Artificial intelligence used in male infertility scoring
Appeared in BioNews 1095
Artificial intelligence (AI) has been used to evaluate tissue samples in men who produce low levels of, or no, sperm.
Researchers at Toho University School of Medicine in Japan tested whether or not the Google Cloud Automated Machine Learning (AutoML) Vision platform could be used to carry out the traditional Johnson scoring method, in place of pathologists. The Johnson scoring method is used to classify the ability of a male patient to create viable sperm, based on examination of tissue samples taken from the testes, and is often the first stage in the treatment of azoospermia (a condition with no sperm in semen). The researchers findings have been published in Scientific Reports in Nature Communication.
Automated machine learning predicts male infertility based on Johnsen score
Infertility affects females and males equally. In male infertility, azoospermia (a medical condition with no sperm in semen) is a major problem that prevents a couple from having a child.
For the treatment of patients with azoospermia, testicular sperm extraction (TESE) is required to obtain mature sperms. When examined, histological specimens are typically given a score, called the Johnsen score, on a scale of 1 to 10, based on the histopathological features of the testis.
The Johnsen score has been widely used in urology since it was first reported 50 years ago. However, histopathological evaluation of the testis is not an easy task and takes much time due to the complexity of testicular tissue arising from the multiple, highly specialized steps in spermatogenesis.