مروری بر کاربردهای هوش مصنوعی در پیش‌بینی رخداد و تشخیص بیماری‌ها در دامپزشکی: چالش‌ها و تکنیک‌ها
محورهای موضوعی : سایر علوم وابسته
مهدی باشی‌زاده
1
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پرهام صوفی‌زاده
2
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مهدی ضمیری
3
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آیدا لامعی
4
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متین ستوده‌نژاد
5
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مهسا دانشمند
6
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ملیکا قدرتی
7
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اریکا عیسوی
8
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حسام الدین اکبرین
9
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1 - بخش اپیدمیولوژی و بیماری‌های مشترک، گروه بهداشت و کنترل مواد غذایی، دانشکده دامپزشکی دانشگاه تهران
2 - دانش آموخته دانشکده دامپزشکی دانشگاه تهران
3 - دانشجوی دکترای عمومی دامپزشکی، دانشکده دامپزشکی دانشگاه تهران
4 - دانشجوی دکترای عمومی دامپزشکی، دانشکده دامپزشکی دانشگاه تهران
5 - دانشجوی دکترای عمومی دامپزشکی، دانشکده دامپزشکی دانشگاه تهران
6 - گروه علوم زیستی مقایسه‌ای، دانشکده دامپزشکی دانشگاه تهران
7 - دانشجوی دکترای عمومی دامپزشکی، دانشکده دامپزشکی دانشگاه تهران
8 - دانشجوی دکترای عمومی دامپزشکی، دانشکده دامپزشکی دانشگاه تهران
9 - بخش اپیدمیولوژی و بیماری‌های مشترک، گروه بهداشت و کنترل مواد غذایی، دانشکده دامپزشکی دانشگاه تهران
کلید واژه: هوش مصنوعی, دامپزشکی, پیش‌بینی, تشخیص,
چکیده مقاله :
تشخیص زودهنگام بیماری‌ها، یکی از هدف‌های اصلی دستگاه‌های بهداشتی و سلامت است. این تشخیص به‌موقع می‌تواند از آسیب‌های بالقوه‌ی بیماری‌ها بکاهد. اهمیت این مسأله در دامپزشکی به‌سبب تلفیق آن با هدف‌های اقتصادی نیز چندین برابر می‌شود. بنابراین وجود یک رویکرد پیش‌بینی‌کننده برای تشخیص زودهنگام بیماری‌ها ضروری است. این رویکرد باید مبتنی بر شواهد بوده و از دقت بالایی برخوردار باشد. هم‌چنین باید از نظر اقتصادی نیز صرفه‌ی بالایی داشته باشد. هوش مصنوعی توانایی یک کامپیوتر یا ربات کنترل‌شده توسط کامپیوتر برای انجام کارهایی است که معمولاً توسط انسان انجام می‌شود و به هوش و تشخیص انسان نیاز دارد. ظهور تکنیک‌های هوش مصنوعی و یادگیری ماشین در دنیای امروز، موجب بهبود عملکردهای موجود در سامانه‌های مراقبتی و بهداشتی شده است، به‌طوری که با به‌کارگیری این تکنولوژی، پیشرفت چشم‌گیری در رویه‌های پیش‌بینی رخداد و تشخیص بیماری‌ها، مدیریت و بهداشت در سطح کلان و ... شده است. هم‌چنین نوع بیماری قابل تشخیص، می‌تواند بسیار گسترده باشد و هرنوع بیماری‌ای که دارای داده‌ی قابل پردازش با الگوریتم‌های هوش مصنوعی باشد، می‌تواند توسط مدل آموزش داده‌شده تشخیص داده شود، اما صحت تشخیص با توجه به شاخص‌های بیماری و داده‌ی جمع‌آوری‌شده و مواردی مانند این متفاوت خواهد بود. در این مقاله به مهم‌ترین کاربردهای هوش مصنوعی در دامپزشکی اشاره خواهد شد و به‌طور کلی، این کاربردها در حوزه‌های گوناگونی مانند تشخیص بیماری‌های شایع، تشخیص تفریقی، پیش‌بینی رخداد بیماری‌ها، تکنیک‌های تصویربرداری تشخیصی دامپزشکی، کلینیکال پاتولوژی دامپزشکی و ... مورد بررسی قرار خواهند گرفت. علاوه بر این به چالش‌های موجود در این زمینه نیز اشاره خواهد شد. این مقاله مروری بر مطالعه‌های موجود در این زمینه است.
