{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T15:20:38Z","timestamp":1779895238084,"version":"3.53.1"},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Research and Graduate Studies at King Khalid University","award":["RGP1\/160\/44"],"award-info":[{"award-number":["RGP1\/160\/44"]}]},{"name":"Deanship of Research and Graduate Studies at King Khalid University","award":["PNURSP2024R510"],"award-info":[{"award-number":["PNURSP2024R510"]}]},{"name":"Deanship of Research and Graduate Studies at King Khalid University","award":["RSPD2024R787"],"award-info":[{"award-number":["RSPD2024R787"]}]},{"name":"Deanship of Research and Graduate Studies at King Khalid University","award":["NBU-FFR-2024-3030-06"],"award-info":[{"award-number":["NBU-FFR-2024-3030-06"]}]},{"name":"Princess Nourah bint Abdulrahman University","award":["RGP1\/160\/44"],"award-info":[{"award-number":["RGP1\/160\/44"]}]},{"name":"Princess Nourah bint Abdulrahman University","award":["PNURSP2024R510"],"award-info":[{"award-number":["PNURSP2024R510"]}]},{"name":"Princess Nourah bint Abdulrahman University","award":["RSPD2024R787"],"award-info":[{"award-number":["RSPD2024R787"]}]},{"name":"Princess Nourah bint Abdulrahman University","award":["NBU-FFR-2024-3030-06"],"award-info":[{"award-number":["NBU-FFR-2024-3030-06"]}]},{"name":"King Saud University","award":["RGP1\/160\/44"],"award-info":[{"award-number":["RGP1\/160\/44"]}]},{"name":"King Saud University","award":["PNURSP2024R510"],"award-info":[{"award-number":["PNURSP2024R510"]}]},{"name":"King Saud University","award":["RSPD2024R787"],"award-info":[{"award-number":["RSPD2024R787"]}]},{"name":"King Saud University","award":["NBU-FFR-2024-3030-06"],"award-info":[{"award-number":["NBU-FFR-2024-3030-06"]}]},{"name":"Deanship of Scientific Research at Northern Border University","award":["RGP1\/160\/44"],"award-info":[{"award-number":["RGP1\/160\/44"]}]},{"name":"Deanship of Scientific Research at Northern Border University","award":["PNURSP2024R510"],"award-info":[{"award-number":["PNURSP2024R510"]}]},{"name":"Deanship of Scientific Research at Northern Border University","award":["RSPD2024R787"],"award-info":[{"award-number":["RSPD2024R787"]}]},{"name":"Deanship of Scientific Research at Northern Border University","award":["NBU-FFR-2024-3030-06"],"award-info":[{"award-number":["NBU-FFR-2024-3030-06"]}]},{"name":"Deanship of Graduate Studies and Scientific Research at University of Bisha","award":["RGP1\/160\/44"],"award-info":[{"award-number":["RGP1\/160\/44"]}]},{"name":"Deanship of Graduate Studies and Scientific Research at University of Bisha","award":["PNURSP2024R510"],"award-info":[{"award-number":["PNURSP2024R510"]}]},{"name":"Deanship of Graduate Studies and Scientific Research at University of Bisha","award":["RSPD2024R787"],"award-info":[{"award-number":["RSPD2024R787"]}]},{"name":"Deanship of Graduate Studies and Scientific Research at University of Bisha","award":["NBU-FFR-2024-3030-06"],"award-info":[{"award-number":["NBU-FFR-2024-3030-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Infertility affects a significant number of humans. A supported reproduction technology was verified to ease infertility problems. In vitro fertilization (IVF) is one of the best choices, and its success relies on the preference for a higher-quality embryo for transmission. These have been normally completed physically by testing embryos in a microscope. The traditional morphological calculation of embryos shows predictable disadvantages, including effort- and time-consuming and expected risks of bias related to individual estimations completed by specific embryologists. Different computer vision (CV) and artificial intelligence (AI) techniques and devices have been recently applied in fertility hospitals to improve efficacy. AI addresses the imitation of intellectual performance and the capability of technologies to simulate cognitive learning, thinking, and problem-solving typically related to humans. Deep learning (DL) and machine learning (ML) are advanced AI algorithms in various fields and are considered the main algorithms for future human assistant technology. This study presents an Embryo Development and Morphology Using a Computer Vision-Aided Swin Transformer with a Boosted Dipper-Throated Optimization (EDMCV-STBDTO) technique. The EDMCV-STBDTO technique aims to accurately and efficiently detect embryo development, which is critical for improving fertility treatments and advancing developmental biology