Comparison of thermal imaging and rectal temperature in the diagnosis of pyrexia in pre-weaned calves using on farm conditions

Detection of canine obstructive nasal disease using infrared thermography: A pilot study

Infrared thermography detects variations in heat signature and is utilized in other species to non-invasively identify respiratory disease. This study aimed to determine if infrared thermography could be used to detect nasal disease in dogs. Eight dogs presenting for nasal disease (ND group) and ten healthy control dogs (C group) were enrolled. Dorsal and rostral images of the nose were acquired using a Fluke TiX580 60Hz thermal imaging camera. Images were analyzed using the accompanying software. Regions of interest were defined over the right and left nasal passages to determine the maximum (max), average (avg), and minimum (min) temperatures. Temperatures were compared between ND and C groups, and correlation to disease state (ND or C) was evaluated. Temperature differences and imaging patterns were subjectively compared with diagnosis based on computed tomography (CT) and histopathology. The ND group consisted of 5 spayed females and 3 neutered males. Clinical sings included unilateral epistaxis (n = 4); bilateral serous discharge and sneezing (n = 1); bilateral mucopurulent discharge, epistaxis, and sneezing (n = 1); unilateral mucoid discharge, epistaxis and sneezing (n = 1); and sneezing and unilateral epistaxis (n = 1). Temperatures were significantly different between ND and C groups on dorsal (max p = <0.001, avg p = 0.001, min p = <0.001) and rostral (max p = <0.001, avg p = <0.001, min p = 0.005) images. Temperature positively correlated to disease status (ND vs C group) in both dorsal and rostral images. Subjective analysis of images allowed correct identification of abnormal or normal 27/36 times. Obstructive nasal disease results in a local temperature increase in the affected nasal passage that can be non-invasively detected by infrared thermography.

Literature Review on Technological Applications to Monitor and Evaluate Calves' Health and Welfare

Precision livestock farming (PLF) research is rapidly increasing and has improved farmers' quality of life, animal welfare, and production efficiency. PLF research in dairy calves is still relatively recent but has grown in the last few years. Automatic milk feeding systems (AMFS) and 3D accelerometers have been the most extensively used technologies in dairy calves. However, other technologies have been emerging in dairy calves' research, such as infrared thermography (IRT), 3D cameras, ruminal bolus, and sound analysis systems, which have not been properly validated and reviewed in the scientific literature. Thus, with this review, we aimed to analyse the state-of-the-art of technological applications in calves, focusing on dairy calves. Most of the research is focused on technology to detect and predict calves' health problems and monitor pain indicators. Feeding and lying behaviours have sometimes been associated with health and welfare levels. However, a consensus opinion is still unclear since other factors, such as milk allowance, can affect these behaviours differently. Research that employed a multi-technology approach showed better results than research focusing on only a single technique. Integrating and automating different technologies with machine learning algorithms can offer more scientific knowledge and potentially help the farmers improve calves' health, performance, and welfare, if commercial applications are available, which, from the authors' knowledge, are not at the moment.

Evaluation of an infrared thermography camera for measuring body temperature in dairy calves