Early diagnosis of diseases is one of the main goals of health and wellness centers. Timely diagnosis can reduce the potential damage of diseases. The importance of this issue in veterinary medicine multiplies due to its combination with economic goals. Therefore, a predictive approach is necessary for early diagnosis of diseases. This approach should be evidence-based and highly accurate. It should also be economically efficient. Artificial intelligence is the simulation of human intelligence and judgment by a computer or a robot that is programmed or trained to perform tasks that normally need human abilities. The emergence of artificial intelligence and machine learning techniques in today's world has improved the existing functions in health care systems. So that with the application of this technology, a significant progress has been made in the procedures of event prediction and disease diagnosis, management and health at the macro level, etc. Furthermore, the scope of diagnosable diseases is extensive, encompassing any ailment for which relevant data can be processed by artificial intelligence algorithms. The trained model has the capability to diagnose a wide range of diseases, with accuracy contingent upon factors such as disease indicators, collected data, and other pertinent variables. In this review article, the most important applications of artificial intelligence in veterinary medicine will be mentioned, and in general, these applications will be examined in various fields such as diagnosis of common diseases, differential diagnosis, prediction of disease occurrence, veterinary diagnostic imaging techniques, veterinary clinical pathology, etc. In addition, the challenges in this field will also be mentioned. This article is a review of recent studies in this fiel.
1. Akbarein H, Zehtabvar O, Enayati A, Soufizadeh P, Ethics in Veterinary Education and Research: dos and don'ts. The 5th National Congress of Basic Veterinary Sciences, November, 2019, Shahid Bahonar University, Kerman. pp: 24-34.
2. Akbarein H, Enayati A, Soufizadeh P. Companion Animals Welfare and Rights: The Need to Pay Serious Attention. Proceedings of the 3rd National Congress of Companion Animal Medicine, October, 2018, Faculty of Veterinary Medicine, University of Tehran, pp: 19-32.
3. Akbarein H, Enayati A. Professional Ethics in Teaching and Research of Biological Sciences: Considerations and Challenges. Articles of the Second Conference of Ethics, December, 2018, University of Tehran, pp: 66-77.
4. Ashfaq M, Razzaq A, Hassan S. Factors affecting the economic losses due to livestock diseases: a case study of district Faisalabad. Pak J Agric Sci. 2015;52(2). https://doi.org/10.21162/PAKJAS
5. Manavian M, Hashemi M, Tavan F, Hosseini SMH, Nikoo D. Prevalence rate and risk factor of bluetongue disease in dairy cattle farms of fars province. Veterinary Research & Biological Products. 2017;30(4):24–8. https://doi.org/10.22092/vj.2017.113187
6. Sotoudehnejad M, Akbarein H. Machine Learning and Artificial Intelligence functions in parasitology and Diagnostic systems: A mini-review. 2nd national Congress of Animal Parasitic Diseases and Zoonoses, October 11-12, 2023, University of Tabriz, P: 51.
7. Thrusfield M. Veterinary epidemiology.4th edition, John Wiley & Sons; 2018 Apr 30; P: 28-35
8. Neapolitan RE, Jiang X. Artificial intelligence: With an introduction to machine learning. CRC Press; 2018. https://doi.org/10.1201/b22400
9. Sheikh H, Prins C, Schrijvers E. Artificial Intelligence: Definition and Background BT - Mission AI: The New System Technology. In: Sheikh H, Prins C, Schrijvers E, editors. Cham: Springer International Publishing; 2023. p. 15–41. Available from: https://doi.org/10.1007/978-3-031-21448-6_2
10. Haenlein M, Kaplan A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. Calif Manage Rev. 2019;61(4):5–14. https://doi.org/10.1177/0008125619864925
11. Helm JM, Swiergosz AM, Haeberle HS, Karnuta JM, Schaffer JL, Krebs VE, et al. Machine learning and artificial intelligence: definitions, applications, and future directions. Curr Rev Musculoskelet Med. 2020;13:69–76. https://doi.org/10.1007/s12178-020-09600-8
12. McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the Dartmouth summer research project on artificial intelligence (1955). Reprinted online at http://www-formal stanford edu/jmc/history/dartmouth/dartmouth html. 2018. https://doi.org/10.1609/aimag.v27i4.1904
13. Wang P. On defining artificial intelligence. Journal of Artificial General Intelligence. 2019;10(2):1–37. https://doi.org/10.2478/jagi-2019-0002
14. Hutter F, Kotthoff L, Vanschoren J. Automated machine learning: methods, systems, challenges. Springer Nature; May, 2019; PP.: 219. https://10.1007/978-3-030-05318-5
15. Khakpour A, Colomo-Palacios R. Convergence of Gamification and Machine Learning: A Systematic Literature Review. Technology, Knowledge and Learning [Internet]. 2021;26(3):597–636. Available from: https://doi.org/10.1007/s10758-020-09456-4
16. Nilsson NJ. Introduction to machine learning. An early draft of a proposed textbook (1998). Software available at http://robotics stanford edu/people/nilsson/mlbook html. 2020.
