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Translational Animal Science, 2022, 6, 1–8 https://doi.org/10.1093/tas/txac119 Advance access publication 28 August 2022 Reproduction Validation of a smartphone-based device to measure concentration, motility, and morphology in swine ejaculates †,‡,1, ║ ║ ║ † Aridany Suárez-Trujillo, Hemanth Kandula, Jasmine Kumar, Anjali Devi, Larissa Shirley, ║ ║ ║ $ Prudhvi Thirumalaraju, Manoj Kumar Kanakasabapathy, Hadi Shafiee, and Liane Hart Department of Animal Science, Purdue University, West Lafayette, IN 47909, USA Department of Animal Science, Berry College, Mount Berry, GA 30149, USA Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02139, USA Verility, Inc., Maxwell, IN 46154, USA Corresponding author: email@example.com ABSTRACT Assessment of swine semen quality is important as it is used as an estimate of the fertility of an ejaculate. There are many methods to measure sperm morphology, concentration, and motility, however, some methods require expensive instrumentation or are not easy to use on-farm. A portable, low-cost, automated device could provide the potential to assess semen quality in field conditions. The objective of this study was to validate the use of Fertile-Eyez (FE), a smartphone-based device, to measure sperm concentration, total motility, and morphology in boar ejaculates. Semen from six sexually mature boars were collected and mixed to create a total of 18 unique semen samples for system evaluations. Each sample was then diluted to 1:4, 1:8, 1:10, and 1:16 (for concentration only) with Androhep Plus semen extender (n = 82 total). Sperm concentration was evaluated using FE and compared to results measured using a Nucleocounter and computer assisted sperm analysis (CASA: Ceros II, Hamilton Thorne). Sperm motility was evaluated using FE and CASA. Sperm morphological assessments were evaluated by a single technician manually counting abnormalities and compared to FE deep-learning technology. Data were analyzed using both descriptive statistics (mean, standard deviation, intra-assay coefficient of variance, and residual standard deviation [RSD]) and statistical tests (correlation analysis between devices and Bland-Altman methods). Concentration analysis was strongly correlated (n = 18; r > 0.967; P < 0.0001) among all devices and dilutions. Analysis of motility showed moderate correlation and was significant when all dilutions are analyzed together ( n = 54; r = 0.558; P < 0.001). The regression analysis for motility also showed the RSD as 3.95% between FE and CASA indicating a tight t fi between devices. This RSD indicates that FE can find boars with unacceptable motility (boars for example with less than 70%) which impact fertility and litter size. The Bland-Altman analysis showed that FE-estimated morphological assessment and the conventionally estimated morphological score were similar, with a mean difference of ~1% (%95 Limits of Agreement: −6.2 to 8.1; n = 17). The results of this experiment demonstrate that FE, a portable and automated smartphone-based device, is capable of assessing concentration, motility, and morphology of boar semen samples. Key words: smartphone-based semen analysis, swine INTRODUCTION methods include nuclear staining to differentiate sperm cells from other particles Nucleocounter (NC) SP-100 (Morrell et Evaluation of boar semen for measures of semen quality is an al., 2010), cell sorting using flow cytometry with fluorescent important component to success when using artificial insemi - cell labeling (Hansen et al., 2006), or computer-assisted sperm nation. Immediately following ejaculation, semen is evaluated analysis (CASA; Zinaman et al., 1996, Amann and Waberski, for volume, concentration, motility, and morphological 2014). These methods can be highly accurate; however, they abnormalities of the sperm cells. The use of poor-quality require either expensive instruments or may not be adaptable semen, with low concentration, motility, or high number of for farm-based use. A portable, low-cost, automated device morphological abnormalities, is correlated with low repro- that could be used in field settings or for smaller operations ductive success after