Embryo pregnancy result prediction device based on multimodality

A prediction device and multi-modal technology, applied in the field of medical artificial intelligence, to achieve the effect of improving classification accuracy

Active Publication Date: 2019-03-29
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there is currently no system for efficient and accurate embryo pregnancy prediction using deep learning algorithms

Method used

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  • Embryo pregnancy result prediction device based on multimodality
  • Embryo pregnancy result prediction device based on multimodality
  • Embryo pregnancy result prediction device based on multimodality

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Experimental program
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Embodiment

[0040] see Figure 1 to Figure 3 In this embodiment, the device for predicting pregnancy outcome based on multimodal embryos includes a memory and a processor, the memory stores computer-executable instructions, the processor communicates with the memory, and is configured to execute the computer-executable instructions stored in the memory, and the memory stores the computer-executable instructions Embryo Pregnancy Outcome Prediction Model.

[0041] The embryo pregnancy outcome prediction model is obtained through the following steps:

[0042] S101 Acquiring annotation data

[0043] Acquire three images of the same embryo developing to the blastocyst stage from reproductive records. Focus the camera on the blastocyst, inner cell mass, and trophoblast cells of the embryo respectively, and take images. This process keeps the camera position unchanged to ensure that the cell positions in the three images are the same. At the same time, after the embryo is transferred to the m...

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Abstract

The invention discloses an embryo pregnancy result prediction device based on multimodality, and belongs to the field of medical artificial intelligence. Firstly, images of embryos developed to blastocyst stage after in vitro fertilization and corresponding pregnancy results are obtained, three pictures of blastocyst, inner cell mass and trophoblast cells of embryos are obtained, and the pregnancyresults are labeled as labels, and data are labeled as raw data. Then the image is smoothed with Gaussian kernel function to remove part of the noise, and then the image is normalized. The image is then data-augmented as input data. Multimodal method is used to fuse the images of the three images so that the input image contains three evaluation features. the fused image is passed to ResNet-50 for training, that network is optimized according to the target tag, and and is iterated until the training is complete. With the model, three images can be taken before embryo transfer to predict the pregnancy outcome, and the embryo with high success rate can be selected according to the output results, which can improve the final pregnancy success rate.

Description

technical field [0001] The invention relates to the field of medical artificial intelligence, in particular to a multimodal-based embryo pregnancy outcome prediction device. Background technique [0002] The infertility rate in my country has climbed from 2.5%-3% 20 years ago to 12.5%-15%, and it is showing a trend of increasing and younger. According to statistics, in 2016, the number of infertility patients in my country has exceeded 5000 Ten thousand. On the other hand, the opening of the two-child policy has brought about a birth peak. In recent years, there are about 16 million newborns in my country every year, of which about 2-2.4 million newborns cannot be born due to infertility. This has directly led to the surge in demand and the expansion of the scale of the assisted reproductive market. [0003] Assisted reproductive technology mainly refers to artificial insemination and in vitro fertilization-embryo transfer (In Vitro Fertilization and Embryo Transfer, IVF-ET...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T5/00G06T5/50G06T3/40G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06T3/4038G06T5/002G06T5/50G06T7/0012G06T2207/30044G06T2207/20216G06V2201/03G06N3/045G06F18/2414G06F18/214
Inventor 吴健刘雪晨马鑫军舒景东王文哲陆逸飞吴福理
Owner ZHEJIANG UNIV
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