Improving Deep Learning-Based Algorithm for Ploidy Status Prediction Through Combined U-NET Blastocyst Segmentation and Sequential Time-Lapse Blastocysts Images Article Swipe
Nining Handayani
,
Gunawan Bondan Danardono
,
Arief Boediono
,
Budi Wiweko
,
Ivan Sini
,
Batara Sirait
,
Arie Adrianus Polim
,
Irham Suheimi
,
Anom Bowolaksono
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.18502/jri.v25i2.16006
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.18502/jri.v25i2.16006
Background: Several approaches have been proposed to optimize the construction of an artificial intelligence-based model for assessing ploidy status. These encompass the investigation of algorithms, refining image segmentation techniques, and discerning essential patterns throughout embryonic development. The purpose of the current study was to evaluate the effectiveness of using U-NET architecture for embryo segmentation and time-lapse embryo image sequence extraction, three and ten hr before biopsy to improve model accuracy for prediction of embryonic ploidy status. Methods: A total of 1.020 time-lapse videos of blastocysts with known ploidy status were used to construct a convolutional neural network (CNN)-based model for ploidy detection. Sequential images of each blastocyst were extracted from the time-lapse videos over a period of three and ten hr prior to the biopsy, generating 31.642 and 99.324 blastocyst images, respectively. U-NET architecture was applied for blastocyst image segmentation before its implementation in CNN-based model development. Results: The accuracy of ploidy prediction model without applying the U-NET segment-ed sequential embryo images was 0.59 and 0.63 over a period of three and ten hr before biopsy, respectively. Improved model accuracy of 0.61 and 0.66 was achieved, respectively with the implementation of U-NET architecture for embryo segmentation on the current model. Extracting blastocyst images over a 10 hr period yields higher accuracy compared to a three-hr extraction period prior to biopsy. Conclusion: Combined implementation of U-NET architecture for blastocyst image segmentation and the sequential compilation of ten hr of time-lapse blastocyst images could yield a CNN-based model with improved accuracy in predicting ploidy status.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18502/jri.v25i2.16006
- OA Status
- green
- Cited By
- 1
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400279753
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4400279753Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18502/jri.v25i2.16006Digital Object Identifier
- Title
-
Improving Deep Learning-Based Algorithm for Ploidy Status Prediction Through Combined U-NET Blastocyst Segmentation and Sequential Time-Lapse Blastocysts ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-02Full publication date if available
- Authors
-
Nining Handayani, Gunawan Bondan Danardono, Arief Boediono, Budi Wiweko, Ivan Sini, Batara Sirait, Arie Adrianus Polim, Irham Suheimi, Anom BowolaksonoList of authors in order
- Landing page
-
https://doi.org/10.18502/jri.v25i2.16006Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/11327420Direct OA link when available
- Concepts
-
Blastocyst, Segmentation, Computer science, Image segmentation, Embryo, Convolutional neural network, Artificial intelligence, Biopsy, Pattern recognition (psychology), Biology, Embryogenesis, Pathology, Medicine, Cell biologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- References (count)
-
31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4400279753 |
|---|---|
| doi | https://doi.org/10.18502/jri.v25i2.16006 |
| ids.doi | https://doi.org/10.18502/jri.v25i2.16006 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39157795 |
| ids.openalex | https://openalex.org/W4400279753 |
| fwci | 1.34576613 |
| type | article |
| title | Improving Deep Learning-Based Algorithm for Ploidy Status Prediction Through Combined U-NET Blastocyst Segmentation and Sequential Time-Lapse Blastocysts Images |
| biblio.issue | 2 |
| biblio.volume | 25 |
| biblio.last_page | 119 |
| biblio.first_page | 110 |
| topics[0].id | https://openalex.org/T10364 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9534000158309937 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2739 |
| topics[0].subfield.display_name | Public Health, Environmental and Occupational Health |
| topics[0].display_name | Reproductive Biology and Fertility |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2778177303 |
| concepts[0].level | 4 |
| concepts[0].score | 0.6595531105995178 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q227717 |
| concepts[0].display_name | Blastocyst |
