Robotics and Artificial Intelligence for Reproductive Medicine provides fundamental principles underpinning robotic and AI techniques used for reproductive medicine. The book provides the state-of-the-art technical advances in clinical infertility treatment, along with the outlook on future challenges and opportunities of robotics and AI in reproductive medicine. It covers robotics, AI, computer vision, biomedical engineering, and reproductive medicine.
1. Chapter 1 Introduction of robotics and artificial intelligence in reproductive medicine - p. 3 2. Chapter 2 Robotic manipulation of human sperm - p. 15 3. Chapter 3 Robotic characterization of the mechanical properties in oocytes and embryos - p. 37 4. Chapter 4 Robotic intracytoplasmic sperm injection - p. 63 5. Chapter 5 Robotic cell surgery for embryo biopsy - p. 75 6. Chapter 6 Robotics in embryo transfer - p. 89 7. Chapter 7 Robot-assisted microsurgery for male infertility treatment - p. 103 8. Part II Artificial intelligence (AI) for reproductive medicine - p. 123 9. Chapter 8 Artificial intelligence for assessment and monitoring of endometrium and follicles - p. 125 10. Chapter 9 Artificial intelligence for semen analysis and sperm selection - p. 133 11. Chapter 10 Artificial intelligence for quality evaluation of embryos - p. 161 12. 10.1 Introduction - p. 161 13. 10.2 Assessment method of embryo morphology - p. 161 14. 10.3 Application of artificial intelligence in embryo selection - p. 162 15. 10.4 Limitations of artificial intelligence assessment - p. 170 16. 10.5 Ethical risks - p. 170 17. 10.6 Conclusion - p. 171 18. References - p. 171 19. Chapter 11 Machine learning and statistical models for prediction of pregnancy outcomes in In vitro fertilization - p. 175 20. 11.1 Introduction - p. 175 21. 11.2 Why is predicting pregnancy outcomes in In vitro fertilization important? - p. 176 22. 11.3 Ingredients for a prediction model - p. 176 23. 11.4 A review of model development - p. 178 24. 11.5 A summary of the best quality In vitro fertilization prediction models - p. 185 25. 11.6 Reporting prediction and prognostic models - p. 207 26. 11.7 Summary - p. 207 27. References - p. 207 28. Chapter 12 Generalization of artificial intelligence models for embryo selection - p. 213 29. Chapter 13 AI-based point-of-care diagnosis and personalization of healthcare in reproductive medicine - p. 229 30. Chapter 14 Artificial intelligence for prediction of perinatal health - p. 273 31. Index - p. 297
Robotics and Artificial Intelligence for Reproductive Medicine provides fundamental principles underpinning robotic and AI techniques used for reproductive medicine. The book provides the state-of-the-art technical advances in clinical infertility treatment, along with the outlook on future challenges and opportunities of robotics and AI in reproductive medicine. It covers robotics, AI, computer vision, biomedical engineering, and reproductive medicine.
1. Chapter 1 Introduction of robotics and artificial intelligence in reproductive medicine - p. 3 2. Chapter 2 Robotic manipulation of human sperm - p. 15 3. Chapter 3 Robotic characterization of the mechanical properties in oocytes and embryos - p. 37 4. Chapter 4 Robotic intracytoplasmic sperm injection - p. 63 5. Chapter 5 Robotic cell surgery for embryo biopsy - p. 75 6. Chapter 6 Robotics in embryo transfer - p. 89 7. Chapter 7 Robot-assisted microsurgery for male infertility treatment - p. 103 8. Part II Artificial intelligence (AI) for reproductive medicine - p. 123 9. Chapter 8 Artificial intelligence for assessment and monitoring of endometrium and follicles - p. 125 10. Chapter 9 Artificial intelligence for semen analysis and sperm selection - p. 133 11. Chapter 10 Artificial intelligence for quality evaluation of embryos - p. 161 12. 10.1 Introduction - p. 161 13. 10.2 Assessment method of embryo morphology - p. 161 14. 10.3 Application of artificial intelligence in embryo selection - p. 162 15. 10.4 Limitations of artificial intelligence assessment - p. 170 16. 10.5 Ethical risks - p. 170 17. 10.6 Conclusion - p. 171 18. References - p. 171 19. Chapter 11 Machine learning and statistical models for prediction of pregnancy outcomes in In vitro fertilization - p. 175 20. 11.1 Introduction - p. 175 21. 11.2 Why is predicting pregnancy outcomes in In vitro fertilization important? - p. 176 22. 11.3 Ingredients for a prediction model - p. 176 23. 11.4 A review of model development - p. 178 24. 11.5 A summary of the best quality In vitro fertilization prediction models - p. 185 25. 11.6 Reporting prediction and prognostic models - p. 207 26. 11.7 Summary - p. 207 27. References - p. 207 28. Chapter 12 Generalization of artificial intelligence models for embryo selection - p. 213 29. Chapter 13 AI-based point-of-care diagnosis and personalization of healthcare in reproductive medicine - p. 229 30. Chapter 14 Artificial intelligence for prediction of perinatal health - p. 273 31. Index - p. 297
