Manual versus deep learning measurements to evaluate cumulus expansion of bovine oocytes and its relationship with embryo development in vitro.

Journal: Computers in biology and medicine
PMID:

Abstract

Cumulus expansion is an important indicator of oocyte maturation and has been suggested to be indicative of greater oocyte developmental capacity. Although multiple methods have been described to assess cumulus expansion, none of them is considered a gold standard. Additionally, these methods are subjective and time-consuming. In this manuscript, the reliability of three cumulus expansion measurement methods was assessed, and a deep learning model was created to automatically perform the measurement. Cumulus expansion of 232 cumulus-oocyte complexes was evaluated by three independent observers using three methods: (1) measurement of the cumulus area, (2) measurement of three distances between the zona pellucida and outer cumulus, and (3) scoring cumulus expansion on a 5-point Likert scale. The reliability of the methods was calculated in terms of intraclass-correlation coefficients (ICC) for both inter- and intra-observer agreements. The area method resulted in the best overall inter-observer agreement with an ICC of 0.89 versus 0.54 and 0.30 for the 3-distance and scoring methods, respectively. Therefore, the area method served as the base to create a deep learning model, AI-xpansion, which reaches a human-level performance in terms of average rank, bias and variance. To evaluate the accuracy of the methods, the results of cumulus expansion calculations were linked to embryonic development. Cumulus expansion had increased significantly in oocytes that achieved successful embryo development when measured by AI-xpansion, the area- or 3-distance method, while this was not the case for the scoring method. Measuring the area is the most reliable method to manually evaluate cumulus expansion, whilst deep learning automatically performs the calculation with human-level precision and high accuracy and could therefore be a valuable prospective tool for embryologists.

Authors

  • Annelies Raes
    imec-itec-KULeuven, Etienne Sabbelaan 51, 8500, Kortrijk, Belgium.
  • Georgios Athanasiou
    Artificial Intelligence Research Institute (IIIA-CSIC), 08193, Bellaterra, Spain; Department of Computer Science, Universitat Autonoma de Barcelona, Spain. Electronic address: gathanasiou@iiia.csic.es.
  • Nima Azari-Dolatabad
    Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium.
  • Hafez Sadeghi
    Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke, Belgium.
  • Sebastian Gonzalez Andueza
    Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium.
  • Josep Lluis Arcos
    Artificial Intelligence Research Institute (IIIA-CSIC), 08193, Bellaterra, Spain.
  • Jesus Cerquides
    Artificial Intelligence Research Institute (IIIA-CSIC), 08193, Bellaterra, Spain. Electronic address: cerquide@iiia.csic.es.
  • Krishna Chaitanya Pavani
    Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium.
  • Geert Opsomer
    Department of Reproduction, Obstetrics and Herd Health, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, B-9820, Merelbeke, Belgium. Electronic address: geert.opsomer@ugent.be.
  • Osvaldo Bogado Pascottini
    Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke, Belgium.
  • Katrien Smits
    Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium.
  • Daniel Angel-Velez
    Department of Internal Medicine, Reproduction and Population Medicine, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium; Research Group in Animal Sciences-INCA-CES, Universidad CES, Medellin, 050021, Colombia.
  • Ann Van Soom