References

Andresen N, Wollhaf M, Hohlbaum K Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis. Plos One. 2020; 15 https://doi.org/10.1371/journal.pone.0228059

Awaysheh A, Wilcke J, Elvinger F Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats. J Vet Diagn Invest. 2016; 28:679-687

Awaysheh A, Wilcke J, Elvinger F Identifying free-text features to improve automated classification of structured histopathology reports for feline small intestinal disease. J Vet Diagn Invest. 2018; 30:211-217 https://doi.org/10.1177/1040638717744002

Awaysheh A, Wilcke J, Elvinger F Review of medical decision support and machine-learning methods. Vet Pathol. 2019; 56:(4)512-525 https://doi.org/10.1177/0300985819829524

Baker LA, Momen M, Chan K Bayesian and machine learning models for genomic prediction of anterior cruciate ligament rupture in the canine model. G3 (Bethesda). 2020; 10:(8)2619-2628 https://doi.org/10.1534/g3.120.401244

Banzato T, Bernardini M, Cherubini GB, Zotti A. A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images. BMC Vet Res. 2018; 14 https://doi.org/10.1186/s12917-018-1638-2

Barnard S, Calderara S, Pistocchi S Quick, accurate, smart: 3D computer vision technology helps assessing confined animals' behaviour. Plos One. 2016; 11 https://doi.org/10.1371/journal.pone.0158748

Baublits JT, Barreda J, Engwall MJ, Vargas H, Chui R. Leveraging machine learning for automated ECG and hemodynamic analyses in the anesthetized canine model. J Pharmacol Toxicol Methods. 2019; 99

Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care?. The J Arthroplasty. 2018; 33:(8)2358-2361 https://doi.org/10.1016/j.arth.2018.02.067

Biourge V, Delmotte S, Feugier A An artificial neural network-based model to predict chronic kidney disease in aged cats. J Vet Intern Med. 2020; 34:1920-1931 https://doi.org/10.1111%2Fjvim.15892

Bleuer-Elsner S, Zamansky A, Fux A Computational analysis of movement patterns of dogs with ADHD-like behavior. Anim. 2019; 9 https://doi.org/10.3390%2Fani9121140

Boissady E, De La Comble A, Zhu X, Hespel AM. Artificial intelligence evaluating primary thoracic lesions has an overall lower error rate compared to veterinarians or veterinarians in conjunction with the artificial intelligence. Vet Radiol Ultrasound. 2020; 61:619-627 https://doi.org/10.1111/vru.12912

Bollig N, Clarke L, Elsmo E, Craven M. Machine learning for syndromic surveillance using veterinary necropsy reports. PLos One. 2020; 15 https://doi.org/10.1371/journal.pone.0228105

Bond RR, Novotny T, Andrsova I Automation bias in medicine: The influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms. J Electrocardiol. 2018; 51:S6-S11 https://doi.org/10.1016/j.jelectrocard.2018.08.007

Bradley R, Tagkopoulos I, Kim M Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning. J Vet Intern Med. 2019; 33:2644-2656

Burrai GP, Gabrieli A, Moccia V, Zappulli V. A statistical analysis of risk factors and biological behavior in canine mammary tumors: a multicenter study. Anim. 2020; 10 https://doi.org/10.3390/ani10091687

Burti S, Longhin Osti V, Zotti A, Banzato T. Use of deep learning to detect cardiomegaly on thoracic radiographs in dogs. Vet J. 2020; 262 https://doi.org/10.1016/j.tvjl.2020.105505

Chen S, Wang L, Li G Machine learning in orthodontics: Introducing a 3D auto-segmentation and auto-landmark finder of CBCT images to assess maxillary constriction in unilateral impacted canine patients. Angle Orthod. 2020; 90:(1)77-84 https://doi.org/10.2319/012919-59.1

Cullell-Dalmau M, Otero-Vinas M, Manzo C. Research techniques made simple: deep learning for the classification of dermatological images. J Invest Dermatol. 2020; 140:507-514 https://doi.org/10.1016/j.jid.2019.12.029

den Ujil I, Gomez Alvarez CB, Bartram D, Dror Y, Holland R, Cook A. External validation of a collar-mounted triaxial accelerometer for second-by-second monitoring of eight behavioural states in dogs. Plos One. 2017; 12 https://doi.org/10.1371/journal.pone.0188481

Dorea FC, Muckle CA, Kelton D Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine. Plos One. 2013; 8:(3) https://doi.org/10.1371/journal.pone.0057334

Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017; 37:(2)505-515 https://doi.org/10.1148/rg.2017160130

Everitt S, Pilnick A, Waring J, Cobb M. The structure of the small animal consultation. J Small Anim Pract. 2013; 54:453-458 https://doi.org/10.1111/jsap.12115

Ferdinandy B, Gerencser L., Corrieri L Challenges of machine learning model validation using correlated behaviour data: Evaluation of cross-validation strategies and accuracy measures. Plos One. 2020; 15

Franzo G, Corso B, Tucciarone CM Comparison and validation of different models and variable selection methods for predicting survival after canine parvovirus infection. Vet Rec. 2020; 187:(9) https://doi.org/10.1136/vr.105283

Gambus PL, Jaramillo S. Machine learning in anaesthesia: reactive, proactive… predictive!. Brit J Anaesth. 2019; 123:401-403 https://doi.org/10.1016/j.bja.2019.07.009

Garcia-Vidal C, Sanjuan G, Puerta-Alcalde P, Moreno-Garcia E, Soriano A. Artificial intelligence to support clinical decision-making processes. EBioMedicine. 2019; 46:27-29 https://doi.org/10.1016/j.ebiom.2019.07.01

Geis JR, Brady A, Wu CC Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Radiology. 2019; 10:1-6 https://doi.org/10.1148/radiol.2019191586

Gergely A, Kiss O, Reicher V Reliability of family dogs' sleep structure scoring based on manual and automated sleep stage identification. Anim (Basel). 2020; 10:(6) https://doi.org/10.3390%2Fani10060927

Griffies JD, Zutty J, Sarzen M, Soorholtz S. Wearable sensor shown to specifically quantify pruritic behaviors in dogs. BMC Vet Res. 2018; 14:124-124 https://doi.org/10.1186/s12917-018-1428-x

Gu Y, Zhang AC, Han Y, Chen C, Lo YH. Machine learning based real-time image-guided cell sorting and classification. Cytometry. 2019; 95:499-509

Hur B, Hardefeldt LY, Verspoor K, Baldwin T, Gilkerson JR. Using natural language processing and VetCompass to understand antimicrobial usage patterns in Australia. Aus Vet J. 2019; 97:298-300

Kaplan A, Haenlein M. Siri, Siri, in my hand: who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons. 2019; 62:(1)15-25 https://doi.org/10.1016/j.bushor.2018.08.004

Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020; 92:807-812 https://doi.org/10.1016/j.gie.2020.06.040

Kershenbaum A, Blumstein DT, Roch MA Acoustic sequences in non-human animals: a tutorial review and prospectus. Biol Rev Camb Philos Soc. 2016; 91:13-52 https://doi.org/10.1111/brv.12160

Kim JY, Lee HE, Choi YH, Lee SJ, Jeon JS. CNN-based diagnosis models for canine ulcerative keratitis. Sci Rep. 2019; 1:(9) https://doi.org/10.1038/s41598-019-50437-0

Kim Y, Jaewon SA, Chung Y, Park D, Lee S. Resource-efficient pet dog sound events classification using LSTM-FCN based on time-series data. Sensors. 2018; 18:(11) https://doi.org/10.3390/s18114019

Kohli M, Prevedello LM, Filice RW, Geis JR. Implementing machine learning in radiology practice and research. AJR Am J Roentgenol. 2017; 208:(28)754-760 https://doi.org/10.2214/ajr.16.17224

La Perle KMD. Machine learning and veterinary pathology: be not afraid!. Vet Pathol. 2019; 56:506-507 https://doi.org/10.1177/0300985819848504

Larranaga A, Bielza C, Pongracz P, Farago T, Balint A, Larranaga P. Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking. Anim Cogn. 2015; 18:405-21 https://doi.org/10.1007/s10071-014-0811-7

Laurenziello M, Montaruli G, Gallo C Determinants of maxillary canine impaction: retrospective clinical and radiographic study. J Clin Exp Dent. 2017; 9:(11)1304-e1309 https://doi.org/10.4317%2Fjced.54095

Li S, Wang Z, Visser LC, Wisner ER, Cheng H. Pilot study: application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs. Vet Radiol Ultrasound. 2020; 61:(6)611-618 https://doi.org/10.1111/vru.12901

Longoni C, Bonezzi A, Morewedge CK. Resistance to medical artificial intelligence. J Cons Res. 2019; 46:629-650 https://doi.org/10.1093/jcr/ucz013

