The need of a proof-of-concept in artificial intelligence-based health-related quality of life instruments in veterinary medicine

Jeff M. Perez

Article ID: 2201
Vol 6, Issue 1, 2023

VIEWS - 202 (Abstract) 110 (PDF)

Abstract


The current state of the art of health-related quality of life (HRQoL) and quality of life in the animal health industry highlights the limitations of existing methodologies and the potential of artificial intelligence (AI) to overcome these limitations. AI has the potential to revolutionize many aspects of healthcare, including HRQoL assessment, leading to more efficient and accurate measurement and personalized medicine. AI in psychometrics can improve cognitive and behavioral assessments and lead to new insights into animal reactions and perceptions. A proof of concept (POC) study is used to assess the feasibility of an AI-based solution. In the next decade, AI-based HRQoL instruments in veterinary medicine are expected to emerge and become widely distributed, making them easily accessible for practical use in daily practice.


Keywords


artificial intelligence; health-related quality of life; quality of life; animal welfare; natural language processing; machine learning

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References


1. Doit H, Dean RS, Duz M, Brennan ML. A systematic review of the quality of life assessment tools for cats in the published literature. Veterinary Journal 2021; 272: 105658. doi: 10.1016/j.tvjl.2021.105658

2. Mwacalimba KK, Contadini FM, Spofford N, et al. Owner and veterinarian perceptions about use of a canine quality of life survey in primary care settings. Frontiers in Veterinary Science 2020; 7: 89. doi: 10.3389/fvets.2020.00089

3. Belshaw Z, Asher L, Harvey ND, Dean RS. Quality of life assessment in domestic dogs: An evidence-based rapid review. Veterinary Journal 2015; 206(2): 203–212. doi: 10.1016/j.tvjl.2015.07.016

4. Wolfensohn S, Shotton J, Bowley H, et al. Assessment of welfare in zoo animals: Towards optimum quality of life. Animals (Basel) 2018; 8(7): 110. doi: 10.3390/ani8070110

5. Long M, Dürnberger C, Jenner F, et al. Quality of life within horse welfare assessment tools: Informing decisions for chronically ill and geriatric horses. Animals (Basel) 2022; 12(14): 1822. doi: 10.3390/ani12141822

6. Fulmer AE, Laven LJ, Hill KE. Quality of life measurement in dogs and cats: A scoping review of generic tools. Animals (Basel) 2022; 12(3): 400. doi: 10.3390/ani12030400

7. Spofford N, Lefebvre SL, McCune S, Niel L. Should the veterinary profession invest in developing methods to assess quality of life in healthy dogs and cats? Journal of the American Veterinary Medical Association 2013; 243(7): 952–956. doi: 10.2460/javma.243.7.952

8. Lambeth SP, Schapiro SJ, Bernacky BJ, Wilkerson GK. Establishing “quality of life” parameters using behavioural guidelines for humane euthanasia of captive non-human primates. Animal Welfare 2013; 22(4): 429–435. doi: 10.7120/09627286.22.4.429

9. Ohl F, Arndt SS, van der Staay FJ. Pathological anxiety in animals. Veterinary Journal 2008; 175(1): 18–26. doi: 10.1016/j.tvjl.2006.12.013

10. Rajapaksha E. Special considerations for diagnosing behavior problems in older pets. The Veterinary Clinics of North America. Small Animal Practice 2018; 48(3): 443–456. doi: 10.1016/j.cvsm.2017.12.010

11. Wiseman-Orr ML, Scott EM, Reid J, Nolan AM. Validation of a structured questionnaire as an instrument to measure chronic pain in dogs on the basis of effects on health-related quality of life. American Journal of Veterinary Research 2006; 67(11): 1826–1836. doi: 10.2460/ajvr.67.11.1826

12. Muir WW. Stress and pain: Their relationship to health related quality of life (HRQL) for horses. Equine Veterinary Journal 2013; 45(6): 653–655. doi: 10.1111/evj.12152

13. Yazbek KVB, Fantoni DT. Validity of a health-related quality-of-life scale for dogs with signs of pain secondary to cancer. Journal of the American Veterinary Medical Association 2005; 226(8): 1354–1358. doi: 10.2460/javma.2005.226.1354

14. Serras AR, Berlato D, Murphy S. Owners’ perception of their dogs’ quality of life during and after radiotherapy for cancer. The Journal of Small Animal Practice 2019; 60(5): 268–273. doi: 10.1111/jsap.12972

15. Lynch S, Savary-Bataille K, Leeuw B, Argyle DJ. Development of a questionnaire assessing health-related quality-of-life in dogs and cats with cancer. Veterinary and Comparative Oncology 2011; 9(3): 172–182. doi: 10.1111/j.1476-5829.2010.00244.x

16. Freeman LM, Rush JE, Farabaugh AE, Must A. Development and evaluation of a questionnaire for assessing health-related quality of life in dogs with cardiac disease. Journal of the American Veterinary Medical Association 2005; 226(11): 1864–1868. doi: 10.2460/javma.2005.226.1864

17. Freeman LM, Rush JE, Oyama MA, et al. Development and evaluation of a questionnaire for assessment of health-related quality of life in cats with cardiac disease. Journal of the American Veterinary Medical Association 2012; 240(10): 1188–1193. doi: 10.2460/javma.240.10.1188

18. Budke CM, Levine JM, Kerwin SC, et al. Evaluation of a questionnaire for obtaining owner-perceived, weighted quality-of-life assessments for dogs with spinal cord injuries. Journal of the American Veterinary Medical Association 2008; 233(6): 925–930. doi: 10.2460/javma.233.6.925

19. Scott EM, Davies V, Nolan AM, et al. Validity and responsiveness of the generic health-related quality of life instrument (VetMetricaTM) in cats with osteoarthritis. Comparison of vet and owner impressions of quality of life impact. Frontiers in Veterinary Science 2021; 8: 733812. doi: 10.3389/fvets.2021.733812

20. Roberts C, Armson B, Bartram D, et al. Construction of a conceptual framework for assessment of health-related quality of life in dogs with osteoarthritis. Frontiers in Veterinary Science 2021; 8: 741864. doi: 10.3389/fvets.2021.741864

21. Niessen SJM, Powney S, Guitian J, et al. Evaluation of a quality-of-life tool for cats with diabetes mellitus. Journal of Veterinary Internal Medicine 2010; 24(5): 1098–1105. doi: 10.1111/j.1939-1676.2010.0579.x

22. Niessen SJM, Powney S, Guitian J, et al. Evaluation of a quality-of-life tool for dogs with diabetes mellitus. Journal of Veterinary Internal Medicine 2012; 26(4): 953–961. doi: 10.1111/j.1939-1676.2012.00947.x

23. Linek M, Favrot C. Impact of canine atopic dermatitis on the health-related quality of life of affected dogs and quality of life of their owners. Veterinary Dermatology 2010; 21(5): 456–462. doi: 10.1111/j.1365-3164.2010.00899.x

24. Noli C. Assessing quality of life for pets with dermatologic disease and their owners. The Veterinary Clinics of North America. Small Animal Practice 2019; 49(1): 83–93. doi: 10.1016/j.cvsm.2018.08.008

25. Wessmann A, Volk HA, Parkin T, et al. Evaluation of quality of life in dogs with idiopathic epilepsy. Journal of Veterinary Internal Medicine 2014; 28(2): 510–514. doi: 10.1111/jvim.12328

26. Bijsmans ES, Jepson RE, Syme HM, et al. Psychometric validation of a general health quality of life tool for cats used to compare healthy cats and cats with chronic kidney disease. Journal of Veterinary Internal Medicine 2016; 30(1): 183–191. doi: 10.1111/jvim.13656

27. Marchetti V, Gori E, Mariotti V, et al. The impact of chronic inflammatory enteropathy on dogs’ quality of life and dog-owner relationship. Veterinary Sciences 2021; 8(8): 166. doi: 10.3390/vetsci8080166

28. Yam PS, Butowski CF, Chitty JL, et al. Impact of canine overweight and obesity on health-related quality of life. Preventive Veterinary Medicine 2016; 127: 64–69. doi: 10.1016/j.prevetmed.2016.03.013

29. Schmutz A, Spofford N, Burghardt W, de Meyer G. Development and initial validation of a dog quality of life instrument. Scientific Reports 2022; 12(1): 12225. doi: 10.1038/s41598-022-16315-y

