Table 1.

Comparison of AI methods regarding wound measurement

Wound Measurement Method	Principle	Application	Merits	Limitations
Automatic image analysis pipeline [23]	Computer vision, object detection algorithms (YOLO)	Automated measurement of wound size and automatic assessment of average wound closure percentage. High fidelity results on unseen data with minimal human intervention	Automated and enables high fidelity results	Not good at dealing with some of the challenges like occlusion and blur.
				Quantitative measurements are not exactly aligned
Fuzzy spectral clustering [25]	Gray scale based fuzzy similarity measure, spectral clustering segmentation algorithm	Accurate depiction of the wound area and automatic calculation of the contrast between wound and non-wound areas	Effective depiction of wound areas in non-uniformly illuminated images	The wounds that are near to heal or the images having very low (i.e. nearly zero) contrast between healed wound area and healthy skin are not accurately segmented.
				The method is not completely automatic.
Vision laser scanner [20]	Use laser ranging scanning to generate 3D point cloud, artificial neural network estimation method	Accurate 3D reconstruction of wound margins and topology	Simultaneous generation of 3D point clouds of wound skin and its edges	The scanner can only deal with small size wound (~3-inch length)
Integrated system [18]	Convolutional encoder-decoder networks (a variant of ConvNet), Hough transformation, computer vision tasks	Wound segmentation in an end-to-end different manner and estimation of wound surface area by transformation of pixel length to actual length	High computational efficiency, validity and reliability as a multifunctional, integrated and unified framework system