Lustgarten JL, Zehnder A, Shipman W, Gancher E, Webb TL. Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives. JAMIA Open. 2020; 3:(2)306-317 https://doi.org/10.1093/jamiaopen/ooaa005

Mamelak AN, Quattrochi JJ, Hobson JA. Automated staging of sleep in cats using neural networks. Electroencephalogr Clin Neurophysiol. 1991; 79:(1)52-61 https://doi.org/10.1016/0013-4694(91)90156-x

McEvoy FJ. Grand challenge veterinary imaging: technology, science, and communication. Front Vet Sci. 2015; 2 https://doi.org/10.3389/fvets.2015.00038

McEvoy FJ, Amigo JM. Using machine learning to classify image features from canine pelvic radiographs: evaluation of partial least squares discriminant analysis and artificial neural network models. Vet Radiol Ultrasound. 2013; 54:(2)122-6 https://doi.org/10.1111/vru.12003

Minas MY, Triantafillou G. Future of the artificial intelligence in daily health applications. Eur J Soc Sci. 2020; 29:3182-3189 https://doi.org/10.15405/ejsbs.2020.08.issue-3

Molnar C, Kaplan F, Roy P Classification of dog barks: a machine learning approach. Anim Cogn. 2008; 11:(3)389-400 https://doi.org/10.1007/s10071-007-0129-9

Mundell P, Liu S, Guerin NA, Berger JM. An automated behavior-shaping intervention reduces signs of separation anxiety-related distress in a mixed-breed dog. J Vet Behav. 2020; 37:71-75 https://doi.org/10.1016/j.jveb.2020.04.006

Nagamori Y, Sedlak RH, Derosa A Evaluation of the VETSCAN IMAGYST: an in-clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm. Parasit Vect. 2020; 13 https://doi.org/10.1186/s13071-020-04215-x

Nasseri M, Kremen V, Nejedly P Semi-supervised training data selection improves seizure forecasting in canines with epilepsy. Biomed Signal Process Control. 2020; 57 https://doi.org/10.1016/j.bspc.2019.101743

Nejedly P, Kremen V, Sladky V Deep-learning for seizure forecasting in canines with epilepsy. J Neural Eng. 2019; 16:(3) https://doi.org/10.1088/1741-2552/ab172d

Nie A, Zehnder A, Page RL DeepTag: inferring diagnoses from veterinary clinical notes. NPJ Digital Med. 2018; 1 https://doi.org/10.1038/s41746-018-0067-8

Nikos F, Stamatis K, Sotiris K, Kyriakos S. Self-trained LMT for semisupervised learning. Comput Intell Neurosci. 2016; 2016 https://doi.org/10.1155/2016/3057481

Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018; 2:(1) https://doi.org/10.1186/s41747-018-0061-6

Rahman M, Islam D, Mukti RJ, Saha I. A deep learning approach based on convolutional LSTM for detecting diabetes. Comput Biol Chem. 2020; 88 https://doi.org/10.1016/j.compbiolchem.2020.107329

RCVS. Telemedicine consultation summary: professionals. 2020. https://www.rcvs.org.uk/document-library/telemedicine-consultation-summary/ (accessed 27 April 2023)

RCVS. Coronavirus: RCVS council temporarily permits vets to remotely prescribe veterinary medicines. 2020. https://www.rcvs.org.uk/news-and-views/news/coronavirus-rcvs-council-temporarily-permits-vets-to-remotely (accessed 27 April 2023)

Reagan KL, Reagan BA, Gilor C. Machine learning algorithm as a diagnostic tool for hypoadrenocorticism in dogs. Domest Anim Endocrinol. 2020; 72 https://doi.org/10.1016/j.domaniend.2019.106396

Reinero CR, Masseau I, Grobman M, Vientos-Plotts A, Williams K. Perspectives in veterinary medicine: Description and classification of bronchiolar disorders in cats. J Vet Intern Med. 2019; 33:(3)1201-1221 https://doi.org/10.1111/jvim.15473

Romero MP, Chang YM, Bruton LA Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making. Prev Vet Med. 2020; 175 https://doi.org/10.1016/j.prevetmed.2019.104860

Samoili S, Lopez-Cobo M, Gomez E AI watch: Defining artificial intelligence: towards an operational definition and taxonomy of artificial intelligence.: Publications Office of the European Union; 2020 https://doi.org/10.2760/019901

