Overview
We are developing "IVF Insight", an AI-powered application to rapidly inform clinical decisions and make comprehensive predictions of fertility treatment. The application will be based on extensive data collected from infertility patients in order to createdetailed unique patient profiles. We will achieve this by using DNN to analyse key digital data collected from electronic patient records, wearable devices, genomic analysis and automated embryo imaging.
AI-powered blastocyst image analysis combined with embryo morphokinetics and time-lapse technology for the automated non-invasive morphological assessment of embryos during culture in the laboratory:Selecting the right embryo during IVF treatment is critical to ensuring a successful outcome. However, embryo selection is typically a manual and imprecise process. One of the biggest challenges is selecting a viable embryo. At present, this is usually done by eye and is based on the good judgment of the embryologist. Business Insight applies AI-driven image analysis that can achieve higher accuracy and reproducibility than human operators in the selection of viable embryos. Instead of a human looking at thousands of images, AI looks at them and is capable of learning all the time. Using a Deep Learning system built with Convolutional Neural Networks (CNN), we train it with past images of embryos of known pregnancy outcomes. Automated embryo imaging will offer further personalization and accuracy allowing an embryo-based selection at the stage of embryo transfer to the uterus. This is an innovative and exciting technology combining state of the art embryology with new advances of AI Computer Vision all with the aim of selecting the best possible embryo for transfer to give patients the best possible chance of having a baby.
The IVFvision.ai application will offer prognostic information at two phases:
Electronic patient records, with detailed and extensive features/variables (deep phenotyping): Incorporating various features related to patient demographics, baseline characteristics, ovarian reserve tests, ovarian stimulation and embryological data will improve pattern formation and greatly enhance the model’s predictive performance.
Wearable devices worn before and during treatment: The measurement of parameters such as heart rate, sleep patterns, physical activity etc will provide information on how lifestyle patterns may affect subsequent infertility treatment.
DNA analysis of patients using the latest technology of whole genome amplification/next generation sequencing:The concept of precision medicine relies on a thorough understanding of the consequences of unique features of individual patients, such as environmental exposures and genetic profiles. A key component of implementing individualized care in this paradigm will be genomic profiling.Compared with single gene tests, whole genome sequencing offers the possibility of a more comprehensive and efficient risk evaluation.