Table 1.
Leveraging Digital Phenotyping for Comprehensive Monitoring and Analysis in the Broiler Industry.
| Digital Phenotyping Applications | Specific Use in Broiler Industry | Sensitivity to Detect Anomalies | Accuracy of Anomaly Detection | Data Used for Analysis | Possible Digital Markers | Associated Genomic Traits |
|---|---|---|---|---|---|---|
| Social Interactions | Monitoring group dynamics, identifying social stressors | High sensitivity in detecting changes in group dynamics that may indicate stress or health issues. | Accuracy depends on the specific methods and technologies used, but generally high for detecting major changes in social interactions. | Social interaction data from sensors and cameras, demographic data, veterinary health records. | Changes in group dynamics, abnormal social behaviors. | Group III Secreted Phospholipase A2 (sPLA2-III), Vasotocin and Mesotocin Receptors, (HTR2C) and (DRD4) |
| Behavioral Patterns | Real-time tracking and analysis of individual and group behaviors. | High sensitivity in detecting changes in behavior patterns that may indicate stress or health issues. | Accuracy depends on the specific methods and technologies used, but generally high for detecting major changes in behavior. | Behavioral data from sensors and cameras, accelerometer training data, demographic data, veterinary health records. | Changes in activity levels, abnormal behaviors. | MC4R gene, insulin signaling pathways, thyroid hormone pathways, and growth hormone pathways, serotonergic activity, genes related to hypothalamic-pituitary-adrenal (HPA) axis |
| Health-Related Phenotypes | Continuous monitoring of physical health parameters, developing disease phenotypes. | High sensitivity in detecting changes in health parameters that may indicate disease. | Accuracy depends on the specific methods and technologies used, but generally high for detecting major health issues. | Health data from sensors and cameras, demographic data, veterinary health records. | Changes in physical health parameters, signs of disease. | Growth hormone (GH), insulin-like growth factor (IGF), AMPD1 gene, involved in energy metabolism, Major Histocompatibility Complex (MHC), Myostatin (MSTN), insulin-like growth factor 2 (IGF2), and growth hormone receptor (GHR), TLR (Toll-like receptor) genes, chicken Mx gene, an antiviral gene. |
| Resilience | Tracking responses to environmental stressors, predicting resilience. | Moderate sensitivity in detecting changes in resilience based on responses to environmental stressors. | Accuracy depends on the specific methods and technologies used, but generally moderate for predicting resilience. | Behavioral data from sensors and cameras, environmental data, demographic data, veterinary health records. | Changes in response to environmental stressors, signs of stress. | Hypothalamic-Pituitary-Adrenal (HPA) Axis, Genetic variation in HSP genes, HSP70, serotonin receptor gene HTR2C and the dopamine receptor gene DRD4, mt-COI gene, a mitochondrial gene, cytokine genes |
| Affective States | Defining measurement paradigms for affective states, identifying symptoms of diverse physiological conditions. | High sensitivity in detecting changes in affective states that may indicate stress or health issues. | Accuracy depends on the specific methods and technologies used, but generally high for detecting major changes in affective states. | Behavioral data from sensors and cameras, physiological data, demographic data, veterinary health records. | Changes in affective states, signs of stress or discomfort. | Brain-Derived Neurotrophic Factor (BDNF) gene, corticotrophin-releasing hormone (CRH), serotonin transporter gene (SERT) and dopamine receptor genes (e.g., DRD2, DRD4), Major Histocompatibility Complex (MHC) or Interleukins genes, Genes involved in mesotocin and vasotocin pathways. |