atomic design where data are stored at a high level of granularity as represented in source systems. Neptune contains robust patient identity management tailored for research; integrates patient data from multiple sources, including electronic health records (EHRs), health plans, and research studies; and includes knowledge for mapping to standard terminologies. Results Neptune contains data for more than 5 million patients longitudinally organized as Health Insurance Portability and Accountability Act (HIPAA) Limited Data with dates and includes structured EHR data, clinical documents, health insurance claims, and research data. Neptune is used as a source for patient data for hundreds of institutional review board-approved research projects by local investigators and for national projects. Discussion The design of Neptune was heavily influenced by the large size of UPMC, the varied data sources, and the rich partnership between the University and the healthcare system. It includes several unique aspects, including the physical warehouse straddling the University and UPMC networks and management under an HIPAA Business Associates Agreement. Conclusion We describe the design and implementation of an RDW at a large academic healthcare system that uses a distinctive atomic design where data are stored at a high level of granularity.
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Affiliation(s)
- Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Brian McLay
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Nickie Cappella
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - John T Milnes
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Steven E Reis
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Jonathan C Silverstein
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Chief Research Information Officer, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michael J Becich
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Mooney SJ, Keil AP, Westreich DJ. Thirteen Questions About Using Machine Learning in Causal Research (You Won't Believe the Answer to Number 10!). Am J Epidemiol 2021; 190:1476-1482. [PMID: 33751024 PMCID: PMC8555423 DOI: 10.1093/aje/kwab047] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 10/16/2020] [Indexed: 11/12/2022] Open
Abstract
Machine learning is gaining prominence in the health sciences, where much of its use has focused on data-driven prediction. However, machine learning can also be embedded within causal analyses, potentially reducing biases arising from model misspecification. Using a question-and-answer format, we provide an introduction and orientation for epidemiologists interested in using machine learning but concerned about potential bias or loss of rigor due to use of "black box" models. We conclude with sample software code that may lower the barrier to entry to using these techniques.
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Affiliation(s)
- Stephen J Mooney
- Correspondence to Dr. Stephen J. Mooney, Department of Epidemiology, School of Public Health, University of Washington, 3980 15th Avenue NE, Seattle, WA 98195 (e-mail: )
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Arabi Belaghi R, Beyene J, McDonald SD. Prediction of preterm birth in nulliparous women using logistic regression and machine learning. PLoS One 2021; 16:e0252025. [PMID: 34191801 PMCID: PMC8244906 DOI: 10.1371/journal.pone.0252025] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 05/10/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE To predict preterm birth in nulliparous women using logistic regression and machine learning. DESIGN Population-based retrospective cohort. PARTICIPANTS Nulliparous women (N = 112,963) with a singleton gestation who gave birth between 20-42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014. METHODS We used data during the first and second trimesters to build logistic regression and machine learning models in a "training" sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent "validation" sample. RESULTS During the first trimester, logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23-5.42; Type II: AOR: 2.68; 95% CI: 2.05-3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80-2.30). During the first trimester, the maximum AUC was 60% (95% CI: 58-62%) with artificial neural networks in the validation sample. During the second trimester, 17 variables were significantly associated with preterm birth, among which complications during pregnancy had the highest AOR (13.03; 95% CI: 12.21-13.90). During the second trimester, the AUC increased to 65% (95% CI: 63-66%) with artificial neural networks in the validation sample. Including complications during the pregnancy yielded an AUC of 80% (95% CI: 79-81%) with artificial neural networks. All models yielded 94-97% negative predictive values for spontaneous PTB during the first and second trimesters. CONCLUSION Although artificial neural networks provided slightly higher AUC than logistic regression, prediction of preterm birth in the first trimester remained elusive. However, including data from the second trimester improved prediction to a moderate level by both logistic regression and machine learning approaches.