30. Vøls KK, Heden MA, Kristensen AT, Sandøe P. Quality of life assessment in dogs and cats receiving chemotherapy—A review of current methods. Veterinary and Comparative Oncology 2017; 15(3): 684–691. doi: 10.1111/vco.12242

31. Rishniw M, Sammarco J, Glass EN, Cerroni B. Effect of doxepin on quality of life in Labradors with laryngeal paralysis: A double-blinded, randomized, placebo-controlled trial. Journal of Veterinary Internal Medicine 2021; 35(4): 1943–1949. doi: 10.1111/jvim.16162

32. Shipley H, Flynn K, Tucker L, et al. Owner evaluation of quality of life and mobility in osteoarthritic cats treated with amantadine or placebo. Journal of Feline Medicine and Surgery 2021; 23(6): 568–574. doi: 10.1177/1098612X20967639

33. Salas-Vega S, Iliopoulos O, Mossialos E. Assessment of overall survival, quality of life, and safety benefits associated with new cancer medicines. JAMA Oncology 2017; 3(3): 382–390. doi: 10.1001/jamaoncol.2016.4166

34. Chen TY, King-Kallimanis BL, Merzoug L, et al. US food and drug administration analysis of patient-reported diarrhea and its impact on function and quality of life in patients receiving treatment for breast cancer. Value Health 2022; 25(4): 566–570. doi: 10.1016/j.jval.2021.09.007

35. Pearce J. Psychometrics in action, science as practice. Advances in Health Sciences Education 2018; 23(3): 653–663. doi: 10.1007/s10459-017-9789-7

36. Wilson M. Seeking a balance between the statistical and scientific elements in psychometrics. Psychometrika 2013; 78(2): 211–236. doi: 10.1007/s11336-013-9327-3

37. Keszei AP, Novak M, Streiner DL. Introduction to health measurement scales. Journal of Psychosomatic Research 2010; 68(4): 319–323. doi: 10.1016/j.jpsychores.2010.01.006

38. Shaw RC, Schmelz M. Cognitive test batteries in animal cognition research: Evaluating the past, present and future of comparative psychometrics. Animal Cognition 2017; 20(6): 1003–1018. doi: 10.1007/s10071-017-1135-1

39. Belshaw Z, Yeates J. Assessment of quality of life and chronic pain in dogs. Veterinary Journal 2018; 239: 59–64. doi: 10.1016/j.tvjl.2018.07.010

40. Tomba E, Bech P. Clinimetrics and clinical psychometrics: Macro- and micro-analysis. Psychotherapy and Psychosomatics 2012; 81(6): 333–343. doi: 10.1159/000341757

41. Bilder RM, Reise SP. Neuropsychological tests of the future: How do we get there from here? The Clinical neuropsychologist 2019; 33(2): 220–245. doi: 10.1080/13854046.2018.1521993

42. Reid J, Wiseman-Orr L, Scott M. Shortening of an existing generic online health-related quality of life instrument for dogs. The Journal of Small Animal Practice 2018; 59(6): 334–342. doi: 10.1111/jsap.12772

43. Bianchi ML, Drudi D, Treggiari E, et al. Quality of life assessment in cancer patients receiving single-agent versus multidrug chemotherapy protocols. Open Veterinary Journal 2021; 11(4): 755–763. doi: 10.5455/OVJ.2021.v11.i4.28

44. Perez JM, Alessi C, Kittleson MD, et al. Psychometric properties of the Spanish version of the functional evaluation of cardiac health questionnaire “FETCH-QTM” for assessing health-related quality of life in dogs with cardiac disease. Topics in Companion Animal Medicine 2020; 39: 100431. doi: 10.1016/j.tcam.2020.100431

45. Brioschi FA, Di Cesare F, Gioeni D, et al. Oral transmucosal cannabidiol oil formulation as part of a multimodal analgesic regimen: Effects on pain relief and quality of life improvement in dogs affected by spontaneous osteoarthritis. Animals (Basel) 2020; 10(9): 1505. doi: 10.3390/ani10091505

46. Milevoj N, Tozon N, Licen S, et al. Health-related quality of life in dogs treated with electrochemotherapy and/or interleukin-12 gene electrotransfer. Veterinary Medicine and Science 2020; 6(3): 290–298. doi: 10.1002/vms3.232

