1
|
Edvinsson C, Björnsson O, Erlandsson L, Hansson SR. Predicting intensive care need in women with preeclampsia using machine learning - a pilot study. Hypertens Pregnancy 2024; 43:2312165. [PMID: 38385188 DOI: 10.1080/10641955.2024.2312165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/02/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND Predicting severe preeclampsia with need for intensive care is challenging. To better predict high-risk pregnancies to prevent adverse outcomes such as eclampsia is still an unmet need worldwide. In this study we aimed to develop a prediction model for severe outcomes using routine biomarkers and clinical characteristics. METHODS We used machine learning models based on data from an intensive care cohort with severe preeclampsia (n=41) and a cohort of preeclampsia controls (n=40) with the objective to find patterns for severe disease not detectable with traditional logistic regression models. RESULTS The best model was generated by including the laboratory parameters aspartate aminotransferase (ASAT), uric acid and body mass index (BMI) with a cross-validation accuracy of 0.88 and an area under the curve (AUC) of 0.91. Our model was internally validated on a test-set where the accuracy was lower, 0.82, with an AUC of 0.85. CONCLUSION The clinical routine blood parameters ASAT and uric acid as well as BMI, were the parameters most indicative of severe disease. Aspartate aminotransferase reflects liver involvement, uric acid might be involved in several steps of the pathophysiologic process of preeclampsia, and obesity is a well-known risk factor for development of both severe and non-severe preeclampsia likely involving inflammatory pathways..[Figure: see text].
Collapse
Affiliation(s)
- Camilla Edvinsson
- Division of Obstetrics and Gynecology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Division of Anaesthesia and Intensive Care, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Anaesthesia and Intensive Care, Helsingborg Hospital, Helsingborg, Sweden
| | - Ola Björnsson
- Division of Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Lund, Sweden
- Department of Energy Sciences, Faculty of Engineering, Lund University, Lund, Sweden
| | - Lena Erlandsson
- Division of Obstetrics and Gynecology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Stefan R Hansson
- Division of Obstetrics and Gynecology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Obstetrics and Gynecology, Skåne University Hospital, Lund/Malmö, Sweden
| |
Collapse
|
2
|
Rahman J, Brankovic A, Tracy M, Khanna S. Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review. Interact J Med Res 2024; 13:e46946. [PMID: 39163610 DOI: 10.2196/46946] [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: 03/02/2023] [Revised: 03/27/2024] [Accepted: 06/26/2024] [Indexed: 08/22/2024] Open
Abstract
BACKGROUND Computational signal preprocessing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility to drive clinical adoption. It further fosters reproducible, ethical, and reliable conduct of studies. This procedure is also crucial for setting up a software quality management system to ensure regulatory compliance in developing software as a medical device aimed at early preclinical detection of clinical deterioration. OBJECTIVE This scoping review focuses on the neonatal intensive care unit setting and summarizes the state-of-the-art computational methods used for preprocessing neonatal clinical physiological signals; these signals are used for the development of machine learning models to predict the risk of adverse outcomes. METHODS Five databases (PubMed, Web of Science, Scopus, IEEE, and ACM Digital Library) were searched using a combination of keywords and MeSH (Medical Subject Headings) terms. A total of 3585 papers from 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2994 (83.51%) papers were screened by title and abstract, and 81 (0.03%) were selected for full-text review. Of these, 52 (64%) were eligible for inclusion in the detailed analysis. RESULTS Of the 52 articles reviewed, 24 (46%) studies focused on diagnostic models, while the remainder (n=28, 54%) focused on prognostic models. The analysis conducted in these studies involved various physiological signals, with electrocardiograms being the most prevalent. Different programming languages were used, with MATLAB and Python being notable. The monitoring and capturing of physiological data used diverse systems, impacting data quality and introducing study heterogeneity. Outcomes of interest included sepsis, apnea, bradycardia, mortality, necrotizing enterocolitis, and hypoxic-ischemic encephalopathy, with some studies analyzing combinations of adverse outcomes. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal preprocessing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. Only 7 (13%) of the 52 reviewed studies reported all the recommended preprocessing steps, which could have impacts on the downstream analysis. CONCLUSIONS The review found heterogeneity in the techniques used and inconsistent reporting of parameters and procedures used for preprocessing neonatal physiological signals, which is necessary to confirm adherence to clinical and software quality management system practices, usefulness, and choice of best practices. Enhancing transparency in reporting and standardizing procedures will boost study interpretation and reproducibility and expedite clinical adoption, instilling confidence in the research findings and streamlining the translation of research outcomes into clinical practice, ultimately contributing to the advancement of neonatal care and patient outcomes.
