Journal of Veterinary Clinics (한국임상수의학회지)

The Korean Society of Veterinary Clinics (한국임상수의학회)
권호 표지
DOI QR코드

DOI QR Code

Scoping Review of Machine Learning and Deep Learning Algorithm Applications in Veterinary Clinics: Situation Analysis and Suggestions for Further Studies

  • Kyung-Duk Min (College of Veterinary Medicine, Chungbuk National University)
  • Received : 2023.07.12
  • Accepted : 2023.08.21
  • Published : 2023.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

Machine learning and deep learning (ML/DL) algorithms have been successfully applied in medical practice. However, their application in veterinary medicine is relatively limited, possibly due to a lack in the quantity and quality of relevant research. Because the potential demands for ML/DL applications in veterinary clinics are significant, it is important to note the current gaps in the literature and explore the possible directions for advancement in this field. Thus, a scoping review was conducted as a situation analysis. We developed a search strategy following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed and Embase databases were used in the initial search. The identified items were screened based on predefined inclusion and exclusion criteria. Information regarding model development, quality of validation, and model performance was extracted from the included studies. The current review found 55 studies that passed the criteria. In terms of target animals, the number of studies on industrial animals was similar to that on companion animals. Quantitative scarcity of prediction studies (n = 11, including duplications) was revealed in both industrial and non-industrial animal studies compared to diagnostic studies (n = 45, including duplications). Qualitative limitations were also identified, especially regarding validation methodologies. Considering these gaps in the literature, future studies examining the prediction and validation processes, which employ a prospective and multi-center approach, are highly recommended. Veterinary practitioners should acknowledge the current limitations in this field and adopt a receptive and critical attitude towards these new technologies to avoid their abuse.

Keywords

Acknowledgement

This work was supported by a funding for the academic research program of Chungbuk National University in 2022. In addition, this work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No. RS-2023-00232301)." Rural Development Administration, Republic of Korea.

