Sperm motility assessed by deep convolutional neural networks into WHO categories.

ABSTRACT: Semen analysis is central in infertility investigation. Manual assessment of sperm motility according to the WHO recommendations is the golden standard, and extensive training is a requirement for accurate and reproducible results. Deep convolutional neural networks (DCNN) are especially suitable for image classification. In this study, we evaluated the performance of the DCNN ResNet-50 in predicting the proportion of sperm in the WHO motility categories. Two models were evaluated using tenfold cross-validation with 65 video recordings of wet semen preparations from an external quality assessment programme for semen analysis. The corresponding manually assessed data was obtained from several of the reference laboratories, and the mean values were used for training of the DCNN models. One m

SUBMITTER: Haugen TB 

PROVIDER: S-EPMC10484948 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature