A Deep Learning Approach for Automated Detection and Segmentation of Pressure Ulcers Using Infrared-Based Thermal Imaging

Pressure ulcers are ailments that require constant care and monitoring. They are painful and sometimes life-threatening. Evaluation of pressure ulcers is an important step towards treatment and healing. Presently, evaluation is carried out invasively using methods like PUSH and Bates-Jensen Wound Assessment Tool, that is not comfortable to the patient. Wounds present themselves with altered thermal profile as the activity of degrading or healing cause changes in the temperature of the affected skin/tissues. Infrared thermography is emerging as a viable medical imaging modality. The major upsides of infrared imaging are it being non-radiating and non-contact. Advances in the field of artificial intelligence, and specifically, deep learning, has widened the application domain of thermal imaging in healthcare. In this study, participants (n
$=10$
) with pre-existing pressure ulcers were recruited and imaged using a FLIR IR thermal camera. The acquired images were used to train an object detection model that successfully detected the existing pressure ulcers by drawing a bounding box over them. This allows the region of interest to contain the wound along with surrounding healthy skin for thermal reference. The developed model detected the pressure ulcers with a confidence level of 96-100% in the test images. The present study promises the potential application of IR-based thermal imaging in automated detection and segmentation of pressure ulcers.