Acute and Chronic Wound Management: Assessment, Therapy and Monitoring Strategies

Kabir, Anisha; Sarkar, Anwita; Barui, Ananya

doi:10.1007/978-981-19-6008-6_6

Cited by 2 publications

(1 citation statement)

References 122 publications

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“…The novel therapies, although not limited, include light applications [8,9], tissue engineering-based approaches [10], cold atmospheric plasma (CAP) applications [11], and nanoparticle-based treatment approaches [12]. In order to assess the efficacy of the methods used in these investigations, the wound-healing process must be monitored continuously [13]. Since the wound healing process in the human body involves a complex series of chemical and physical interactions happening simultaneously, the observation and monitoring of wounds have become challenging [14].…”

Section: Introductionmentioning

confidence: 99%

An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology

Doğru,

Özdemir,

Özdemir

et al. 2024

BMC Med Imaging

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Background The assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the wound healing process. Existing methods suffer significant limitations, such as user dependency, time-consuming nature, and lack of sensitivity, thus paving the way for automated analysis approaches. Methods Hereby, three structurally different variations of U-net architectures based on convolutional neural networks (CNN) were implemented for the segmentation of in vitro wound healing microscopy images. The developed models were fed using two independent datasets after applying a novel augmentation method aimed at the more sensitive analysis of edges after the preprocessing. Then, predicted masks were utilized for the accurate calculation of wound areas. Eventually, the therapy efficacy-indicator wound areas were thoroughly compared with current well-known tools such as ImageJ and TScratch. Results The average dice similarity coefficient (DSC) scores were obtained as 0.958$$\sim$$ ∼ 0.968 for U-net-based deep learning models. The averaged absolute percentage errors (PE) of predicted wound areas to ground truth were 6.41%, 3.70%, and 3.73%, respectively for U-net, U-net++, and Attention U-net, while ImageJ and TScratch had considerable averaged error rates of 22.59% and 33.88%, respectively. Conclusions Comparative analyses revealed that the developed models outperformed the conventional approaches in terms of analysis time and segmentation sensitivity. The developed models also hold great promise for the prediction of the in vitro wound area, regardless of the therapy-of-interest, cell line, magnification of the microscope, or other application-dependent parameters.

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