| concepts[1].id | https://openalex.org/C89600930 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6308048367500305 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[1].display_name | Segmentation |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5393794178962708 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C124504099 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5260108709335327 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q56933 |
| concepts[3].display_name | Image segmentation |
| concepts[4].id | https://openalex.org/C196843134 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5141125321388245 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q33196 |
| concepts[4].display_name | Embryo |
| concepts[5].id | https://openalex.org/C81363708 |
| concepts[5].level | 2 |
| concepts[5].score | 0.482431024312973 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[5].display_name | Convolutional neural network |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.45614951848983765 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C2775934546 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4124377369880676 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q179991 |
| concepts[7].display_name | Biopsy |
| concepts[8].id | https://openalex.org/C153180895 |
| concepts[8].level | 2 |
| concepts[8].score | 0.367403507232666 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[8].display_name | Pattern recognition (psychology) |
| concepts[9].id | https://openalex.org/C86803240 |
| concepts[9].level | 0 |
| concepts[9].score | 0.32337844371795654 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[9].display_name | Biology |
| concepts[10].id | https://openalex.org/C87073359 |
| concepts[10].level | 3 |
| concepts[10].score | 0.20058134198188782 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q33196 |
| concepts[10].display_name | Embryogenesis |
| concepts[11].id | https://openalex.org/C142724271 |
| concepts[11].level | 1 |
| concepts[11].score | 0.11851266026496887 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7208 |
| concepts[11].display_name | Pathology |
| concepts[12].id | https://openalex.org/C71924100 |
| concepts[12].level | 0 |
| concepts[12].score | 0.08913308382034302 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[12].display_name | Medicine |
| concepts[13].id | https://openalex.org/C95444343 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7141 |
| concepts[13].display_name | Cell biology |
| keywords[0].id | https://openalex.org/keywords/blastocyst |
| keywords[0].score | 0.6595531105995178 |
| keywords[0].display_name | Blastocyst |
| keywords[1].id | https://openalex.org/keywords/segmentation |
| keywords[1].score | 0.6308048367500305 |
| keywords[1].display_name | Segmentation |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5393794178962708 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/image-segmentation |
| keywords[3].score | 0.5260108709335327 |
| keywords[3].display_name | Image segmentation |
| keywords[4].id | https://openalex.org/keywords/embryo |
| keywords[4].score | 0.5141125321388245 |
| keywords[4].display_name | Embryo |
| keywords[5].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[5].score | 0.482431024312973 |
| keywords[5].display_name | Convolutional neural network |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.45614951848983765 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/biopsy |
| keywords[7].score | 0.4124377369880676 |
| keywords[7].display_name | Biopsy |
| keywords[8].id | https://openalex.org/keywords/pattern-recognition |
| keywords[8].score | 0.367403507232666 |
| keywords[8].display_name | Pattern recognition (psychology) |
| keywords[9].id | https://openalex.org/keywords/biology |
| keywords[9].score | 0.32337844371795654 |
| keywords[9].display_name | Biology |
| keywords[10].id | https://openalex.org/keywords/embryogenesis |
| keywords[10].score | 0.20058134198188782 |
| keywords[10].display_name | Embryogenesis |
| keywords[11].id | https://openalex.org/keywords/pathology |
| keywords[11].score | 0.11851266026496887 |
| keywords[11].display_name | Pathology |
| keywords[12].id | https://openalex.org/keywords/medicine |
| keywords[12].score | 0.08913308382034302 |
| keywords[12].display_name | Medicine |
| language | en |
| locations[0].id | doi:10.18502/jri.v25i2.16006 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S2764457158 |
| locations[0].source.issn | 2228-5482, 2251-676X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 2228-5482 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Journal of Reproduction & Infertility |
| locations[0].source.host_organization | https://openalex.org/P4310319396 |
| locations[0].source.host_organization_name | Knowledge E |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319396 |
| locations[0].source.host_organization_lineage_names | Knowledge E |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Journal of Reproduction & Infertility |
| locations[0].landing_page_url | https://doi.org/10.18502/jri.v25i2.16006 |