insemination (Flowers, 1997). There are could be useful if accurate and repeatable. multiple methods to asses concentration of sperm cells within Smartphone-based devices for semen evaluation have an ejaculate, most commonly, direct cell counting using a he- been previously tested in humans, stallions, and dogs mocytometer (Jasko, 1992) or spectrophotometry (Camus (Kanakasabapathy et al., 2017, Thirumalaraju et al., 2018, et al., 2011). These two methods are relatively inexpensive, Buss et al., 2019, Dini et al., 2019, Thirumalaraju et al., 2019, however, hemocytometer hand counting of sperm cells can be Bulkeley et al., 2021, Kanakasabapathy et al., 2021). Those time-consuming limiting its practicality for use on every ejac- smartphone-based devices, similar to CASA systems, use ulate in a commercial setting (Dini et al., 2019). Additional Received January 8, 2022 Accepted August 26, 2022. © The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. 2 Suárez-Trujillo et al. images captured from semen samples loaded in a chamber SP-100 machine and evaluated for the total number of sperm slide to evaluate sperm cell concentration, motility, and mor- cells. Each sample was evaluated in triplicate by loading three phology (Mortimer et al., 2015). individual cartridges from the same semen/Reagent S100 mix. The aim of this study was to evaluate the accuracy and FE. FE device is a smartphone-based device developed by repeatability of measures of sperm concentration, motility, Kanakasabapathy et al. (2017) and Kanakasabapathy et al. and morphology in boar ejaculates using Fertile-Eyez (FE), (2021). Briefly, an optical hardware smartphone attachment, a smartphone-based semen evaluation device. To achieve composed of a pair of lenses, a small battery, an LED light, this aim, the concentration of sperm cells in ejaculates and and a 3-D printed support base, was used for sperm cells motility were compared to two devices generally accepted imaging. The recorded videos of fresh semen samples and as highly repeatable and accurate in the swine industry, images of smeared stained sperm cells were used for meas- the NC SP-100 and CASA (Ceros II, Hamilton Thorne, uring sperm concentration, motility, and morphology using USA). Morphology estimations were compared to conven- a deep learning-based framework. A 2-mL aliquot of each tional manual assessments performed using phase-contrast diluted ejaculate was transferred to a clean polypropylene microscopy. tube and incubated at 37 °C for 20 min. After warming, the sample was mixed by hand and 3 µL loaded in a pre-warmed MATERIALS AND METHODS 2-chamber slide (Leja, IMV, USA). The chamber slide was then inserted into the support base of the FE device for anal- Animals ysis of concentration and total motility. The smartphone ap- Six sexually mature boars (20 months old) with known semen plication records 1s duration videos (30 fps) and processes quality above 75% total motility and 85% morphologically each frame to obtain sperm concentration and motility. normal sperm cells were used for this study. Boars were col- lected under a protocol reviewed and approved by Purdue CASA. The same warmed 2 mL sample of semen was used University Institutional Animal Care and Use Committee to evaluate concentration and motility on the CASA system (#2012002099). Boars were housed in individual stalls and (Ceros II, Hamilton Thorne, USA). A 3 µL aliquot was loaded fed a maintenance diet once per day. into a 4-chamber slide (Leja, IMV, USA) and placed on a warmed (37 °C) microscope stage of an AxioLAB A1 Zeiss Semen Collection and Dilutions microscope equipped with a 20× FINH objective. Within each Semen was collected using the double-gloved-hand method to chamber, six fields were analyzed and the average concentra - minimize bacterial contamination of the ejaculates. Ejaculates tion and motility reported. Each sample was analyzed in trip- from six boars were collected and mixed in pairs to create licate using three slide chambers. a total of 18 unique semen samples. The main objective of mixing semen samples to create 18 unique samples was to Morphology Assessment increase the sample size for this study. Mixed ejaculates were then diluted with