Smartphone based sperm counting - an alternative way to the visual assessment technique in sperm concentration analysis

İlhan, Hamza Osman; Aydın, Nizamettin

doi:10.1007/s11042-019-08421-3

Cited by 7 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sperm analysis systems are validated, and protocols are standardized to improve the accuracy of sperm concentration estimates. Altogether, this would result in better knowledge of methods used to estimate parameters of semen quality [55][56][57][58] and represent new opportunities for producers that can access new technologies [23,28,29,59,60] with greater objectivity [61][62][63] that could complement or replace methods for manual or visual analyses of semen quality, and this could help them make more informed decisions about breeding and reproduction [16,64,65]. Further research is nonetheless needed to refine the estimation methods of boar sperm concentration and motility and to determine the most appropriate method for different applications and animal production systems and conditions.…”

Section: Discussionmentioning

confidence: 99%

Are There Differences between Methods Used for the Objective Estimation of Boar Sperm Concentration and Motility?

Sevilla,

Soler,

Araya-Zúñiga

et al. 2023

Animals

View full text Add to dashboard Cite

Artificial insemination in the swine industry, as in other species, demands adequate semen handling and accurate evaluation for the preparation of seminal doses. Sperm concentration and motility estimates are part of the semen evaluation process and are considered important for maximizing the yield of doses for insemination. In this study, methods were examined for their accuracy in the estimation of boar sperm concentration and motility. Assessments of sperm concentration were carried out using iSperm®, ISAS® v1, Open CASA v2, and the Accuread® photometer. Analyses of sperm motility were performed with iSperm®, ISAS® v1, and Open CASA v2 systems. In this study, boar semen samples were collected from 10 healthy males from two genetic lines. There were no relevant differences between sire lines when sperm concentration was assessed. A Bayesian analysis was applied to the four methods used to assess sperm concentration to examine whether there are relevant differences between them. Results suggested differences in the four methods, with a probability of relevance (PR) of 0.86–1.00. The iSperm® method revealed higher concentration values within the highest posterior density region at 95% confidence interval (HPD95%) = 167.0, 224.2 M/mL, whereas Open CASA v2 showed the lowest values, with HPD95% = 99.3, 155.9 M/mL. The iSperm® demonstrated higher reliability in measuring sperm concentration compared to other methods or devices within the given range of confidence. ANOVAs revealed relevant differences in the three methods of motility estimation. Overall, differences in boar sperm concentration and motility estimates were found using various methods, but further studies are needed for better characterization of these differences.

show abstract

Section: Discussionmentioning

confidence: 99%

Are There Differences between Methods Used for the Objective Estimation of Boar Sperm Concentration and Motility?

Sevilla,

Soler,

Araya-Zúñiga

et al. 2023

Animals

View full text Add to dashboard Cite

show abstract

“…Research conducted by Ilhan et al (2021), discusses a new alternative in sperm counting using smartphones, replacing visual assessment techniques in sperm concentration analysis [36]. The first stage in infertility diagnosis, the sperm count, involves two main evaluation techniques: Computer Aided Sperm Analysis (CASA) and Visual Assessment (VA) [36]. VA involves observation of sperm in the counting chamber, which makes the diagnosis highly dependent on individual expertise and experience.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, CASA uses computer-based techniques but is more expensive and requires complex parameter settings. In this study, a new method that utilizes smartphones and computers for sperm counting analysis was proposed [36]. A smartphone is used to take pictures similar to the VA technique, and the sample video is then transferred to a computer.…”

Section: Introductionmentioning

confidence: 99%

Abnormality Determination of Spermatozoa Motility Using Gaussian Mixture Model and Matching-based Algorithm

Mas Diyasa,

Saputra,

Gunawan

et al. 2024

Journal of Robotics and Control

View full text Add to dashboard Cite

Sperm analysis is an initial step in the examination conducted to identify infertility cases in humans. One aspect of sperm analysis involves observing the movement of spermatozoa and determining whether it is normal or abnormal. Normal spermatozoa movement is characterized by progressive motion at an average speed of 20 µm/second, while abnormal movement includes slow or non-motile spermatozoa. Traditional methods can be employed to assess the normality or abnormality of sperm movement, but they have drawbacks such as time-consuming procedures and diverse results depending on the expertise of the examiner. On the other hand, utilizing Computer-Assisted Sperm Analysis (CASA) equipment provides consistent results, albeit at a relatively high cost. Therefore, this research proposes an alternative method for determining sperm movement abnormalities using the Gaussian Mixture Model (GMM) for background subtraction and a matching-based algorithm to track and analyze the formed trajectories, distinguishing between normal and abnormal sperm movement. Human spermatozoa in real-time are used, and their movements are recorded in video format using a bright field microscope. The testing results for determining sperm movement abnormalities based on the GMM method and matching-based algorithm were successful, particularly in videos recorded at 50 fps recording speed, 20 minutes of liquefaction time, and 40x microscope lens magnification. This condition exhibited the highest average accuracy, with a tracking accuracy of 77.3% and an average accuracy for determining sperm motility abnormalities of 87.7%. Therefore, the combined tracking of sperm movement based on the GMM method and matching-based algorithm can be utilized to identify abnormalities in the movement of human spermatozoa.

show abstract

“…Only sperm with high motility have physiological significance. Ilhan et al ( 22 ) developed a sperm counting system based on smart phone is developed, which can be used as an alternative method for sperm concentration analysis in visual evaluation technology. However, none of them are equipped with microfluidic chips, which leads to complicated operation steps and a large number of reagents, making them unsuitable for on-site testing.…”

Section: Introductionmentioning

confidence: 99%

A Sperm Quality Detection System Based on Microfluidic Chip and Micro-Imaging System

et al. 2022

View full text Add to dashboard Cite

Sperm quality assessment is the main method to predict the reproductive ability of livestock. The detection of sperm quality of livestock is of great significance to the application of artificial insemination and in vitro fertilization. In order to comprehensively evaluate sperm quality and improve the real-time and portability of sperm quality detection, a portable microscopic imaging system based on microfluidic chip is developed in this paper. The system can realize the comprehensive evaluation of sperm quality by detecting sperm vitality and survival rate. On the hardware side, a microfluidic chip is designed, which can automatically mix samples. A set of optical system with a magnification of 400 times was developed for microscopic observation of sperm. In the aspect of software, aiming at the comprehensive evaluation of sperm quality based on OpenCV, a set of algorithms for identifying sperm motility and survival rate is proposed. The accuracy of the system in detecting sperm survival rate is 94.0%, and the error rate is 0.6%. The evaluation results of sperm motility are consistent with those of computer-aided sperm analysis (CASA). The system's identification time is 9 s. Therefore, the system is absolutely suitable for sperm quality detection.

show abstract

Smartphone based sperm counting - an alternative way to the visual assessment technique in sperm concentration analysis

Cited by 7 publications

References 38 publications

Are There Differences between Methods Used for the Objective Estimation of Boar Sperm Concentration and Motility?

Are There Differences between Methods Used for the Objective Estimation of Boar Sperm Concentration and Motility?

Abnormality Determination of Spermatozoa Motility Using Gaussian Mixture Model and Matching-based Algorithm

A Sperm Quality Detection System Based on Microfluidic Chip and Micro-Imaging System

Contact Info

Product

Resources

About