| locations[1].id | pmid:39157795 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Journal of reproduction & infertility |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39157795 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:11327420 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | other-oa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/other-oa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | J Reprod Infertil |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11327420 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5001284371 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8123-1203 |
| authorships[0].author.display_name | Nining Handayani |
| authorships[0].countries | ID |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I29617571 |
| authorships[0].affiliations[0].raw_affiliation_string | Doctoral Program in Biomedical Sciences, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia. |
| authorships[0].affiliations[1].raw_affiliation_string | IRSI Research and Training Centre, Jakarta, Indonesia. |
| authorships[0].institutions[0].id | https://openalex.org/I29617571 |
| authorships[0].institutions[0].ror | https://ror.org/0116zj450 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I29617571 |
| authorships[0].institutions[0].country_code | ID |
| authorships[0].institutions[0].display_name | University of Indonesia |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Nining Handayani |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Doctoral Program in Biomedical Sciences, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia., IRSI Research and Training Centre, Jakarta, Indonesia. |
| authorships[1].author.id | https://openalex.org/A5020978945 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Gunawan Bondan Danardono |
| authorships[1].affiliations[0].raw_affiliation_string | IRSI Research and Training Centre, Jakarta, Indonesia. |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Gunawan Bondan Danardono |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | IRSI Research and Training Centre, Jakarta, Indonesia. |
| authorships[2].author.id | https://openalex.org/A5072632992 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-1535-703X |
| authorships[2].author.display_name | Arief Boediono |
| authorships[2].countries | ID |
| authorships[2].affiliations[0].raw_affiliation_string | IRSI Research and Training Centre, Jakarta, Indonesia. |
| authorships[2].affiliations[1].raw_affiliation_string | Morula IVF Jakarta Clinic, Jakarta, Indonesia. |
| authorships[2].affiliations[2].institution_ids | https://openalex.org/I21881038 |
| authorships[2].affiliations[2].raw_affiliation_string | Department of Anatomy, Physiology and Pharmacology, IPB University, Bogor, Indonesia. |
| authorships[2].institutions[0].id | https://openalex.org/I21881038 |
| authorships[2].institutions[0].ror | https://ror.org/05smgpd89 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I21881038 |
| authorships[2].institutions[0].country_code | ID |
| authorships[2].institutions[0].display_name | IPB University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Arief Boediono |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Anatomy, Physiology and Pharmacology, IPB University, Bogor, Indonesia., IRSI Research and Training Centre, Jakarta, Indonesia., Morula IVF Jakarta Clinic, Jakarta, Indonesia. |
| authorships[3].author.id | https://openalex.org/A5066738157 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-5898-7034 |
| authorships[3].author.display_name | Budi Wiweko |
| authorships[3].countries | ID |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210155152 |
| authorships[3].affiliations[0].raw_affiliation_string | Yasmin IVF Clinic, Dr Cipto Mangunkusumo General Hospital, Jakarta, Indonesia. |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I29617571 |
| authorships[3].affiliations[1].raw_affiliation_string | Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia. |
| authorships[3].affiliations[2].institution_ids | https://openalex.org/I29617571 |
| authorships[3].affiliations[2].raw_affiliation_string | Human Reproduction, Infertility, and Family Planning Cluster, Indonesia Reproductive Medicine Research and Training Center, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia. |
| authorships[3].institutions[0].id | https://openalex.org/I4210155152 |
| authorships[3].institutions[0].ror | https://ror.org/05am7x020 |
| authorships[3].institutions[0].type | healthcare |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210155152 |
| authorships[3].institutions[0].country_code | ID |
| authorships[3].institutions[0].display_name | Rumah Sakit Umum Pusat Nasional Dr. Cipto Mangunkusumo |
| authorships[3].institutions[1].id | https://openalex.org/I29617571 |
| authorships[3].institutions[1].ror | https://ror.org/0116zj450 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I29617571 |