Androhep Plus (Minitube, USA) semen A subsample (1 mL) of each semen mixture was preserved extender to dilutions of 1:4, 1:8, 1:10, and 1:16. Extended with 100 µL of 10% formalin for evaluation of sperm cell semen samples were placed into a cooler at 17 °C overnight morphology. Using phase-contrast, bright-field microscopy until analysis. All dilutions were used for determination of (40×), 200 randomly selected sperm cells were categorized as concentration, but only concentration 1:4, 1:8, and 1:10 were morphologically normal or containing proximal or distal cy- used for determination of motility. Dilution 1:16 was not used toplasmic droplets, distal midpiece reflex, abnormal heads, or for motility determination due to low number of sperm cells tails. Each of the 18 semen samples was manually counted per field to have a representative number sample for this de - a single time by a single technician. The preserved samples termination. CASA system settings would need to have been were then mixed with eosin stain, smeared on a cleaned glass adjusted to accurately measure at this low level of sperm. slide, covered with a glass coverslip, and sealed with clear nail polish. Stained slides were shipped to Dr. Shafiee’s laboratory Determination of Sperm Concentration and Total at Brigham and Women’s Hospital where the FE deep learning Motility technology was used to evaluate sperm cell morphological NC. Semen samples were diluted with Reagent S100 fol- abnormalities. The smeared microscope slide was inserted lowing manufacturer recommendations based on the appro- into the device, similarly to the Leja slide for motility analysis, priate dilution factor, vortexed for 10 s and loaded into an and evaluated for morphological abnormalities using a deep SP-100 cassette. The cassette was then inserted into the NC learning algorithm. Table 1. Mean concentration (10 cells/mL) and coefficient of variation (CV) of serial-diluted swine semen samples measured with Nucleocounter, Fertile-Eyez, and CASA Nucleocounter Fertile-Eyez CASA Dilution Mean SD CV Mean SD CV Mean SD CV 1:4 87.88 1.72 1.98 76.22 8.58 11.15 84.80 6.07 7.28 1:8 37.89 0.78 2.03 35.96 6.05 15.04 36.26 3.62 10.26 1:10 26.65 0.87 3.40 21.39 3.56 16.97 30.56 9.39 13.80 1:16 19.26 0.54 2.81 16.39 2.49 15.31 18.83 3.00 15.36 Smartphone-based device to evaluate sperm quality in swine ejaculates 3 Table 2. Correlation among concentrations measured by NucleoCounter Python 3.6 using PyTorch (v1.5.0) was used to implement (NC), Fertile-Eyez (FE), and CASA in serial‐diluted swine semen samples the deep learning algorithm used in this study (MDnets) (Kanakasabapathy et al., 2021) and public libraries such as Method Correlation (r) P-value OS, time, csv, sklearn, math, copy, Itertools, random, and NumPy were used. The network was built on a computer All dilutions (n = 82) running Ubuntu 18.04 Linux. The network training was NC FE 0.967 <0.0001 GPU-bound, and the training was performed using 3 GeForce NC CASA 0.982 <0.0001 GTX 1080Ti GPUs (Nvidia). The MDnet framework consists FE CASA 0.964 <0.0001 of a base network architecture with a final flattened layer Dilution 1:4 (~82 × 10 cell/mL) (n =18) linked to a classifier block and an adversarial block. MD-nets NC FE 0.819 <0.0001 are trained by limiting the classification loss created by the classification block using the source data while maximizing NC CASA 0.880 <0.0001 the discriminator loss, which increases domain confusion FE CASA 0.778 0.0001 (Kanakasabapathy et al., 2021). One of the 18 samples was Dilution 1:8 (~37 × 10 cell/mL) (n=18) randomly selected to be used as a control sample for device NC FE 0.583 0.011 calibration, leaving 17 samples evaluated by both the techni- NC CASA 0.893 <0.0001 cian and FE technologies. FE CASA 0.615 0.007 Individual sperm images annotated through manual as- Dilution 1:10 (~25 × 10 cell/mL) (n = 18) sessment by expert-technical staff was used to evaluate the NC FE 0.896 <0.0001 trained algorithm at the single-cell level. We utilized images collected using a benchtop microscope for this section of the NC CASA 0.839 <0.0001 analysis similar to a previous study by Kanakasabapathy et FE CASA 0.809 <0.0001 al. (2021). The algorithm was evaluated using 270 individual Dilution 1:16 (~18 × 10 cell/mL) (n = 18) sperm cell images and through a receiver operating character- NC FE 0.705 0.001 istic (ROC) analysis, an area under the curve (AUC) of 0.994 NC CASA 0.854 <0.0001 (P < 0.001) was obtained, which indicated that the algorithm FE CASA 0.641 0.004 excelled at differentiating between sperm cells based on their morphology (normal vs. abnormal). Figure 1. Linear regression between sperm concentration values measured with Fertile-Eyez, compared to Nucleocounter at 1:4 (A), 1:8 (B), 1:10 (C), and 1:16 (D) dilution. 4 Suárez-Trujillo et al. Statistical Analysis All analyses were performed using SAS v9.4 (SAS Institute, Cary, NC), Prism v9.2 (Graphpad, CA), and MedCalc v20.009 (MedCalc Software, Belgium). Statistical analyses were performed in agreement with previous research testing similar devices (Dini et al., 2019). The mean and standard de- viation (SD) were calculated from the three replicates for each diluted semen sample. These factors were used to calculate the coefficient of variation (CV) as an evaluation of repeat - ability. Accuracy assessments were performed using Pearson correlation coefficients in PROC CORR and linear regres - sion analysis was performed with PROC REG, both in SAS. Band-Altman plots were created by comparing the difference in response (concentration, motility, or morphology) between two methods for each sample and at each dilution (concentra- tion and motility only), to compare similarities between the two approaches (Bland and Altman, 1986). Statistical signif- icance was established as P ≤ 0.05 and P-values > 0.05 and ≤0.10 were considered a tendency. Coefficients of correlation greater than 0.40 were considered moderately correlated, and coefficients greater than 0.70 were considered as strongly correlated (Ratner, 2009, Mukaka, 2012, Schober et al., 2018). RESULTS AND DISCUSSION Assessment of Sperm Concentration The results for evaluation of concentration for the three devices at all four dilutions are shown in Table 1. The de- scriptive statistics SD and intra-assay CV provide information about the repeatability of the instruments. The NC had the lowest SD (range 0.54–1.72) and CV (range 1.98%–3.4%) for concentration at all dilutions. The CASA system and FE had similar SD (range for CASA 3.0–9.39 and FE 2.49–8.58) and CV (range for CASA 7.28–15.36 and FE 11.15–16.97). Analysis of canine sperm concentration with an iPad-based device found similar to slightly higher CV (22.97%) when the repeatability of the concentration was assessed (Bulkeley et al., 2021). Figure 2. Linear regression between sperm concentration values Correlation analysis allowed evaluation of whether measured with Fertile-Eyez, compared with Nucleocounter (A) and CASA differences in concentration had similar variation for each (B), and between Nucleocounter and CASA (C). of the three devices. Comparison between the three devices showed significant correlation ( P < 0.05) when all dilutions were analyzed as well as when samples were separated The Bland-Altman analysis is used to assess agreement be- by dilution factor (Table 2). All the devices were strongly tween two evaluation methods (Bland and Altman, 1986). In correlated except for at the 1:8 dilution, which was mod- the current study, Bland-Altman analysis showed similarity erately correlated, for the comparison between FE and NC, between NC and CASA at all dilutions (<10%; Figure 3). and between FE and CASA, and at the 1:16 dilution for FE NC and FE, as well as CASA and FE, has the greatest sim- and CASA. Representation of the concentration measured ilarity at the 1:8 dilution with slightly less similarity at the using NC and FE by dilution factor are shown in Figure 1. other dilutions (Table 3). Concentrations reported by NC and The lower r value at 1:8 dilution (r = 0.583) could be due CASA were greater than those reported by FE at all dilutions to 2 or 3 data points that deviate from the regression line. with variation above 10% at all dilutions except 1:8. Devices were also evaluated for accuracy using linear regres- The NC is the gold standard for accuracy and repeata- sion where a significant P-value (P < 0.05) indicates