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Anomaly Detection in Embryo Development and Morphology Using Medical Computer Vision-Aided Swin Transformer with

Alanoud Al Mazroa1, Mashael Maashi2, Yahia Said3

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces a new AI technique, EDMCV-STBDTO, for assessing embryo development in fertility treatments. The method improves accuracy and efficiency over traditional embryo evaluation, aiding in better assisted reproduction outcomes.

Keywords:
boosted dipper-throated optimizationcomputer visionembryo developmentimage preprocessingswin transformer

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Area of Science:

  • Reproductive biology
  • Artificial intelligence in medicine
  • Computer vision for medical imaging

Background:

  • Infertility affects many individuals, with in vitro fertilization (IVF) being a key assisted reproduction technology.
  • Current embryo assessment relies on manual microscopic evaluation, which is time-consuming, labor-intensive, and prone to subjective bias.
  • Advancements in artificial intelligence (AI) and computer vision (CV) offer potential solutions to enhance the accuracy and efficiency of embryo evaluation.

Purpose of the Study:

  • To develop and validate an advanced AI-driven technique, EDMCV-STBDTO, for accurate and efficient detection of human embryo development and morphology.
  • To improve the quality assessment of embryos for successful IVF outcomes and advance developmental biology research.
  • To overcome the limitations of traditional manual embryo assessment methods.

Main Methods:

  • The proposed Embryo Development and Morphology Using a Computer Vision-Aided Swin Transformer with a Boosted Dipper-Throated Optimization (EDMCV-STBDTO) technique was employed.
  • Image preprocessing utilized a bilateral filter (BF) for noise reduction.
  • Feature extraction was performed using the swin transformer method, followed by variational autoencoder (VAE) for embryo development classification.
  • Hyperparameter optimization for the VAE was achieved using the boosted dipper-throated optimization (BDTO) technique.

Main Results:

  • The EDMCV-STBDTO technique demonstrated superior performance in accurately detecting embryo development compared to existing methods.
  • Comprehensive validation using a benchmark dataset confirmed the method's effectiveness and efficiency.
  • The AI-driven approach showed significant improvements over traditional manual embryo assessment.

Conclusions:

  • The EDMCV-STBDTO technique presents a robust and efficient AI-based solution for embryo development assessment in assisted reproduction.
  • This advanced computer vision approach has the potential to significantly improve IVF success rates and contribute to developmental biology research.
  • The study highlights the transformative impact of AI and deep learning in modern fertility treatments.