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Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index.

Roopini Sathiasai Kumar1, Swapnil Sharma2, Arunima Halder1

  • 1Manipal Hospitals Pvt. Ltd, Bengaluru, Karnataka, India.

Journal of Human Reproductive Sciences
|June 12, 2023
PubMed
Summary

This study developed an AI model to predict sperm DNA fragmentation index (DFI) using the sperm chromatin dispersion (SCD) test. The AI model achieved 80.15% accuracy in binary classification, aiding in accurate sperm health assessment.

Keywords:
Clinical outcomeDNA fragmentation indexmachine learning

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Area of Science:

  • Reproductive Medicine
  • Artificial Intelligence in Healthcare
  • Sperm Analysis

Background:

  • Sperm chromatin dispersion (SCD) test manually determines DNA fragmentation index (DFI).
  • Manual counting in SCD tests is labor-intensive and subjective.
  • Accurate DFI assessment is crucial for male fertility evaluations.

Purpose of the Study:

  • To develop an artificial intelligence (AI)-based solution for predicting DFI.
  • To automate and standardize the analysis of sperm images for DFI.
  • To improve the accuracy and efficiency of DFI determination in in vitro fertilization (IVF) settings.

Main Methods:

  • Retrospective experimental study using 24,415 images from 30 patients undergoing SCD testing.
  • Development of a pre-processing method for automatic segmentation and detection of sperm-like regions.
  • Training and prediction phases using binary (halo/no halo) and multiclass (halo size/degraded) classifications.

Main Results:

  • The AI model achieved 80.15% accuracy for the binary classification and 75.25% for the multiclass classification.
  • F1 scores were 0.81 for binary and 0.72 for multiclass datasets.
  • The binary approach demonstrated superior performance compared to the multiclass approach.

Conclusions:

  • The proposed AI model standardizes DFI assessment and aids in achieving accurate results without expensive software.
  • The model provides accurate information on healthy and degraded sperm, potentially improving clinical outcomes.
  • While the binary approach is more accurate, the multiclass approach offers insights into the distribution of fragmented sperm.