Artificial Intelligence and Machine Learning for Women’s Health Issues

1st Edition - April 26, 2024
Imprint: Academic Press
Editors: Meenu Gupta, D. Jude Hemanth
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 1 8 8 9 - 7
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 1 8 9 0 - 3

Artificial Intelligence and Machine Learning for Women’s Health Issues discusses the applications, challenges, and solutions that machine learning can bring to women’s heal… Read more

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Artificial Intelligence and Machine Learning for Women’s Health Issues discusses the applications, challenges, and solutions that machine learning can bring to women’s health challenges. The book illustrates advanced, innovative techniques, frameworks, concepts, and methodologies of machine learning which enhance the future healthcare system. This book's primary focus is on women’s health issues and machine learning's role in providing solutions to these challenges, providing novel ideas for feasible implementation. It also provides an early-stage analysis for early diagnosis of women’s health issues.

Cover image
Title page
Table of Contents
Copyright
Contributors
Chapter 1 Role of artificial intelligence in gynecology and obstetrics
Abstract
1.1 Introduction
1.2 Clinical benefits of GYN/OB via AI
1.3 AI in obstetrics
1.4 AI in IVF
1.5 AI in obstetric ultrasound
1.6 AI application in trimester (first)
1.7 AI application in second and last trimesters
1.8 AI application in gynecology
1.9 Application of ML in the early examination of maternal-fetal conditions
1.10 Existing constraints and future prospects
1.11 Research gap
1.12 Discussion on obstacles of AI
1.13 Conclusion
References
Chapter 2 Prediction of female pregnancy complication using artificial intelligence
Abstract
2.1 Introduction
2.2 Review
2.3 Machine learning applications of maternal-fetal issues
2.4 Pregnancy and artificial intelligence
2.5 Understand maternal and fetal health outcomes from pharmacologic through recent uses of artificial intelligence
2.6 Maternal and fetal health outcomes based future application of artificial intelligence
2.7 Limitations
2.8 Conclusion
References
Chapter 3 Early stage prediction of endometriosis cancer using fuzzy machine learning technique
Abstract
3.1 Introduction
3.2 Multicriteria decision making
3.3 Aggregation
3.4 Decision making
3.5 Medical diagnosis
3.6 Some preliminaries
3.7 Endometrial cancer types and treatments
3.8 Possible treatments for endometrial cancer
3.9 Relation between symptoms and treatments for endometrial cancer
3.10 Intuitionistic trapezoidal fuzzy prioritized weighted average (ITrFPWA) operators: An algorithm for the selection of suitable treatment of endometrial cancer
3.11 Evaluation of case study
3.12 Result and discussion
3.13 Conclusion
References
Chapter 4 Artificial intelligence approaches for ultrasound examination in pregnancy
Abstract
4.1 Introduction
4.2 Machine learning and deep learning models used in ultrasound examination in pregnancy
4.3 Applications of artificial intelligence in ultrasound examination during pregnancy
4.4 Conclusion
References
Chapter 5 Early assessment of pregnancy using machine learning
Abstract
5.1 Introduction
5.2 Modeling early assessment of pregnancy using ML
5.3 A comprehensive approach to pregnancy
5.4 Significant clinical obstacles in pregnancy
5.5 Conclusion
References
Chapter 6 Ensemble learning-based analysis of perinatal disorders in women
Abstract
6.1 Introduction
6.2 Literature survey
6.3 Proposed methodology
6.4 Conclusion
6.5 Research gaps
6.6 Future scope
References
Chapter 7 Machine learning approaches to predict gestational diabetes in early pregnancy
Abstract
7.1 Introduction
7.2 Case study
7.3 Future scope
7.4 Conclusion
References
Chapter 8 Contribution of artificial intelligence to improving women’s health in pregnancy
Abstract
8.1 Introduction
8.2 Future scope of AI in pregnancy
8.3 Conclusion
References
Chapter 9 Artificial intelligence-based prediction of health risks among women during menopause
Abstract
9.1 Introduction
9.2 Literature survey
9.3 Comparison
9.4 Conclusion
9.5 Future scope
References
Chapter 10 Mammography screening of women in their forties: Benefits and risks
Abstract
10.1 Introduction
10.2 Mammography
10.3 Benefits of screening mammography
10.4 Risk of screening mammography
10.5 Supporting informed decision-making
10.6 Conclusion
References
Chapter 11 Machine learning approach for early prediction of postpartum depression
Abstract
11.1 Introduction
11.2 Assessment of postpartum depression by machine learning
11.3 Machine learning-based postpartum depression prediction studies
11.4 Challenges
11.5 Future work
References
Chapter 12 Improving women’s mental health through AI-powered interventions and diagnoses
Abstract
12.1 Introduction
12.2 AI-powered mental health diagnosis
12.3 AI-powered mental health interventions
12.4 Challenges of mental health in women
12.5 Impact of societal and cultural factors on women’s mental health
12.6 Significance of gender-specific methods in mental health
12.7 Case studies of AI-powered mental health interventions for women
12.8 Mental health and physical activity during the COVID-19 pandemic
12.9 Discussion of the results and impact of the interventions
12.10 Potential future applications of AI in Women’s mental health
12.11 Ethical considerations in using AI for women’s mental health
12.12 The potential impact of AI on women’s mental health
12.13 Conclusion
References
Chapter 13 Artificial intelligence and machine learning for early-stage breast cancer diagnosis in women using vision transformers
Abstract
13.1 Introduction
13.2 Vision transformer
13.3 Methodology
13.4 CNN-based breast cancer detection—Vision transformer
13.5 Discussion
13.6 Conclusion
References
Chapter 14 Recent and future applications of artificial intelligence in obstetric ultrasound examination
Abstract
14.1 Introduction
14.2 Utilization of AI in the first trimester
14.3 Utilization of AI-assisted ultrasonography in the second and last trimesters
14.4 Ultrasound by utilizing Doppler
14.5 Preterm birth
14.6 Postpartum period
14.7 Conclusion and future perspective
References
Chapter 15 Deadly cancer of cervix tackled with early diagnosis using machine learning
Abstract
15.1 Introduction
15.2 Statistics—Global and Indian scenario
15.3 Current method available for cervical cancer diagnosis
15.4 Resolutions using machine intelligence to detect cervical cancer
15.5 Classification methods/algorithms to analyze datasets for cervical cancer
15.6 Conclusion
References
Chapter 16 AI, women’s health care, and trust: Problems and prospects
Abstract
16.1 Introduction
16.2 Overview of technology and trust
16.3 Gender divide in trusting technology
16.4 Women’s health care and AI: The prospects
16.5 Women’s health care and AI: Trust issues
16.6 Ways to mitigate trust and bias
16.7 Conclusion
References
Chapter 17 Role of artificial intelligence and machine learning in women’s health
Abstract
17.1 Introduction
17.2 Aim
17.3 Role of artificial intelligence in science
17.4 Importance of artificial intelligence (AI) in health care and medicine for women
17.5 Using Ml to predict complications in pregnancy
17.6 The effect of AI on working lives of women
17.7 Conclusion
References
Index