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Affiliation(s)
- Reza Arabi Belaghi
- Department of Obstetrics and Gynecology, McMaster University, Hamilton, Ontario, Canada
- Department of Statistics, University of Tabriz, Tabriz, Iran
| | - Joseph Beyene
- Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Ontario, Canada
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
| | - Sarah D. McDonald
- Department of Obstetrics and Gynecology, McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Ontario, Canada
- Department of Obstetrics and Gynecology (Division of Maternal-Fetal Medicine), McMaster University, Hamilton, Ontario, Canada
- Department of Radiology, McMaster University, Hamilton, Ontario, Canada
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15
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Validation of Questionnaire-based Case Definitions for Chronic Obstructive Pulmonary Disease. Epidemiology 2021; 31:459-466. [PMID: 32028323 DOI: 10.1097/ede.0000000000001176] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Various questionnaire-based definitions of chronic obstructive pulmonary disease (COPD) have been applied using the US representative National Health and Nutrition Examination Survey (NHANES), but few have been validated against objective lung function data. We validated two prior definitions that incorporated self-reported physician diagnosis, respiratory symptoms, and/or smoking. We also validated a new definition that we developed empirically using gradient boosting, an ensemble machine learning method. METHODS Data came from 7,996 individuals 40-79 years who participated in NHANES 2007-2012 and underwent spirometry. We considered participants "true" COPD cases if their ratio of postbronchodilator forced expiratory volume in 1 second to forced vital capacity was below 0.7 or the lower limit of normal. We stratified all analyses by smoking history. We developed a gradient boosting model for smokers only; predictors assessed (25 total) included sociodemographics, inhalant exposures, clinical variables, and respiratory symptoms. RESULTS The spirometry-based COPD prevalence was 26% for smokers and 8% for never smokers. Among smokers, using questionnaire-based definitions resulted in a COPD prevalence ranging from 11% to 16%, sensitivity ranging from 18% to 35%, and specificity ranging from 88% to 92%. The new definition classified participants based on age, bronchodilator use, body mass index (BMI), smoking pack-years, and occupational organic dust exposure, and resulted in the highest sensitivity (35%) and specificity (92%) among smokers. Among never smokers, the COPD prevalence ranged from 4% to 5%, and we attained good specificity (96%) at the expense of sensitivity (9-10%). CONCLUSION Our results can be used to parametrize misclassification assumptions for quantitative bias analysis when pulmonary function data are unavailable.
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16
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Oskar S, Stingone JA. Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps. Curr Environ Health Rep 2021; 7:170-184. [PMID: 32578067 DOI: 10.1007/s40572-020-00282-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE OF REVIEW The goal of this article is to review the use of machine learning (ML) within studies of environmental exposures and children's health, identify common themes across studies, and provide recommendations to advance their use in research and practice. RECENT FINDINGS We identified 42 articles reporting upon the use of ML within studies of environmental exposures and children's health between 2017 and 2019. The common themes among the articles were analysis of mixture data, exposure prediction, disease prediction and forecasting, analysis of complex data, and causal inference. With the increasing complexity of environmental health data, we anticipate greater use of ML to address the challenges that cannot be handled by traditional analytics. In order for these methods to beneficially impact public health, the ML techniques we use need to be appropriate for our study questions, rigorously evaluated and reported in a way that can be critically assessed by the scientific community.
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Affiliation(s)
- Sabine Oskar
- Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th St, Room 1608, New York, NY, 10032, USA
| | - Jeanette A Stingone
- Department of Epidemiology, Columbia University Mailman School of Public Health, 722 West 168th St, Room 1608, New York, NY, 10032, USA.
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17
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Davidson L, Boland MR. Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes. Brief Bioinform 2021; 22:6065792. [PMID: 33406530 PMCID: PMC8424395 DOI: 10.1093/bib/bbaa369] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 10/13/2020] [Accepted: 11/18/2020] [Indexed: 12/16/2022] Open
Abstract
Objective Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes. Materials and methods We searched English articles on EMBASE, PubMed and SCOPUS. Search terms included ML, AI, pregnancy and informatics. We included research articles and book chapters, excluding conference papers, editorials and notes. Results We identified 127 distinct studies from our queries that were relevant to our topic and included in the review. We found that supervised learning methods were more popular (n = 69) than unsupervised methods (n = 9). Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). Methods such as DL are beginning to gain traction (n = 13). Common areas within the pregnancy domain where AI and ML methods were used the most include prenatal care (e.g. fetal anomalies, placental functioning) (n = 73); perinatal care, birth and delivery (n = 20); and preterm birth (n = 13). Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9). Conclusions Overall, we found that ML and AI methods are being employed to optimize pregnancy outcomes, including modern DL methods (n = 13). Future research should focus on less-studied pregnancy domain areas, including postnatal and postpartum care (n = 2). Also, more work on clinical adoption of AI methods and the ethical implications of such adoption is needed.