17. Zhou ZH. Machine learning. Springer Nature; August, 2021; PP.: 457. https://doi.org/10.1186/s40813-022-00280-z
18. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science (1979). 2015;349(6245):255–60. https://doi.org/10.1126/science.aaa8415
19. Mahesh B. Machine learning algorithms-a review. International Journal of Science and Research (IJSR)[Internet]. 2020;9(1):381–6. https://doi:10.21275/ART20203995
20. Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput Sci [Internet]. 2021;2(3):160. Available from: https://doi.org/10.1007/s42979-021-00592-x
21. Jiang T, Gradus JL, Rosellini AJ. Supervised machine learning: a brief primer. Behav Ther. 2020;51(5):675–87. https://doi.org/10.1016/j.beth.2020.05.002
22. Jo T. Machine learning foundations. Supervised, Unsupervised, and Advanced Learning Cham: Springer International Publishing, First Edition. 2021; PP. 411. https://doi.org/10.1007/978-3-030-65900-4
23. Khanum M, Mahboob T, Imtiaz W, Ghafoor HA, Sehar R. A survey on unsupervised machine learning algorithms for automation, classification and maintenance. Int J Comput Appl. 2015;119(13). https://doi.org/10.5120/21131-4058
24. Morales EF, Escalante HJ. A brief introduction to supervised, unsupervised, and reinforcement learning. In: Biosignal processing and classification using computational learning and intelligence. Elsevier; 2022. p. 111–29. https://doi.org/10.1016/B978-0-12-820125-1.00017-8
25. Nasteski V. An overview of the supervised machine learning methods. Horizons b. 2017;4:51–62. https://doi:10.20544/HORIZONS.B.04.1.17.P05
26. Osisanwo FY, Akinsola JET, Awodele O, Hinmikaiye JO, Olakanmi O, Akinjobi J. Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology (IJCTT). 2017;48(3):128–38. https://doi:10.14445/22312803/IJCTT-V48P12
27. Reddy Y, Viswanath P, Reddy BE. Semi-supervised learning: A brief review. Int J Eng Technol. 2018;7(1.8):81. https://doi.org/10.14419/ijet.v7i1.8.997
28. Shanthamallu US, Spanias A, Tepedelenlioglu C, Stanley M. A brief survey of machine learning methods and their sensor and IoT applications. In: 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA). IEEE; 2017. p. 1–8.
29. Zhu X, Goldberg AB. Introduction to semi-supervised learning. Springer Nature; May, 2022. PP.:130
30. Krenker A, Bešter J, Kos A. Introduction to the artificial neural networks. Artificial Neural Networks: Methodological Advances and Biomedical Applications InTech. 2011;1–18.
31. Krogh A. What are artificial neural networks? Nat Biotechnol. 2008;26(2):195–7. https://doi.org/10.1038/nbt1386
32. Mijwel MM. Artificial neural networks advantages and disadvantages. Retrieved from LinkedIn https//www linkedin com/pulse/artificial-neuralnet Work. 2018;21.
33. Sarker IH. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Comput Sci [Internet]. 2021;2(6):420. Available from: https://doi.org/10.1007/s42979-021-00815-1
34. Yegnanarayana B. Artificial neural networks. PHI Learning Pvt. Ltd.; 2009; PP.: 480
35. Moein S. Definition of artificial neural network. In: Artificial Intelligence: Concepts, Methodologies, Tools, and Applications. IGI Global; 2017. p. 1–11.
36. Nunes I, Da Silva HS. Artificial neural networks: a practical course. Springer, First Edition; 2018; PP. 303
37. Walczak S. Artificial neural networks. In: Advanced methodologies and technologies in artificial intelligence, computer simulation, and human-computer interaction. IGI global; 2019. p. 40–53.