Collapse
Affiliation(s)
- Jessica Rahman
- Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australian e-Health Research Centre, Australia, Sydney, Australia
| | - Aida Brankovic
- Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australian e-Health Research Centre, Australia, Brisbane, Australia
| | - Mark Tracy
- Neonatal Intensive Care Unit, Westmead, Sydney, Australia
| | - Sankalp Khanna
- Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australian e-Health Research Centre, Australia, Brisbane, Australia
| |
Collapse
|
3
|
Kwok WH, Zhang Y, Wang G. Artificial intelligence in perinatal mental health research: A scoping review. Comput Biol Med 2024; 177:108685. [PMID: 38838557 DOI: 10.1016/j.compbiomed.2024.108685] [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: 07/19/2023] [Revised: 04/28/2024] [Accepted: 06/01/2024] [Indexed: 06/07/2024]
Abstract
The intersection of Artificial Intelligence (AI) and perinatal mental health research presents promising avenues, yet uncovers significant challenges for innovation. This review explicitly focuses on this multidisciplinary field and undertakes a comprehensive exploration of existing research therein. Through a scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we searched relevant literature spanning a decade (2013-2023) and selected fourteen studies for our analysis. We first provide an overview of the main AI techniques and their development, including traditional methods across different categories, as well as recent emerging methods in the field. Then, through our analysis of the literature, we summarize the predominant AI and ML techniques adopted and their applications in perinatal mental health studies, such as identifying risk factors, predicting perinatal mental health disorders, voice assistants, and Q&A chatbots. We also discuss existing limitations and potential challenges that hinder AI technologies from improving perinatal mental health outcomes, and suggest several promising directions for future research to meet real needs in the field and facilitate the translation of research into clinical settings.
Collapse
Affiliation(s)
- Wai Hang Kwok
- School of Nursing and Midwifery, Edith Cowan University, WA, Australia
| | - Yuanpeng Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, China
| | - Guanjin Wang
- School of Information Technology, Murdoch University, Murdoch, WA, Australia.
| |
Collapse
|
4
|
Yaseen I, Rather RA. A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery. Int J Womens Health 2024; 16:903-915. [PMID: 38800118 PMCID: PMC11128252 DOI: 10.2147/ijwh.s454127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in feto-maternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.
Collapse
Affiliation(s)
- Ishfaq Yaseen
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Riyaz Ahmad Rather
- Department of Biotechnology, College of Natural and Computational Science, Wachemo University, Hossana, Ethiopia
| |
Collapse
|
5
|
Calhoun BC, Uselman H, Olle EW. Development of Artificial Intelligence Image Classification Models for Determination of Umbilical Cord Vascular Anomalies. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:881-897. [PMID: 38279605 DOI: 10.1002/jum.16418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/05/2024] [Accepted: 01/07/2024] [Indexed: 01/28/2024]
Abstract
OBJECTIVE The goal of this work was to develop robust techniques for the processing and identification of SUA using artificial intelligence (AI) image classification models. METHODS Ultrasound images obtained retrospectively were analyzed for blinding, text removal, AI training, and image prediction. After developing and testing text removal methods, a small n-size study (40 images) using fastai/PyTorch to classify umbilical cord images. This data set was expanded to 286 lateral-CFI images that were used to compare: different neural network performance, diagnostic value, and model predictions. RESULTS AI-Optical Character Recognition method was superior in its ability to remove text from images. The small n-size mixed single umbilical artery determination data set was tested with a pretrained ResNet34 neural network and obtained and error rate average of 0.083 (n = 3). The expanded data set was then tested with several AI models. The majority of the tested networks were able to obtain an average error rate of <0.15 with minimal modifications. The ResNet34-default performed the best with: an image-classification error rate of 0.0175, sensitivity of 1.00, specificity of 0.97, and ability to correctly infer classification. CONCLUSION This work provides a robust framework for ultrasound image AI classifications. AI could successfully classify umbilical cord types of ultrasound image study with excellent diagnostic value. Together this study provides a reproducible framework to develop AI-specific ultrasound classification of umbilical cord or other diagnoses to be used in conjunction with physicians for optimal patient care.
