Thermal Image Scanning for the Early Detection of Fever Induced by Highly Pathogenic Avian Influenza Virus Infection in Chickens and Ducks and Its Application in Farms

Abstract

Highly pathogenic avian influenza (HPAI) is considered as one of the most devastating poultry diseases. It is imperative to immediately report any known outbreaks to the World Organization for Animal Health. Early detection of infected birds is of paramount importance to control virus spread, thus minimizing the associated economic loss. In this study, thermal imaging camera devices were used to detect change in the maximum surface temperature (MST) of chickens (n = 5) and ducks (n = 2) as an early indicator of experimental HPAI infection. The MST of both chickens and ducks increased at least 24 h before the manifestation of clinical signs of HPAI infection, depending on the severity of the infection. The basal MST was recorded for broiler chickens housed under small pen and normal farm conditions without intentional infection. A threshold cutoff of MST was established based on the circadian rhythm of normal MST. This study suggests that thermal imaging of chickens and ducks is a promising tool to screen any potential HPAI-infected flock in order to expedite HPAI diagnosis.

Synchronously in vivo real‐time monitoring bacterial load and temperature with evaluating immune response to decipher bacterial infection

“…40,41 High temperature also induced the increase expression level of HSP60 and HSP90. 40,41 The increased expression level of both HSPs then played a negative feedback role in regulating body temperature, leading to a decrease in temperature. 11 Moreover, the delay in the immune response compared with the temperature elevation may be attributed to the fact that immune response are initiated by febrile-range hyperthermia, which could accelerate pathogen clearance, but also enhance collateral tissue injury.…”

Diagnosing Infectious Diseases in Poultry Requires a Holistic Approach: A Review

“…Avian influenza Imaging (thermal images) experimental setting [29] Campylobacter jejuni imaging (flock movement-optical flow) dataset from broiler buildings [30] Clostridium perfringens sound analysis (vocalizations) experimental setting [31] Coccidiosis sensor (volatile organic compounds) experimental setting + broiler building [32] Coccidiosis sensor (volatile organic compounds) dataset from broiler buildings [33] Coccidiosis + Salmonella spp. imaging (feces) dataset of images [20] Ektoparasites wearable sensor (activity) dataset from poultry building [34] Infectious bronchitis sound analysis (rales) experimental setting [35] Infectious bronchitis sound analysis (rales) experimental setting [36] Infectious bronchitis + Newcastle disease sound analysis (vocalizations) experimental setting [37] Newcastle disease sound analysis (sneezes) experimental setting [38] Newcastle disease imaging (posture and mobility) experimental setting [39] Newcastle disease sound analysis (vocalizations) experimental setting [40] Non-specific, clinical signs imaging (feces) dataset of images [41] Non-specific, clinical signs imaging and sound analysis dataset of audio samples [42] Non-specific, clinical signs imaging (feces) dataset from broiler building [43] Non-specific, clinical signs imaging (head motion, appearance) experimental setting [44] Non-specific, clinical signs imaging (posture, appearance) experimental setting [45] Non-specific, clinical signs sound analysis (abnormal respiratory sounds) dataset from broiler building [46] Non-specific, clinical signs imaging (posture, appearance) dataset of images [47] Pasteurella spp.…”

“…imaging (feces) dataset of images [20] Ektoparasites wearable sensor (activity) dataset from poultry building [34] Infectious bronchitis sound analysis (rales) experimental setting [35] Infectious bronchitis sound analysis (rales) experimental setting [36] Infectious bronchitis + Newcastle disease sound analysis (vocalizations) experimental setting [37] Newcastle disease sound analysis (sneezes) experimental setting [38] Newcastle disease imaging (posture and mobility) experimental setting [39] Newcastle disease sound analysis (vocalizations) experimental setting [40] Non-specific, clinical signs imaging (feces) dataset of images [41] Non-specific, clinical signs imaging and sound analysis dataset of audio samples [42] Non-specific, clinical signs imaging (feces) dataset from broiler building [43] Non-specific, clinical signs imaging (head motion, appearance) experimental setting [44] Non-specific, clinical signs imaging (posture, appearance) experimental setting [45] Non-specific, clinical signs sound analysis (abnormal respiratory sounds) dataset from broiler building [46] Non-specific, clinical signs imaging (posture, appearance) dataset of images [47] Pasteurella spp. imaging (thermal images) experimental setting [48] * All studies were perfomed with chickens, except Noh et al [29] who also used ducks.…”

Poultry disease early detection methods using deep learning technology

“…Chicken body temperature can be measured by applying this technology after changes in diet, stress levels, and environmental conditions [23]. Noh et al [29] developed an innovative system for real-time surveillance of infected poultry before disease manifestation. It showed if it would be possible to employ thermal imaging to find changes in the surface temperature of ducks and chickens as an early sign of highly pathogenic avian influenza (HPAI) infection.…”