using medical CV techniques. Primarily, the EDMCV-STBDTO method performs image preprocessing using a bilateral filter (BF) model to remove the noise. Next, the swin transformer method is implemented for the feature extraction technique. The EDMCV-STBDTO model employs the variational autoencoder (VAE) method to classify human embryo development. Finally, the hyperparameter selection of the VAE method is implemented using the boosted dipper-throated optimization (BDTO) technique. The efficiency of the EDMCV-STBDTO method is validated by comprehensive studies using a benchmark dataset. The experimental result shows that the EDMCV-STBDTO method performs better than the recent techniques.<\/jats:p>","DOI":"10.3390\/bioengineering11101044","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T03:57:09Z","timestamp":1729483029000},"page":"1044","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Anomaly Detection in Embryo Development and Morphology Using Medical Computer Vision-Aided Swin Transformer with Boosted Dipper-Throated Optimization Algorithm"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6201-0410","authenticated-orcid":false,"given":"Alanoud Al","family":"Mazroa","sequence":"first","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0446-5430","authenticated-orcid":false,"given":"Mashael","family":"Maashi","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh 11543, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0613-4037","authenticated-orcid":false,"given":"Yahia","family":"Said","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7066-2945","authenticated-orcid":false,"given":"Mohammed","family":"Maray","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer Science, King Khalid University, Abha 62521, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1573-0367","authenticated-orcid":false,"given":"Ahmad A.","family":"Alzahrani","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, College of Computing, Umm-AlQura University, Makkah 24382, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulwhab","family":"Alkharashi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0938-4969","authenticated-orcid":false,"given":"Ali M.","family":"Al-Sharafi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha 67714, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"ref_1","first-page":"10","article-title":"Live Birth after Cleavage-Stage versus Blastocyst-Stage Embryo Transfer in Assisted Reproductive Technology: A Randomised Controlled Study","volume":"18","author":"Mahmoudinia","year":"2024","journal-title":"Int. J. Fertil. Steril."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1109\/TMI.2021.3116986","article-title":"Predicting Embryo Viability Based on Self-Supervised Alignment of Time-Lapse Videos","volume":"41","author":"Kragh","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.rbmo.2021.11.003","article-title":"Artificial intelligence in the embryology laboratory: A review","volume":"44","author":"Dimitriadis","year":"2022","journal-title":"Reprod. Biomed. Online"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1093\/humrep\/deaa013","article-title":"Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF","volume":"35","author":"VerMilyea","year":"2020","journal-title":"Hum. Reprod."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liao, Q., Zhang, Q., Feng, X., Huang, H., Xu, H., Tian, B., Liu, J., Yu, Q., Guo, N., and Liu, Q. (2021). Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring. Commun. Biol., 4.","DOI":"10.1038\/s42003-021-01937-1"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Berntsen, J., Rimestad, J., Lassen, J.T., Tran, D., and Kragh, M.F. (2022). Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0262661"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chavez-Badiola, A., Flores-Saiffe Farias, A., Mendizabal-Ruiz, G., Garcia-Sanchez, R., Drakeley, A.J., and Garcia-Sandoval, J.P. (2020). Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-61357-9"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e55301","DOI":"10.7554\/eLife.55301","article-title":"Performance of a deep learning based neural network in the selection of human blastocysts for implantation","volume":"9","author":"Bormann","year":"2020","journal-title":"eLife"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gao, J., Yuan, Y., Li, J., Tian, T., Lian, Y., Liu, P., Li, R., Qiao, J., Long, X., and Wang, H. (2023). Sequential embryo transfer versus double cleavage-stage embryo or double blastocyst transfer in patients with recurrent implantation failure with frozen-thawed embryo transfer cycles: A cohort study. Front. Endocrinol., 14.","DOI":"10.3389\/fendo.2023.1238251"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.rbmo.2020.07.003","article-title":"Embryo Ranking Intelligent Classification Algorithm (ERICA): Artificial intelligence clinical assistant predicting embryo ploidy and implantation","volume":"41","author":"Drakeley","year":"2020","journal-title":"Reprod. Biomed. Online"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102773","DOI":"10.1016\/j.artmed.2024.102773","article-title":"A clinical consensus-compliant deep learning approach to quantitatively evaluate human in vitro fertilization early embryonic development with optical microscope images","volume":"149","author":"Liao","year":"2024","journal-title":"Artif. Intell. Med."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sharma, A., Alawad, F., Kakulavarapu, R., Iliceto, M., Riegler, M.A., Stensen, M.H., and Hammer, H.L. (2024, January 16). Exploring Embryo Development at the Morula Stage-an AI-based Approach to Determine Whether to Use or Discard an Embryo. Proceedings of the 2024 4th International Conference on Applied Artificial Intelligence (ICAPAI), Halden, Norway.","DOI":"10.1109\/ICAPAI61893.2024.10541175"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1093\/biolre\/ioae056","article-title":"Machine learning in time-lapse imaging to differentiate embryos from young vs old mice","volume":"110","author":"Yang","year":"2024","journal-title":"Biol. Reprod."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Raymahapatra, P., Khang, A., and Chaudhuri, A.K. (2024). A Novel Human Embryo Microscope Image Classification Technique Based on ConvNeXtLarge Model. Medical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry, IGI Global.","DOI":"10.4018\/979-8-3693-2105-8.ch015"},{"key":"ref_15","first-page":"2426601","article-title":"Local Binary Convolutional Neural Networks\u2019 Long Short-Term Memory Model for Human Embryos\u2019 Anomaly Detection","volume":"2023","author":"Einy","year":"2023","journal-title":"Sci. Program."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sharma, A., Dorobantiu, A., Ali, S., Iliceto, M., Stensen, M.H., Delbarre, E., Riegler, M.A., and Hammer, H.L. (2024). Deep learning methods to forecasting human embryo development in time-lapse videos. bioRxiv.","DOI":"10.1101\/2024.03.04.583269"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/s41556-024-01367-1","article-title":"Distinct pathways drive anterior hypoblast specification in the implanting human embryo","volume":"26","author":"Weatherbee","year":"2024","journal-title":"Nat. Cell Biol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1038\/s41556-023-01341-3","article-title":"Low-input lipidomics reveals lipid metabolism remodelling during early mammalian embryo development","volume":"26","author":"Zhang","year":"2024","journal-title":"Nat. Cell Biol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2300366","DOI":"10.1002\/aisy.202300366","article-title":"Advancing Predictive Risk Assessment of Chemicals via Integrating Machine Learning, Computational Modeling, and Chemical\/Nano-Quantitative Structure-Activity Relationship Approaches","volume":"6","author":"Singh","year":"2024","journal-title":"Adv. Intell. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1109\/TUFFC.2023.3311879","article-title":"COMFormer: Classification of maternal-fetal and brain anatomy using a residual cross-covariance attention guided transformer in ultrasound","volume":"70","author":"Sarker","year":"2023","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103229","DOI":"10.1016\/j.media.2024.103229","article-title":"Hierarchical online contrastive anomaly detection for fetal arrhythmia diagnosis in ultrasound","volume":"97","author":"Yang","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1109\/JBHI.2023.3328954","article-title":"TransFSM: Fetal anatomy segmentation and biometric measurement in ultrasound images using a hybrid transformer","volume":"28","author":"Zhao","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_23","first-page":"1","article-title":"Enhanced Multi-Class Fetal Plane Detection with Limb Localization in Ultrasound Images","volume":"Volume 1","author":"Sindhu","year":"2024","journal-title":"Proceedings of the 2024 IEEE International Conference on Contemporary Computing and Communications (InC4)"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tang, J., Han, J., Xie, B., Xue, J., Zhou, H., Jiang, Y., Hu, L., Chen, C., Zhang, K., and Zhu, F. (2023). The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20032377"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Degala, S.K.B., Tewari, R.P., Kamra, P., Kasiviswanathan, U., and Pandey, R. (2024). Segmentation and Estimation of Fetal Biometric Parameters using an Attention Gate Double U-Net with Guided Decoder Architecture. Comput. Biol. Med., 180.","DOI":"10.1016\/j.compbiomed.2024.109000"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, S., Xia, W., Fenster, A., Gan, H., and Zhou, R. (2023). LDW-RS Loss: Label Density-Weighted Loss with Ranking Similarity Regularization for Imbalanced Deep Fetal Brain Age Regression. International Conference on Neural Information Processing, Springer Nature.","DOI":"10.1007\/978-981-99-8141-0_10"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, F., Li, P., Wu, X., Zeng, P., Lyu, G., Fan, Y., Liu, P., Song, H., and Liu, Z. (2024). FHUSP-NET: A multi-task model for fetal heart ultrasound standard plane recognition and key anatomical structures detection. Comput. Biol. Med., 168.","DOI":"10.1016\/j.compbiomed.2023.107741"},{"key":"ref_28","first-page":"193","article-title":"Detection of tuberculosis using optimized deep learning approach with enhanced selective median (esmf) filter","volume":"6","author":"Abirami","year":"2024","journal-title":"Afr. J. Biol. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"10235","DOI":"10.3934\/math.2024500","article-title":"Advancements in remote sensing: Harnessing the power of artificial intelligence for scene image classification","volume":"9","author":"Khadidos","year":"2024","journal-title":"AIMS Math."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Falola, Y., Churilova, P., Liu, R., Huang, C.K., Delgado, J.F., and Misra, S. (2024). Generating extremely low-dimensional representation of subsurface earth models using vector quantization and deep Autoencoder. Pet. Res.","DOI":"10.1016\/j.ptlrs.2024.07.001"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e26415","DOI":"10.1016\/j.heliyon.2024.e26415","article-title":"Boosted dipper throated optimization algorithm-based Xception neural network for skin cancer diagnosis: An optimal approach","volume":"10","author":"Tang","year":"2024","journal-title":"Heliyon"},{"key":"ref_32","unstructured":"(2024, September 21). Embryo Classification Based on Microscopic Images. Available online: https:\/\/www.kaggle.com\/competitions\/world-championship-2023-embryo-classification\/data."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wu, C., Yan, W., Li, H., Li, J., Wang, H., Chang, S., Yu, T., Jin, Y., Ma, C., and Luo, Y. (2021). A classification system of day 3 human embryos using deep learning. Biomed. Signal Process. Control, 70.","DOI":"10.1016\/j.bspc.2021.102943"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e06298","DOI":"10.1016\/j.heliyon.2021.e06298","article-title":"Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality","volume":"7","author":"Thirumalaraju","year":"2021","journal-title":"Heliyon"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"122153","DOI":"10.1109\/ACCESS.2019.2937765","article-title":"Multi-task deep learning with dynamic programming for embryo early development stage classification from time-lapse videos","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"200110","DOI":"10.1016\/j.sasc.2024.200110","article-title":"A hybrid machine learning model for classifying gene mutations in cancer using LSTM, BiLSTM, CNN, GRU, and GloVe","volume":"6","author":"Aburass","year":"2024","journal-title":"Syst. Soft Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"100312","DOI":"10.1016\/j.xhgg.2024.100312","article-title":"DeepFace: Deep learning-based framework to contextualize orofacial cleft-related variants during human embryonic craniofacial development","volume":"5","author":"Dai","year":"2024","journal-title":"Hum. Genet. Genom. Adv."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/10\/1044\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:16:28Z","timestamp":1760112988000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/11\/10\/1044"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,18]]},"references-count":37,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["bioengineering11101044"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering11101044","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,18]]}}}