Collapse
Affiliation(s)
- Byron C Calhoun
- Department of Obstetrics and Gynecology, WVU School of Medicine, Charleston Division, Charleston, West Virginia, USA
- Maternal-Fetal Medicine, WVU School of Medicine, Charleston Division, Charleston, West Virginia, USA
| | - Heather Uselman
- Resident Department of Obstetrics and Gynecology, Charleston Area Medical Center, Charleston, West Virginia, USA
| | - Eric W Olle
- Research and Development, SynXBio Inc., Charleston, West Virginia, USA
| |
Collapse
|
6
|
Bülez A, Hansu K, Çağan ES, Şahin AR, Dokumacı HÖ. Artificial Intelligence in Early Diagnosis of Preeclampsia. Niger J Clin Pract 2024; 27:383-388. [PMID: 38528360 DOI: 10.4103/njcp.njcp_222_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 02/08/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND Every day, 810 women die of preventable causes related to pregnancy and childbirth worldwide, and preeclampsia is among the top three causes of maternal deaths. AIM To develop a diagnostic system with artificial intelligence for the early diagnosis of preeclampsia. METHODS This retrospective study included pregnant women who were screened for the inclusion criteria on the hospital's database, and the sample consisted of the data of 1158 pregnant women diagnosed with preeclampsia and 9194 pregnant women who were not diagnosed with preeclampsia at Kahramanmaras Necip Fazıl City Hospital Gynecology and Pediatrics Additional Service Building, Kahramanmaras/Turkey. The statistical analysis was performed using the Statistical Package for social sciences (SPSS) version 22 for windows. Artificial intelligence models were created using Python, scikit-learn, and TensorFlow. RESULTS The model achieved 73.7% sensitivity (95% confidence interval (CI): 70.2%-77.1%) and 92.7% specificity (95% CI: 91.7%-93.6%) on the test set. Furthermore, the model had 90.6% accuracy (95% CI: 90.1% - 91.1%) and an area under the curve (AUC) value of 0.832 (95% CI: 0.818-0.846). The significant parameters in predicting preeclampsia in the model were hemoglobin (HGB), age, aspartate transaminase level (AST), alanine transferase level (ALT), and the blood group. CONCLUSION Artificial intelligence is effective in the prediction and diagnosis of preeclampsia.
Collapse
Affiliation(s)
- A Bülez
- Department of Midwifery, Kahramanmaras Sutcu Imam University, Turkey
| | - K Hansu
- Department of Gynecology and Obstetrics, Kahramanmaras Sutcu Imam University, Turkey
| | - E S Çağan
- Department of Midwifery, Agri Ibrahim Cecen University, Turkey
| | - A R Şahin
- Department of Infectious Diseases and Clinic Microbiology, University of Health Sciences, Adana City Health Research Center, Turkey
| | - H Ö Dokumacı
- Department of Electrical and Electronic Engineering, Kahramanmaras Sutcu Imam University, Turkey
| |
Collapse
|
7
|
Togunwa TO, Babatunde AO, Abdullah KUR. Deep hybrid model for maternal health risk classification in pregnancy: synergy of ANN and random forest. Front Artif Intell 2023; 6:1213436. [PMID: 37476504 PMCID: PMC10354509 DOI: 10.3389/frai.2023.1213436] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 06/16/2023] [Indexed: 07/22/2023] Open
Abstract
Introduction Maternal health is a critical aspect of public health that affects the wellbeing of both mothers and infants. Despite medical advancements, maternal mortality rates remain high, particularly in developing countries. AI-based models provide new ways to analyze and interpret medical data, which can ultimately improve maternal and fetal health outcomes. Methods This study proposes a deep hybrid model for maternal health risk classification in pregnancy, which utilizes the strengths of artificial neural networks (ANN) and random forest (RF) algorithms. The proposed model combines the two algorithms to improve the accuracy and efficiency of risk classification in pregnant women. The dataset used in this study consists of features such as age, systolic and diastolic blood pressure, blood sugar, body temperature, and heart rate. The dataset is divided into training and testing sets, with 75% of the data used for training and 25% used for testing. The output of the ANN and RF classifier is considered, and a maximum probability voting system selects the output with the highest probability as the most correct. Results Performance is evaluated using various metrics, such as accuracy, precision, recall, and F1 score. Results showed that the proposed model achieves 95% accuracy, 97% precision, 97% recall, and an F1 score of 0.97 on the testing dataset. Discussion The deep hybrid model proposed in this study has the potential to improve the accuracy and efficiency of maternal health risk classification in pregnancy, leading to better health outcomes for pregnant women and their babies. Future research could explore the generalizability of this model to other populations, incorporate unstructured medical data, and evaluate its feasibility for clinical use.
