Artificial Intelligence-Assisted Disease and Welfare Detection in Farm Animals: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis

Muhammet Kuddusi Erhan; Aycan Mutlu Yağanoğlu

doi:10.56430/japro.1979815

EN

Artificial Intelligence-Assisted Disease and Welfare Detection in Farm Animals: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis

Abstract

To estimate, against a reference standard, how accurately artificial intelligence methods detect disease and welfare conditions in farm animals. PubMed, Web of Science, and Scopus were searched for primary diagnostic accuracy studies, with an updated search during revision for additional animal-level lameness studies. When a 2×2 table was not reported, the table was reconstructed from the reported metrics and checked for internal consistency. Eligible studies were pooled with a bivariate random-effects model, and the results were checked with independent estimators. Heterogeneity, publication bias, sensitivity analyses, risk of bias, and certainty of evidence were assessed. Seventy-four of 154 full texts met the inclusion criteria. Under an eligibility criterion amended in review to keep within-group sample sizes comparable (animal-level N ≤ 500 cows), three large-sample studies were excluded and one small-sample study was added through the updated search; ten studies entered the primary meta-analysis (seven on mastitis and three on lameness). A single-study reproduction/metritis dataset was excluded from pooling and synthesised narratively. Pooled sensitivity was 83.5% (95% confidence interval 74.0–90.0) and pooled specificity was 90.1% (95% confidence interval 85.4–93.3), with an area under the curve of 0.936 and a diagnostic odds ratio of 46. The mastitis subgroup (k=7) reached 83.5% sensitivity and 88.9% specificity; the three-study lameness subgroup is reported as exploratory. Heterogeneity was substantial, and most studies could not be pooled because they reported performance at the record or quarter level rather than per animal. Artificial intelligence-based detection appears promising, combining high specificity with good sensitivity, but wide heterogeneity, the small number of poolable studies, and low certainty of evidence all call for cautious interpretation.

Keywords

Artificial intelligence, Diagnostic test accuracy, Lameness, Mastitis, Meta-analysis, Precision livestock farming

Ethical Statement

As the study is based on the synthesis of published data, ethics committee approval was not required.

Thanks

Registration and data availability: The study was registered retrospectively on the Open Science Framework (OSF) after data extraction and analysis were completed (osf.io/5xzvh); it was not prospectively registered (PROSPERO). The data, analysis code, and outputs supporting this study are available from the corresponding author on reasonable request.

References

Beer, G., Alsaaod, M., Starke, A., Schuepbach-Regula, G., Müller, H., Kohler, P., & Steiner, A. (2016). Use of extended characteristics of locomotion and feeding behavior for automated identification of lame dairy cows. PLOS One, 11(5), e0155796. https://doi.org/10.1371/journal.pone.0155796
Borghart, G. M., O’Grady, L. E., & Somers, J. R. (2021). Prediction of lameness using automatically recorded activity, behavior and production data in post-parturient Irish dairy cows. Irish Veterinary Journal, 74(1), 4. https://doi.org/10.1186/s13620-021-00182-6
Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., Kressel, H. Y., Rifai, N., Golub, R. M., Altman, D. G., Hooft, L., Korevaar, D. A., Cohen, J. F., & STARD Group. (2015). STARD 2015: An updated list of essential items for reporting diagnostic accuracy studies. Radiology, 277(3), 826-832. https://doi.org/10.1136/bmj.h5527
Collins, G. S., Dhiman, P., Andaur Navarro, C. L., Ma, J., Hooft, L., Reitsma, J. B., Logullo, P., Beam, A. L., Peng, L., Van Calster, B., van Smeden, M., Riley, R. D., & Moons, K. G. (2021). Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open, 11(7), e048008. https://doi.org/10.1136/bmjopen-2020-048008
Collins, G. S., Moons, K. G., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., ... & Logullo, P. (2024). TRIPOD+ AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 385, e078378. https://doi.org/10.1136/bmj-2023-078378
Deeks, J. J., Bossuyt, P. M., Leeflang, M. M., & Takwoingi, Y. (2023). Cochrane handbook for systematic reviews of diagnostic test accuracy. John Wiley & Sons.
Deeks, J. J., Macaskill, P., & Irwig, L. (2005). The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. Journal of Clinical Epidemiology, 58(9), 882-893. https://doi.org/10.1016/j.jclinepi.2005.01.016
Dervić, E., Matzhold, C., Egger-Danner, C., Steininger, F., & Klimek, P. (2024). Improving lameness detection in cows: A machine learning algorithm application. Journal of Dairy Science, 107(12), 11550-11562. https://doi.org/10.3168/jds.2024-24730
Distante, D., Albanello, C., Zaffar, H., Faralli, S., & Amalfitano, D. (2025). Artificial intelligence applied to precision livestock farming: A tertiary study. Smart Agricultural Technology, 11, 100889. https://doi.org/10.1016/j.atech.2025.100889
Džermeikaitė, K., Krištolaitytė, J., & Antanaitis, R. (2025a). Application of machine learning models for the early detection of metritis in dairy cows based on physiological, behavioural and milk quality indicators. Animals, 15(11), 1674. https://doi.org/10.3390/ani15111674

