Danardono, Gunawan Bondan and Erwin, Alva and Purnama, James (2022) A Homogeneous Ensemble of Robust Pre-defined Neural Network Enables Automated Annotation of Human Embryo Morphokinetics. Journal of Reproduction & Infertility, 23 (4). pp. 250-256. ISSN 22285482, 2251676X
|
Text
A Homogeneous Ensemble of Robust Pre-defined Neural Network Enables Automated Annotation of Human Embryo Morphokinetics.pdf Download (343kB) | Preview |
Abstract
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 net�work (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 compila�tion 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 ma�chine 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, build�ing AI models with larger data is required to properly increase AI model reliability.
| Item Type: | Article |
|---|---|
| Subjects: | T Technology > T Technology (General) > T58.5 Information technology T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing |
| Divisions: | Faculty of Engineering and Information Technology > Department of Information Technology |
| Depositing User: | James Purnama |
| Date Deposited: | 11 Apr 2023 01:09 |
| Last Modified: | 11 Apr 2023 01:09 |
| URI: | http://repository.sgu.ac.id/id/eprint/2508 |
Actions (login required)
 |
View Item |