38. Zamiri M, Lamei A, Akbarein H. Blockchain in Veterinary Medicine,The fourth international conference on new findings in medical and health sciences with a health promotion approach; May, 2023.
39. Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. In: Proceedings of the 23rd international conference on Machine learning. 2006. p. 233–40.
40. Goutte C, Gaussier E. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: European conference on information retrieval. Springer; 2005. p. 345–59.
41. Yacouby R, Axman D. Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. In: Proceedings of the first workshop on evaluation and comparison of NLP systems. 2020. p. 79–91.
42. Zheng A. Evaluating machine learning models: a beginner’s guide to key concepts and pitfalls. O’Reilly Media, First Edition; 2015; PP.: 58
43. Juba B, Le HS. Precision-recall versus accuracy and the role of large data sets. In: Proceedings of the AAAI conference on artificial intelligence. 2019. p. 4039–48. https://doi.org/10.1609/aaai.v33i01.33014039
44. Mokarram-Ghotoorlar S, Ghamsari SM, Nowrouzian I, Mokarram-Ghotoorlar S, Ghidary SS. Lameness scoring system for dairy cows using force plates and artificial intelligence. Veterinary Record. 2012 Feb;170(5):126-130. https://doi.org/10.1136/vr.100429
45. Vajhi A, Norouzi M, Molazem M, Vasei H, Amini E, Ramzi Sh, et al. Application of artificial intelligence in diagnostic radiology of laminitis in horses, The 5th National Congress of equine Health and Diseases. December 2021, Shahid Bahonar University, Kerman. pp: 168-178.
46. Hyde RM, Down PM, Bradley AJ, Breen JE, Hudson C, Leach KA, et al. Automated prediction of mastitis infection patterns in dairy herds using machine learning. Sci Rep. 2020;10(1):4289. https://doi.org/10.1038/s41598-020-61126-8
47. Ghadiri A. Deep Learning, Machine Learning and Artificial In-telligence in Veterinary Diagnostic Imaging. InThe 2nd Regional Conference on Cow Comfort & Lameness 18-20 July 2022 University of Tehran, Iran 2022 Jul 16, p. 76.
48. 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. Veterinary Radiology & Ultrasound. 2020;61(6):611–8. https://doi.org/10.1111/vru.12901
49. 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. https://doi.org/10.3389/fvets.2021.721167
50. Awaysheh A, Wilcke J, Elvinger F, Rees L, Fan W, Zimmerman KL. Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats. Journal of veterinary diagnostic investigation. 2016;28(6):679–87. https://doi.org/10.1177/1040638716
51. 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(1):1–6. https://doi.org/10.1186/s12917-018-1638-2
52. 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(5):847–55. https://doi.org/10.1111/evj.13528
53. Zamiri M, Lamei A, Akbarein H. 2023. Applications of AI in Veterinary Medicine,The first international congress of artificial intelligence in medical sciences, Kish Island; P. 320
54. Mitchell TM. Machine learning (WBC/McGraw-Hill, Boston). MA. 1997.
55. Dórea FC, Sanchez J, Revie CW. Veterinary syndromic surveillance: current initiatives and potential for development. Prev Vet Med. 2011;101(1–2):1–17. https://doi.org/10.1016/j.prevetmed.2011.05.004
56. Bollig N, Clarke L, Elsmo E, Craven M. Machine learning for syndromic surveillance using veterinary necropsy reports. PLoS One. 2020;15(2):e0228105. https://doi.org/10.1371/journal.pone.0228105
57. Das DK, Ghosh M, Pal M, Maiti AK, Chakraborty C. Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron. 2013;45:97–106. https://doi.org/10.1016/j.micron.2012.11.002
58. Ligda P, Claerebout E, Kostopoulou D, Zdragas A, Casaert S, Robertson LJ, et al. Cryptosporidium and Giardia in surface water and drinking water: Animal sources and towards the use of a machine-learning approach as a tool for predicting contamination. Environmental Pollution. 2020;264:114766. https://doi.org/10.1016/j.envpol.2020.114766
59. 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. https://doi.org/10.1016/j.jevs.2020.102973
60. 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(6):2644–56. https://doi.org/10.1111/jvim.15623
