Deep learning-based robust automated system for predicting human sperm DNA fragmentation index

Abstract

A BSTRACT Background: Determining the DNA fragmentation index (DFI) by the sperm chromatin dispersion (SCD) test involves manual counting of stained sperms with halo and no halo. Aims: The aim of this study is to build a robust artificial intelligence-based solution to predict the DFI. Settings and Design: This is a retrospective experimental study conducted in a secondary in vitro fertilisation setup. Materials and Methods: We obtained 24,415 images from 30 patients after the SCD test using a phase-contrast microscope. We classified the dataset into two, binary (halo/no halo) and multiclass (big/medium/small halo/degraded (DEG)/dust). Our approach consists of a training and prediction phase. The 30 patients’ images were divided into training (24) and prediction (6) sets. A pre-processing method M was developed to automatically segment the images to detect sperm-like regions and was annotated by three embryologists. Statistical Analysis Used: To interpret the findings, the precision-recall curve and F1 score were utilised. Results: Binary and multiclass datasets containing 8887 and 15,528 cropped sperm image regions showed an accuracy of 80.15% versus 75.25%. A precision-recall curve was determined and the binary and multiclass datasets obtained an F1 score of 0.81 versus 0.72. A confusion matrix was applied for predicted and actuals for the multiclass approach where small halo and medium halo confusion were found to be highest. Conclusion: Our proposed machine learning model can standardise and aid in arriving at accurate results without using expensive software. It provides accurate information about healthy and DEG sperms in a given sample, thereby attaining better clinical outcomes. The binary approach performed better with our model than the multiclass approach. However, the multiclass approach can highlight the distribution of fragmented and non-fragmented sperms.

Gaussian clustering and quantification of the sperm chromatin dispersion test using convolutional neural networks

“…Recently, models for automation of the SCD test have been introduced, [39][40][41] but most of them are semi-quantitative classifier models. We establish our modified standard upon the commonly used definition of the relative halo radius and take into consideration the variable staining status and inter-operator variability.…”

Biomarker-based human and animal sperm phenotyping: the good, the bad and the ugly

“…New deep learning–based models have also been calculated to track human sperm motility to improve upon kinematic profiles [ 242 ]. Others have taken the approach of automating traditional cell counting in assays such as that of the sperm chromatin dispersion (SCD) test to determining the DNA fragmentation index (DFI) [ 243 ]. A recent review highlights various machine and deep learning methods that could be used and adapted to the andrology field, which lags in artificial intelligence incorporation compared to other fields of medicine [ 244 ].…”