Collapse
Affiliation(s)
- Taofeeq Oluwatosin Togunwa
- Department of Medicine and Surgery, Faculty of Clinical Sciences, College of Medicine, University of Ibadan, Ibadan, Oyo, Nigeria
- College Research and Innovation Hub, University College Hospital, Ibadan, Oyo, Nigeria
| | - Abdulhammed Opeyemi Babatunde
- Department of Medicine and Surgery, Faculty of Clinical Sciences, College of Medicine, University of Ibadan, Ibadan, Oyo, Nigeria
- College Research and Innovation Hub, University College Hospital, Ibadan, Oyo, Nigeria
- MyBelle Digital Maternal and Child Health Organisation, Ibadan, Nigeria
- Public Health Interest Group Africa (PHIGA), Lagos, Nigeria
| | - Khalil-ur-Rahman Abdullah
- Faculty of Clinical Sciences, College of Health Sciences, University of Ilorin, Ilorin, Nigeria
- MCON Institute of Medical Research, Ilorin, Nigeria
| |
Collapse
|
8
|
Basu M, Howdeshell KL, Rasmussen SA, Rychlik KA, Knudsen TB, Shuey DL, Slikker W. Society for birth defects research and prevention's multidisciplinary research needs workshop 2022: A call to action. Birth Defects Res 2023; 115:959-966. [PMID: 37218073 PMCID: PMC10641708 DOI: 10.1002/bdr2.2186] [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: 04/15/2023] [Accepted: 04/21/2023] [Indexed: 05/24/2023]
Abstract
The Society for Birth Defects Research and Prevention (BDRP) strives to understand and protect against potential hazards to developing embryos, fetuses, children, and adults by bringing together scientific knowledge from diverse fields. The theme of 62nd Annual Meeting of BDRP, "From Bench to Bedside and Back Again", represented the cutting-edge research areas of high relevance to public health and significance in the fields of birth defects research and surveillance. The multidisciplinary Research Needs Workshop (RNW) convened at the Annual Meeting continues to identify pressing knowledge gaps and encourage interdisciplinary research initiatives. The multidisciplinary RNW was first introduced at the 2018 annual meeting to provide an opportunity for annual meeting attendees to participate in breakout discussions on emerging topics in birth defects research and to foster collaboration between basic researchers, clinicians, epidemiologists, drug developers, industry partners, funding agencies, and regulators to discuss state-of-the-art methods and innovative projects. Initially, a list of workshop topics was compiled by the RNW planning committee and circulated among the members of BDRP to obtain the most popular topics for the Workshop discussions. Based on the pre-meeting survey results, the top three discussion topics selected were, A) Inclusion of pregnant and lactating women in clinical trials. When, why, and how? B) Building multidisciplinary teams across disciplines: What cross-training is needed? And C) Challenges in applications of Artificial Intelligence (AI) and machine learning for risk factor analysis in birth defects research. This report summarizes the key highlights of the RNW workshop and specific topic discussions.
