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ARTIFICIAL INTELLIGENCE IN INFERTILITY
Prof. Dr. G. A. Rama Raju, Medical Director, Krishna IVF Clinic, Visakhapatnam
AI in Reproductive Medicine
Brief introduction to
AI, machine learning,
and predictive
models.
Overview of potential
applications of AI in
reproductive
medicine.
Discussion on the use
of AI in Assisted
Reproduction
EDWARD H SHORTLIFFE
• FIRST PERSON TO WRITE A PROGRAM FOR AI IN
MEDICINE
• MYCIN AN FROM STANFORD UNIVERSITY 1970
Rule based
expert system
Machine learning ?
AI in Reproductive Medicine
Ovary
Semen
analysis
Uterus
embryo
assessment
Automated detection and segmentation of follicle in 3d ultrasound
Segmentation and post processing
Case Study: AI in Ovarian Assessment
How to check AFC ?
USING SUPER RESOLUTION:
Super resolution
5d follicle and Asrm Eshre guideline on PCO
Case Study: AI in
Longitudinal Follicular
Growth Tracking
•
Summary: Tracking follicles between days 1-4 is sufficient to
predict their size at
the end of cycle
Average Rate of Growth
(in mm/day)
Between days Inference
>1 1-4
Follicle will grow to
>10mm at end of cycle
0.6 – 1
(increasing)
1-4
Follicle will be between
4mm-10mm at the end
of cycle
0.6 – 0.1
(decreasing)
1-4
Follicle will have size
<4mm at end of cycle
Case Study: AI for
Follicle Tracking
longitudinal tracking of follicles in IVF cycles
Subject 1 Subject 2
Longitudinal analysis of follicular growth
Deep learning and unsupervised learning in follicle assement and
tracking
Discussion -
Interpretation in
context of literature
• This is a first of its kind attempt
to track spatial locations of
follicles in the ovary across IVF
cycles using blood vessels as
anatomical landmarks
• Earlier attempts have examined
tracking in normal cycles only
• We have attempted to study
follicular growth in an IVF
cycle
8/7/2023
Understanding the
Junctional Zone
Our work on
green journal
Predicting implantation success using junctional
zone assessment in assisted reproduction
Junctional zone and implantation
Junctional zone and cesarean section deliveries
Uterine
anomaly and
junction
zone
: AI in Sperm Analysis
Overview of the importance of sperm analysis in
infertility treatment.
Discussion on how AI can be used to analyse
sperm parameters more accurately and efficiently.
Presentation of any results or case studies you
have related to using AI in sperm analysis.
AI in Sperm Analysis
Progress of ai in semen analysis
AI in Time-Lapse
Embryo Monitoring
• Overview of the
importance of embryo
monitoring in IVF.
•
Embryo assessment
Time-lapse assessment
Morphokinetics events occurring during
fertilization
Combined. Data. To live birth Non-Implantation
Awards and
Recognition
Conclusion
• AI algorithms may help practitioners from around the globe to
standardize, automate, and improve IVF outcomes for the benefit of
patients. Collaboration is required between AI developers and
healthcare professionals to make this happen.

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Artificial intelligence and reproductive Medicine.pptx

Editor's Notes

  1. Valedictory Address Script Ladies and Gentlemen, As we conclude this enlightening conference, I would like to reflect on the transformative power of artificial intelligence (AI) in the realm of fertility treatments. From automating the detection of ovarian follicles to streamlining sperm analysis, AI has proven to be a game-changer in assisted reproductive technology (ART). In my own research, I have seen firsthand the potential of AI in revolutionizing fertility treatments. (Here, you can briefly discuss your work and its impact). However, as we celebrate these advancements, we must also acknowledge the challenges that lie ahead, including ethical considerations and data privacy issues. Despite these hurdles, the potential of AI in fertility treatments is undeniable. This is underscored by the recognition this work has received, including the prestigious Samsung Best External Partner Award. As we look to the future, let us continue to innovate and explore the potential of AI in fertility treatments. Together, we can make a difference and bring hope to millions of couples worldwide. Thank you for your attention and for your valuable contributions to this conference. Let's continue this journey of exploration and innovation together. Thank you.
  2. Predicting implantation success using junctional zone assessment in assisted reproduction
  3. Temporal distributions of morphokinetics events occurring during fertilization and relevant to meiotic resumption, pronuclear dynamics, chromatin organization and cytoplasmic/cortical modifications.