{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T06:22:22Z","timestamp":1787552542695,"version":"build-2736575974"},"reference-count":42,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:00:00Z","timestamp":1787443200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:00:00Z","timestamp":1787443200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Exp Zool Pt B"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Accurate staging of embryonic development is crucial for applied studies that require precise determination of embryonic age, such as developmental biology research and phenotype\u2010based assessments using microinjection. However, the determination of embryonic development stages in the Mediterranean fruit fly,\n                    <jats:italic>Ceratitis capitata<\/jats:italic>\n                    , still largely relies on manual morphological assessment, which is labor\u2010intensive and susceptible to observer bias. In this study, we evaluated deep learning models for the automatic classification of embryonic development stages in\n                    <jats:italic>C. capitata<\/jats:italic>\n                    using time\u2010lapse microscopy images. Embryos were monitored in vivo at 25\u00b0C\u2009\u00b1\u20091\u00b0C, and high\u2010resolution 4\u2009K phase\u2010contrast images (3840\u2009\u00d7\u20092160 pixels) were acquired at hourly intervals throughout the developmental process (50\u2009h). After quality control, a balanced dataset of 3000 images representing six development stages was created, including blastoderm formation (BF), early gastrulation (EG), germband elongation (GE), germband retraction (GR), dorsal closure (DC), and muscle movement (MM). Four pre\u2010trained architectures (EfficientNetV2M, EfficientNetV2S, ResNet50, and DenseNet121) were standardized and compared under the same conditions. Model performance was evaluated using overall accuracy, sensitivity, specificity, precision, F1 score, ROC, precision\u2013recall analysis, confusion matrices, and t\u2010SNE visualization. With the single training\u2013validation\u2013test split used here, EfficientNetV2M reached a test accuracy of 86.22% and a macro\u2010F1 of 86.12%, and ResNet50 gave comparable results. As each model was trained only once, these small differences are treated descriptively rather than as evidence of statistical superiority. Classification was most reliable for the BF, EG, and MM stages, whereas more confusion arose at the intermediate stages, where morphological features overlap. Overall, these findings show that deep learning offers a practical framework for the time\u2010dependent classification of embryonic development stages in\n                    <jats:italic>C. capitata<\/jats:italic>\n                    and could support future work in embryology and applied entomology.\n                  <\/jats:p>","DOI":"10.1002\/jez.b.70042","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T06:11:53Z","timestamp":1787551913000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning\u2010Based Classification of In Vivo Embryonic Development Stages in\n                    <i>Ceratitis capitata<\/i>\n                    (Wiedemann) (Diptera: Tephritidae) Using Real\u2010Time Microscopy Images"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4478-5834","authenticated-orcid":false,"given":"Tugberk","family":"Tuncyurek","sequence":"first","affiliation":[{"name":"School of Graduate Studies, Terzio\u011flu Campus \u00c7anakkale Onsekiz Mart University \u00c7anakkale T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mevlut","family":"Akcura","sequence":"additional","affiliation":[{"name":"Department of Field Crops, Faculty of Agriculture, Terzio\u011flu Campus \u00c7anakkale Onsekiz Mart University \u00c7anakkale T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8180-4307","authenticated-orcid":false,"given":"Hanife Yandayan","family":"Genc","sequence":"additional","affiliation":[{"name":"Department of Agricultural Biotechnology, Faculty of Agriculture, Terzio\u011flu Campus \u00c7anakkale Onsekiz Mart University \u00c7anakkale T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,23]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10722-026-02864-y"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-61907-7"},{"key":"e_1_2_8_4_1","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-662-02454-6","volume-title":"The Embryonic Development of Drosophila melanogaster","author":"Campos\u2010Ortega J. 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