Validation of Vetscan Imagyst®, a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples
Background Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst’s skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intelligence (AI) algorithm is a viable, emerging alterna...
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| Published in | Parasites & vectors Vol. 17; no. 1; p. 465 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
12.11.2024
BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1756-3305 1756-3305 |
| DOI | 10.1186/s13071-024-06525-w |
Cover
| Abstract | Background
Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst’s skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intelligence (AI) algorithm is a viable, emerging alternative that can mitigate operator variation compared to conventional methods in companion animal fecal parasite diagnostics. Vetscan Imagyst is a novel fecal parasite detection system that uploads the scanned image to the cloud where proprietary software analyzes captured images for diagnostic recognition by a deep learning, object detection AI algorithm. The study describes the use and validation of Vetscan Imagyst in equine parasitology.
Methods
The primary objective of the study was to evaluate the performance of the Vetscan Imagyst system in terms of diagnostic sensitivity and specificity in testing equine fecal samples (
n
= 108) for ova from two parasites that commonly infect horses, strongyles and
Parascaris
spp., compared to reference assays performed by expert parasitologists using a Mini-FLOTAC technique. Two different fecal flotation solutions were used to prepare the sample slides, NaNO
3
and Sheather’s sugar solution.
Results
Diagnostic sensitivity of the Vetscan Imagyst algorithm for strongyles versus the manual reference test was 99.2% for samples prepared with NaNO
3
solution and 100.0% for samples prepared with Sheather’s sugar solution. Sensitivity for
Parascaris
spp. was 88.9% and 99.9%, respectively, for samples prepared with NaNO
3
and Sheather’s sugar solutions. Diagnostic specificity for strongyles was 91.4% and 99.9%, respectively, for samples prepared with NaNO
3
and Sheather’s sugar solutions. Specificity for
Parascaris
spp. was 93.6% and 99.9%, respectively, for samples prepared with NaNO
3
and Sheather’s sugar solutions. Lin’s concordance correlation coefficients for VETSCAN IMAGYST eggs per gram counts versus those determined by the expert parasitologist were 0.924–0.978 for strongyles and 0.944–0.955 for
Parascaris
spp., depending on the flotation solution.
Conclusions
Sensitivity and specificity results for detecting strongyles and
Parascaris
spp. in equine fecal samples showed that Vetscan Imagyst can consistently provide diagnostic accuracy equivalent to manual evaluations by skilled parasitologists. As an automated method driven by a deep learning AI algorithm, VETSCAN IMAGYST has the potential to avoid variations in analyst characteristics, thus providing more consistent results in a timely manner, in either clinical or laboratory settings.
Graphical Abstract |
|---|---|
| AbstractList | Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst's skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intelligence (AI) algorithm is a viable, emerging alternative that can mitigate operator variation compared to conventional methods in companion animal fecal parasite diagnostics. Vetscan Imagyst is a novel fecal parasite detection system that uploads the scanned image to the cloud where proprietary software analyzes captured images for diagnostic recognition by a deep learning, object detection AI algorithm. The study describes the use and validation of Vetscan Imagyst in equine parasitology.
The primary objective of the study was to evaluate the performance of the Vetscan Imagyst system in terms of diagnostic sensitivity and specificity in testing equine fecal samples (n = 108) for ova from two parasites that commonly infect horses, strongyles and Parascaris spp., compared to reference assays performed by expert parasitologists using a Mini-FLOTAC technique. Two different fecal flotation solutions were used to prepare the sample slides, NaNO
and Sheather's sugar solution.
