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Journal of Reproduction and Infertility، جلد ۲۳، شماره ۴، صفحات ۲۵۰-۲۵۷
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عنوان انگلیسی A Homogeneous Ensemble of Robust Pre-defined Neural Network Enables Automated Annotation of Human Embryo Morphokinetics
چکیده انگلیسی مقاله Background: The purpose of the current study was to reduce the risk of human bias in assessing embryos by automatically annotating embryonic development based on their morphological changes at specified time-points with convolutional neural network (CNN) and artificial intelligence (AI). Methods: Time-lapse videos of embryo development were manually annotated by the embryologist and extracted for use as a supervised dataset, where the data were split into 14 unique classifications based on morphological differences. A compilation of homogeneous pre-trained CNN models obtained via TensorFlow Hub was tested with various hyperparameters on a controlled environment using transfer learning to create a new model. Subsequently, the performances of the AI models in correctly annotating embryo morphologies within the 14 designated classifications were compared with a collection of AI models with different built-in configurations so as to derive a model with the highest accuracy. Results: Eventually, an AI model with a specific configuration and an accuracy score of 67.68% was obtained, capable of predicting the embryo developmental stages (t1, t2, t3, t4, t5, t6, t7, t8, t9+, tCompaction, tM, tSB, tB, tEB). Conclusion: Currently, the technology and research of artificial intelligence and machine learning in the medical field have significantly and continuingly progressed in an effort to develop computer-assisted technology which could potentially increase the efficiency and accuracy of medical personnel's performance. Nonetheless, building AI models with larger data is required to properly increase AI model reliability.
کلیدواژه‌های انگلیسی مقاله Artificial intelligence, Automation, Computer-assisted image processing, Embryonic development, In vitro fertilization, Machine learning, Neural networks
نویسندگان مقاله | Gunawan B Danardono
Faculty of Engineering and Information Technology, Swiss German University, Tangerang, Indonesia


| Alva Erwin
Faculty of Engineering and Information Technology, Swiss German University, Tangerang, Indonesia


| James Purnama
Faculty of Engineering and Information Technology, Swiss German University, Tangerang, Indonesia


| Nining Handayani
Morula IVF Jakarta Clinic, Jakarta, Indonesia


| Arie A Polim
Department of Obstetrics and Gynecology, School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia


| Arief Boediono
Department of Anatomy, Physiology and Pharmacology, IPB University, Bogor, Indonesia


| Ivan Sini
Morula IVF Jakarta Clinic, Jakarta, Indonesia
نشانی اینترنتی https://www.jri.ir/article/140161
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