The objective of this diagnostic accuracy study was to validate an infrared thermography (IRT) camera and its software (FLIR One, FLIR, Global) for accuracy and precision for ocular temperature readings to serve as a proxy for rectal temperature in commercially housed calves. A total of 318 male Holstein calves were enrolled into this study from the day of arrival to a calf rearing facility until 14 d later. Researchers took an ocular temperature reading using an IRT camera, and a rectal temperature on each calf each day in the morning. The reference standard method for body temperature in the calves was rectal temperature. We assessed the data for agreement between the IRT and the reference standard using Pearson correlations by calf (accuracy), coefficients of determination (precision), and Bland-Altman plots for bias. In addition, a logistic regression model was built using the reference method as the outcome, with IRT as an explanatory variable to assess the diagnostic accuracy of IRT as an indicator of fever (rectal temperature ≥39.5°C). There was a negligible correlation between the IRT readings and rectal temperature (r = 0.22) and the coefficient of determination for IRT to predict rectal temperature was negligible (R² = 0.05), suggesting poor precision. The average mean difference between the IRT data and rectal temperature was 0.55°C, and the differences between IRT and rectal formed a linear line around the mean difference, suggesting the Bland-Altman analyses showed proportional error and bias. The optimal probability cut-off for IRT readings for fever was at 39.5°C, and had a receiver operating characteristic area under the curve of 0.67, a sensitivity of 61%, a specificity of 71%, and 78% (3,134/4,427) of the samples were correctly labeled as either having a fever or not using IRT readings. In summary, the IRT camera and software were not validated for serving as a proxy for rectal temperature in commercially housed calves due to poor precision, and proportional error partially explained by ambient environmental conditions. We suggest that this infrared thermography system should not replace rectal temperature readings for use in commercially housed calves.

Thermography for disease detection in livestock: A scoping review

Infra-red thermography (IRT) offers potential opportunities as a tool for disease detection in livestock. Despite considerable research in this area, there are no common standards or protocols for managing IRT parameters in animal disease detection research. In this review, we investigate parameters that are essential to the progression of this tool and make recommendations for their use based on the literature found and the veterinary thermography guidelines from the American Academy of Thermology. We analyzed a defined set of 109 articles concerned with the use of IRT in livestock related to disease and from these articles, parameters for accurate IRT were identified and sorted into the fields of camera-, animal- or environment-related categories to assess the practices of each article in reporting parameters. This review demonstrates the inconsistencies in practice across peer-reviewed articles and reveals that some important parameters are completely unreported while others are incorrectly captured and/or under-represented in the literature. Further to this, our review highlights the lack of measured emissivity values for live animals in multiple species. We present guidelines for the standards of parameters that should be used and reported in future experiments and discuss potential opportunities and challenges associated with using IRT for disease detection in livestock.

A Systematic Review of Automatic Health Monitoring in Calves: Glimpsing the Future From Current Practice

Infectious diseases, particularly bovine respiratory disease (BRD) and neonatal calf diarrhea (NCD), are prevalent in calves. Efficient health-monitoring tools to identify such diseases on time are lacking. Common practice (i.e., health checks) often identifies sick calves at a late stage of disease or not at all. Sensor technology enables the automatic and continuous monitoring of calf physiology or behavior, potentially offering timely and precise detection of sick calves. A systematic overview of automated disease detection in calves is still lacking. The objectives of this literature review were hence: to investigate previously applied sensor validation methods used in the context of calf health, to identify sensors used on calves, the parameters these sensors monitor, and the statistical tools applied to identify diseases, to explore potential research gaps and to point to future research opportunities. To achieve these objectives, systematic literature searches were conducted. We defined four stages in the development of health-monitoring systems: (1) sensor technique, (2) data interpretation, (3) information integration, and (4) decision support. Fifty-four articles were included (stage one: 26; stage two: 19; stage three: 9; and stage four: 0). Common parameters that assess the performance of these systems are sensitivity, specificity, accuracy, precision, and negative predictive value. Gold standards that typically assess these parameters include manual measurement and manual health-assessment protocols. At stage one, automatic feeding stations, accelerometers, infrared thermography cameras, microphones, and 3-D cameras are accurate in screening behavior and physiology in calves. At stage two, changes in feeding behaviors, lying, activity, or body temperature corresponded to changes in health status, and point to health issues earlier than manual health checks. At stage three, accelerometers, thermometers, and automatic feeding stations have been integrated into one system that was shown to be able to successfully detect diseases in calves, including BRD and NCD. We discuss these findings, look into potentials at stage four, and touch upon the topic of resilience, whereby health-monitoring system might be used to detect low resilience (i.e., prone to disease but clinically healthy calves), promoting further improvements in calf health and welfare.