61. 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(5):1920–31. https://doi.org/10.1111/jvim.15892
62. Pereira AI, Franco-Gonçalo P, Leite P, Ribeiro A, Alves-Pimenta MS, Colaço B, et al. Artificial Intelligence in Veterinary Imaging: An Overview. Vet Sci. 2023;10(5):320. https://doi.org/10.3390/vetsci10050320
63. McEvoy FJ, Proschowsky HF, Müller A V, Moorman L, Bender‐Koch J, Svalastoga EL, et al. Deep transfer learning can be used for the detection of hip joints in pelvis radiographs and the classification of their hip dysplasia status. Veterinary Radiology & Ultrasound. 2021;62(4):387–93. https://doi.org/10.1111/vru.12968
64. Ergün GB, Güney S. Classification of Canine Maturity and Bone Fracture Time Based on X-Ray Images of Long Bones. IEEE Access. 2021;9:109004–11. https://doi:10.1109/ACCESS.2021.3101040
65. Yoon Y, Hwang T, Lee H. Prediction of radiographic abnormalities by the use of bag-of-features and convolutional neural networks. The Veterinary Journal. 2018;237:43–8. https://doi.org/10.1016/j.tvjl.2018.05.009
66. 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(4):624–31. https://doi.org/10.1111/vco.12747
67. 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. The Veterinary Journal. 2018;233:35–40. https://doi.org/10.1016/j.tvjl.2017.12.026
68. Vinicki K, Ferrari P, Belic M, Turk R. Using convolutional neural networks for determining reticulocyte percentage in cats. Published online March 13, 2018. doi: 10.48550. ArXiv. 1803;
69. Cihan P, Gökçe E, Atakişi O, Kırmzıgül AH, Erdoğan HM. Prediction of immunoglobulin G in lambs with artificial intelligence methods. Kafkas Univ Vet Fak Derg; 27 (1): 21-27, 2021. https://acikerisim.aksaray.edu.tr/dx.doi.org/%2010.9775/kvfd.2020.24642
70. Zapata, Lorena, et al. “Detection of Cutaneous Tumors in Dogs Using Deep Learning Techniques.” Advances in Intelligent Systems and Computing, Springer International Publishing, June 2019, pp. 83–91. Crossref, https://doi.org/10.1007/978-3-030-20454-9_8.
71. Zuraw A, Aeffner F. Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review. Vet Pathol. 2022;59(1):6–25. https://doi.org/10.1177/03009858211040484
72. Sotoudehnejad M, Akbarein H. Machine Learning and AI at the service of Microbiologists, 24th Iran’s International Congress of Microbiology, Tehran, Iran. September, 2023; P. 342.
73. Bashizadeh M. (Supervised by Akbarein H & Nikbakht-Boroijeni Gh.). Developing a machine learning method for prediction of Bovine Leukemia Virus (BLV) disease progress by BoLA-DRB 3 alleles. DVM thesis, Faculty of Veterinary Medicine, University of Tehran, Tehran- Iran, September. 2023. p:22-37
74. Bokaie S, Mosa Farkhani E. Epidemiological investigation of waterborne and foodborne disease outbreaks in Iran: 2012-2018. J Mil Med. 2019 Jan 1;21(6):637-46.
75. Bashizade M, Madani K, Akbarein H. The Application of Artificial Intelligence in the Success Rate of In Vitro Fertilization, The First International Congress of Artificial Intelligence in Medical Sciences, Kish Island. 2023; P. 327.
76. Medeiros CM, Baêta FDC, Oliveira RD, Tinôco I, Albino LFT, Cecon PR. Efeitos da temperatura, umidade relativa e velocidade do ar em frangos de corte. Engenharia na Agricultura 13 (4): 277-286. 2014.
77. Campos OF, Cunha D, Pereira JC, Junqueira MM, Martustello JA, Pires MFA, et al. Utilização de diferentes abrigos para bezerros de rebanhos leiteiros em condições tropicais durante a época das águas: temperatura retal, frequência respiratória e consumo de água. Proceedings of the XLI Reunião Anual da Sociedade Brasileira de Zootecnia, Brazil. 2004;1–5.
78. Lopes AZ, Junior TY, Lacerda WS, RABELO D. Predicting rectal temperature of broiler chickens with artificial neural network. International Journal of Engineering e Technology. 2014;14(5):29–33. Paper ID:145205-8383-IJET-IJENS
79. Milosevic B, Ciric S, Lalic N, Milanovic V, Savic Z, Omerovic I, et al. Machine learning application in growth and health prediction of broiler chickens. Worlds Poult Sci J. 2019;75(3):401–10. https://doi.org/10.1017/S0043933919000254