System for predicting whether embryo can be capsulated or not based on time-delay camera system and deep learning algorithm

A camera system and deep learning technology, applied in the field of artificial intelligence learning and analyzing medical images, can solve the problems of data not having diagnostic value, not suitable for clinical use, and not showing the advantages of predicting blastocysts and embryo screening.

Pending Publication Date: 2021-07-06
TONGJI HOSPITAL ATTACHED TO TONGJI MEDICAL COLLEGE HUAZHONG SCI TECH
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Problems solved by technology

However, the accuracy of the EevaTM system for automatic annotation of early embryos needs to be improved: when compared with manual annotation of the same embryo video, EevaTM did not show an advantage in predicting bla

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  • System for predicting whether embryo can be capsulated or not based on time-delay camera system and deep learning algorithm
  • System for predicting whether embryo can be capsulated or not based on time-delay camera system and deep learning algorithm
  • System for predicting whether embryo can be capsulated or not based on time-delay camera system and deep learning algorithm

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Embodiment Construction

[0033] The implementation of the present invention will be described in detail below in conjunction with the examples of implementation, but they do not constitute a limitation of the present invention, and are only examples. At the same time, the advantages of the present invention will become clearer and easier to understand.

[0034] The system for predicting whether an embryo can form a cyst based on a time-lapse camera system and a deep learning algorithm of the present invention includes a single-multiple cell recognition module, a prokaryotic recognition module, a prokaryotic disappearance recognition module, a five-category cell recognition module, a time-domain prediction module, and an airspace prediction module , and the spatio-temporal prediction module (STEM);

[0035]The single and multi-cell identification module is used to identify single cells and multi-cells in the video of the time-lapse camera system; the single-multi-cell identification module utilizes the...

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Abstract

The invention discloses a system for predicting whether an embryo can be capsulated or not based on a time-delay camera system and a deep learning algorithm. The system comprises a single-cell and multi-cell identification module, a pronucleus identification module, a pronucleus disappearance identification module, a five-classification cell identification module, a time domain prediction module, a space domain prediction module and a time-space domain combination prediction module. According to the system for predicting whether the embryo can be capsulated or not based on the time-delay camera system and the deep learning algorithm, time sequence and form information of a video are separately identified by using a time domain model and a space domain model, and then the time sequence and form information are integrated, so that the prediction accuracy is remarkably improved.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence learning and analyzing medical images, in particular to a system for predicting whether an embryo can form a cyst based on a time-lapse camera system and a deep learning algorithm. Background technique [0002] At present, the standard of embryo evaluation and screening widely used in clinical practice is still the traditional morphological method, that is, embryologists evaluate the morphology and developmental stages of embryos at several fixed time points after embryo fertilization to determine their developmental ability. Morphological assessment, however, is subject to the subjectivity of the embryologist. More importantly, embryonic development is a process of dynamic changes, and the observation of intermittent time points cannot obtain comprehensive embryonic development information, thereby missing key events in the embryonic development process. In recent years, the time...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06N3/04G06N3/08
CPCG06T7/0016G06N3/08G06T2207/10016G06T2207/30044G06T2207/20081G06T2207/20084G06N3/044
Inventor 李科珍马丁徐树公张麒艾继辉廖秋月冯雪
Owner TONGJI HOSPITAL ATTACHED TO TONGJI MEDICAL COLLEGE HUAZHONG SCI TECH
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