Diagnostic sensitivity of the Vetscan Imagyst algorithm for strongyles versus the manual reference test was 99.2% for samples prepared with NaNO
solution and 100.0% for samples prepared with Sheather's sugar solution. Sensitivity for Parascaris spp. was 88.9% and 99.9%, respectively, for samples prepared with NaNO
and Sheather's sugar solutions. Diagnostic specificity for strongyles was 91.4% and 99.9%, respectively, for samples prepared with NaNO
and Sheather's sugar solutions. Specificity for Parascaris spp. was 93.6% and 99.9%, respectively, for samples prepared with NaNO
and Sheather's sugar solutions. Lin's concordance correlation coefficients for VETSCAN IMAGYST eggs per gram counts versus those determined by the expert parasitologist were 0.924-0.978 for strongyles and 0.944-0.955 for Parascaris spp., depending on the flotation solution.
Sensitivity and specificity results for detecting strongyles and Parascaris spp. in equine fecal samples showed that Vetscan Imagyst can consistently provide diagnostic accuracy equivalent to manual evaluations by skilled parasitologists. As an automated method driven by a deep learning AI algorithm, VETSCAN IMAGYST has the potential to avoid variations in analyst characteristics, thus providing more consistent results in a timely manner, in either clinical or laboratory settings. Abstract Background Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst’s skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intelligence (AI) algorithm is a viable, emerging alternative that can mitigate operator variation compared to conventional methods in companion animal fecal parasite diagnostics. Vetscan Imagyst is a novel fecal parasite detection system that uploads the scanned image to the cloud where proprietary software analyzes captured images for diagnostic recognition by a deep learning, object detection AI algorithm. The study describes the use and validation of Vetscan Imagyst in equine parasitology. Methods The primary objective of the study was to evaluate the performance of the Vetscan Imagyst system in terms of diagnostic sensitivity and specificity in testing equine fecal samples (n = 108) for ova from two parasites that commonly infect horses, strongyles and Parascaris spp., compared to reference assays performed by expert parasitologists using a Mini-FLOTAC technique. Two different fecal flotation solutions were used to prepare the sample slides, NaNO3 and Sheather’s sugar solution. Results Diagnostic sensitivity of the Vetscan Imagyst algorithm for strongyles versus the manual reference test was 99.2% for samples prepared with NaNO3 solution and 100.0% for samples prepared with Sheather’s sugar solution. Sensitivity for Parascaris spp. was 88.9% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather’s sugar solutions. Diagnostic specificity for strongyles was 91.4% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather’s sugar solutions. Specificity for Parascaris spp. was 93.6% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather’s sugar solutions. Lin’s concordance correlation coefficients for VETSCAN IMAGYST eggs per gram counts versus those determined by the expert parasitologist were 0.924–0.978 for strongyles and 0.944–0.955 for Parascaris spp., depending on the flotation solution. Conclusions Sensitivity and specificity results for detecting strongyles and Parascaris spp. in equine fecal samples showed that Vetscan Imagyst can consistently provide diagnostic accuracy equivalent to manual evaluations by skilled parasitologists. As an automated method driven by a deep learning AI algorithm, VETSCAN IMAGYST has the potential to avoid variations in analyst characteristics, thus providing more consistent results in a timely manner, in either clinical or laboratory settings. Graphical Abstract Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst's skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intelligence (AI) algorithm is a viable, emerging alternative that can mitigate operator variation compared to