Džermeikaitė, K., Krištolaitytė, J., Grigė, S., Girdauskaitė, A., Šertvytytė, G., Lembovičiūtė, G., Televičius, M., Riškevičienė, V., & Antanaitis, R. (2025b). Creation of machine learning models trained on multimodal physiological, behavioural, blood biochemical, and milk composition parameters for the identification of lameness in dairy cows. Biosensors, 15(11), 722. https://doi.org/10.3390/bios15110722
Foris, B., Sheng, K., Dürnberger, C., Oczak, M., & Rault, J.-L. (2025). AI for One Welfare: The role of animal welfare scientists in developing valid and ethical AI-based welfare assessment tools. Frontiers in Veterinary Science, 12, 1645901. https://doi.org/10.3389/fvets.2025.1645901
Ghafoor, N. A., & Sitkowska, B. (2021). MasPA: A machine learning application to predict risk of mastitis in cattle from AMS sensor data. AgriEngineering, 3(3), 575-583. https://doi.org/10.3390/agriengineering3030037
Jayakumar, S., Sounderajah, V., Normahani, P., Harling, L., Markar, S. R., Ashrafian, H., & Darzi, A. (2022). Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: A meta-research study. npj Digital Medicine, 5, 11. https://doi.org/10.1038/s41746-021-00544-y
Liu, C., Cui, M., Zhang, D., Lu, Y., Yan, X., Li, Y., Wang, Z., Zhang, Y., Li, M., Zhao, G., & Liu, X. (2025). Machine learning-based prediction of clinical mastitis in dairy cows: A comparative analysis of 9 algorithms using production and management data. Journal of Dairy Science, 109(5), 5260-5275. https://doi.org/10.3168/jds.2025-27483
Maciel-Guerra, A., Esener, N., Giebel, K., Lea, D., Green, M. J., Bradley, A. J., & Dottorini, T. (2021). Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning. Scientific Reports, 11(1), 7736. https://doi.org/10.1038/s41598-021-87300-0
Mammadova, N. M., & Keskin, I. (2015). Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle. Indian Journal of Animal Research, 49(5), 671-679. https://doi.org/10.18805/ijar.5581
Michelena, Á., Fontenla-Romero, Ó., & Calvo-Rolle, J. L. (2024). A review and future trends of precision livestock over dairy and beef cow cattle with artificial intelligence. Logic Journal of the IGPL, 33(4), jzae111. https://doi.org/10.1093/jigpal/jzae111
Michielon, A., Litta, P., Bonelli, F., Don, G., Farisè, S., Giannuzzi, D., Milanesi, M., Pietrucci, D., Vezzoli, A., Cecchinato, A., Chillemi, G., Gallo, L., Mele, M., & Furlanello, C. (2024). Mind the step: An artificial intelligence-based monitoring platform for animal welfare. Sensors, 24(24), 8042. https://doi.org/10.3390/s24248042
Moses, L. E., Shapiro, D., & Littenberg, B. (1993). Combining independent studies of a diagnostic test into a summary ROC curve: Data-analytic approaches and some additional considerations. Statistics in Medicine, 12(14), 1293-1316. https://doi.org/10.1002/sim.4780121403
Neupane, R., Aryal, A., Haeussermann, A., Hartung, E., Pinedo, P., & Paudyal, S. (2024). Evaluating machine learning algorithms to predict lameness in dairy cattle. PLOS One, 19(7), e0301167. https://doi.org/10.1371/journal.pone.0301167
Pan, L., Chen, X., Han, D., Li, N., Chen, D., Wang, J., Chen, J., & Huo, X. (2025). Machine learning-based clinical mastitis detection in dairy cows using milk electrical conductivity and somatic cell count. Frontiers in Veterinary Science, 12, 1671186. https://doi.org/10.3389/fvets.2025.1671186
Rasmussen, P., Barkema, H. W., Osei, P. P., Taylor, J., Shaw, A. P., Conrady, B., Chaters, G., Muñoz, V., Hall, D. C., Apenteng, O. O., Rushton, J., & Torgerson, P. R. (2024). Global losses due to dairy cattle diseases: A comorbidity-adjusted economic analysis. Journal of Dairy Science, 107(9), 6945-6970. https://doi.org/10.3168/jds.2023-24626
