Validation of artificial neural network models for predicting biochemical markers associated with male infertility.
Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres.


BACKGROUND
AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous epididymal sperm spiration; RBFN: radical basis function network; SRNN: simple recurrent neural network; SVM: support vector machines; TSE: testicular sperm extraction; WHO: World Health Organization.
a Industrial Biotechnology Division , School of Biosciences and Technology, VIT University , Tamil Nadu , India.
A S
AS
Vickram
b Bangalore Assisted Conception Centre Healthcare Pvt. Ltd., Andrology, Banglore , Karnataka , India.
A Rao
AR
Kamini
c School of Advanced Sciences, VIT University , Tamil Nadu , India.
Raja
R
Das
d School of Biosciences and Technology, VIT University , Tamil Nadu , India.
M Ramesh
MR
Pathy
d School of Biosciences and Technology, VIT University , Tamil Nadu , India.
R
R
Parameswari
d School of Biosciences and Technology, VIT University , Tamil Nadu , India.
K
K
Archana
d School of Biosciences and Technology, VIT University , Tamil Nadu , India.
T B
TB
Sridharan
D015415:Biomarkers / Q000378:metabolism*; D006801:Humans; D007248:Infertility, Male / Q000175:diagnosis / Q000378:metabolism*; D008297:Male; D008954:Models, Biological; D016571:Neural Networks, Computer
D015415:Biomarkers
Artificial neural networks; biochemical markers; human seminal plasma; prediction