conventional methods in companion animal fecal parasite diagnostics. Vetscan Imagyst is a novel fecal parasite detection system that uploads the scanned image to the cloud where proprietary software analyzes captured images for diagnostic recognition by a deep learning, object detection AI algorithm. The study describes the use and validation of Vetscan Imagyst in equine parasitology.BACKGROUNDCurrent methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst's skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intelligence (AI) algorithm is a viable, emerging alternative that can mitigate operator variation compared to conventional methods in companion animal fecal parasite diagnostics. Vetscan Imagyst is a novel fecal parasite detection system that uploads the scanned image to the cloud where proprietary software analyzes captured images for diagnostic recognition by a deep learning, object detection AI algorithm. The study describes the use and validation of Vetscan Imagyst in equine parasitology.The primary objective of the study was to evaluate the performance of the Vetscan Imagyst system in terms of diagnostic sensitivity and specificity in testing equine fecal samples (n = 108) for ova from two parasites that commonly infect horses, strongyles and Parascaris spp., compared to reference assays performed by expert parasitologists using a Mini-FLOTAC technique. Two different fecal flotation solutions were used to prepare the sample slides, NaNO3 and Sheather's sugar solution.METHODSThe primary objective of the study was to evaluate the performance of the Vetscan Imagyst system in terms of diagnostic sensitivity and specificity in testing equine fecal samples (n = 108) for ova from two parasites that commonly infect horses, strongyles and Parascaris spp., compared to reference assays performed by expert parasitologists using a Mini-FLOTAC technique. Two different fecal flotation solutions were used to prepare the sample slides, NaNO3 and Sheather's sugar solution.Diagnostic sensitivity of the Vetscan Imagyst algorithm for strongyles versus the manual reference test was 99.2% for samples prepared with NaNO3 solution and 100.0% for samples prepared with Sheather's sugar solution. Sensitivity for Parascaris spp. was 88.9% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Diagnostic specificity for strongyles was 91.4% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Specificity for Parascaris spp. was 93.6% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Lin's concordance correlation coefficients for VETSCAN IMAGYST eggs per gram counts versus those determined by the expert parasitologist were 0.924-0.978 for strongyles and 0.944-0.955 for Parascaris spp., depending on the flotation solution.RESULTSDiagnostic sensitivity of the Vetscan Imagyst algorithm for strongyles versus the manual reference test was 99.2% for samples prepared with NaNO3 solution and 100.0% for samples prepared with Sheather's sugar solution. Sensitivity for Parascaris spp. was 88.9% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Diagnostic specificity for strongyles was 91.4% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Specificity for Parascaris spp. was 93.6% and 99.9%, respectively, for samples prepared with NaNO3 and Sheather's sugar solutions. Lin's concordance correlation coefficients for VETSCAN IMAGYST eggs per gram counts versus those determined by the expert parasitologist were 0.924-0.978 for strongyles and 0.944-0.955 for Parascaris spp., depending on the flotation solution.Sensitivity and specificity results for detecting strongyles and Parascaris spp. in equine fecal samples showed that Vetscan Imagyst can consistently provide diagnostic accuracy equivalent to manual evaluations by skilled parasitologists. As an automated method driven by a deep learning AI algorithm, VETSCAN IMAGYST has the potential to avoid variations in analyst characteristics, thus providing more consistent results in a timely manner, in either clinical or laboratory settings.CONCLUSIONSSensitivity