Collapse
Affiliation(s)
- Madhumita Basu
- Center for Cardiovascular Research and Heart Center, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, Ohio, United States of America
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, United States of America
- MelliCell Inc. Newton, Massachusetts, United States of America
| | - Kembra L. Howdeshell
- Division of Translational Toxicology, National Institute of Environmental Health Sciences (NIEHS), North Carolina, United States of America
| | - Sonja A. Rasmussen
- Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Kristal A. Rychlik
- Public Health Program, School of Exercise and Sport Science, University of Mary Hardin-Baylor, Belton, Texas, United States of America
| | - Thomas B. Knudsen
- US Environmental Protection Agency, Center for Computational Toxicology and Exposure, Research Triangle Park, North Carolina, United States of America
| | - Dana L. Shuey
- Incyte Corporation, Wilmington, Delaware, United States of America
| | - William Slikker
- Retired, Formerly of the Office of the Director, National Center for Toxicological Research, US Food and Drug Administration (FDA), Jefferson, Arkansas, United States of America
| |
Collapse
|
9
|
Mennickent D, Rodríguez A, Opazo MC, Riedel CA, Castro E, Eriz-Salinas A, Appel-Rubio J, Aguayo C, Damiano AE, Guzmán-Gutiérrez E, Araya J. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Front Endocrinol (Lausanne) 2023; 14:1130139. [PMID: 37274341 PMCID: PMC10235786 DOI: 10.3389/fendo.2023.1130139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/04/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology. Aim To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications. Methodology Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations. Current state ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used. Future challenges To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models. Conclusion The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.
Collapse
Affiliation(s)
- Daniela Mennickent
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| | - Andrés Rodríguez
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
- Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad del Bío-Bío, Chillán, Chile
| | - Ma. Cecilia Opazo
- Instituto de Ciencias Naturales, Facultad de Medicina Veterinaria y Agronomía, Universidad de Las Américas, Santiago, Chile
- Millennium Institute on Immunology and Immunotherapy, Santiago, Chile
| | - Claudia A. Riedel
- Millennium Institute on Immunology and Immunotherapy, Santiago, Chile
- Departamento de Ciencias Biológicas, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago, Chile
| | - Erica Castro
- Departamento de Obstetricia y Puericultura, Facultad de Ciencias de la Salud, Universidad de Atacama, Copiapó, Chile
| | - Alma Eriz-Salinas
- Departamento de Obstetricia y Puericultura, Facultad de Medicina, Universidad de Concepción, Concepción, Chile
| | - Javiera Appel-Rubio
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
| | - Claudio Aguayo
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
| | - Alicia E. Damiano
- Cátedra de Biología Celular y Molecular, Departamento de Ciencias Biológicas, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina
- Laboratorio de Biología de la Reproducción, Instituto de Fisiología y Biofísica Bernardo Houssay (IFIBIO-Houssay)- CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Enrique Guzmán-Gutiérrez
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| | - Juan Araya
- Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| |
Collapse
|
10
|
Darsareh F, Ranjbar A, Farashah MV, Mehrnoush V, Shekari M, Jahromi MS. Application of machine learning to identify risk factors of birth asphyxia. BMC Pregnancy Childbirth 2023; 23:156. [PMID: 36890453 PMCID: PMC9993370 DOI: 10.1186/s12884-023-05486-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 03/02/2023] [Indexed: 03/10/2023] Open
Abstract
BACKGROUND Developing a prediction model that incorporates several risk factors and accurately calculates the overall risk of birth asphyxia is necessary. The present study used a machine learning model to predict birth asphyxia. METHODS Women who gave birth at a tertiary Hospital in Bandar Abbas, Iran, were retrospectively evaluated from January 2020 to January 2022. Data were extracted from the Iranian Maternal and Neonatal Network, a valid national system, by trained recorders using electronic medical records. Demographic factors, obstetric factors, and prenatal factors were obtained from patient records. Machine learning was used to identify the risk factors of birth asphyxia. Eight machine learning models were used in the study. To evaluate the diagnostic performance of each model, six metrics, including area under the receiver operating characteristic curve, accuracy, precision, sensitivity, specificity, and F1 score were measured in the test set. RESULTS Of 8888 deliveries, we identified 380 women with a recorded birth asphyxia, giving a frequency of 4.3%. Random Forest Classification was found to be the best model to predict birth asphyxia with an accuracy of 0.99. The analysis of the importance of the variables showed that maternal chronic hypertension, maternal anemia, diabetes, drug addiction, gestational age, newborn weight, newborn sex, preeclampsia, placenta abruption, parity, intrauterine growth retardation, meconium amniotic fluid, mal-presentation, and delivery method were considered to be the weighted factors. CONCLUSION Birth asphyxia can be predicted using a machine learning model. Random Forest Classification was found to be an accurate algorithm to predict birth asphyxia. More research should be done to analyze appropriate variables and prepare big data to determine the best model.
