Course summary

Human clinical embryology studies human fertilisation and the development of embryos. It focuses on assisted reproductive technology (ART). This is used to help people with infertility conceive. Artificial intelligence (AI) uses machines designed to think and learn like humans. Infertility is a global problem. Around 80 million people worldwide have been diagnosed as infertile. Demand for ART is increasing, and opportunities for careers in this field are growing. Data science and AI is a fast-growing area found in many medical fields, including ART. Technological advancements in this area are already present on the market. They are used in IVF labs and in clinical practice. This is revolutionising the way medically assisted reproduction intervention works. Recent research highlights the emergence of a new professional role: the computational embryologist. More professionals need training in applying data analysis/AI technologies to reproductive medicine. This master's course gives you a robust education in embryology, andrology, and ART. It looks at all this from a clinical, research, and laboratory perspective. You'll also learn about the field of data analysis and AI, and how it applies to reproductive medicine. The course combines both areas to give you the skills needed to future proof your career. You'll get access to clinics in a real Assisted Conception Unit (ACU) in Ninewells Hospital. This means you can follow real patients through their fertility journey. You'll also shadow experienced embryologists in the lab. This helps you deliver the most advanced medical care to patients with infertility. We welcome students with a background in biology, allied health science, medicine, nursing, midwifery, or informatics. You'll get basic knowledge in both reproductive biology and computing disciplines. What you learn on this course You'll get an in-depth understanding of a range of reproductive medicine and AI topics. These include:

• the science of human reproductive biology

• the causes of infertility

• assisted reproductive technology

• clinical embryology

• new technologies using AI in the field of reproductive medicine

• artificial intelligence and analytical modelling

• machine learning techniques and applications

• deep learning implementation and optimisation

• recognising and using evidence-based medicine

• how to retrieve, review and analyse scientific data

• ethical and research skills

• legislation and regulatory frameworks.

• sperm analysis

• embryo (oocyte) selection

• techniques used in the preparation and preservation of sperm and eggs

• dish preparation

• generating, evaluating, interpreting and presenting practical work

• health and safety in the lab and Good Laboratory Practice (GLP).

• how to recruit patients and donors for research

• the validation of processes and equipment.