
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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Request a sales quote- Provides fundamental concepts and analysis of machine learning algorithms used to aid in the diagnosis of women’s health issues
- Guides researchers to specific ideas, tools, and practices most applicable to product/service development, innovation problems, and opportunities
- Provides hands-on chapters that describe frameworks, applications, best practices, and case studies of future directions of applied machine learning in women’s healthcare
- 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
- Edition: 1
- Published: April 26, 2024
- Imprint: Academic Press
- No. of pages: 300
- Language: English
- Paperback ISBN: 9780443218897
- eBook ISBN: 9780443218903
MG
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.
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