End-to-end deep learning for recognition of ploidy status using time-lapse videos

doi:10.1007/s10815-021-02228-8

中山醫學大學機構典藏 CSMUIR > 中山醫學大學研究成果 > 期刊論文 > Item 310902500/23609

Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/23609

Title:	End-to-end deep learning for recognition of ploidy status using time-lapse videos
Authors:	Lee, CI;Su, YR;Chen, CH;Chang, TA;Kuo, EES;Zheng, WL;Hsieh, WT;Huang, CC;Lee, MS;Liu, M
Keywords:	Time-lapse;Deep learning;Preimplantation genetic testing for aneuploidy (PGT-A);Ploidy status
Date:	2021
Issue Date:	2022-08-05T09:40:03Z (UTC)
Publisher:	SPRINGER/PLENUM PUBLISHERS
ISSN:	1058-0468
Abstract:	Purpose Our retrospective study is to investigate an end-to-end deep learning model in identifying ploidy status through raw time-lapse video. Methods By randomly dividing the dataset of time-lapse videos with known outcome of preimplantation genetic testing for aneuploidy (PGT-A), a deep learning model on raw videos was trained by the 80% dataset, and used to test the remaining 20%, by feeding time-lapse videos as input and the PGT-A prediction as output. The performance was measured by an average area under the curve (AUC) of the receiver operating characteristic curve. Result(s) With 690 sets of time-lapse video image, combined with PGT-A results, our deep learning model has achieved an AUC of 0.74 from the test dataset (138 videos), in discriminating between aneuploid embryos (group 1) and others (group 2, including euploid and mosaic embryos). Conclusion Our model demonstrated a proof of concept and potential in recognizing the ploidy status of tested embryos. A larger scale and further optimization on the exclusion criteria would be included in our future investigation, as well as prospective approach.
URI:	http://dx.doi.org/10.1007/s10815-021-02228-8 https://www.webofscience.com/wos/woscc/full-record/WOS:000652922400001 https://ir.csmu.edu.tw:8080/handle/310902500/23609
Relation:	JOURNAL OF ASSISTED REPRODUCTION AND GENETICS ,2021,v38,issue 7, P1655-1663
Appears in Collections:	[中山醫學大學研究成果] 期刊論文

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