Reitsma, J. B., Glas, A. S., Rutjes, A. W. S., Scholten, R. J. P. M., Bossuyt, P. M., & Zwinderman, A. H. (2005). Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. Journal of Clinical Epidemiology, 58(10), 982-990. https://doi.org/10.1016/j.jclinepi.2005.02.022
Rohan, A., Rafaq, M. S., Hasan, M. J., Asghar, F., Bashir, A. K., & Dottorini, T. (2024). Application of deep learning for livestock behaviour recognition: A systematic literature review. Computers and Electronics in Agriculture, 224, 109115. https://doi.org/10.1016/j.compag.2024.109115
Siachos, N., Neary, J. M., Smith, R. F., & Oikonomou, G. (2024). Automated dairy cattle lameness detection utilizing the power of artificial intelligence; current status quo and future research opportunities. The Veterinary Journal, 304, 106091. https://doi.org/10.1016/j.tvjl.2024.106091
Sounderajah, V., Guni, A., Liu, X., Collins, G. S., Karthikesalingam, A., Markar, S. R., Golub, R. M., Denniston, A. K., Shetty, S., Moher, D., Bossuyt, P. M., Darzi, A., & Ashrafian, H. (2025). The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence. Nature Medicine, 31(10), 3283-3289. https://doi.org/10.1038/s41591-025-03953-8
Tedeschi, L. O., Guarnido-Lopez, P., Menendez, H. M., III, & Seo, S. (2026). Advancing precision livestock farming: Integrating artificial intelligence and emerging technologies for sustainable livestock management. Animal Bioscience, 39(4), 250-289. https://doi.org/10.5713/ab.25.0289
Van Hertem, T., Bahr, C., Tello, A. S., Viazzi, S., Steensels, M., Romanini, C. E. B., Lokhorst, C., Maltz, E., Halachmi, I., & Berckmans, D. (2016). Lameness detection in dairy cattle: Single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing. Animal, 10(9), 1525-1532. https://doi.org/10.1017/S1751731115001457
Velasco-Bolaños, J., Ceballes-Serrano, C. C., Velásquez-Mejía, D., Riaño-Rojas, J. C., Giraldo, C. E., Carmona, J. U., & Ceballos-Márquez, A. (2021). Application of udder surface temperature by infrared thermography for diagnosis of subclinical mastitis in Holstein cows located in tropical highlands. Journal of Dairy Science, 104(9), 10310-10323. https://doi.org/10.3168/jds.2020-19894
Volkmann, N., Kulig, B., Hoppe, S., Stracke, J., Hensel, O., & Kemper, N. (2021). On-farm detection of claw lesions in dairy cows based on acoustic analyses and machine learning. Journal of Dairy Science, 104(5), 5921-5931. https://doi.org/10.3168/jds.2020-19206
Wang, Y., Kang, X., He, Z., Feng, Y., & Liu, G. (2022). Accurate detection of dairy cow mastitis with deep learning technology: A new and comprehensive detection method based on infrared thermal images. Animal, 16(9), 100646. https://doi.org/10.1016/j.animal.2022.100646
Zecconi, A., Zaghen, F., Meroni, G., Sommariva, F., Ferrari, S., & Sora, V. (2025). Machine learning approach for early lactation mastitis diagnosis using total and differential somatic cell counts. Animals, 15(8), 1125. https://doi.org/10.3390/ani15081125
Zhang, L., Guo, W., Lv, C., Guo, M., Yang, M., Fu, Q., & Liu, X. (2024). Advancements in artificial intelligence technology for improving animal welfare: Current applications and research progress. Animal Research and One Health, 2(1), 93-109. https://doi.org/10.1002/aro2.44
Zhang, X., Li, Y., Zhang, Y., Yao, Z., Zou, W., Nie, P., & Yang, L. (2024). A new method to detect buffalo mastitis using udder ultrasonography based on deep learning network. Animals, 14(5), 707. https://doi.org/10.3390/ani14050707
Zhou, X., Qu, Y., Xu, C., Wang, H., Lang, D., Jia, B., & Jiang, N. (2026). Machine learning and SHapley Additive exPlanation-Based interpretation for predicting mastitis in dairy cows. Animals, 16(2), 204. https://doi.org/10.3390/ani16020204