| authorships[3].institutions[1].country_code | ID |
| authorships[3].institutions[1].display_name | University of Indonesia |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Budi Wiweko |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia., Human Reproduction, Infertility, and Family Planning Cluster, Indonesia Reproductive Medicine Research and Training Center, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia., Yasmin IVF Clinic, Dr Cipto Mangunkusumo General Hospital, Jakarta, Indonesia. |
| authorships[4].author.id | https://openalex.org/A5070863211 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-5364-0381 |
| authorships[4].author.display_name | Ivan Sini |
| authorships[4].affiliations[0].raw_affiliation_string | Morula IVF Jakarta Clinic, Jakarta, Indonesia. |
| authorships[4].affiliations[1].raw_affiliation_string | IRSI Research and Training Centre, Jakarta, Indonesia. |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Ivan Sini |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | IRSI Research and Training Centre, Jakarta, Indonesia., Morula IVF Jakarta Clinic, Jakarta, Indonesia. |
| authorships[5].author.id | https://openalex.org/A5080621799 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-9073-809X |
| authorships[5].author.display_name | Batara Sirait |
| authorships[5].countries | ID |
| authorships[5].affiliations[0].raw_affiliation_string | Morula IVF Jakarta Clinic, Jakarta, Indonesia. |
| authorships[5].affiliations[1].raw_affiliation_string | IRSI Research and Training Centre, Jakarta, Indonesia. |
| authorships[5].affiliations[2].institution_ids | https://openalex.org/I102555177 |
| authorships[5].affiliations[2].raw_affiliation_string | Department of Obstetrics and Gynaecology, Faculty of Medicine, Universitas Kristen Indonesia, Jakarta, Indonesia. |
| authorships[5].institutions[0].id | https://openalex.org/I102555177 |
| authorships[5].institutions[0].ror | https://ror.org/03xqzzq44 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I102555177 |
| authorships[5].institutions[0].country_code | ID |
| authorships[5].institutions[0].display_name | Universitas Kristen Indonesia |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Batara Sirait |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Obstetrics and Gynaecology, Faculty of Medicine, Universitas Kristen Indonesia, Jakarta, Indonesia., IRSI Research and Training Centre, Jakarta, Indonesia., Morula IVF Jakarta Clinic, Jakarta, Indonesia. |
| authorships[6].author.id | https://openalex.org/A5013701104 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-5876-4968 |
| authorships[6].author.display_name | Arie Adrianus Polim |
| authorships[6].countries | ID |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I161010349 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Obstetrics and Gynecology, School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia. |
| authorships[6].institutions[0].id | https://openalex.org/I161010349 |
| authorships[6].institutions[0].ror | https://ror.org/02hd2zk59 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I161010349 |
| authorships[6].institutions[0].country_code | ID |
| authorships[6].institutions[0].display_name | Atma Jaya Catholic University of Indonesia |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Arie A Polim |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Department of Obstetrics and Gynecology, School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia. |
| authorships[7].author.id | https://openalex.org/A5092515101 |
| authorships[7].author.orcid | https://orcid.org/0009-0002-0189-6067 |
| authorships[7].author.display_name | Irham Suheimi |
| authorships[7].affiliations[0].raw_affiliation_string | Morula IVF Jakarta Clinic, Jakarta, Indonesia. |
| authorships[7].affiliations[1].raw_affiliation_string | IRSI Research and Training Centre, Jakarta, Indonesia. |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Irham Suheimi |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | IRSI Research and Training Centre, Jakarta, Indonesia., Morula IVF Jakarta Clinic, Jakarta, Indonesia. |
| authorships[8].author.id | https://openalex.org/A5084021110 |
| authorships[8].author.orcid | https://orcid.org/0000-0003-3180-1460 |
| authorships[8].author.display_name | Anom Bowolaksono |
| authorships[8].countries | ID |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I29617571 |
| authorships[8].affiliations[0].raw_affiliation_string | Cellular and Molecular Mechanisms in Biological System (CEMBIOS) Research Group, Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia. |
| authorships[8].institutions[0].id | https://openalex.org/I29617571 |
| authorships[8].institutions[0].ror | https://ror.org/0116zj450 |
| authorships[8].institutions[0].type | education |
| authorships[8].institutions[0].lineage | https://openalex.org/I29617571 |
| authorships[8].institutions[0].country_code | ID |