Collapse
Affiliation(s)
- Fatemeh Darsareh
- Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Amene Ranjbar
- Fertility and Infertility Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | | | - Vahid Mehrnoush
- Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
| | - Mitra Shekari
- Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | | |
Collapse
|
11
|
Pammi M, Aghaeepour N, Neu J. Multiomics, artificial intelligence, and precision medicine in perinatology. Pediatr Res 2023; 93:308-315. [PMID: 35804156 PMCID: PMC9825681 DOI: 10.1038/s41390-022-02181-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/12/2022] [Accepted: 05/30/2022] [Indexed: 01/11/2023]
Abstract
Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.
Collapse
Affiliation(s)
- Mohan Pammi
- Section of Neonatology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, TX, USA.
| | - Nima Aghaeepour
- Departments of Anesthesiology, Pediatrics, and Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Josef Neu
- Section of Neonatology, Department of Pediatrics, University of Florida, Gainesville, FL, USA
| |
Collapse
|
12
|
Silva Rocha ED, de Morais Melo FL, de Mello MEF, Figueiroa B, Sampaio V, Endo PT. On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature. BMC Med Inform Decis Mak 2022; 22:334. [PMID: 36536413 PMCID: PMC9764498 DOI: 10.1186/s12911-022-02082-3] [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] [Received: 08/13/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Care during pregnancy, childbirth and puerperium are fundamental to avoid pathologies for the mother and her baby. However, health issues can occur during this period, causing misfortunes, such as the death of the fetus or neonate. Predictive models of fetal and infant deaths are important technological tools that can help to reduce mortality indexes. The main goal of this work is to present a systematic review of literature focused on computational models to predict mortality, covering stillbirth, perinatal, neonatal, and infant deaths, highlighting their methodology and the description of the proposed computational models. METHODS We conducted a systematic review of literature, limiting the search to the last 10 years of publications considering the five main scientific databases as source. RESULTS From 671 works, 18 of them were selected as primary studies for further analysis. We found that most of works are focused on prediction of neonatal deaths, using machine learning models (more specifically Random Forest). The top five most common features used to train models are birth weight, gestational age, sex of the child, Apgar score and mother's age. Having predictive models for preventing mortality during and post-pregnancy not only improve the mother's quality of life, as well as it can be a powerful and low-cost tool to decrease mortality ratios. CONCLUSION Based on the results of this SRL, we can state that scientific efforts have been done in this area, but there are many open research opportunities to be developed by the community.
Collapse
Affiliation(s)
- Elisson da Silva Rocha
- grid.26141.300000 0000 9011 5442Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil
| | - Flavio Leandro de Morais Melo
- grid.26141.300000 0000 9011 5442Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil
| | | | - Barbara Figueiroa
- Programa Mãe Coruja Pernambucana, Secretaria de Saúde do Estado de Pernambuco, Recife, Brazil
| | | | - Patricia Takako Endo
- grid.26141.300000 0000 9011 5442Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil
| |
Collapse
|
13
|
Zhu J, Jin J, Tang J. Inflammatory pathophysiological mechanisms implicated in postpartum depression. Front Pharmacol 2022; 13:955672. [PMID: 36408212 PMCID: PMC9669749 DOI: 10.3389/fphar.2022.955672] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 10/24/2022] [Indexed: 09/10/2023] Open
Abstract
Postpartum Depression (PPD) is a serious psychiatric disorder of women within the first year after delivery. It grievously damages women's physical and mental health. Inflammatory reaction theory is well-established in depression, and also has been reported associated with PPD. This review summarized the inflammatory pathophysiological mechanisms implicated in PPD, including decreased T cell activation, increased proinflammatory cytokines secretion, active kynurenine pathway, and initiated NLRP3 inflammasome. Clinical and preclinical research are both gathered. Potential therapeutical alternatives targeting the inflammatory mechanisms of PPD were introduced. In addition, this review briefly discussed the differences of inflammatory mechanisms between PPD and depression. The research of inflammation in PPD is limited and seems just embarking, which indicates the direction we can further study. As a variety of risky factors contribute to PPD collectively, therapy for women with PPD should be comprehensive, and clinical heterogeneity should be taken into consideration. As PPD has a predictability, early clinical screening and interventions are also needed. This review aims to help readers better understand the inflammatory pathological mechanisms in PPD, so as to identify biomarkers and potential therapeutic targets in the future.
