{"doi":"10.1101/2025.04.25.650556","title":"<i>ImplantoMetrics</i>\n                  - Multidimensional trophoblast invasion assessment by combining 3D-\n                  <i>in-vitro</i>\n                  modeling and deep learning analysis","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Infertility affects millions of couples worldwide, and\n                  <jats:italic>in vitro</jats:italic>\n                  fertilization is a key therapeutic strategy for achieving parenthood. Despite advances, the first IVF attempt fails in ∼60% of patients, highlighting the need for innovative solutions to improve clinical outcomes. Challenges include the limited ability to study embryo implantation, inadequate methods to test therapeutic drugs, and lack of metrics to evaluate implantation images. To address these issues, we developed\n                  <jats:italic>ImplantoMetrics</jats:italic>\n                  , a Fiji plugin for quantitative assessment of trophoblast invasion in combination with a 3D-\n                  <jats:italic>in-vitro</jats:italic>\n                  model.\n                  <jats:italic>ImplantoMetrics</jats:italic>\n                  uses Convolutional Neural Network and XGBoosting to accurately measure multidimensional expansion patterns. It allows quantitative evaluation of therapeutic interventions, and enables a complex study of trophoblast invasion. Compared to manual methods,\n                  <jats:italic>ImplantoMetrics</jats:italic>\n                  is ∼13-times faster and reduces errors through automation. Beyond implantation research,\n                  <jats:italic>ImplantoMetrics</jats:italic>\n                  offers a comprehensive tool to study spheroid invasion in different biological contexts, as e.g. demonstrated here for cancer research.\n                </jats:p>","journal":null,"year":null,"id":645461,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1678402,"name":"Marlene Rezk-Füreder","orcid":null,"position":1,"is_corresponding":false},{"id":1678403,"name":"Celine Kapper","orcid":null,"position":2,"is_corresponding":false},{"id":322789,"name":"Gil Mor","orcid":"0000-0002-5499-3912","position":3,"is_corresponding":false},{"id":1330329,"name":"Omar Shebl","orcid":"0000-0002-8583-6071","position":4,"is_corresponding":false},{"id":1079928,"name":"Peter Oppelt","orcid":"0000-0002-3557-8449","position":5,"is_corresponding":false},{"id":663781,"name":"Patrick Stelzl","orcid":"0000-0002-5915-7788","position":6,"is_corresponding":false},{"id":258880,"name":"Barbara Arbeithuber","orcid":"0000-0001-8367-1560","position":7,"is_corresponding":false},{"id":1678401,"name":"Ayberk Alp Gyunesh","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"<i>ImplantoMetrics</i>\n                  - Multidimensional trophoblast invasion assessment by combining 3D-\n                  <i>in-vitro</i>\n                  modeling and deep learning analysis","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>\n                  Infertility affects millions of couples worldwide, and\n                  <jats:italic>in vitro</jats:italic>\n                  fertilization is a key therapeutic strategy for achieving parenthood. Despite advances, the first IVF attempt fails in ∼60% of patients, highlighting the need for innovative solutions to improve clinical outcomes. Challenges include the limited ability to study embryo implantation, inadequate methods to test therapeutic drugs, and lack of metrics to evaluate implantation images. To address these issues, we developed\n                  <jats:italic>ImplantoMetrics</jats:italic>\n                  , a Fiji plugin for quantitative assessment of trophoblast invasion in combination with a 3D-\n                  <jats:italic>in-vitro</jats:italic>\n                  model.\n                  <jats:italic>ImplantoMetrics</jats:italic>\n                  uses Convolutional Neural Network and XGBoosting to accurately measure multidimensional expansion patterns. It allows quantitative evaluation of therapeutic interventions, and enables a complex study of trophoblast invasion. Compared to manual methods,\n                  <jats:italic>ImplantoMetrics</jats:italic>\n                  is ∼13-times faster and reduces errors through automation. Beyond implantation research,\n                  <jats:italic>ImplantoMetrics</jats:italic>\n                  offers a comprehensive tool to study spheroid invasion in different biological contexts, as e.g. demonstrated here for cancer research.\n                </jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4409959742","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"cc-by-nd","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2025/04/28/2025.04.25.650556.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2025/04/28/2025.04.25.650556.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2025.04.25.650556","host_type":"publisher"},{"url":"https://doi.org/10.1101/2025.04.25.650556","host_type":"repository"}],"fields_of_study":["Pregnancy and preeclampsia studies","Pelvic and Acetabular Injuries"],"mesh_terms":[],"keywords":["Trophoblast","In vitro","Cell biology","Computational biology","Biology","Genetics","Placenta"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T06:05:25.658942Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}