The UTrack framework for segmenting and measuring dermatological ulcers through telemedicine

Convolution Neural Network with Coordinate Attention for Real-Time Wound Segmentation and Automatic Wound Assessment

BACKGROUND

Wound treatment in emergency care requires the rapid assessment of wound size by medical staff. Limited medical resources and the empirical assessment of wounds can delay the treatment of patients, and manual contact measurement methods are often inaccurate and susceptible to wound infection. This study aimed to prepare an Automatic Wound Segmentation Assessment (AWSA) framework for real-time wound segmentation and automatic wound region estimation.

METHODS

This method comprised a short-term dense concatenate classification network (STDC-Net) as the backbone, realizing a segmentation accuracy-prediction speed trade-off. A coordinated attention mechanism was introduced to further improve the network segmentation performance. A functional relationship model between prior graphics pixels and shooting heights was constructed to achieve wound area measurement. Finally, extensive experiments on two types of wound datasets were conducted.

RESULTS

The experimental results showed that real-time AWSA outperformed state-of-the-art methods such as mAP, mIoU, recall, and dice score. The AUC value, which reflected the comprehensive segmentation ability, also reached the highest level of about 99.5%. The FPS values of our proposed segmentation method in the two datasets were 100.08 and 102.11, respectively, which were about 42% higher than those of the second-ranked method, reflecting better real-time performance. Moreover, real-time AWSA could automatically estimate the wound area in square centimeters with a relative error of only about 3.1%.

CONCLUSION

The real-time AWSA method used the STDC-Net classification network as its backbone and improved the network processing speed while accurately segmenting the wound, realizing a segmentation accuracy-prediction speed trade-off.

COMPUTER SYSTEMS TO AID IN WOUND HEALING: SCOPE REVIEW

Objective: To investigate studies that present computational systems to aid healing and systems which refer to the use of low-level laser.Method: Scope review that aimed to answer the question: Which computer systems help in wound healing? A subquestion was: Which of the computer systems refer to the use of low-level laser? Results: From the search, applying the eligibility criteria, 49 articles made up the final sample. The systems served multiple purposes in support of wound healing; the majority presented the health professional as a user of the system; medicine was the most mentioned professional area despite nursing being involved in the management of care for people with wounds. Innovation in care using the computer system was frequently reported, demonstrating the importance of this type of tool for clinical practice. There was a high frequency of the mobile platform, showing that this is a current trend. Conclusion:Computer systems have been used as tools to support patients and especially professionals in wound healing. Regarding the systems aimed at the low intensity laser, there was a shortage of computer systems for this purpose, with a study.

SISTEMAS COMPUTACIONAIS PARA AUXÍLIO NA CICATRIZAÇÃO DE FERIDAS: REVISÃO DE ESCOPO

Objetivo:Investigar estudos que apresentem sistemas computacionais de auxílio à cicatrização de feridas e quais sistemas se referem ao uso de laser de baixa intensidade. Método: Revisão de escopo que visou responder à questão de pesquisa: Quais sistemas computacionais auxiliam na cicatrização de feridas? Uma subquestão foi: quais sistemas computacionais se referem ao uso do laser de baixa intensidade? Resultados: A partir da busca, aplicando os critérios de elegibilidade, 49 artigos compuseram a amostra final. Os sistemas apresentaram várias finalidades de apoio à cicatrização de feridas, em que a maioria apresentou como usuário do sistema o profissional de saúde, sendo a medicina a área profissional mais mencionada, embora a enfermagem esteja envolvida com o manejo do cuidado às pessoas com feridas. Foi relatada com frequência a inovação na assistência a partir do uso do sistema computacional, o que demonstra a importância desse tipo de ferramenta para a prática clínica. Verificou-se com frequência o uso de plataforma mobile, como tendência da atualidade. Conclusão: Os sistemas computacionais têm sido utilizados como ferramentas para apoiar pacientes e principalmente profissionais na cicatrização de feridas. Quanto ao laser de baixa intensidade, houve escassez de sistemas computacionais com essa finalidade, com apenas um estudo.

A multi-task convolutional neural network for classification and segmentation of chronic venous disorders

Chronic Venous Disorders (CVD) of the lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. Due to the exponential growth of the aging population and the worsening of CVD with age, it is expected that the healthcare costs and the resources needed for the treatment of CVD will increase in the coming years. The early diagnosis of CVD is fundamental in treatment planning, while the monitoring of its treatment is fundamental to assess a patient's condition and quantify the evolution of CVD. However, correct diagnosis relies on a qualitative approach through visual recognition of the various venous disorders, being time-consuming and highly dependent on the physician's expertise. In this paper, we propose a novel automatic strategy for the joint segmentation and classification of CVDs. The strategy relies on a multi-task deep learning network, denominated VENet, that simultaneously solves segmentation and classification tasks, exploiting the information of both tasks to increase learning efficiency, ultimately improving their performance. The proposed method was compared against state-of-the-art strategies in a dataset of 1376 CVD images. Experiments showed that the VENet achieved a classification performance of 96.4%, 96.4%, and 97.2% for accuracy, precision, and recall, respectively, and a segmentation performance of 75.4%, 76.7.0%, 76.7% for the Dice coefficient, precision, and recall, respectively. The joint formulation increased the robustness of both tasks when compared to the conventional classification or segmentation strategies, proving its added value, mainly for the segmentation of small lesions.

Quantification of Efflorescences in Pustular Psoriasis Using Deep Learning

Objectives

Pustular psoriasis (PP) is one of the most severe and chronic skin conditions. Its treatment is difficult, and measurements of its severity are highly dependent on clinicians’ experience. Pustules and brown spots are the main efflorescences of the disease and directly correlate with its activity. We propose an automated deep learning model (DLM) to quantify lesions in terms of count and surface percentage from patient photographs.

Methods

In this retrospective study, two dermatologists and a student labeled 151 photographs of PP patients for pustules and brown spots. The DLM was trained and validated with 121 photographs, keeping 30 photographs as a test set to assess the DLM performance on unseen data. We also evaluated our DLM on 213 unstandardized, out-of-distribution photographs of various pustular disorders (referred to as the pustular set), which were ranked from 0 (no disease) to 4 (very severe) by one dermatologist for disease severity. The agreement between the DLM predictions and experts’ labels was evaluated with the intraclass correlation coefficient (ICC) for the test set and Spearman correlation (SC) coefficient for the pustular set.

Results

On the test set, the DLM achieved an ICC of 0.97 (95% confidence interval [CI], 0.97–0.98) for count and 0.93 (95% CI, 0.92–0.94) for surface percentage. On the pustular set, the DLM reached a SC coefficient of 0.66 (95% CI, 0.60–0.74) for count and 0.80 (95% CI, 0.75–0.83) for surface percentage.

Conclusions

The proposed method quantifies efflorescences from PP photographs reliably and automatically, enabling a precise and objective evaluation of disease activity.