that the bility of measuring concentration in semen samples. The data is linear and the R value is interpreted as the percentage low CV for all dilutions for the NC supports this idea. The of the data variation explained by the linear model. Linear smartphone-based device (FE) showed similar results to the regression analysis showed that concentration measured computer-based device (CASA) when used to measure sperm with the three devices significantly ( P < 0.001) fitted a linear concentration, as demonstrated by similarity in repeatability model. In addition, R values >0.9 were found between FE and accuracy of measurements. FE results were accurate, as and NC, and FE and CASA (Figure 2), which indicates that demonstrated by the high correlation with NC and CASA the three devices have similar variation in their measurements results. Finally, Bland-Altman analysis demonstrated that di- of concentration. lution 1:8 was most correlated among the three devices. Smartphone-based device to evaluate sperm quality in swine ejaculates 5 Figure 3. Bland-Altman plots of sperm concentration measured with Fertile-Eyez (FE), Nucleocounter (NC) and CASA in 1:4, 1:8, 1:10, and 1:16 serial- diluted swine semen. Analysis was performed for the match between (A) NC-FE; (B) NC-CASA; and (C) FE-CASA. The red solid line represents the average of the differences, blue solid line represents 0, the red dashed lines are the ±2× SD; and the green dashed lines represent ±3× SD. All values are given as 10 cells/mL. 6 Suárez-Trujillo et al. Evaluation of FE as a Device to Measure Total that have found strong correlations (r > 0.70) when comparing Motility in Boar Semen devices for assessment of motility using frozen semen with more variation in motility for their analyses (10% to 60%, Testing of the accuracy of FE to measure total motility was Dini et al., 2019; 0% to 80%, Kanakasabapathy et al., 2017). assessed by comparing data measured in serial-diluted boar Therefore, the lower variation between samples most likely semen samples with results obtained by CASA. The average have influenced the correlation analysis, suggesting future total motility, SD, and CV for both devices are presented in studies should be performed with a wider range of motility Table 4. The range in SD for the dilutions using CASA was that include sub-fertile boars with 50%–70% motility and 1.24–1.77 and for FE 0.89–1.18. The range in CV for CASA less. Bland-Altman analysis showed that the mean difference was 2.45–7.10 and for FE 3.46–6.03. Correlation analysis between devices was equal or lower than 10% of the means, was significant when all dilutions were evaluated together ( P indicating high similarity in the measurement of motility be- < 0.001) and the correlation coefficient showed a moderated tween devices (Table 6). correlation (r = 0.558, Table 5). When each dilution was analyzed individually, only the 1:10 dilution was significant Evaluation of FE as a Device to Assess Sperm (P = 0.044) and moderately correlated (r = 0.479). The 1:8 Morphology in Boar Semen dilution showed a tendency (P = 0.098) to correlate motility For morphological assessment, 17 samples were evaluated by between FE and CASA with moderate correlation (r = 0.403). the FE artificial intelligence algorithm, and its results were Lower coefficient of correlation values found for motility data compared with manual counts obtained by a trained tech- may be a result of using samples with similar motility. The nician (Figure 4). Bland-Altman analysis showed that the RSD calculated in the regression analysis showed that overall, the measurement of motility showed a 3.95% variation be- Table 5. Correlation among total motility measured with Fertile-Eyez (FE) tween FE and CASA. Semen samples ranged from a min- and CASA in serial‐diluted swine semen samples imum motility value of 62.7% to a maximum value of 92.5% indicating that FE could measure data spanning values above Method Correlation (r) P-value and below the industry threshold of 70%. Previous authors All dilutions Table 3. Bland-Altman analysis of the sperm concentration measured Fertile-Eyez CASA 0.558 < using NucleoCounter (NC), Fertile-Eyez (FE), and CASA in serial‐diluted 