Artificial intelligence: Is it wizardry, witchcraft, or a helping hand for an equine veterinarian?

1. McCarthy J. From here to human-level AI. Artif Intell 2007;171:1174-82.
   doi: 10.1016/j.artint.2007.10.009google scholar: lookup
2. French RM. The Turing test: the first fifty years. Trends Cogn Sci 2000;4:115-21.
3. Jovel J, Greiner R. An introduction to machine learning approaches for biomedical research. Front Med 2021;8:1-15.
   doi: 10.3389/fmed.2021.771607google scholar: lookup
4. Pfob A, Lu SC, Sidey-Gibbons C. Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison. BMC Med Res Methodol 2022;22:1-15.
   doi: 10.1186/s12874-022-01758-8google scholar: lookup
5. Cain JL, Peters KT, Suri P, Roher A, Rutledge MH, Nielsen MK. The effect of analyst training on fecal egg counting variability. Parasitol Res 2021;120:1363-70.
   doi: 10.1007/s00436-021-07074-2google scholar: lookup
6. Marzahl C, Aubreville M, Bertram CA, Stayt J, Jasensky A-K, Bartenschlager F. Deep learning-based quantification of pulmonary hemosiderophages in cytology slides. Sci Rep 2020;10:9795.
   doi: 10.1038/s41598-020-65958-2google scholar: lookup
7. Bertram CA, Marzahl C, Bartel A, Stayt J, Bonsembiante F, Beeler-Marfisi J. Cytologic scoring of equine exercise-induced pulmonary hemorrhage: performance of human experts and a deep learning-based algorithm. Vet Pathol 2023;60:75-85.
   doi: 10.1177/03009858221137582google scholar: lookup
8. Lencioni GC, de Sousa RV, de Souza Sardinha EJ, Corrêa RR, Zanella AJ. Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling. PLoS One 2021;16:1-12.
   doi: 10.1371/journal.pone.0258672google scholar: lookup
9. May A, Gesell-May S, Müller T, Ertel W. Artificial intelligence as a tool to aid in the differentiation of equine ophthalmic diseases with an emphasis on equine uveitis. Equine Vet J 2022;54:847-55.
   doi: 10.1111/evj.13528google scholar: lookup
10. Zhou M, Elmore JA, Samiappan S, Evans KO, Pfeiffer MB, Blackwell BF. Improving animal monitoring using small unmanned aircraft systems (sUAS) and deep learning networks. Sensors 2021;21:1-13.
    doi: 10.3390/s21175697google scholar: lookup
11. Schobesberger H, Peham C. Computerized detection of supporting forelimb lameness in the horse using an artificial neural network. Vet J 2002;163:77-84.
    doi: 10.1053/tvjl.2001.0608google scholar: lookup
12. Lawin FJ, Byström A, Roepstorff C, Rhodin M, Almlöf M, Silva M. Is markerless more or less? Comparing a smartphone computer vision method for equine lameness assessment to multi-camera motion capture. Animals 2023;13:390.
    doi: 10.3390/ani13030390google scholar: lookup
13. Feuser A-K, Gesell-May S, Müller T, May A. Artificial intelligence for lameness detection in horses-a preliminary study. Animals 2022;12:37-45.
    doi: 10.3390/ani12202804google scholar: lookup
14. Darbandi H, Bragança FS, Van der Zwaag BJ, Voskamp J, Gmel AI, Haraldsdóttir EH. Using different combinations of body-mounted IMU sensors to estimate speed of horses--a machine learning approach. Sensors 2021;21:1-12.
    doi: 10.3390/s21030798google scholar: lookup
15. Schmutz A, Chèze L, Jacques J, Martin P. A method to estimate horse speed per stride from one IMU with a machine learning method. Sensors 2020;20:1-12.
    doi: 10.3390/s20020518google scholar: lookup
16. Serra Bragança FM, Broomé S, Rhodin M, Björnsdóttir S, Gunnarsson V, Voskamp JP. Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning. Sci Rep 2020;10:17785.
    doi: 10.1038/s41598-020-73215-9google scholar: lookup
17. Lee J-N, Lee M-W, Byeon Y-H, Lee W-S, Kwak K-C. Classification of horse gaits using FCM-based neuro-fuzzy classifier from the transformed data information of inertial sensor. Sensors 2016;16:664.
    doi: 10.3390/s16050664google scholar: lookup
18. Parmentier JIM, Bosch S, van der Zwaag BJ, Weishaupt MA, Gmel AI, Havinga PJM. Prediction of continuous and discrete kinetic parameters in horses from inertial measurement units data using recurrent artificial neural networks. Sci Rep 2023;13:740.
    doi: 10.1038/s41598-023-27899-4google scholar: lookup
19. Sarin JK, Te Moller NCR, Mohammadi A, Prakash M, Torniainen J, Brommer H. Machine learning augmented near-infrared spectroscopy: in vivo follow-up of cartilage defects. Osteoarthr Cartil 2021;29:423-32.
    doi: 10.1016/j.joca.2020.12.007google scholar: lookup
20. Sarin JK, Nykänen O, Tiitu V, Mancini IAD, Brommer H, Visser J. Arthroscopic determination of cartilage proteoglycan content and collagen network structure with near-infrared spectroscopy. Ann Biomed Eng 2019;47:1815-26.
    doi: 10.1007/s10439-019-02280-7google scholar: lookup
21. Sarin JK, Rieppo L, Brommer H, Afara IO, Saarakkala S, Töyräs J. Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure. Sci Rep 2017;7:1-9.
    doi: 10.1038/s41598-017-10973-zgoogle scholar: lookup
22. Mirmojarabian SA, Kajabi AW, Ketola JHJ, Nykänen O, Liimatainen T, Nieminen MT. Machine learning prediction of collagen fiber orientation and proteoglycan content from multiparametric quantitative MRI in articular cartilage. J Magn Reson Imaging 2023;57:1056-68.
    doi: 10.1002/jmri.28353google scholar: lookup
23. Huang YH, Alexeenko V, Tse G, Huang CL-H, Marr CM, Jeevaratnam K. ECG restitution analysis and machine learning to detect paroxysmal atrial fibrillation: insight from the equine athlete as a model for human athletes. Function 2020;2:1-10.
    doi: 10.1093/function/zqaa031google scholar: lookup
24. Qin H, Xiao J, Gao X, Wang H. Horse-expert: an aided expert system for diagnosing horse diseases. Pol J Vet Sci 2016;19:907-15.
    doi: 10.1515/pjvs-2016-0112google scholar: lookup
25. Baker A, Perov Y, Middleton K, Baxter J, Mullarkey D, Sangar D. A comparison of artificial intelligence and human doctors for the purpose of triage and diagnosis. Front Artif Intell 2020;3:1-9.
    doi: 10.3389/frai.2020.543405google scholar: lookup
26. Cohen EB, Gordon IK. First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology. Vet Radiol Ultrasound 2022;63(S1):840-50.
    doi: 10.1111/vru.13171google scholar: lookup
27. Van der Stigchel B, Van Diggelen J, Haselager WFG, Van den Bosch K. Intelligent decision support in medical triage: are people robust to biased advice?. J Public Health 2023.
    doi: 10.1093/pubmed/fdad005google scholar: lookup

Citations