| authorships[8].institutions[0].display_name | University of Indonesia |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Anom Bowolaksono |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Cellular and Molecular Mechanisms in Biological System (CEMBIOS) Research Group, Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia. |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11327420 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Improving Deep Learning-Based Algorithm for Ploidy Status Prediction Through Combined U-NET Blastocyst Segmentation and Sequential Time-Lapse Blastocysts Images |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10364 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9534000158309937 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2739 |
| primary_topic.subfield.display_name | Public Health, Environmental and Occupational Health |
| primary_topic.display_name | Reproductive Biology and Fertility |
| related_works | https://openalex.org/W2129050989, https://openalex.org/W2058346480, https://openalex.org/W2181839420, https://openalex.org/W2971755608, https://openalex.org/W2353791190, https://openalex.org/W2024778227, https://openalex.org/W2086636586, https://openalex.org/W2099138420, https://openalex.org/W4296027837, https://openalex.org/W2934111382 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:pubmedcentral.nih.gov:11327420 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764455111 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | PubMed Central |
| best_oa_location.source.host_organization | https://openalex.org/I1299303238 |
| best_oa_location.source.host_organization_name | National Institutes of Health |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| best_oa_location.license | other-oa |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Text |
| best_oa_location.license_id | https://openalex.org/licenses/other-oa |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | J Reprod Infertil |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11327420 |
| primary_location.id | doi:10.18502/jri.v25i2.16006 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S2764457158 |
| primary_location.source.issn | 2228-5482, 2251-676X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2228-5482 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Journal of Reproduction & Infertility |
| primary_location.source.host_organization | https://openalex.org/P4310319396 |
| primary_location.source.host_organization_name | Knowledge E |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319396 |
| primary_location.source.host_organization_lineage_names | Knowledge E |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Journal of Reproduction & Infertility |
| primary_location.landing_page_url | https://doi.org/10.18502/jri.v25i2.16006 |
| publication_date | 2024-07-02 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2560346242, https://openalex.org/W3132804113, https://openalex.org/W4282929045, https://openalex.org/W3015106848, https://openalex.org/W3031291303, https://openalex.org/W3176290932, https://openalex.org/W4200140956, https://openalex.org/W4304184138, https://openalex.org/W2787002947, https://openalex.org/W3160943435, https://openalex.org/W3132659982, https://openalex.org/W2913215965, https://openalex.org/W3091827510, https://openalex.org/W2914897901, https://openalex.org/W4306322075, https://openalex.org/W2754900231, https://openalex.org/W3041764944, https://openalex.org/W1986583481, https://openalex.org/W2809415996, https://openalex.org/W2802990218, https://openalex.org/W4308738489, https://openalex.org/W2603671750, https://openalex.org/W4221030726, https://openalex.org/W2109334201, https://openalex.org/W3039494404, https://openalex.org/W2082874617, https://openalex.org/W2949941775, https://openalex.org/W2953611911, https://openalex.org/W4377104709, https://openalex.org/W3037122226, https://openalex.org/W3134989184 |
| referenced_works_count | 31 |
| abstract_inverted_index.A | 77 |
| abstract_inverted_index.a | 93, 114, 167, 204, 213, 243 |
| abstract_inverted_index.10 | 205 |
| abstract_inverted_index.an | 11 |
| abstract_inverted_index.hr | 63, 120, 173, 206, 236 |
| abstract_inverted_index.in | 143, 249 |
| abstract_inverted_index.of | 10, 23, 38, 47, 72, 79, 83, 104, 116, 150, 169, 180, 190, 223, 234, 237 |
| abstract_inverted_index.on | 196 |
| abstract_inverted_index.to | 6, 43, 66, 91, 122, 212, 218 |
| abstract_inverted_index.The | 36, 148 |
| abstract_inverted_index.and | 29, 54, 61, 118, 127, 164, 171, 182, 230 |
| abstract_inverted_index.for | 15, 51, 70, 99, 136, 193, 226 |
| abstract_inverted_index.its | 141 |
| abstract_inverted_index.ten | 62, 119, 172, 235 |
| abstract_inverted_index.the | 8, 21, 39, 45, 110, 123, 156, 188, 197, 231 |
| abstract_inverted_index.was | 42, 134, 162, 184 |
| abstract_inverted_index.0.59 | 163 |
