| dc.description.abstract | Diabetic foot ulcers (DFUs) are serious complications of diabetes that reduce mobility
and quality of life and increase the risk of lower-limb amputation. The early detection
of DFUs is important to avoid serious outcomes, however, the diagnostic techniques
presently used rely much on subjective methods, thus delaying early treatment. This
review underlines the new developments regarding the detection of DFUs that are
being addressed using image processing and AI in disciplines of thermal imaging,
wearable sensors, and deep learning models like Convolutional Neural Networks
(CNNs). Non-invasive thermography detects skin temperature abnormalities that herald
early DFU formation, and wearable sensors track temperature, pressure, and moisture
to monitor foot health continuously. Deep learning algorithms, especially CNNs, excel
in the identification, classification, and segmentation of DFUs with a high degree of
diagnostic accuracy, because they greatly reduce human error. Most AI-based models
report a precision of over 90%, hence their potential to transform DFU detection and
management. Challenges include the need for standardized diagnostic tools, improved
sensor accuracy, and resolving issues related to limited datasets. Multidisciplinary
collaboration is essential to develop explainable AI models, larger datasets, and reliable
tools for clinical use. Moreover, patient education and engagement with wearable
devices and mobile applications are crucial for preventing DFU progression. This
research highlights the importance of combining AI and image processing to enhance
early detection and management of DFUs, ultimately aiming to reduce the risk of limb
loss. Future research should focus on incorporating these technologies into clinical
practice and mobile platforms for real-time patient-centred care. Overcoming the
existing barriers, AI-driven solutions can significantly reduce the global burden of DFUs
and improve patient outcomes. | |