and specificity results for detecting strongyles and Parascaris spp. in equine fecal samples showed that Vetscan Imagyst can consistently provide diagnostic accuracy equivalent to manual evaluations by skilled parasitologists. As an automated method driven by a deep learning AI algorithm, VETSCAN IMAGYST has the potential to avoid variations in analyst characteristics, thus providing more consistent results in a timely manner, in either clinical or laboratory settings. BACKGROUND: Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst’s skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intelligence (AI) algorithm is a viable, emerging alternative that can mitigate operator variation compared to conventional methods in companion animal fecal parasite diagnostics. Vetscan Imagyst is a novel fecal parasite detection system that uploads the scanned image to the cloud where proprietary software analyzes captured images for diagnostic recognition by a deep learning, object detection AI algorithm. The study describes the use and validation of Vetscan Imagyst in equine parasitology. METHODS: The primary objective of the study was to evaluate the performance of the Vetscan Imagyst system in terms of diagnostic sensitivity and specificity in testing equine fecal samples (n = 108) for ova from two parasites that commonly infect horses, strongyles and Parascaris spp., compared to reference assays performed by expert parasitologists using a Mini-FLOTAC technique. Two different fecal flotation solutions were used to prepare the sample slides, NaNO₃ and Sheather’s sugar solution. RESULTS: Diagnostic sensitivity of the Vetscan Imagyst algorithm for strongyles versus the manual reference test was 99.2% for samples prepared with NaNO₃ solution and 100.0% for samples prepared with Sheather’s sugar solution. Sensitivity for Parascaris spp. was 88.9% and 99.9%, respectively, for samples prepared with NaNO₃ and Sheather’s sugar solutions. Diagnostic specificity for strongyles was 91.4% and 99.9%, respectively, for samples prepared with NaNO₃ and Sheather’s sugar solutions. Specificity for Parascaris spp. was 93.6% and 99.9%, respectively, for samples prepared with NaNO₃ and Sheather’s sugar solutions. Lin’s concordance correlation coefficients for VETSCAN IMAGYST eggs per gram counts versus those determined by the expert parasitologist were 0.924–0.978 for strongyles and 0.944–0.955 for Parascaris spp., depending on the flotation solution. CONCLUSIONS: Sensitivity and specificity results for detecting strongyles and Parascaris spp. in equine fecal samples showed that Vetscan Imagyst can consistently provide diagnostic accuracy equivalent to manual evaluations by skilled parasitologists. As an automated method driven by a deep learning AI algorithm, VETSCAN IMAGYST has the potential to avoid variations in analyst characteristics, thus providing more consistent results in a timely manner, in either clinical or laboratory settings. Background Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst’s skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intelligence (AI) algorithm is a viable, emerging alternative that can mitigate operator variation compared to conventional methods in companion animal fecal parasite diagnostics. Vetscan Imagyst is a novel fecal parasite detection system that uploads the scanned image to the cloud where proprietary software analyzes captured images for diagnostic recognition by a deep learning, object detection AI algorithm. The study describes the use and validation of Vetscan Imagyst in equine parasitology. Methods The primary objective of the study was to evaluate the performance of the Vetscan Imagyst system in terms of diagnostic sensitivity and specificity in testing equine fecal samples ( n = 108) for ova from two parasites that commonly infect horses, strongyles and Parascaris spp., compared to reference assays performed by expert parasitologists using a Mini-FLOTAC technique. Two different fecal flotation solutions were used to prepare the sample slides, NaNO 