Quantification of human sperm concentration using machine learning-based spectrophotometry

Spectrophotometry is an indirect non-invasive and quantitative method for specifying materials with unknown contents based on absorption behavior. This paper presents the first application of artificial neural network in spectrophotometry for quantification of human sperm concentration. A well-trained full spectrum neural network (FSNN) model is developed by examining the absorption response of sperm samples from 41 human subjects to different light spectra (wavelength from 390 to 1100 nm). It is shown that this FSNN accurately estimates sperm concentration based on the full absorption spectrum with over 93% prediction accuracy, and provides 100% agreement with clinical assessments in differentiating the samples of healthy donor from patient samples. We suggest the machine learning-based spectrophotometry approach with the trained FSNN model as a rapid, low-cost, and powerful technique to quantify sperm concentration. The performance of this technique is superior to available spectrophotometry methods currently used for semen analysis and will provide novel research and clinical opportunities for tackling male infertility.