47. Basran PS, Appleby RB. The unmet potential of artificial intelligence in veterinary medicine. American Journal of Veterinary Research 2022; 83(5): 385–392. doi: 10.2460/ajvr.22.03.0038

48. Liao WW, Hsieh YW, Lee TH, et al. Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke. Scientific Reports 2022; 12(1): 11235. doi: 10.1038/s41598-022-14986-1

49. Wolff J, Pauling J, Keck A, Baumbach J. The economic impact of artificial intelligence in health care: Systematic review. Journal of Medical Internet Research 2020; 22(2): e16866. doi: 10.2196/16866

50. Liyanage H, Liaw ST, Jonnagaddala J, et al. Artificial intelligence in primary health care: Perceptions, issues, and challenges. Yearbook of Medical Informatics 2019; 28(1): 41–46. doi: 10.1055/s-0039-1677901

51. Ahmadi M, Nopour R. Clinical decision support system for quality of life among the elderly: An approach using artificial neural network. BMC Medical Informatics and Decision Making 2022; 22(1): 293. doi: 10.1186/s12911-022-02044-9

52. Maron JL. Impact of artificial intelligence on clinical decision-making in health care. Clinical Therapeutics 2022; 44(4): 825–826. doi: 10.1016/j.clinthera.2022.05.005

53. Ellahham S, Ellahham N, Simsekler MCE. Application of artificial intelligence in the health care safety context: Opportunities and challenges. American Journal of Medical Quality 2020; 35(4): 341–348. doi: 10.1177/1062860619878515

54. Cohen EB, Gordon IK. First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology. Veterinary Radiology & Ultrasound 2022; 63(S1): 840–850. doi: 10.1111/vru.13171

55. Gonzalez O. Psychometric and machine learning approaches to reduce the length of scales. Multivariate Behavioral Research 2021; 56(6): 903–919. doi: 10.1080/00273171.2020.1781585

56. Katsuki M, Narita N, Matsumori Y, et al. Preliminary development of a deep learning-based automated primary headache diagnosis model using Japanese natural language processing of medical questionnaire. Surgical Neurology International 2020; 11: 475. doi: 10.25259/SNI_827_2020

57. Falissard B, Simpson EL, Guttman-Yassky E, et al. Qualitative assessment of adult patients’ perception of atopic dermatitis using natural language processing analysis in a cross-sectional study. Dermatology and Therapy 2020; 10(2): 297–305. doi: 10.1007/s13555-020-00356-0

58. Nguyen DD, Luo JW, Lu XH, et al. Estimating the health-related quality of life of kidney stone patients: Initial results from the Wisconsin Stone Quality of Life Machine-Learning Algorithm (WISQOL-MLA). BJU International 2021; 128(1): 88–94. doi: 10.1111/bju.15300

59. Crumpei-Tanasă I, Crumpei I. A machine learning approach to predict stress hormones and inflammatory markers using illness perception and quality of life in breast cancer patients. Current Oncology 2021; 28(4): 3150–3171. doi: 10.3390/curroncol28040275

60. Wang S, Jiang H, Qiao Y, et al. The research progress of vision-based artificial intelligence in smart pig farming. Sensors (Basel) 2022; 22(17): 6541. doi: 10.3390/s22176541

61. Sakamoto N, Murata T. Analytical technologies of animal behavior using artificial intelligence. Nihon Yakurigaku Zasshi. Folia Pharmacologica Japonica 2022; 157(2): 156. doi: 10.1254/fpj.21111

62. Bernardes RC, Lima MAP, Guedes RNC, et al. Ethoflow: Computer vision and artificial intelligence-based software for automatic behavior analysis. Sensors (Basel) 2021; 21(9): 3237. doi: 10.3390/s21093237

63. Bouhali O, Bensmail H, Sheharyar A, et al. A review of radiomics and artificial intelligence and their application in veterinary diagnostic imaging. Veterinary Sciences 2022; 9(11): 620. doi: 10.3390/vetsci9110620

64. Basran PS, Porter I. Radiomics in veterinary medicine: Overview, methods, and applications. Veterinary Radiology & Ultrasound 2022; 63(S1): 828–839. doi: 10.1111/vru.13156




DOI: https://doi.org/10.24294/irr.v6i1.2201

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