Collapse
Affiliation(s)
| | | | - Jing Tang
- Department of Pharmacy, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| |
Collapse
|
14
|
Gulzar Ahmad S, Iqbal T, Javaid A, Ullah Munir E, Kirn N, Ullah Jan S, Ramzan N. Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review. SENSORS 2022; 22:s22124362. [PMID: 35746144 PMCID: PMC9228894 DOI: 10.3390/s22124362] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/20/2022] [Accepted: 05/23/2022] [Indexed: 02/01/2023]
Abstract
Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential for maternal and infant health, since maternal and infant health is vital for a healthy society. Over the last few years, researchers have delved into sensing and artificially intelligent healthcare systems for maternal and infant health. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. Since these healthcare systems deal with large amounts of data, significant development is also noted in the computing platforms. The relevant literature reports the potential impact of ICT-enabled systems for improving maternal and infant health. This article reviews wearable sensors and AI algorithms based on existing systems designed to predict the risk factors during and after pregnancy for both mothers and infants. This review covers sensors and AI algorithms used in these systems and analyzes each approach with its features, outcomes, and novel aspects in chronological order. It also includes discussion on datasets used and extends challenges as well as future work directions for researchers.
Collapse
Affiliation(s)
- Saima Gulzar Ahmad
- Department of Computer Science, Wah Campus, COMSATS University Islamabad, Islamabad 45040, Pakistan; (S.G.A.); (T.I.); (A.J.)
| | - Tassawar Iqbal
- Department of Computer Science, Wah Campus, COMSATS University Islamabad, Islamabad 45040, Pakistan; (S.G.A.); (T.I.); (A.J.)
| | - Anam Javaid
- Department of Computer Science, Wah Campus, COMSATS University Islamabad, Islamabad 45040, Pakistan; (S.G.A.); (T.I.); (A.J.)
| | - Ehsan Ullah Munir
- Department of Computer Science, Wah Campus, COMSATS University Islamabad, Islamabad 45040, Pakistan; (S.G.A.); (T.I.); (A.J.)
- Correspondence:
| | - Nasira Kirn
- School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Glasgow G72 0LH, UK;
| | - Sana Ullah Jan
- School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK; (S.U.J.); (N.R.)
| | - Naeem Ramzan
- School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK; (S.U.J.); (N.R.)
| |
Collapse
|
15
|
Malaba TR, Newell ML, Myer L, Ramokolo V. Methodological Considerations for Preterm Birth Research. Front Glob Womens Health 2022; 2:821064. [PMID: 35088058 PMCID: PMC8787258 DOI: 10.3389/fgwh.2021.821064] [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: 11/23/2021] [Accepted: 12/14/2021] [Indexed: 11/28/2022] Open
Abstract
Complications from preterm birth are a leading cause of infant mortality, with long-term implications for morbidity and quality of life of preterm infants. There are many important risk factors for preterm births however in this article, we focus on the maternal infection etiological pathway, given its significance in low-to-middle income countries. In high preterm birth settings such as sub-Saharan Africa, maternal HIV infection and antiretroviral therapy (ART) use have been associated with an increased risk of preterm births. Consequently, we highlight methodological considerations related to selection and measurement bias in preterm birth research. We further illustrate the potential impact of these biases in studies investigating the relationship between HIV/ART and preterm births. We also briefly discuss issues related to population-level estimations based on routinely collected clinical or civil registration data. We conclude by emphasizing the importance of strengthening of antenatal care services to improve quality of population data as well as optimizing current and future study designs, by taking into account the important methodological considerations described in this article.
Collapse
Affiliation(s)
- Thokozile R Malaba
- Division of Epidemiology and Biostatistics, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa
| | - Marie-Louise Newell
- School of Human Development and Health, University of Southampton, Southampton, United Kingdom.,School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Landon Myer
- Division of Epidemiology and Biostatistics, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa.,Centre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa
| | - Vundli Ramokolo
- HIV Prevention Research Unit, South African Medical Research Council, Cape Town, South Africa.,Gertrude H. Sergievsky Center, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, United States
| |
Collapse
|