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Affiliation(s)
- Lena Davidson
- MS degree at College of St. Scholastica, Duluth, MN, USA
| | - Mary Regina Boland
- Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania
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18
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Ananth CV, Brandt JS. Fetal growth and gestational age prediction by machine learning. LANCET DIGITAL HEALTH 2020; 2:e336-e337. [PMID: 33328092 DOI: 10.1016/s2589-7500(20)30143-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 05/27/2020] [Indexed: 10/24/2022]
Affiliation(s)
- Cande V Ananth
- Division of Epidemiology and Biostatistics, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, Cardiovascular Institute of New Jersey, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA; Environmental and Occupational Health Sciences Institute, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA; Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ, USA.
| | - Justin S Brandt
- Division of Maternal-Fetal Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
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19
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Bodnar LM, Cartus AR, Kirkpatrick SI, Himes KP, Kennedy EH, Simhan HN, Grobman WA, Duffy JY, Silver RM, Parry S, Naimi AI. Machine learning as a strategy to account for dietary synergy: an illustration based on dietary intake and adverse pregnancy outcomes. Am J Clin Nutr 2020; 111:1235-1243. [PMID: 32108865 PMCID: PMC7266693 DOI: 10.1093/ajcn/nqaa027] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 01/31/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Conventional analytic approaches for studying diet patterns assume no dietary synergy, which can lead to bias if incorrectly modeled. Machine learning algorithms can overcome these limitations. OBJECTIVES We estimated associations between fruit and vegetable intake relative to total energy intake and adverse pregnancy outcomes using targeted maximum likelihood estimation (TMLE) paired with the ensemble machine learning algorithm Super Learner, and compared these with results generated from multivariable logistic regression. METHODS We used data from 7572 women in the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be. Usual daily periconceptional intake of total fruits and total vegetables was estimated from an FFQ. We calculated the marginal risk of preterm birth, small-for-gestational-age (SGA) birth, gestational diabetes, and pre-eclampsia according to density of fruits and vegetables (cups/1000 kcal) ≥80th percentile compared with <80th percentile using multivariable logistic regression and Super Learner with TMLE. Models were adjusted for confounders, including other Healthy Eating Index-2010 components. RESULTS Using logistic regression, higher fruit and high vegetable densities were associated with 1.1% and 1.4% reductions in pre-eclampsia risk compared with lower densities, respectively. They were not associated with the 3 other outcomes. Using Super Learner with TMLE, high fruit and vegetable densities were associated with fewer cases of preterm birth (-4.0; 95% CI: -4.9, -3.0 and -3.7; 95% CI: -5.0, -2.3), SGA (-1.7; 95% CI: -2.9, -0.51 and -3.8; 95% CI: -5.0, -2.5), and pre-eclampsia (-3.2; 95% CI: -4.2, -2.2 and -4.0; 95% CI: -5.2, -2.7) per 100 births, respectively, and high vegetable densities were associated with a 0.9% increase in risk of gestational diabetes. CONCLUSIONS The differences in results between Super Learner with TMLE and logistic regression suggest that dietary synergy, which is accounted for in machine learning, may play a role in pregnancy outcomes. This innovative methodology for analyzing dietary data has the potential to advance the study of diet patterns.
