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dc.contributor.authorButola, Ankit
dc.contributor.authorPopova, Daria
dc.contributor.authorPrasad, Dilip K.
dc.contributor.authorAhmad, Azeem
dc.contributor.authorHabib, Anowarul
dc.contributor.authorTinguely, Jean-Claude
dc.contributor.authorBasnet, Purusotam
dc.contributor.authorAcharya, Ganesh
dc.contributor.authorParamasivam, Senthilkumaran
dc.contributor.authorMehta, Dalip Singh
dc.contributor.authorAhluwalia, Balpreet Singh
dc.date.accessioned2021-01-23T13:00:31Z
dc.date.available2021-01-23T13:00:31Z
dc.date.issued2020-08-04
dc.description.abstractSperm cell motility and morphology observed under the bright field microscopy are the only criteria for selecting a particular sperm cell during Intracytoplasmic Sperm Injection (ICSI) procedure of Assisted Reproductive Technology (ART). Several factors such as oxidative stress, cryopreservation, heat, smoking and alcohol consumption, are negatively associated with the quality of sperm cell and fertilization potential due to the changing of subcellular structures and functions which are overlooked. However, bright field imaging contrast is insufficient to distinguish tiniest morphological cell features that might influence the fertilizing ability of sperm cell. We developed a partially spatially coherent digital holographic microscope (PSC-DHM) for quantitative phase imaging (QPI) in order to distinguish normal sperm cells from sperm cells under different stress conditions such as cryopreservation, exposure to hydrogen peroxide and ethanol. Phase maps of total 10,163 sperm cells (2,400 control cells, 2,750 spermatozoa after cryopreservation, 2,515 and 2,498 cells under hydrogen peroxide and ethanol respectively) are reconstructed using the data acquired from the PSC-DHM system. Total of seven feedforward deep neural networks (DNN) are employed for the classification of the phase maps for normal and stress affected sperm cells. When validated against the test dataset, the DNN provided an average sensitivity, specificity and accuracy of 85.5%, 94.7% and 85.6%, respectively. The current QPI + DNN framework is applicable for further improving ICSI procedure and the diagnostic efficiency for the classification of semen quality in regard to their fertilization potential and other biomedical applications in general.en_US
dc.identifier.citationButola A, Popova DA, Prasad DK, Ahmad A, Habib A, Tinguely J, Basnet P, Acharya G, Paramasivam, Mehta DS, Ahluwalia BS. High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition. Scientific Reports. 2020;10(1)en_US
dc.identifier.cristinIDFRIDAID 1848977
dc.identifier.doihttps://doi.org/10.1038/s41598-020-69857-4
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/10037/20415
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.ispartofPopova, D. (2021). Advanced methods in reproductive medicine: Application of optical nanoscopy, artificial intelligence-assisted quantitative phase microscopy and mitochondrial DNA copy numbers to assess human sperm cells. (Doctoral thesis). <a href=https://hdl.handle.net/10037/22598>https://hdl.handle.net/10037/22598</a>.
dc.relation.journalScientific Reports
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/NANO2021/288565/Norway/Integrated photonic chip-based nanoscopy for pathology & the clinic//en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Medical disciplines: 700::Basic medical, dental and veterinary science disciplines: 710en_US
dc.subjectVDP::Medisinske Fag: 700::Basale medisinske, odontologiske og veterinærmedisinske fag: 710en_US
dc.titleHigh spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed conditionen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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