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End-to-end deep learning for recognition of ploidy status using time-lapse videos.
Lee, Chun-I; Su, Yan-Ru; Chen, Chien-Hong; Chang, T Arthur; Kuo, Esther En-Shu; Zheng, Wei-Lin; Hsieh, Wen-Ting; Huang, Chun-Chia; Lee, Maw-Sheng; Liu, Mark.
Afiliación
  • Lee CI; Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan.
  • Su YR; Department of Obstetrics and Gynecology, Chung Shan Medical University, Taichung, Taiwan.
  • Chen CH; Division of Infertility, Lee Women's Hospital, Taichung, Taiwan.
  • Chang TA; Binflux Inc., Taipei, Taiwan.
  • Kuo EE; Division of Infertility, Lee Women's Hospital, Taichung, Taiwan.
  • Zheng WL; Department of Obstetrics and Gynecology, University of Texas Health Science Center, San Antonio, TX, USA.
  • Hsieh WT; Binflux Inc., Taipei, Taiwan.
  • Huang CC; Binflux Inc., Taipei, Taiwan.
  • Lee MS; Binflux Inc., Taipei, Taiwan.
  • Liu M; Division of Infertility, Lee Women's Hospital, Taichung, Taiwan.
J Assist Reprod Genet ; 38(7): 1655-1663, 2021 Jul.
Article en En | MEDLINE | ID: mdl-34021832
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diagnóstico Preimplantación / Imagen de Lapso de Tiempo / Aprendizaje Profundo / Aneuploidia Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Female / Humans Idioma: En Revista: J Assist Reprod Genet Asunto de la revista: GENETICA / MEDICINA REPRODUTIVA Año: 2021 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diagnóstico Preimplantación / Imagen de Lapso de Tiempo / Aprendizaje Profundo / Aneuploidia Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Female / Humans Idioma: En Revista: J Assist Reprod Genet Asunto de la revista: GENETICA / MEDICINA REPRODUTIVA Año: 2021 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Países Bajos