0.001 swine semen samples Dilution 1:4 Fertile-Eyez CASA 0.043 0.866 Method Mean difference (%) Dilution 1:8 Dilution 1:4 Fertile-Eyez CASA 0.403 0.098 NC FE 11.65(+14.2%) Dilution 1:10 NC CASA 3.08(+3.7%) Fertile-Eyez CASA 0.479 0.044 FE CASA −8.57(−17.0%) Dilution 1:8 NC FE 1.93(+5.2%) Table 6. Bland-Altman analysis of the semen total motility measured NC CASA 1.63(+4.5%) using NucleoCounter, Fertile-Eyez, and CASA in serial‐diluted swine FE CASA −0.30(−0.8%) semen samples Dilution 1:10 Method Mean difference (%) NC FE 5.26(+21.9%) NC CASA −0.24(−0.9%) Dilution 1:4 FE CASA −5.50(−22.8%) FE CASA -3.37(-3.9%) Dilution 1:16 Dilution 1:8 NC FE 2.87(+16.1%) FE CASA -2.45(-3.0%) NC CASA 0.43(+2.3%) Dilution 1:10 FE CASA −2.44(−13.9%) FE CASA +3.34(+4.2%) Data represent the difference of the means between devices for the same Data represent the difference of the means between devices for the same dilution factors, and the percentage that the difference represents of the dilution actors, and the percentage that the difference represents of the mean for sperm concentration. mean of for sperm concentration. Table 4. Mean total motility measured in serial-diluted boar samples using Fertile-Eyez and CASA CASA Fertile-Eyez Dilution Mean SD CV Mean SD CV 1:4 84.00 1.24 2.45 82.57 0.93 3.46 1:8 80.66 1.77 6.08 81.52 0.89 6.08 1:10 81.35 1.62 7.10 80.00 1.18 6.03 Smartphone-based device to evaluate sperm quality in swine ejaculates 7 Figure 4. Evaluation of sperm morphology using Fertile-Eyez artificial intelligence. (A) Sperm images as imaged with a Fertile-Eyez device and a benchtop microscope. (B) Receiver operating characteristic curve when analyzing the artificial intelligence algorithm’s ability to differentiate sperm cells based on their morphological quality (normal vs. abnormal) (n = 270). (C) Bland-Altman plot comparing conventionally estimated morphology scores and the scores estimated by Fertile-Eyez (n = 17). difference between the two methods on average was 0.95, Acknowledgements with its 95% limits of agreement ranging from -6.20% to Authors want to thank the collaboration of the Purdue 8.11%, indicating the similarity of measurement between the Animal Science Research and Education Center and Acuity technician and the FE technology. The result indicated that Genetics for the partial sponsorship of this study. the assessment of morphology by FE was like conventional assessments of morphology of expert human technicians. Conflict of Interest Statement H.S. is the inventor of patent application PCT/US2016/038739. CONCLUSION H.S., P.T., and M.K.K. are the inventors of the patent appli- cation PCT/US2019/049367. H.S., P.T., M.K.K. and H.K. are The repeatability and accuracy of using FE for evaluating the inventors of the patent application PCT/US2021/039718. concentration of boar semen samples were similar to NC and L.H. is the CEO and Co-Founder of Verility, Inc. H.S. has CASA, with the greatest accuracy at the 1:8 dilution. Despite a financial interest in Verility Inc, a company developing being moderately correlated, the repeatability and accuracy devices with application in animal breeding including semen of using FE for evaluating motility in boar semen samples analysis. The listed patents are under a licensing agreement were similar to CASA. Further studies, with a wider range with Verility Inc. The section of research, primarily sperm of motilities, should be performed to further evaluate the morphology assessments involving H.S., P.T., M.K.K., and precision of FE to assess sperm motility. FE artificial intelli- H.K., was supported by a sponsored research agreement with gence is also capable of performing automated morphology Verility Inc. Interests of H.S., P.T., and H.K. were reviewed assessments of sperm cells similar to a trained expert tech- and are managed by Brigham and Women’s Hospital and nician. FE is a portable, smartphone-based device capable of Mass General Brigham in accordance with their conflict-of- assessing concentration, motility, and morphology of boar interest policies. semen samples. 8 Suárez-Trujillo et al. diagnostic assay for point-of-care semen analysis. Sci. Transl. Med. LITERATURE CITED 9:eaai7863. doi:10.1126/scitranslmed.aai7863. Amann, R. P., and D. Waberski. 