Description:
Reference #: 01648
The University of South Carolina is offering licensing opportunities for Using Infrared Technology to Detect Tissue Damage Below the Skin.
Background:
Thermography is a technique that can measure variations in skin temperature, which often indicates underlying damage. The use of artificial intelligence enables precise and consistent monitoring of these temperature changes over time, identifying early signs of damage and allowing for preventative treatment, improving the overall health and wellbeing of patients.
Invention Description:
This innovation combines infrared thermography and artificial intelligence (AI) to detect underlying skin damage, specifically in patients with diabetes mellitus (DM). By tracking changes in skin temperature, which correspond to alterations in blood flow, this non-invasive approach identifies potential tissue damage at an early stage. The AI component aids in accurately interpreting these temperature changes from sequential images, pinpointing the onset of potential diabetic foot ulcers (DFU). This approach not only improves patient outcomes through early detection but also makes the process affordable and widely accessible.
Potential Applications:
This innovation offers a solution for early detection and management of DFUs, particularly beneficial for patients in rural and underserved areas who face challenges in accessing regular podiatric care. By combining infrared thermography and AI to monitor skin temperature changes linked to underlying tissue damage, potential DFUs can be identified earlier, reducing the risk of complications and expensive treatments. Furthermore, this system can potentially bridge the gap in DFU detection and care among diverse populations, addressing disparities in healthcare outcomes.
Advantages and Benefits:
This innovative solution stands out by utilizing affordable infrared thermography integrated with AI for comprehensive monitoring of the whole foot, rather than just the sole, significantly improving the early detection of DFUs. By overcoming the limitations of current expensive systems and bypassing the need for human analysis, this approach increases accuracy, minimizes risk, and broadens accessibility for healthcare providers and home health agencies, promising to make DFU prevention more feasible and effective.