References

  1. Ahmadi N, Peng Y, Wolfien M, Zoch M, Sedlmayr M. OMOP CDM can facilitate data-driven studies for cancer prediction: a systematic review. Int J Mol Sci 2022; 23: 11834. 
  2. Ali O, Abdelbaki W, Shrestha A, Elbasi E, Alryalat MAA, Dwivedi YK. A systematic literature review of artificial intelligence in the healthcare sector: benefits, challenges, methodologies, and functionalities. J Innov Knowl 2023; 8: 100333. 
  3. Aubreville M, Bertram CA, Marzahl C, Gurtner C, Dettwiler M, Schmidt A, et al. Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region. Sci Rep 2020; 10: 16447. 
  4. 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: 317. 
  5. Banzato T, Bonsembiante F, Aresu L, Gelain ME, Burti S, Zotti A. Use of transfer learning to detect diffuse degenerative hepatic diseases from ultrasound images in dogs: a methodological study. Vet J 2018; 233: 35-40.  https://doi.org/10.1016/j.tvjl.2017.12.026
  6. Banzato T, Cherubini GB, Atzori M, Zotti A. Development of a deep convolutional neural network to predict grading of canine meningiomas from magnetic resonance images. Vet J 2018; 235: 90-92.  https://doi.org/10.1016/j.tvjl.2018.04.001
  7. Banzato T, Wodzinski M, Burti S, Osti VL, Rossoni V, Atzori M, et al. Automatic classification of canine thoracic radiographs using deep learning. Sci Rep 2021; 11: 3964. 
  8. Banzato T, Wodzinski M, Tauceri F, Dona C, Scavazza F, Muller H, et al. An AI-based algorithm for the automatic classification of thoracic radiographs in cats. Front Vet Sci 2021; 8: 731936. 
  9. Bauer EA, Jagusiak W. The use of multilayer perceptron artificial neural networks to detect dairy cows at risk of ketosis. Animals (Basel) 2022; 12: 332. 
  10. Benfodil K, Benbouras MA, Ansel S, Mohamed-Cherif A, Ait-Oudhia K. Prediction of Trypanosoma evansi infection in dromedaries using artificial neural network (ANN). Vet Parasitol 2022; 306: 109716. 
  11. Biercher A, Meller S, Wendt J, Caspari N, Schmidt-Mosig J, De Decker S, et al. Using deep learning to detect spinal cord diseases on thoracolumbar magnetic resonance images of dogs. Front Vet Sci 2021; 8: 721167. 
  12. Biourge V, Delmotte S, Feugier A, Bradley R, McAllister M, Elliott J. 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/jvim.15892
  13. Boissady E, De La Comble A, Zhu X, Abbott J, Adrien-Maxence H. Comparison of a deep learning algorithm vs. humans for vertebral heart scale measurements in cats and dogs shows a high degree of agreement among readers. Front Vet Sci 2021; 8: 764570. 
  14. Bonicelli L, Trachtman AR, Rosamilia A, Liuzzo G, Hattab J, Mira Alcaraz E, et al. Training convolutional neural networks to score pneumonia in slaughtered pigs. Animals (Basel) 2021; 11: 3290. 
  15. Bradley R, Tagkopoulos I, Kim M, Kokkinos Y, Panagiotakos T, Kennedy J, et al. 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.  https://doi.org/10.1111/jvim.15623
  16. Broughton-Neiswanger LE, Rivera-Velez SM, Suarez MA, Slovak JE, Pineyro PE, Hwang JK, et al. Urinary chemical fingerprint left behind by repeated NSAID administration: discovery of putative biomarkers using artificial intelligence. PLoS One 2020; 15: e0228989. 
  17. 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: 105505. 
  18. Dinh-Le C, Chuang R, Chokshi S, Mann D. Wearable health technology and electronic health record integration: scoping review and future directions. JMIR Mhealth Uhealth 2019; 7: e12861. 
  19. Dumortier L, Guepin F, Delignette-Muller ML, Boulocher C, Grenier T. Deep learning in veterinary medicine, an approach based on CNN to detect pulmonary abnormalities from lateral thoracic radiographs in cats. Sci Rep 2022; 12: 11418. 
  20. Ebrahimi M, Mohammadi-Dehcheshmeh M, Ebrahimie E, Petrovski KR. Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: deep learning and gradient-boosted trees outperform other models. Comput Biol Med 2019; 114: 103456. 
  21. ELKhamary AN, Keenihan EK, Schnabel LV, Redding WR, Schumacher J. Leveraging MRI characterization of longitudinal tears of the deep digital flexor tendon in horses using machine learning. Vet Radiol Ultrasound 2022; 63: 580-592.  https://doi.org/10.1111/vru.13090
  22. Fernandez-Carrion E, Barasona JA, Sanchez A, Jurado C, Cadenas-Fernandez E, Sanchez-Vizcaino JM. Computer vision applied to detect lethargy through animal motion monitoring: a trial on African swine fever in wild boar. Animals (Basel) 2020; 10: 2241. 
  23. Figueirinhas P, Sanchez A, Rodriguez O, Vilar JM, Rodriguez-Altonaga J, Gonzalo-Orden JM, et al. Development of an artificial neural network for the detection of supporting hindlimb lameness: a pilot study in working dogs. Animals (Basel) 2022; 12: 1755. 
  24. Fraiwan MA, Abutarbush SM. Using artificial intelligence to predict survivability likelihood and need for surgery in horses presented with acute abdomen (Colic). J Equine Vet Sci 2020; 90: 102973. 
  25. Ghotoorlar SM, Ghamsari SM, Nowrouzian I, Ghotoorlar SM, Ghidary SS. Lameness scoring system for dairy cows using force plates and artificial intelligence. Vet Rec 2012; 170: 126. 