2014. Computer-assisted sperm analysis Kanakasabapathy, M. K., P. Thirumalaraju, H. Kandula, F. Doshi, A. D. (CASA): capabilities and potential developments. Theriogenology. Sivakumar, D. Kartik, R. Gupta, R. Pooniwala, J. A. Branda, A. M. 81:5–17.e11-13. doi:10.1016/j.theriogenology.2013.09.004. Tsibris, et al. 2021. Adaptive adversarial neural networks for the Bland, J. M., and D. G. Altman. 1986. Statistical methods for assessing analysis of lossy and domain-shifted datasets of medical images. agreement between two methods of clinical measurement. Lancet. Nat. Biomed. Eng. 5:571–585. doi:10.1038/s41551-021-00733-w. 1:307–310. doi:10.1016/S0140-6736(86)90837-8. Morrell, J. M., A. Johannisson, L. Juntilla, K. Rytty, L. Bäckgren, A. Bulkeley, E., C. Collins, A. Foutouhi, K. Gonzales, H. Power, and S. M. Dalin, and H. Rodriguez-Martinez. 2010. Stallion sperm via- Meyers. 2021. Assessment of an iPad-based Sperm motility ana- bility, as measured by the nucleocounter sp-100, is affected by ex- lyzer for determination of canine sperm motility. Transl. Anim. Sci. tender and enhanced by single layer centrifugation. Vet Med Int. 5:txab066. doi:10.1093/tas/txab066. 2010:659862. doi:10.4061/2010/659862. Buss, T., J. Aurich, and C. Aurich. 2019. Evaluation of a portable device Mortimer, S. T., G. van der Horst, and D. Mortimer. 2015. The future for assessment of motility in stallion semen. Reprod Domest Anim of computer-aided sperm analysis. Asian J. Androl. 17:545–553. 54:514–519. doi:10.1111/rda.13390. doi:10.4103/1008-682X.154312. Camus, A., S. Camugli, C. Lévêque, E. Schmitt, and C. Staub. 2011. Mukaka, M. M. 2012. Statistics corner: a guide to appropriate use of Is photometry an accurate and reliable method to assess boar se- correlation coefficient in medical research. Malawi Med J. 24:69– men concentration? Theriogenology 75:577–583. doi:10.1016/j. 71. doi:10.4314/MMJ.V24I3. theriogenology.2010.09.025. Ratner, B. 2009. The correlation coefficient: its values range between Dini, P., L. Troch, I. Lemahieu, P. Deblende, and P. Daels. 2019. Val- +1/−1, or do they? J Target. Meas. Anal. Mark. 17:139–142. idation of a portable device (iSperm ) for the assessment of stal- doi:10.1057/jt.2009.5. lion sperm motility and concentration. Reprod. Domest. Anim. Schober, P., C. Boer, and L. A. Schwarte. 2018. Correlation coefficients: 54:1113–1120. doi:10.1111/rda.13487. appropriate use and interpretation. Anesth Analg. 126:1763–1768. Flowers, W. L. 1997. Management of boars for efficient semen produc - doi:10.1213/ANE.0000000000002864. tion. J. Reprod. Fertil. Suppl. 52:67–78. doi: Thirumalaraju, P., C. Bormann, M. Kanakasabapathy, F. Doshi, I. Hansen, C., T. Vermeiden, J. P. W. Vermeiden, C. Simmet, B. C. Day, and Souter, I. Dimitriadis, and H. Shafiee. 2018. Automated sperm H. Feitsma. 2006. Comparison of FACSCount AF system, Improved morpshology testing using artificial intelligence. Fertil. Steril. Neubauer hemocytometer, Corning 254 photometer, SpermVision, 110:e432. doi:10.1016/J.FERTNSTERT.2018.08.039. UltiMate and NucleoCounter SP-100 for determination of sperm Thirumalaraju, P., M. K. Kanakasabapathy, C. L. Bormann, H. concentration of boar semen. Theriogenology 66:2188–2194. Kandula, S. K. Sai Pavan, D. Yarravarapu, and H. Shafiee. 2019. doi:10.1016/j.theriogenology.2006.05.020. Human sperm morphology analysis using smartphone micros- Jasko, D. J. 1992. Evaluation of stallion semen. Vet. Clin. North Am. copy and deep learning. Fertil. Steril. 112:e41. doi:10.1016/j. Equine Pract. 8:129–148. doi:10.1016/s0749-0739(17)30471-6. fertnstert.2019.07.237. Kanakasabapathy, M. K., M. Sadasivam, A. Singh, C. Preston, P. Zinaman, M. J., M. L. Uhler, E. Vertuno, S. G. Fisher, and E. D. Clegg. 1996. Thirumalaraju, M. Venkataraman, C. L. Bormann, M. S. Draz, J. C. Evaluation of computer-assisted semen analysis (CASA) with IDENT Petrozza, and H. Shafiee. 2017. An automated smartphone-based stain to determine sperm concentration. J. Androl. 17:288–292.
Translational Animal Science – Oxford University Press
Published: Aug 28, 2022
Keywords: smartphone; based semen analysis; swine
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