Can the Sperm Class Analyser (SCA) CASA-Mot system for human sperm motility analysis reduce imprecision and operator subjectivity and improve semen analysis?

Dearing, Chey; Jayasena, Channa N.; Lindsay, K.S.

doi:10.1080/14647273.2019.1610581

Cited by 20 publications

(10 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…No significant difference was reported also by Lammers et al (2014) when using both the SQA V-GOLD and CEROS systems and similarly by Agarwal et al when using the LensHooke X1 PRO (22). Conversely, the studies conducted by Dearing et al and Talarczyk et al reported a significant difference between the results (P<0.001 and P<0.0001, respectively) (14,24). A high correlation coefficient between manual and CASA-based analyses were reported when SQA Vision (r=0.86; P value not reported) (18), and a novel CASA system using a multitarget tracking algorithms (r=0.85; P<0.0001) (17) were used.…”

Section: Progressive Motilitymentioning

confidence: 79%

See 1 more Smart Citation

The validity and reliability of computer-aided semen analyzers in performing semen analysis: a systematic review

Finelli¹,

Leisegang²,

Tumallapalli³

et al. 2021

Transl Androl Urol

View full text Add to dashboard Cite

Background: Computer-aided sperm analyzers (CASA) are currently used worldwide for semen analysis.However, there are doubts about their reliability to fully substitute the human operator. Therefore, this study aimed to systematically review the current literature comparing results from semen evaluation by both CASA-based and manual approaches.Methods: A systematic screening of the literature was performed based on the PRISMA guidelines and by searching on PubMed, Scopus, and Embase databases.Results: A total of 14 studies were included. Our results showed a high degree of correlation for sperm concentration and motility when analysis was performed either manually or by using a CASA system. However, CASA results showed increased variability in low (<15 million/mL) and high (>60 million/ mL) concentration specimens, while sperm motility assessment was inaccurate in samples with higher concentration or in the presence of non-sperm cells and debris. Morphology results showed the highest level of difference, due to the high amount of heterogeneity seen between the shapes of the spermatozoa either in one sample or across multiple samples from the same subject.Conclusions: Overall, our study suggests CASA systems as a valid alternative for the evaluation of semen parameters in clinical practice, especially for sperm concentration and motility. However, further technological improvements are required before these devices can one day completely replace the human operator. Artificial intelligence-based CASA devices promise to offer higher efficiency of the analysis and improve the reliability of results.

show abstract

Section: Progressive Motilitymentioning

confidence: 79%

“…Seven articles compared results for progressive motility by analyzing samples manually or using SCA (15,18,24), LensHooke X1 PRO ( 22), SQA-Vision ( 16), SQA-V GOLD and CEROS (11), and an unnamed system (17). An agreement between both manual and SCA analysis was reported analyzing 30 semen samples (15).…”

Section: Progressive Motilitymentioning

confidence: 99%

The validity and reliability of computer-aided semen analyzers in performing semen analysis: a systematic review

Finelli¹,

Leisegang²,

Tumallapalli³

et al. 2021

Transl Androl Urol

View full text Add to dashboard Cite

show abstract

“…Sperm sample evaluation in a modern andrology lab might be measured by the means of CASA. The use of computer aid does not exclude additional evaluation by the human eye [70,71]…”

Section: Sperm Sample Evaluation and Referencesmentioning

confidence: 99%

The Sperm: Parameters and Evaluation

Milachich¹,

Dyulgerova-Nikolova²

2020

Innovations in Assisted Reproduction Technology

View full text Add to dashboard Cite

Sperm abnormalities are a major factor of human infertility. Since 1987, there are several references in different editions of World Health Organization (WHO) manual defining optimal sperm parameters. Over the years, many reproductive specialists have been constantly debating, suggesting and remodeling the frame values in those guidelines. Semen parameters have a leading role both in natural conception and assisted reproduction technologies (ART) outcomes. Deviations expressed in lower sperm count, impaired motility, abnormal morphology, and high percentage of sperm DNA fragmentation are linked to reduced chances to achieve pregnancy. In cases with low sperm count, severe oligoasthenozoospermia (OA) or azoospermia, karyotyping or evaluation with sperm aneuploidy test (SAT) could be an option and genetic counseling will be necessary if there is an obvious deviation or aberration (e.g., translocation, aneuploidy, etc.). Taking care of lifestyle factors as body mass index (BMI), diets, alcohol intake, smoking, using some additional nutrition and vitamin supplements might affect sperm parameters and contribute to the chances of a couple to conceive.

show abstract

“…The automated interpretation of semen parameters produced by computed-aided systems such as CASA has shown to decrease operator subjectivity when compared to manual testing. [ 5 ] Although the precision of results is being maintained, according to Talarczyk-Desole et al ., the technician's personal interpretation could skew the computer-assisted study. The technicians need not be experts in semen analysis, however, basic knowledge of seminology is required and to get accurate findings, it is crucial to strictly follow CASA's producer settings and to evaluate human sperm in Leja chambers.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index

Kumar¹,

Sharma²,

Halder³

et al. 2023

Journal of Human Reproductive Sciences

View full text Add to dashboard Cite

A BSTRACT Background: Determining the DNA fragmentation index (DFI) by the sperm chromatin dispersion (SCD) test involves manual counting of stained sperms with halo and no halo. Aims: The aim of this study is to build a robust artificial intelligence-based solution to predict the DFI. Settings and Design: This is a retrospective experimental study conducted in a secondary in vitro fertilisation setup. Materials and Methods: We obtained 24,415 images from 30 patients after the SCD test using a phase-contrast microscope. We classified the dataset into two, binary (halo/no halo) and multiclass (big/medium/small halo/degraded (DEG)/dust). Our approach consists of a training and prediction phase. The 30 patients’ images were divided into training (24) and prediction (6) sets. A pre-processing method M was developed to automatically segment the images to detect sperm-like regions and was annotated by three embryologists. Statistical Analysis Used: To interpret the findings, the precision-recall curve and F1 score were utilised. Results: Binary and multiclass datasets containing 8887 and 15,528 cropped sperm image regions showed an accuracy of 80.15% versus 75.25%. A precision-recall curve was determined and the binary and multiclass datasets obtained an F1 score of 0.81 versus 0.72. A confusion matrix was applied for predicted and actuals for the multiclass approach where small halo and medium halo confusion were found to be highest. Conclusion: Our proposed machine learning model can standardise and aid in arriving at accurate results without using expensive software. It provides accurate information about healthy and DEG sperms in a given sample, thereby attaining better clinical outcomes. The binary approach performed better with our model than the multiclass approach. However, the multiclass approach can highlight the distribution of fragmented and non-fragmented sperms.

show abstract

Can the Sperm Class Analyser (SCA) CASA-Mot system for human sperm motility analysis reduce imprecision and operator subjectivity and improve semen analysis?

Cited by 20 publications

References 38 publications

The validity and reliability of computer-aided semen analyzers in performing semen analysis: a systematic review

The validity and reliability of computer-aided semen analyzers in performing semen analysis: a systematic review

The Sperm: Parameters and Evaluation

Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index

Contact Info

Product

Resources

About