Robotics and Artificial Intelligence for Reproductive Medicine

Erscheinungsjahr 2025
ISBN: 978-0-443-26745-1
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 191 mm x 235 mm

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, and 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.

Autoren/Hrsg.

Shan, Guanqiao

Sun, Yu

Liu, Hang

Zhang, Zhuoran

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe

Inhaltsverzeichnis

Part I: Robotics for reproductive medicine
1. Introduction of robotics and AI in reproductive medicine
2. Robotic manipulation of human sperm
3. Robotic characterization of the mechanical properties in oocytes and embryos
4. Robotic Intracytoplasmic sperm injection (ICSI)
5. Robotic cell surgery for embryo biopsy
6. Microrobots for embryo transfer
7. Robot-assisted microsurgery for infertility treatment

Part II: Artificial intelligence (AI) for reproductive medicine
8. Artificial intelligence for assessment and monitoring of follicles and endometrium
9. Artificial intelligence for semen analysis and sperm selection
10. Artificial intelligence for quality evaluation of oocytes and embryos
11. Artificial intelligence for prediction of clinical outcomes in IVF
12. Generalization of AI models for embryo selection using transfer learning
13. AI-based point-of-care diagnosis and personalization of healthcare in reproductive medicine
14. Genomic risk prediction of common diseases in preimplantation genetic testing
15. Ethics of artificial intelligence in reproductive medicine

Über Autor(innen)

Shan, Guanqiao
Guanqiao Shan is currently a Postdoctoral Fellow at the University of Toronto. His research interests include robotic cell manipulation and characterization, automation at micro-nano scales, intelligent control systems, and medical robotics

Zhang, Zhuoran
Dr. Zhuoran Zhang is currently an Assistant Professor and Presidential Young Fellow at the Chinese University of Hong Kong, Shenzhen. He obtained his PhD degree from University of Toronto (UofT) in 2019. His research interests include micro-robotics, medical robotics, robotic cell manipulation, and biomedical instrumentation with applications for clinical assisted reproductive medicine. His techniques are currently under clinical trials in multiple hospitals. His research has been recognized by both the robotics automation society and clinical reproductive medicine society by winning multiple best paper awards, including the Best Paper in Automation Award from the IEEE International Conference on Robotics and Automation (ICRA), IEEE Robotics and Automation Letters (RAL) Honorable Mention Award, and the Prize for Technical Achievement Award from the American Society for Reproductive Medicine (ASRM) for three subsequent years (2020, 2021, 2022). He is also an associate editor for RAL with an Outstanding AE award.

Sun, Yu
Dr. Sun is a Professor at the University of Toronto. He is a Tier I Canada Research Chair and the Director of Robotic Institute, University of Toronto. His Advanced Micro and Nanosystems Laboratory specializes in developing innovative robotic and AI technologies for reproductive medicine. He was elected Fellow of ASME (American Society of Mechanical Engineers), IEEE (Institute of Electrical and Electronics Engineers), AAAS (American Association for the Advancement of Science), NAI (US National Academy of Inventors), and AIMBE (American Institute of Medicine and Biomedical Engineering) for his work on micro/nano devices, robotic systems and AI technologies.

Liu, Hang
Dr. Hang Liu is currently a Postdoctoral Fellow at University of Toronto. He obtained his Ph.D. degree in mechanical engineering from University of Toronto in 2023. His research interests lie at the intersection of Artificial Intelligence (AI) and In Vitro Fertilization (IVF), specifically focusing on applying AI techniques to enhance various stages of the IVF process. By harnessing machine learning and image analysis, he aims to develop advanced models capable of discerning key features indicative of embryo quality. Additionally, his research delves into the refinement of assessment methodologies for embryos at critical developmental milestones, notably on day 5 and day 3.