Details

Primary Language

English

Subjects

Animal Science, Genetics and Biostatistics

Journal Section

Review

Authors

Muhammet Kuddusi Erhan ^*
0000-0003-4115-8747
Türkiye

Aycan Mutlu Yağanoğlu
0000-0002-0163-6989
Türkiye

Publication Date

September 30, 2026

Submission Date

June 26, 2026

Acceptance Date

August 16, 2026

Published in Issue

Year 2026 Volume: 7 Number: 3

DOI

https://doi.org/10.56430/japro.1979815

IZ

https://izlik.org/JA66JU24KL

APA

Erhan, M. K., & Yağanoğlu, A. M. (2026). Artificial Intelligence-Assisted Disease and Welfare Detection in Farm Animals: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis. Journal of Agricultural Production, 7(3), 170-181. https://doi.org/10.56430/japro.1979815

AMA

1.Erhan MK, Yağanoğlu AM. Artificial Intelligence-Assisted Disease and Welfare Detection in Farm Animals: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis. J Agri Pro. 2026;7(3):170-181. doi:10.56430/japro.1979815

Chicago

Erhan, Muhammet Kuddusi, and Aycan Mutlu Yağanoğlu. 2026. “Artificial Intelligence-Assisted Disease and Welfare Detection in Farm Animals: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis”. Journal of Agricultural Production 7 (3): 170-81. https://doi.org/10.56430/japro.1979815.

EndNote

Erhan MK, Yağanoğlu AM (September 1, 2026) Artificial Intelligence-Assisted Disease and Welfare Detection in Farm Animals: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis. Journal of Agricultural Production 7 3 170–181.

IEEE

[1]M. K. Erhan and A. M. Yağanoğlu, “Artificial Intelligence-Assisted Disease and Welfare Detection in Farm Animals: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis”, J Agri Pro, vol. 7, no. 3, pp. 170–181, Sept. 2026, doi: 10.56430/japro.1979815.

ISNAD

Erhan, Muhammet Kuddusi - Yağanoğlu, Aycan Mutlu. “Artificial Intelligence-Assisted Disease and Welfare Detection in Farm Animals: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis”. Journal of Agricultural Production 7/3 (September 1, 2026): 170-181. https://doi.org/10.56430/japro.1979815.

JAMA

1.Erhan MK, Yağanoğlu AM. Artificial Intelligence-Assisted Disease and Welfare Detection in Farm Animals: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis. J Agri Pro. 2026;7:170–181.

MLA

Erhan, Muhammet Kuddusi, and Aycan Mutlu Yağanoğlu. “Artificial Intelligence-Assisted Disease and Welfare Detection in Farm Animals: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis”. Journal of Agricultural Production, vol. 7, no. 3, Sept. 2026, pp. 170-81, doi:10.56430/japro.1979815.

Vancouver

1.Muhammet Kuddusi Erhan, Aycan Mutlu Yağanoğlu. Artificial Intelligence-Assisted Disease and Welfare Detection in Farm Animals: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis. J Agri Pro. 2026 Sep. 1;7(3):170-81. doi:10.56430/japro.1979815

Artificial Intelligence-Assisted Disease and Welfare Detection in Farm Animals: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis

Abstract

Keywords

Ethical Statement

Thanks

Abstract

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite