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

Experimental investigation of brain contusion characteristics and dynamic response in low-age children using an animal model

INTRODUCTION

Brain contusion is a prevalent traumatic brain injury (TBI) in low-age children, bearing the potential for coma and fatality. Hence, it is imperative to undertake comprehensive research in this field.

METHODS

This study employed 4-week-old piglets as surrogates for children and introduced self-designed devices for both free-fall drop impact tests and drop-hammer impact tests. The study explored the characteristics of brain contusion and dynamic responses of brain under these distinct testing conditions.

RESULTS

Brain contusions induced by free-fall and drop-hammer conditions both were categorized as the coup injury, except that slight difference in the contusion location was observed, with contusion occurring mainly in the surrounding regions beneath the impact location under free-fall condition and the region just right beneath the impact location under drop-hammer condition. Analysis of impact force and intracranial pressure (ICP) curves indicated similar trends in impact forces under both conditions, yet different trends in ICPs. Further examination of the peak impact forces and ICPs elucidated that, with increasing impact energy, the former followed a combined power and first-order polynomial function, while the latter adhered to a power function. The brain contusion was induced at the height (energy) of 2 m (17.2 J), but not at the heights of 0.4, 0.7, 1, 1.35 and 1.7 m, when the vertex of the piglet head collided with a rigid plate. In the case of a cylindrical rigid hammer (cross-sectional area constituting 40 % of the parietal bone) striking the head, the brain contusion was observed under the energy of 21.9 J, but not under energies of 8.1 J, 12.7 J and 20.3 J. Notably, the incidence of brain contusion was more pronounced under the free-fall condition.

CONCLUSIONS

These findings not only facilitate a comprehensive understanding of brain contusion dynamics in pediatric TBIs, but also contribute to the validation of theories and finite element models for piglet heads, which are commonly employed as surrogates for children.

Measurement of perineal tears as an additional tool for laceration assessment during vaginal birth

INTRODUCTION

Spontaneous lacerations at vaginal birth are everyday events, but their classification and management still challenge midwifery care. This study aims to measure and describe first-degree and second-degree perineal lacerations resulting from vaginal birth, describe their repair, and the education provided for care.

METHODS

A descriptive study was conducted in a public maternity hospital in São Paulo, Brazil, with 87 parturients. Data were collected between October 2017 and June 2018 using a structured instrument containing obstetric variables and a description of lacerations. The obstetricians and nurse midwives assisted with births, determining the degree of laceration and intervention, and the researchers measured and reported them.

RESULTS

The majority of parturients (82.7%) had lacerations only in the anterior region, 8% had them in the posterior region, and 9.2% in both regions. The lacerations were classified as first-degree (78.1%) or second-degree (21.8%). Among the 32 nulliparous parturients, 27.6% had first-degree lacerations, and 9.2% had second-degree. Of the 55 multiparous parturients, 50.6% had first-degree, and 12.6% had second-degree. Among the lacerations assessed as first-degree, 25% had deeper tissue layers compromised in addition to the skin and mucosa. There were 180 lacerations, with an average length of 33.1 mm, depth of 19.8 mm, and width of 23.8 mm. Half of the parturients did not receive guidance on laceration care. There was no association between parity and size, number, location, or degree classification of lacerations.

CONCLUSIONS

This study provides a broad description of the characteristics of perineal lacerations and presents measurement techniques as a complementary resource for evaluating lacerations.

Simultaneous Segmentation and Classification of Pressure Injury Image Data Using Mask-R-CNN

Background

Pressure injuries (PIs) impose a substantial burden on patients, caregivers, and healthcare systems, affecting an estimated 3 million Americans and costing nearly $18 billion annually. Accurate pressure injury staging remains clinically challenging. Over the last decade, object detection and semantic segmentation have evolved quickly with new methods invented and new application areas emerging. Simultaneous object detection and segmentation paved the way to segment and classify anatomical structures. In this study, we utilize the Mask-R-CNN algorithm for segmentation and classification of stage 1-4 pressure injuries.

Methods

Images from the eKare Inc. pressure injury wound data repository were segmented and classified manually by two study authors with medical training. The Mask-R-CNN model was implemented using the Keras deep learning and TensorFlow libraries with Python. We split 969 pressure injury images into training (87.5%) and validation (12.5%) subsets for Mask-R-CNN training.

Results

We included 121 random pressure injury images in our test set. The Mask-R-CNN model showed overall classification accuracy of 92.6%, and the segmentation demonstrated 93.0% accuracy. Our F1 scores for stages 1-4 were 0.842, 0.947, 0.907, and 0.944, respectively. Our Dice coefficients for stages 1-4 were 0.92, 0.85, 0.93, and 0.91, respectively.

Conclusions

Our Mask-R-CNN model provides levels of accuracy considerably greater than the average healthcare professional who works with pressure injury patients. This tool can be easily incorporated into the clinician's workflow to aid in the hospital setting.

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

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.

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

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.

Hard-to-heal wounds: a randomised trial of an oral proline-containing supplement to aid repair

OBJECTIVE

We evaluated the effects of a specialised oral nutritional supplement (ONS) containing arginine and proline, with high vitamin A, C and E, zinc and selenium content, on the repair of hard-to-heal wounds.

METHOD

Patients with hard-to-heal wounds were evaluated at five timepoints (S0-S4) over four consecutive weeks. At S0 patients were randomised to the specialised ONS (n=15; 25 wounds) or control (n=15; 25 wounds) groups. Posology was 200ml twice daily over the research period. Wound surface area and perimeter were monitored. In addition to the metric data, it was also possible to calculate the rate of wound contraction and the linear growth of the wound edges, looking for wound-healing predictive factors.

RESULTS

A total of 30 patients took part in the study. Mean age was 65 years and 50% of patients had diabetes. Of the total evaluated wounds, 78% were <50cm², 14% were 50-150cm² and 8% were >250cm². In 96% of cases, the wounds were in the lower limbs. A statistically significant reduction (p=0.004) in surface area of the wounds due to the specialised ONS, with a performance peak between S1 and S2, was observed. This specialised ONS did not induce changes in blood pressure, blood glucose level or renal function. A mean weekly wound edge growth of 1.85mm in patients with diabetes and 3.0mm in those without diabetes was observed. These results were 2.9 and 4.6 times, respectively, higher than expected, according to the literature.

CONCLUSION

Specialised ONS can be a therapeutic option for hard-to-heal wounds.