Impact of Complex Wounds on Health-Related Quality of Life: A Descriptive Study

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

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 ∼ 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.

Treatment of Complex Wounds with NovoSorb® Biodegradable Temporising Matrix (BTM)-A Retrospective Analysis of Clinical Outcomes

Complex and chronic wounds represent a highly prevalent condition worldwide that requires a multimodal and interdisciplinary treatment approach to achieve good functional and aesthetic outcomes. Due to increasing costs of health care, an aging population and an increase in difficult-to-treat microbial colonization of wounds, complex wounds will become a substantial clinical, social and economic challenge in the upcoming years. In plastic reconstructive surgery, a variety of dermal skin substitutes have been established for clinical use. Since its approval as a dermal skin substitute in Germany, NovoSorb^® Biodegradable Temporising Matrix (BTM) has become a valuable therapeutic option for the treatment of full-thickness wound defects. The clinical data published to date are limited to case reports and small-scale case series with the main focus on single wounds. The aim of this single-center study was a retrospective analysis of our own patient collective that has received treatment with BTM for complex wounds. Overall, BTM showed to be a reliable and versatile reconstructive option, especially for patients with multiple co-morbidities and microbiologically colonized wounds. Although the preliminary findings have produced promising results, further investigation and research are warranted regarding long-term outcomes and additional clinical applications.