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Affiliation(s)
- Lisa M Bodnar
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Obstetrics, Gynecology, and Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Magee-Womens Research Institute, Pittsburgh, PA, USA
| | - Abigail R Cartus
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sharon I Kirkpatrick
- School of Public Health and Health Systems, University of Waterloo, Waterloo, Ontario, Canada
| | - Katherine P Himes
- Department of Obstetrics, Gynecology, and Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Magee-Womens Research Institute, Pittsburgh, PA, USA
| | - Edward H Kennedy
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Hyagriv N Simhan
- Department of Obstetrics, Gynecology, and Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Magee-Womens Research Institute, Pittsburgh, PA, USA
| | - William A Grobman
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jennifer Y Duffy
- Department of Obstetrics & Gynecology, School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Robert M Silver
- Department of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT, USA
| | - Samuel Parry
- Department of Obstetrics and Gynecology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Ashley I Naimi
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
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20
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Bellavia A, Rotem RS, Dickerson AS, Hansen J, Gredal O, Weisskopf MG. The use of Logic regression in epidemiologic studies to investigate multiple binary exposures: an example of occupation history and amyotrophic lateral sclerosis. ACTA ACUST UNITED AC 2020; 9. [PMID: 33224709 DOI: 10.1515/em-2019-0032] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Investigating the joint exposure to several risk factors is becoming a key component of epidemiologic studies. Individuals are exposed to multiple factors, often simultaneously, and evaluating patterns of exposures and high-dimension interactions may allow for a better understanding of health risks at the individual level. When jointly evaluating high-dimensional exposures, common statistical methods should be integrated with machine learning techniques that may better account for complex settings. Among these, Logic regression was developed to investigate a large number of binary exposures as they relate to a given outcome. This method may be of interest in several public health settings, yet has never been presented to an epidemiologic audience. In this paper, we review and discuss Logic regression as a potential tool for epidemiological studies, using an example of occupation history (68 binary exposures of primary occupations) and amyotrophic lateral sclerosis in a population-based Danish cohort. Logic regression identifies predictors that are Boolean combinations of the original (binary) exposures, fully operating within the regression framework of interest (e.g. linear, logistic). Combinations of exposures are graphically presented as Logic trees, and techniques for selecting the best Logic model are available and of high importance. While highlighting several advantages of the method, we also discuss specific drawbacks and practical issues that should be considered when using Logic regression in population-based studies. With this paper, we encourage researchers to explore the use of machine learning techniques when evaluating large-dimensional epidemiologic data, as well as advocate the need of further methodological work in the area.
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Affiliation(s)
- Andrea Bellavia
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Ran S Rotem
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Aisha S Dickerson
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115
| | - Johnni Hansen
- Danish Cancer Society, Institute of Cancer Epidemiology, DK-2100 Copenhagen, Denmark
| | - Ole Gredal
- Danish Cancer Society, Institute of Cancer Epidemiology, DK-2100 Copenhagen, Denmark
| | - Marc G Weisskopf
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115
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21
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Garcia-Canadilla P, Sanchez-Martinez S, Crispi F, Bijnens B. Machine Learning in Fetal Cardiology: What to Expect. Fetal Diagn Ther 2020; 47:363-372. [PMID: 31910421 DOI: 10.1159/000505021] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 11/25/2019] [Indexed: 11/19/2022]
Abstract
In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this review we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities.
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Affiliation(s)
- Patricia Garcia-Canadilla
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain, .,Institute of Cardiovascular Science, University College London, London, United Kingdom,
| | | | - Fatima Crispi
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain.,Fetal Medicine Research Center, BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia Obstetricia i Neonatologia, Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Bart Bijnens
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain.,Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.,ICREA, Barcelona, Spain
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22
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Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning. Artif Intell Med 2019; 102:101748. [PMID: 31980089 DOI: 10.1016/j.artmed.2019.101748] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 10/06/2019] [Accepted: 10/27/2019] [Indexed: 10/25/2022]
Abstract
Obstetric ultrasound examination of physiological parameters has been mainly used to estimate the fetal weight during pregnancy and baby weight before labour to monitor fetal growth and reduce prenatal morbidity and mortality. However, the problem is that ultrasound estimation of fetal weight is subject to population's difference, strict operating requirements for sonographers, and poor access to ultrasound in low-resource areas. Inaccurate estimations may lead to negative perinatal outcomes. This study aims to predict fetal weight at varying gestational age in the absence of ultrasound examination within a certain accuracy. We consider that machine learning can provide an accurate estimation for obstetricians alongside traditional clinical practices, as well as an efficient and effective support tool for pregnant women for self-monitoring. We present a robust methodology using a data set comprising 4212 intrapartum recordings. The cubic spline function is used to fit the curves of several key characteristics that are extracted from ultrasound reports. A number of simple and powerful machine learning algorithms are trained, and their performance is evaluated with real test data. We also propose a novel evaluation performance index called the intersection-over-union (loU) for our study. The results are encouraging using an ensemble model consisting of Random Forest, XGBoost, and LightGBM algorithms. The experimental results show the loU between predicted range of fetal weight at any gestational age that is given by the ensemble model and ultrasound respectively. The machine learning based approach applied in our study is able to predict, with a high accuracy, fetal weight at varying gestational age in the absence of ultrasound examination.
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