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Assessment of reproductive knowledge among young adults utilizing Amazon mechanical Turk survey.
Ther Adv Reprod Health
December 2024
Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, 1150 NW 14th Street, Miami, FL 33136, USA.
Background: Reproductive health technology has evolved significantly since the introduction of in vitro fertilization in 1978, enhancing the possibility of conceiving children at later stages in life. Despite these advancements, there remains a critical gap in fertility knowledge among young adults, as demonstrated by recent studies. This gap is compounded by the growing influence of social media on health information, where misinformation can distort public understanding of fertility-related issues.
View Article and Find Full Text PDFA clinical consensus-compliant deep learning approach to quantitatively evaluate human in vitro fertilization early embryonic development with optical microscope images.
Artif Intell Med
March 2024
Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China; Key Laboratory of Tropical Disease Control (Ministry of Education), Sun Yat-Sen University, Guangzhou, China. Electronic address:
Article Synopsis
- The study focuses on improving the selection of embryos for in vitro fertilization (IVF) by developing a deep learning model called Esava, which evaluates IVF embryo quality using optical microscope images.
- Esava utilizes advanced techniques like Faster R-CNN for blastomere detection and an innovative Crowd-NMS algorithm to enhance detection accuracy and quantify embryonic cells, achieving high precision and consistency with expert embryologists.
- The model demonstrated superior performance compared to other computational methods, significantly enhancing the accuracy and efficiency of embryo assessment in accordance with clinical standards.
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