Spiteri M, Knowler SP, Rusbridge C, Wells K. Using machine learning to understand neuromorphological change and image-based biomarker identification in Cavalier King Charles Spaniels with Chiari-like malformation-associated pain and syringomyelia. J Vet Intern Med. 2019; 33:2665-2674 https://doi.org/10.1111/jvim.15621

Spruijt BM, Devisser L. Advanced behavioural screening: automated home cage ethology. Drug Discov Today Technol. 2006; 3:(2)231-7 https://doi.org/10.1016/j.ddtec.2006.06.010

Svetnik V, Wang TC, Xu Y, Hansen BJ, Fox S. A deep learning approach for automated sleep-wake scoring in pre-clinical animal models. J Neurosci Methods. 2020; 337 https://doi.org/10.1016/j.jneumeth.2020.108668

Varatharajah Y, Iyer RK, Berry BM, Worrell GA, Brinkmann BH. Seizure forecasting and the preictal state in canine epilepsy. Int J Neural Syst. 2017; 27:(1) https://doi.org/10.1142/s01290657165004652017;27:1650046

Waters A. Stress levels for vets will lead to ‘implosion’. Vet Rec. 2018; 183:(20) https://doi.org/10.1136/vr.k4956

Watts JM. Animats: computer-simulated animals in behavioral research. J Anim Sci. 1998; 76:(10)2596-604 https://doi.org/10.2527/1998.76102596x

Yoon Y, Hwang T, Choi H, Lee H. Classification of radiographic lung pattern based on texture analysis and machine learning. J Vet Sci. 2019; 20 https://doi.org/10.4142/jvs.2019.20.e44

Zamansky A, Van der Linden D, Hadar I, Bleuer-Elsner S. Log my dog: perceived impact of dog activity tracking. Computer. 2019; 52:(9)35-43 https://doi.org/10.1109/MC.2018.2889637

Zhang Y, Nie A, Zehnder A, Page RL, Zou J. VetTag: improving automated veterinary diagnosis coding via large-scale language modeling. NPJ Digit Med. 2019; 2 https://doi.org/10.1038/s41746-019-0113-1

A review of applications of artificial intelligence in veterinary medicine

02 June 2023
17 mins read
Volume 28 · Issue 6
Figure 1. Schematic illustration showing deep learning as a subfield of machine learning, while both are part of artificial intelligence.
Figure 1. Schematic illustration showing deep learning as a subfield of machine learning, while both are part of artificial intelligence.

Abstract

Artificial intelligence is a newer concept in veterinary medicine than human medicine, but its existing benefits illustrate the significant potential it may also have in this field. This article reviews the application of artificial intelligence to various fields of veterinary medicine. Successful integration of different artificial intelligence strategies can offer practical solutions to issues, such as time pressure, in practice. Several databases were searched to identify literature on the application of artificial intelligence in veterinary medicine. Exclusion and inclusion criteria were applied to obtain relevant papers. There was evidence for an acceleration of artificial intelligence research in recent years, particularly for diagnostics and imaging. Some of the benefits of using artificial intelligence included standardisation, increased efficiency, and a reduction in the need for expertise in particular fields. However, limitations identified in the literature included a requirement for ideal situations for artificial intelligence to achieve accuracy and other inherent, unresolved issues. Ethical considerations and a hesitancy to engage with artificial intelligence, by both the public and veterinarians, are further barriers that must be addressed for artificial intelligence to be fully integrated in daily practice. The rapid growth in artificial intelligence research substantiates its potential to improve veterinary practice.

John McCarthy first coined the term artificial intelligence (AI) in 1956 while lecturing at Dartmouth College (Bini, 2018). Although the term has now been integrated into everyday life, there is no standard accepted definition for AI (Samoili et al, 2020). One of the numerous definitions for AI is ‘a system's ability to interpret external data correctly, to learn from such data and to use that learning to achieve specific goals and tasks through flexible adaptation’ (Kaplan and Haenlein, 2019). Currently, AI is more integrated into the practice and research of human medicine than it is into veterinary medicine, but many of its applications, such as imaging, diagnostics, and health records, are equally relevant to veterinary medicine. As an example, medical coding infrastructure of health records to aid doctors and improve clinical research is already established in human medicine. Similarly, veterinary research is now examining the large scale use of electronic health records to predict diagnoses from free text clinician notes (Zhang et al, 2019). This information can then be used for several purposes including research and public health.

Register now to continue reading

Thank you for visiting UK-VET Companion Animal and reading some of our peer-reviewed content for veterinary professionals. To continue reading this article, please register today.