Measurement of wound area for early analysis of the scar predictive factor

Mehl, Adriano Antonio; Schneider, Bertoldo; Schneider, Fábio Kurt; Carvalho, Bruno Henrique Kamarowski De

doi:10.1590/1518-8345.3708.3299

Cited by 17 publications

(16 citation statements)

References 14 publications

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“…Unlike conventional methods, the present study successfully extracted the scar mask prediction from the Mask RCNN, confirming that scar area assessment using Mask RCNN is more effective than approaches in previous studies [ 57 ]. One limitation of the conventional method is its need to remove the non-scar area to calculate the entire scar area in the dermis; thus, it takes time to calculate the scar area in the WSI.…”

Section: Discussionsupporting

confidence: 74%

Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning

et al. 2022

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An analysis of scar tissue is necessary to understand the pathological tissue conditions during or after the wound healing process. Hematoxylin and eosin (HE) staining has conventionally been applied to understand the morphology of scar tissue. However, the scar lesions cannot be analyzed from a whole slide image. The current study aimed to develop a method for the rapid and automatic characterization of scar lesions in HE-stained scar tissues using a supervised and unsupervised learning algorithm. The supervised learning used a Mask region-based convolutional neural network (RCNN) to train a pattern from a data representation using MMDetection tools. The K-means algorithm characterized the HE-stained tissue and extracted the main features, such as the collagen density and directional variance of the collagen. The Mask RCNN model effectively predicted scar images using various backbone networks (e.g., ResNet50, ResNet101, ResNeSt50, and ResNeSt101) with high accuracy. The K-means clustering method successfully characterized the HE-stained tissue by separating the main features in terms of the collagen fiber and dermal mature components, namely, the glands, hair follicles, and nuclei. A quantitative analysis of the scar tissue in terms of the collagen density and directional variance of the collagen confirmed 50% differences between the normal and scar tissues. The proposed methods were utilized to characterize the pathological features of scar tissue for an objective histological analysis. The trained model is time-efficient when used for detection in place of a manual analysis. Machine learning-assisted analysis is expected to aid in understanding scar conditions, and to help establish an optimal treatment plan.

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Section: Discussionsupporting

confidence: 74%

Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning

et al. 2022

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“…Then, the samples were centrifuged for 10 min at 1500 rpm and the absorbance of the obtained supernatant was measured at 545 nm using a Microplate Reader (Anthos 2020, Biochrom, Berlin, Germany). The hemolysis percent was calculated using equation (3).…”

Section: Morphologicalmentioning

confidence: 99%

“…2 In many cases, the proper treatment using wound healing materials and substances minimizes the scar formation, a physiological and undesirable healing process. 3 Therefore, unprecedented attention has been given to develop effective and promising wound healing materials. 4 Hydrogel, a three-dimensional structure of hydrophilic polymers, and nanofibers based biomaterials are important as wound healing materials due to their advantageous properties, such as water absorption and retention capability, providing a moist environment in the wound site, ease to apply to the wound bed, full coverage of wound bed, and ability to carry and release the various drugs.…”

Section: Introductionmentioning

confidence: 99%

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In vitro and in vivo evaluation of porous alginate hydrogel containing retinoic acid for skin wound healing applications

Nazarnezhad

Salehi

Samadian

et al. 2022

Journal of Bioactive and Compatible Polymers

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The current study’s main aim was to fabricate and evaluate alginate (Alg) hydrogel containing retinoic acid (RA) as wound healing materials. Different RA concentrations (2, 10, and 50% w/w) were incorporated into the hydrogel. The results showed that the prepared hydrogels had a porous structure with a pore size of 69.69 ± 22.1 µm for pure Alg hydrogel and 78.44 ± 27.8 µm for Alg/RA hydrogel. The swelling measurement showed that the hydrogels swelled up to 65% and the incorporation of RA reduced the degree of swelling . The in vitro studies confirmed the hemo- and biocompatibility of the Alg/RA 2% and increasing the RA concentration induced hemolysis and toxic effects. The animal studies showed that the lowest RA concentration resulted in the best treatment outcome while increasing the RA concentration suppressed the healing process. In conclusion, these results showed that RA induced wound healing process at low concentration, and the prepared hydrogel could be used as the wound healing materials.

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“…However, they are lagging technologically, and most caregivers only depend on imprecise optical assessment [2], which brings some complications, such as infection risks, inaccurate measurements, and discomfort to patients [3]. Advanced computer vision methods assist the accurate monitoring of wound healing [4]. Image processing and machine learning automate the evaluation of medical images [5].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous wound border segmentation and tissue classification using a conditional generative adversarial network

Sarp

Kuzlu

Pipattanasomporn

et al. 2021

J. eng.

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Generative adversarial network (GAN) applications on medical image synthesis have the potential to assist caregivers in deciding a proper chronic wound treatment plan by understanding the border segmentation and the wound tissue classification visually. This study proposes a hybrid wound border segmentation and tissue classification method utilising conditional GAN, which can mimic real data without expert knowledge. We trained the network on chronic wound datasets with different sizes. The performance of the GAN algorithm is evaluated through the mean squared error, Dice coefficient metrics and visual inspection of generated images. This study also analyses the optimum number of training images as well as the number of epochs using GAN for wound border segmentation and tissue classification. The results show that the proposed GAN model performs efficiently for wound border segmentation and tissue classification tasks with a set of 2000 images at 200 epochs.

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Measurement of wound area for early analysis of the scar predictive factor

Cited by 17 publications

References 14 publications

Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning

Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning

In vitro and in vivo evaluation of porous alginate hydrogel containing retinoic acid for skin wound healing applications

Simultaneous wound border segmentation and tissue classification using a conditional generative adversarial network

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