| abstract_inverted_index.0.61 | 181 |
| abstract_inverted_index.0.63 | 165 |
| abstract_inverted_index.0.66 | 183 |
| abstract_inverted_index.been | 4 |
| abstract_inverted_index.each | 105 |
| abstract_inverted_index.from | 109 |
| abstract_inverted_index.have | 3 |
| abstract_inverted_index.over | 113, 166, 203 |
| abstract_inverted_index.used | 90 |
| abstract_inverted_index.were | 89, 107 |
| abstract_inverted_index.with | 85, 187, 246 |
| abstract_inverted_index.1.020 | 80 |
| abstract_inverted_index.These | 19 |
| abstract_inverted_index.U-NET | 49, 132, 157, 191, 224 |
| abstract_inverted_index.could | 241 |
| abstract_inverted_index.image | 26, 57, 138, 228 |
| abstract_inverted_index.known | 86 |
| abstract_inverted_index.model | 14, 68, 98, 145, 153, 178, 245 |
| abstract_inverted_index.prior | 121, 217 |
| abstract_inverted_index.study | 41 |
| abstract_inverted_index.three | 60, 117, 170 |
| abstract_inverted_index.total | 78 |
| abstract_inverted_index.using | 48 |
| abstract_inverted_index.yield | 242 |
| abstract_inverted_index.31.642 | 126 |
| abstract_inverted_index.99.324 | 128 |
| abstract_inverted_index.before | 64, 140, 174 |
| abstract_inverted_index.biopsy | 65 |
| abstract_inverted_index.embryo | 52, 56, 160, 194 |
| abstract_inverted_index.higher | 209 |
| abstract_inverted_index.images | 103, 161, 202, 240 |
| abstract_inverted_index.model. | 199 |
| abstract_inverted_index.neural | 95 |
| abstract_inverted_index.period | 115, 168, 207, 216 |
| abstract_inverted_index.ploidy | 17, 74, 87, 100, 151, 251 |
| abstract_inverted_index.status | 88 |
| abstract_inverted_index.videos | 82, 112 |
| abstract_inverted_index.yields | 208 |
| abstract_inverted_index.Several | 1 |
| abstract_inverted_index.applied | 135 |
| abstract_inverted_index.biopsy, | 124, 175 |
| abstract_inverted_index.biopsy. | 219 |
| abstract_inverted_index.current | 40, 198 |
| abstract_inverted_index.images, | 130 |
| abstract_inverted_index.improve | 67 |
| abstract_inverted_index.network | 96 |
| abstract_inverted_index.purpose | 37 |
| abstract_inverted_index.status. | 18, 75, 252 |
| abstract_inverted_index.without | 154 |
| abstract_inverted_index.Combined | 221 |
| abstract_inverted_index.Improved | 177 |
| abstract_inverted_index.Methods: | 76 |
| abstract_inverted_index.Results: | 147 |
| abstract_inverted_index.accuracy | 69, 149, 179, 210, 248 |
| abstract_inverted_index.applying | 155 |
| abstract_inverted_index.compared | 211 |
| abstract_inverted_index.evaluate | 44 |
| abstract_inverted_index.improved | 247 |
| abstract_inverted_index.optimize | 7 |
| abstract_inverted_index.patterns | 32 |
| abstract_inverted_index.proposed | 5 |
| abstract_inverted_index.refining | 25 |
| abstract_inverted_index.sequence | 58 |
| abstract_inverted_index.three-hr | 214 |
| abstract_inverted_index.CNN-based | 144, 244 |
| abstract_inverted_index.achieved, | 185 |
| abstract_inverted_index.assessing | 16 |
| abstract_inverted_index.construct | 92 |
| abstract_inverted_index.embryonic | 34, 73 |
| abstract_inverted_index.encompass | 20 |
| abstract_inverted_index.essential | 31 |
| abstract_inverted_index.extracted | 108 |
| abstract_inverted_index.Extracting | 200 |
| abstract_inverted_index.Sequential | 102 |
| abstract_inverted_index.approaches | 2 |
| abstract_inverted_index.artificial | 12 |
| abstract_inverted_index.blastocyst | 106, 129, 137, 201, 227, 239 |
| abstract_inverted_index.detection. | 101 |
| abstract_inverted_index.discerning | 30 |
| abstract_inverted_index.extraction | 215 |
| abstract_inverted_index.generating | 125 |
| abstract_inverted_index.predicting | 250 |
| abstract_inverted_index.prediction | 71, 152 |
| abstract_inverted_index.segment-ed | 158 |
| abstract_inverted_index.sequential | 159, 232 |
| abstract_inverted_index.throughout | 33 |
| abstract_inverted_index.time-lapse | 55, 81, 111, 238 |
| abstract_inverted_index.(CNN)-based | 97 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.Conclusion: | 220 |
| abstract_inverted_index.algorithms, | 24 |
| abstract_inverted_index.blastocysts | 84 |
| abstract_inverted_index.compilation | 233 |
| abstract_inverted_index.extraction, | 59 |
| abstract_inverted_index.techniques, | 28 |
| abstract_inverted_index.architecture | 50, 133, 192, 225 |
| abstract_inverted_index.construction | 9 |
| abstract_inverted_index.development. | 35, 146 |
| abstract_inverted_index.respectively | 186 |
| abstract_inverted_index.segmentation | 27, 53, 139, 195, 229 |
| abstract_inverted_index.convolutional | 94 |
| abstract_inverted_index.effectiveness | 46 |
| abstract_inverted_index.investigation | 22 |
| abstract_inverted_index.respectively. | 131, 176 |
| abstract_inverted_index.implementation | 142, 189, 222 |
| abstract_inverted_index.intelligence-based | 13 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.71874951 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |