1
|
Francis F, Luz S, Wu H, Stock SJ, Townsend R. Machine learning on cardiotocography data to classify fetal outcomes: A scoping review. Comput Biol Med 2024; 172:108220. [PMID: 38489990 DOI: 10.1016/j.compbiomed.2024.108220] [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: 06/09/2023] [Revised: 02/02/2024] [Accepted: 02/25/2024] [Indexed: 03/17/2024]
Abstract
INTRODUCTION Uterine contractions during labour constrict maternal blood flow and oxygen delivery to the developing baby, causing transient hypoxia. While most babies are physiologically adapted to withstand such intrapartum hypoxia, those exposed to severe hypoxia or with poor physiological reserves may experience neurological injury or death during labour. Cardiotocography (CTG) monitoring was developed to identify babies at risk of hypoxia by detecting changes in fetal heart rate (FHR) patterns. CTG monitoring is in widespread use in intrapartum care for the detection of fetal hypoxia, but the clinical utility is limited by a relatively poor positive predictive value (PPV) of an abnormal CTG and significant inter and intra observer variability in CTG interpretation. Clinical risk and human factors may impact the quality of CTG interpretation. Misclassification of CTG traces may lead to both under-treatment (with the risk of fetal injury or death) or over-treatment (which may include unnecessary operative interventions that put both mother and baby at risk of complications). Machine learning (ML) has been applied to this problem since early 2000 and has shown potential to predict fetal hypoxia more accurately than visual interpretation of CTG alone. To consider how these tools might be translated for clinical practice, we conducted a review of ML techniques already applied to CTG classification and identified research gaps requiring investigation in order to progress towards clinical implementation. MATERIALS AND METHOD We used identified keywords to search databases for relevant publications on PubMed, EMBASE and IEEE Xplore. We used Preferred Reporting Items for Systematic Review and Meta-Analysis for Scoping Reviews (PRISMA-ScR). Title, abstract and full text were screened according to the inclusion criteria. RESULTS We included 36 studies that used signal processing and ML techniques to classify CTG. Most studies used an open-access CTG database and predominantly used fetal metabolic acidosis as the benchmark for hypoxia with varying pH levels. Various methods were used to process and extract CTG signals and several ML algorithms were used to classify CTG. We identified significant concerns over the practicality of using varying pH levels as the CTG classification benchmark. Furthermore, studies needed to be more generalised as most used the same database with a low number of subjects for an ML study. CONCLUSION ML studies demonstrate potential in predicting fetal hypoxia from CTG. However, more diverse datasets, standardisation of hypoxia benchmarks and enhancement of algorithms and features are needed for future clinical implementation.
Collapse
Affiliation(s)
| | | | - Honghan Wu
- Institute of Health Informatics, University College London, UK
| | | | | |
Collapse
|
2
|
Kim HY, Cho GJ, Kwon HS. Applications of artificial intelligence in obstetrics. Ultrasonography 2023; 42:2-9. [PMID: 36588179 PMCID: PMC9816710 DOI: 10.14366/usg.22063] [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: 04/15/2022] [Accepted: 06/20/2022] [Indexed: 01/13/2023] Open
Abstract
Artificial intelligence, which has been applied as an innovative technology in multiple fields of healthcare, analyzes large amounts of data to assist in disease prediction, prevention, and diagnosis, as well as in patient monitoring. In obstetrics, artificial intelligence has been actively applied and integrated into our daily medical practice. This review provides an overview of artificial intelligence systems currently used for obstetric diagnostic purposes, such as fetal cardiotocography, ultrasonography, and magnetic resonance imaging, and demonstrates how these methods have been developed and clinically applied.
Collapse
Affiliation(s)
- Ho Yeon Kim
- Department of Obstetrics and Gynecology, Korea University College of Medicine, Seoul, Korea
| | - Geum Joon Cho
- Department of Obstetrics and Gynecology, Korea University College of Medicine, Seoul, Korea,Correspondence to: Geum Joon Cho, MD, PhD, Department of Obstetrics and Gynecology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul 08308, Korea Tel. +82-2-2626-3141 Fax. +82-2-838-1560 E-mail:
| | - Han Sung Kwon
- Division of Maternal and Fetal Medicine, Department of Obstetrics and Gynecology, Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea
| |
Collapse
|
3
|
Wang PH, Huo TI. Winners of the 2021 honor awards for excellence at the annual meeting of the Chinese Medical Association-Taipei : Part I. J Chin Med Assoc 2022; 85:963-964. [PMID: 35947026 DOI: 10.1097/jcma.0000000000000789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Affiliation(s)
- Peng-Hui Wang
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Female Cancer Foundation, Taipei, Taiwan, ROC
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan, ROC
| | - Teh-Ia Huo
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, ROC
- Institute of Pharmacology, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| |
Collapse
|
4
|
Wang PH, Huo TI. Outstanding research paper awards of the Journal of the Chinese Medical Association in 2021. J Chin Med Assoc 2022; 85:887-888. [PMID: 36150102 DOI: 10.1097/jcma.0000000000000786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/14/2022] Open
Affiliation(s)
- Peng-Hui Wang
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Female Cancer Foundation, Taipei, Taiwan, ROC
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan, ROC
| | - Teh-Ia Huo
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Pharmacology, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| |
Collapse
|
5
|
Cheng H, Wu K, Tian J, Ma K, Gu C, Guan X. Colon tissue image segmentation with MWSI-NET. Med Biol Eng Comput 2022; 60:727-737. [PMID: 35044614 DOI: 10.1007/s11517-022-02501-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 12/28/2021] [Indexed: 11/26/2022]
Abstract
Developments in deep learning have resulted in computer-aided diagnosis for many types of cancer. Previously, pathologists manually performed the labeling work in the analysis of colon tissues, which is both time-consuming and labor-intensive. Results are easily affected by subjective conditions. Therefore, it is beneficial to identify the cancerous regions of colon cancer with the assistance of computer-aided technology. Pathological images are often difficult to process due to their irregularity, similarity between cancerous and non-cancerous tissues and large size. We propose a multi-scale perceptual field fusion structure based on a dilated convolutional network. Using this model, a structure of dilated convolution kernels with different aspect ratios is inserted, which can process cancerous regions of different sizes and generate larger receptive fields. Thus, the model can fuse detailed information at different scales for better semantic segmentation. Two different attention mechanisms are adopted to highlight the cancerous areas. A large, open-source dataset was used to verify improved efficacy when compared to previously disclosed methods.
Collapse
Affiliation(s)
- Hao Cheng
- Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Kaijie Wu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
| | - Jie Tian
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Kai Ma
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Chaocheng Gu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Xinping Guan
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
6
|
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.
Collapse
|
7
|
Yang ST, Yeh CC, Lee WL, Lee FK, Chang CC, Wang PH. A symptomatic near-term pregnant woman recovered from SARS-CoV-2 infection. Taiwan J Obstet Gynecol 2021; 60:945-948. [PMID: 34507682 PMCID: PMC8328571 DOI: 10.1016/j.tjog.2021.07.046] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2021] [Indexed: 01/18/2023] Open
Abstract
OBJECTIVE Coronavirus-2019 (COVID-19) is a global health crisis. Although pregnant women are a vulnerable population during the infectious pandemics, extremely rare cases of pregnant women infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are described in Taiwan. We share our experience to manage a pregnant women with COVID-19 in the third trimester and subsequent delivery at term. CASE REPORT A 43-year-old woman presented with sore throat, cough and rhinorrhea was diagnosed as laboratory-confirmed SARS-CoV-2 infection at the 35 gestational weeks (GW). During the hospitalization, the disease progressed with a need of oxygen supplement and prednisolone therapy. She was discharged uneventfully at 37 GW. Finally, she delivered a female baby with Apgar score of 8-9 points at 38 GW by cesarean section due to the deformity of pelvic cavity resulted from previous surgery for pelvic bone tumor. Both mother and her offspring (without SARS-CoV-2 infection) were discharged uneventfully. CONCLUSION Our report adds the growing body of experience toward management of pregnant women with SARS-CoV-2 infection. Decision making of timing and method of delivery is regarding to individualized condition and hospital setting.
Collapse
Affiliation(s)
- Szu-Ting Yang
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chang-Chin Yeh
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wen-Ling Lee
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Medicine, Cheng-Hsin General Hospital, Taipei, Taiwan; Department of Nursing, Oriental Institute of Technology, New Taipei City, 220, Taiwan
| | - Fa-Kung Lee
- Department of Obstetrics and Gynecology, Cathy General Hospital, Taipei, Taiwan
| | - Cheng-Chang Chang
- Department of Obstetrics and Gynecology, Tri-service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Peng-Hui Wang
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Medical Research, China Medical University Hospital, Taichung, Taiwan; Female Cancer Foundation, Taipei, Taiwan.
| |
Collapse
|