Meenu Gupta

Dr. Meenu Gupta is an Associate Professor at the UIE-CSE Department, Chandigarh University, India. She completed her Ph.D. in Computer Science and Engineering with an emphasis on Traffic Accident Severity Problems from Ansal University, Gurgaon, India, in 2020. She has more than 15 years of teaching experience. Her research areas cover Machine Learning, Intelligent Systems, and Data mining, with a specific interest in Artificial Intelligence, Image Processing and Analysis, Smart Cities, Data Analysis, and Human/Brain-machine Interaction (BMI). She has five edited and four authored books. She has also authored or co-authored more than 20 book chapters and over 80 papers in refereed international journals and conferences. She has five filled patents and was awarded the best faculty and department researcher in 2021 and 2022.

Affiliations and expertise

Associate Professor, Department of Computer Science and Engineering University Centre of Research and Development Chandigarh University, Punjab, India

D. Jude Hemanth

Dr. D. Jude Hemanth is currently working as a professor in Department of ECE, Karunya University, Coimbatore, India. He also holds the position of “Visiting Professor” in Faculty of Electrical Engineering and Information Technology, University of Oradea, Romania. He also serves as the “Research Scientist” of Computational Intelligence and Information Systems (CI2S) Lab, Argentina; LAPISCO research lab, Brazil; RIADI Lab, Tunisia; Research Centre for Applied Intelligence, University of Craiova, Romania and e-health and telemedicine group, University of Valladolid, Spain. Dr. Hemanth received his B.E degree in ECE from Bharathiar University in 2002, M.E degree in communication systems from Anna University in 2006 and Ph.D. from Karunya University in 2013. He has published 37 edited books with reputed publishers such as Elsevier, Springer and IET. His research areas include Computational Intelligence and Image processing. He has authored more than 200 research papers in reputed SCIE indexed International Journals and Scopus indexed International Conferences.

Affiliations and expertise

Professor, ECE Department, Karunya Institute of Technology and Sciences, Coimbatore, India

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

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