  26. Hennessey E, DiFazio M, Hennessey R, Cassel N. Artificial intelligence in veterinary diagnostic imaging: a literature review. Vet Radiol Ultrasound 2022; 63 Suppl 1: 851-870.  https://doi.org/10.1111/vru.13163
  27. Hwang EJ, Park S, Jin KN, Kim JI, Choi SY, Lee JH, et al. Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Netw Open 2019; 2: e191095. Erratum in: JAMA Netw Open 2019; 2: e193260. 
  28. Jones PH, Dawson S, Gaskell RM, Coyne KP, Tierney A, Setzkorn C, et al. Surveillance of diarrhoea in small animal practice through the Small Animal Veterinary Surveillance Network (SAVSNET). Vet J 2014; 201: 412-418.  https://doi.org/10.1016/j.tvjl.2014.05.044
  29. Joslyn S, Alexander K. Evaluating artificial intelligence algorithms for use in veterinary radiology. Vet Radiol Ultrasound 2022; 63 Suppl 1: 871-879.  https://doi.org/10.1111/vru.13159
  30. Kang X, Zhang XD, Liu G. Accurate detection of lameness in dairy cattle with computer vision: a new and individualized detection strategy based on the analysis of the supporting phase. J Dairy Sci 2020; 103: 10628-10638.  https://doi.org/10.3168/jds.2020-18288
  31. 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
  32. Keegan KG, Arafat S, Skubic M, Wilson DA, Kramer J. Detection of lameness and determination of the affected forelimb in horses by use of continuous wavelet transformation and neural network classification of kinematic data. Am J Vet Res 2003; 64: 1376-1381.  https://doi.org/10.2460/ajvr.2003.64.1376
  33. Kil N, Ertelt K, Auer U. Development and validation of an automated video tracking model for stabled horses. Animals (Basel) 2020; 10: 2258. 
  34. Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol 2019; 20: 405-410.  https://doi.org/10.3348/kjr.2019.0025
  35. Kim JY, Lee HE, Choi YH, Lee SJ, Jeon JS. CNN-based diagnosis models for canine ulcerative keratitis. Sci Rep 2019; 9: 14209. 
  36. Kittichai V, Kaewthamasorn M, Thanee S, Jomtarak R, Klanboot K, Naing KM, et al. Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks. Sci Rep 2021; 11: 16919. 
  37. Kokkinos Y, Morrison J, Bradley R, Panagiotakos T, Ogeer J, Chew D, et al. An early prediction model for canine chronic kidney disease based on routine clinical laboratory tests. Sci Rep 2022; 12: 14489. 
  38. Krone LM, Brown CM, Lindenmayer JM. Survey of electronic veterinary medical record adoption and use by independent small animal veterinary medical practices in Massachusetts. J Am Vet Med Assoc 2014; 245: 324-332.  https://doi.org/10.2460/javma.245.3.324
  39. 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: 611-618.  https://doi.org/10.1111/vru.12901
  40. Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health 2019; 1: e271-e297. Erratum in: Lancet Digit Health 2019; 1: e334. 
  41. Mao A, Giraudet CSE, Liu K, De Almeida Nolasco I, Xie Z, Xie Z, et al. Automated identification of chicken distress vocalizations using deep learning models. J R Soc Interface 2022; 19: 20210921. 
  42. Marzahl C, Aubreville M, Bertram CA, Stayt J, Jasensky AK, Bartenschlager F, et al. Deep learning-based quantification of pulmonary hemosiderophages in cytology slides. Sci Rep 2020; 10: 9795. 
  43. May A, Gesell-May S, Muller 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: 847-855.  https://doi.org/10.1111/evj.13528
  44. McGreevy P, Thomson P, Dhand NK, Raubenheimer D, Masters S, Mansfield CS, et al. VetCompass Australia: a national big data collection system for veterinary science. Animals (Basel) 2017; 7: 74. 
  45. Meller S, Zamansky A, Sinitca A, Kaplun D, Meyerhoff N, Stein V, et al. Sounds of seizures-acoustic information enables immediate recognition and detection of generalized tonic-clonic seizures in dogs. J Vet Intern Med 2022; 36: 305. 
  46. Motahari-Nezhad H, Fgaier M, Mahdi Abid M, Pentek M, Gulacsi L, Zrubka Z. Digital biomarker-based studies: scoping review of systematic reviews. JMIR Mhealth Uhealth 2022; 10: e35722. 
  47. Mouloodi S, Rahmanpanah H, Burvill C, Davies HMS. Prediction of displacement in the equine third metacarpal bone using a neural network prediction algorithm. Biocybern Biomed Eng 2020; 40: 849-863.  https://doi.org/10.1016/j.bbe.2019.09.001
  48. Mouloodi S, Rahmanpanah H, Burvill C, Davies HMS. Prediction of load in a long bone using an artificial neural network prediction algorithm. J Mech Behav Biomed Mater 2020; 102: 103527. 
  49. Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis. Lancet Digit Health 2021; 3: e195-e203.  https://doi.org/10.1016/S2589-7500(20)30292-2
  50. Nagamori Y, Hall Sedlak R, DeRosa A, Pullins A, Cree T, Loenser M, et al. Evaluation of the VETSCAN IMAGYST: an in-clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm. Parasit Vectors 2020; 13: 346. 
  51. Nagamori Y, Sedlak RH, DeRosa A, Pullins A, Cree T, Loenser M, et al. Further evaluation and validation of the VETSCAN IMAGYST: in-clinic feline and canine fecal parasite detection system integrated with a deep learning algorithm. Parasit Vectors 2021; 14: 89. 
  52. Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 2020; 368: m689. 
  53. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372: n71. 