3 and Sheather’s sugar solution. Results Diagnostic sensitivity of the Vetscan Imagyst algorithm for strongyles versus the manual reference test was 99.2% for samples prepared with NaNO 3 solution and 100.0% for samples prepared with Sheather’s sugar solution. Sensitivity for Parascaris spp. was 88.9% and 99.9%, respectively, for samples prepared with NaNO 3 and Sheather’s sugar solutions. Diagnostic specificity for strongyles was 91.4% and 99.9%, respectively, for samples prepared with NaNO 3 and Sheather’s sugar solutions. Specificity for Parascaris spp. was 93.6% and 99.9%, respectively, for samples prepared with NaNO 3 and Sheather’s sugar solutions. Lin’s concordance correlation coefficients for VETSCAN IMAGYST eggs per gram counts versus those determined by the expert parasitologist were 0.924–0.978 for strongyles and 0.944–0.955 for Parascaris spp., depending on the flotation solution. Conclusions Sensitivity and specificity results for detecting strongyles and Parascaris spp. in equine fecal samples showed that Vetscan Imagyst can consistently provide diagnostic accuracy equivalent to manual evaluations by skilled parasitologists. As an automated method driven by a deep learning AI algorithm, VETSCAN IMAGYST has the potential to avoid variations in analyst characteristics, thus providing more consistent results in a timely manner, in either clinical or laboratory settings. Graphical Abstract |
| ArticleNumber | 465 |
| Author | Daniel, Ian Boggan, SaraBeth Lin, Dan Penn, Cory Steuer, Ashley Fritzler, Jason Goldstein, Richard Cowles, Bobby |
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| Cites_doi | 10.3390/pathogens9020139 10.1186/s13071-022-05266-y 10.1371/journal.pntd.0010500 10.1016/j.vetpar.2017.12.012 10.1186/s13071-020-04215-x 10.1016/j.vetpar.2007.04.014 10.1016/j.crpvbd.2021.100046 10.1645/GE-2058.1 10.1186/s13071-022-05168-z 10.1186/s13071-020-04396-5 10.1016/bs.apar.2022.07.002 10.1016/j.vetpar.2020.109199 10.1128/JCM.00511-20 10.1016/j.vetpar.2014.05.009 10.3390/ani10081254 10.7717/peerj-cs.1065 10.1016/j.cmi.2020.03.012 10.1093/clinchem/hvab165 10.1016/j.vetpar.2017.10.005 10.1186/s13071-021-04591-y 10.1186/1756-3305-2-S2-S1 10.1371/journal.pone.0175646 10.1186/1756-3305-7-271 10.1007/s00436-022-07627-z 10.1038/nprot.2017.067 |
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| Keywords | Deep learning Sheather’s sugar solution Diagnostic Equine Fecal egg Strongyles Artificial intelligence Parascaris |
| Language | English |
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| References | Y Nagamori (6525_CR6) 2020; 13 6525_CR26 KP Smith (6525_CR17) 2020; 26 JL Cain (6525_CR20) 2022; 121 LR Ballweber (6525_CR4) 2014; 204 B Barda (6525_CR7) 2014; 7 P Ward (6525_CR15) 2022; 16 JL Cain (6525_CR5) 2020; 284 SV Inácio (6525_CR10) 2020; 9 MK Nielsen (6525_CR27) 2018; 250 MC Gates (6525_CR8) 2009; 95 H Boelow (6525_CR1) 2022; 15 DS Herman (6525_CR16) 2021; 67 JA Scare (6525_CR14) 2017; 247 A Ghafar (6525_CR2) 2021; 1 KM Naing (6525_CR11) 2022; 8 AM Zajac (6525_CR24) 2012 G Cringoli (6525_CR23) 2017; 12 F Tyson (6525_CR3) 2020; 10 DD Rhoads (6525_CR12) 2020; 58 AE Steuer (6525_CR22) 2022; 15 L Rinaldi (6525_CR13) 2022; 118 S Corning (6525_CR21) 2009; 2 A Alva (6525_CR9) 2017; 12 A Pereckiene (6525_CR25) 2007; 149 Y Nagamori (6525_CR18) 2021; 14 JL Bellaw (6525_CR19) 2020; 13 |
| References_xml | – volume: 9 start-page: 139 year: 2020 ident: 6525_CR10 publication-title: Pathogens doi: 10.3390/pathogens9020139 – volume: 15 start-page: 166 year: 2022 ident: 6525_CR1 publication-title: Parasit Vectors doi: 10.1186/s13071-022-05266-y – volume: 16 year: 2022 ident: 6525_CR15 publication-title: PLoS Negl Trop Dis doi: 10.1371/journal.pntd.0010500 – volume: 250 start-page: 45 year: 2018 ident: 6525_CR27 publication-title: Vet Parasitol doi: 10.1016/j.vetpar.2017.12.012 – volume: 13 start-page: 346 year: 2020 ident: 6525_CR6 publication-title: Parasit Vectors doi: 10.1186/s13071-020-04215-x – volume: 149 start-page: 111 year: 2007 ident: 6525_CR25 publication-title: Vet Parasitol doi: 10.1016/j.vetpar.2007.04.014 – volume: 1 year: 2021 ident: 6525_CR2 publication-title: Curr Res Parasitol Vector Borne Dis doi: 10.1016/j.crpvbd.2021.100046 – volume: 95 start-page: 1213 year: 2009 ident: 6525_CR8 publication-title: J Parasitol doi: 10.1645/GE-2058.1 – volume: 15 start-page: 50 year: 2022 ident: 6525_CR22 publication-title: Parasit Vectors doi: 10.1186/s13071-022-05168-z – volume: 13 start-page: 509 year: 2020 ident: 6525_CR19 publication-title: Parasit Vectors doi: 10.1186/s13071-020-04396-5 – volume: 118 start-page: 85 year: 2022 ident: 6525_CR13 publication-title: Adv Parasitol doi: 10.1016/bs.apar.2022.07.002 – volume: 284 year: 2020 ident: 6525_CR5 publication-title: Vet Parasitol doi: 10.1016/j.vetpar.2020.109199 – ident: 6525_CR26 – volume: 58 start-page: e00511 year: 2020 ident: 6525_CR12 publication-title: J Clin Microbiol doi: 10.1128/JCM.00511-20 – volume: 204 start-page: 73 year: 2014 ident: 6525_CR4 publication-title: Vet Parasitol doi: 10.1016/j.vetpar.2014.05.009 – volume: 10 start-page: 1254 year: 2020 ident: 6525_CR3 publication-title: Animals (Basel) doi: 10.3390/ani10081254 – volume: 8 year: 2022 ident: 6525_CR11 publication-title: PeerJ Comput Sci doi: 10.7717/peerj-cs.1065 – volume: 26 start-page: 1318 year: 2020 ident: 6525_CR17 publication-title: Clin Microbiol Infect doi: 10.1016/j.cmi.2020.03.012 – volume: 67 start-page: 1466 year: 2021 ident: 6525_CR16 publication-title: Clin Chem doi: 10.1093/clinchem/hvab165 – volume: 247 start-page: 85 year: 2017 ident: 6525_CR14 publication-title: Vet Parasitol doi: 10.1016/j.vetpar.2017.10.005 – start-page: 1 volume-title: Veterinary clinical parasitology year: 2012 ident: 6525_CR24 – volume: 14 start-page: 89 year: 2021 ident: 6525_CR18 publication-title: Parasit Vectors doi: 10.1186/s13071-021-04591-y – volume: 2 start-page: 1 year: 2009 ident: 6525_CR21 publication-title: Parasit Vectors doi: 10.1186/1756-3305-2-S2-S1 – volume: 12 year: 2017 ident: 6525_CR9 publication-title: PLoS ONE doi: 10.1371/journal.pone.0175646 – volume: 7 start-page: 271 year: 2014 ident: 6525_CR7 publication-title: Parasit Vectors doi: 10.1186/1756-3305-7-271 – volume: 121 start-page: 2775 year: 2022 ident: 6525_CR20 publication-title: Parasitol Res doi: 10.1007/s00436-022-07627-z – volume: 12 start-page: 1723 year: 2017 ident: 6525_CR23 publication-title: Nat Protoc doi: 10.1038/nprot.2017.067 |
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Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst’s skill and experience.... Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst's skill and experience. Automated digital... BACKGROUND: Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst’s skill and experience.... Abstract Background Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst’s skill and experience.... |
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| SubjectTerms | Algorithms Animals Artificial Intelligence Ascaridida Infections - diagnosis Ascaridida Infections - parasitology Ascaridida Infections - veterinary Ascaridoidea - isolation & purification automation Biomedical and Life Sciences Biomedicine computer software Deep Learning Diagnostic diagnostic sensitivity diagnostic specificity diagnostic techniques Diagnostic Tests, Routine - methods Diagnostic Tests, Routine - veterinary eggs Entomology Equine Fecal egg feces Feces - parasitology Horse Diseases - diagnosis Horse Diseases - parasitology horses Horses - parasitology Infectious Diseases Parascaris Parasite Egg Count - methods Parasite Egg Count - veterinary parasites parasitic diseases Parasitology pets Sensitivity and Specificity Strongyle Infections, Equine - diagnosis Strongyle Infections, Equine - parasitology Strongylidae sugars Tropical Medicine Veterinary Medicine/Veterinary Science Virology |
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| Title | Validation of Vetscan Imagyst®, a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples |
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