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AI-Powered Models for Detection of Metabolic Diseases
Context: According to a new study published in Cell Metabolism, trained AI-powered models may help identify early-stage metabolic diseases like diabetes and fatty liver by tracing the temperature.
Findings Detection Mechanism:
- Study collected thermal facial images of 2,811 individuals aged 21 to 88 years.
- Developed ‘‘ThermoFace’’ (TF) method combining facial recognition and temperature extraction.
- AI models detect temperature differences too small to be sensed by touch by Utilising a thermal camera to detect temperature differences in various parts of the face.
- Temperature differences provide insights into body ageing and health.
- Example: People with diabetes and fatty liver have higher eye area temperatures compared to healthy counterparts of the same age.
AI-Powered Thermal Imaging for Early Disease Detection:
- Body temperature affects cell function and organism survival:
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- Lower body temperature is linked to longer lifespan and slower ageing in endothermic species. Increased body temperatures can be triggered by high metabolic rates due to psychological and metabolic stress.
- Increase in temperatures around eyes and cheeks due to spike in cellular activities related to inflammation (e.g., repairing damaged DNA, fighting infections).
- Decrease in temperatures in the nose, cheeks, and eyebrows with age, starting around 50 years in females and 60 years in males.
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- Metabolic Disorders:
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- Faster thermal ageing observed in individuals with diabetes and fatty liver.
- Higher eye area temperatures compared to healthy counterparts of the same age.
- Elevated blood pressure associated with higher cheek temperatures.
- Participants who exercised reduced their thermal age by five years.