  54. Park J, Choi B, Ko J, Chun J, Park I, Lee J, et al. Deep-learning-based automatic segmentation of head and neck organs for radiation therapy in dogs. Front Vet Sci 2021; 8: 721612. 
  55. Parra C, Grijalva F, Nunez B, Nunez A, Perez N, Benitez D. Automatic identification of intestinal parasites in reptiles using microscopic stool images and convolutional neural networks. PLoS One 2022; 17: e0271529. 
  56. Pastell ME, Kujala M. A probabilistic neural network model for lameness detection. J Dairy Sci 2007; 90: 2283-2292.  https://doi.org/10.3168/jds.2006-267
  57. Pichler M, Hartig F. Machine learning and deep learning-a review for ecologists. Methods Ecol Evol 2023; 14: 994-1016. 
  58. Porter IR, Wieland M, Basran PS. Feasibility of the use of deep learning classification of teat-end condition in Holstein cattle. J Dairy Sci 2021; 104: 4529-4536.  https://doi.org/10.3168/jds.2020-19642
  59. Post C, Rietz C, Buscher W, Muller U. Using sensor data to detect lameness and mastitis treatment events in dairy cows: a comparison of classification models. Sensors (Basel) 2020; 20: 3863. 
  60. Rai T, Morisi A, Bacci B, Bacon NJ, Dark MJ, Aboellail T, et al. Deep learning for necrosis detection using canine Perivascular Wall Tumour whole slide images. Sci Rep 2022; 12: 10634. 
  61. Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med 2022; 28: 31-38.  https://doi.org/10.1038/s41591-021-01614-0
  62. Rose N, Toews L, Pang DS. A systematic review of clinical audit in companion animal veterinary medicine. BMC Vet Res 2016; 12: 40. 
  63. Roush WB, Wideman RF Jr, Cahaner A, Deeb N, Cravener TL. Minimal number of chicken daily growth velocities for artificial neural network detection of pulmonary hypertension syndrome (PHS). Poult Sci 2001; 80: 254-259.  https://doi.org/10.1093/ps/80.3.254
  64. Salvi M, Molinari F, Iussich S, Muscatello LV, Pazzini L, Benali S, et al. Histopathological classification of canine cutaneous round cell tumors using deep learning: a multi-center study. Front Vet Sci 2021; 8: 640944. 
  65. Sanchez-Vizcaino F, Jones PH, Menacere T, Heayns B, Wardeh M, Newman J, et al. Small animal disease surveillance. Vet Rec 2015; 177: 591-594.  https://doi.org/10.1136/vr.h6174
  66. Schobesberger H, Peham C. Computerized detection of supporting forelimb lameness in the horse using an artificial neural network. Vet J 2002; 163: 77-84.  https://doi.org/10.1053/tvjl.2001.0608
  67. Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak 2021; 21: 125. 
  68. Shahinfar S, Khansefid M, Haile-Mariam M, Pryce JE. Machine learning approaches for the prediction of lameness in dairy cows. Animal 2021; 15: 100391. 
  69. Song KD, Kim M, Do S. The latest trends in the use of deep learning in radiology illustrated through the stages of deep learning algorithm development. J Korean Soc Radiol 2019; 80: 202-212.  https://doi.org/10.3348/jksr.2019.80.2.202
  70. Teixeira VA, Lana AMQ, Bresolin T, Tomich TR, Souza GM, Furlong J, et al. Using rumination and activity data for early detection of anaplasmosis disease in dairy heifer calves. J Dairy Sci 2022; 105: 4421-4433.  https://doi.org/10.3168/jds.2021-20952
  71. Theodoropoulos G, Loumos V, Anagnostopoulos C, Kayafas E, Martinez-Gonzales B. A digital image analysis and neural network based system for identification of third-stage parasitic strongyle larvae from domestic animals. Comput Methods Programs Biomed 2000; 62: 69-76.  https://doi.org/10.1016/S0169-2607(99)00056-5
  72. Trachtman AR, Bergamini L, Palazzi A, Porrello A, Capobianco Dondona A, Del Negro E, et al. Scoring pleurisy in slaughtered pigs using convolutional neural networks. Vet Res 2020; 51: 51. 
  73. Yakubu A, Oluremi OIA, Ekpo EI. Predicting heat stress index in Sasso hens using automatic linear modeling and artificial neural network. Int J Biometeorol 2018; 62: 1181-1186.  https://doi.org/10.1007/s00484-018-1521-7
  74. Ye Y, Sun WW, Xu RX, Selmic LE, Sun M. Intraoperative assessment of canine soft tissue sarcoma by deep learning enhanced optical coherence tomography. Vet Comp Oncol 2021; 19: 624-631.  https://doi.org/10.1111/vco.12747
  75. Yoon Y, Hwang T, Lee H. Prediction of radiographic abnormalities by the use of bag-of-features and convolutional neural networks. Vet J 2018; 237: 43-48.  https://doi.org/10.1016/j.tvjl.2018.05.009
  76. You SC, Lee S, Choi B, Park RW. Establishment of an international evidence sharing network through common data model for cardiovascular research. Korean Circ J 2022; 52: 853-864.  https://doi.org/10.4070/kcj.2022.0294
  77. ZareBidaki M, Allahyari E, Zeinali T, Asgharzadeh M. Occurrence and risk factors of brucellosis among domestic animals: an artificial neural network approach. Trop Anim Health Prod 2022; 54: 62. 
  78. Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med 2018; 15: e1002683. 
  79. Zhang M, Zhang K, Yu D, Xie Q, Liu B, Chen D, et al. Computerized assisted evaluation system for canine cardiomegaly via key points detection with deep learning. Prev Vet Med 2021; 193: 105399. 
  80. 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: 35.