Utilization of smartphone and tablet camera photographs to predict healing of diabetes-related foot ulcers

Kim, Renaid B.; Gryak, Jonathan; Mishra, Abinash; Cui, Can; Soroushmehr, S. M. Reza; Najarian, Kayvan; Wrobel, James S.

doi:10.1016/j.compbiomed.2020.104042

Cited by 24 publications

(16 citation statements)

References 20 publications

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“…In the same year, Kim et al performed a study aimed at predicting the healing of DFU, using both clinical features extracted from patients’ electronic health records (EHRs) and image features extracted from photographs (simply taken by a smartphone or tablet camera) [ 51 ]. A wide set of clinical features (48 variables) were considered, as derived from the EHRs of 2291 visits over a three-year period, for 381 DFUs from 155 patients.…”

Section: Artificial Intelligence In Diabetic Foot Syndrome: Methodolo...mentioning

confidence: 99%

Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review

et al. 2022

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Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient’s quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.

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Section: Artificial Intelligence In Diabetic Foot Syndrome: Methodolo...mentioning

confidence: 99%

Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review

et al. 2022

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“…Plataforma web e mobile de uso profissional para tratamento de feridas com escolha de produtos Tolins et al 42 Wound care follow-up from the emergency department using a mobile application: A pilot study Coorte prospectivo Plataforma mobile de uso do paciente para monitoramento de feridas pós-operatórias de um serviço de urgência Kim et al 43 Utilization of smartphone and tablet camera photographs to predict healing of diabetes-related foot ulcers Metodológico Plataforma mobile de uso profissional para avaliação de feridas em pé diabético pelo processamento de cor e textura pelas imagens das feridas para prevê uma eventual cura ou infecção Maddah et al 44 Use of a smartphone thermometer to monitor thermal conductivity changes in diabetic foot ulcers: A pilot study…”

Section: Metodológicounclassified

Sistemas Computacionais Para Auxílio Na Cicatrização De Feridas: Revisão De Escopo

Rodrigues

Bezerra

Calçada

2023

ESTIMA, Braz. J. Enterostomal Ther.

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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.

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“…The model achieved 94.28% accuracy in binary classification and 87.70% in multilevel classifications. Kim et al [ 15 ] proposed a model for the prognosis of DFU using thermal images. The ResNet50 model is used as the feature extractor, in which machine learning (ML) algorithms random forest and support vector machine (SVM) are used as a classifier.…”

Section: Related Workmentioning

confidence: 99%

Automated Detection of Infection in Diabetic Foot Ulcer Images Using Convolutional Neural Network

Yogapriya¹,

Elakkiya³

et al. 2022

Journal of Healthcare Engineering

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A bacterial or bone infection in the feet causes diabetic foot infection (DFI), which results in reddish skin in the wound and surrounding area. DFI is the most prevalent and dangerous type of diabetic mellitus. It will mainly occur in people with heart disease, renal illness, or eye disease. The clinical signs and symptoms of local inflammation are used to diagnose diabetic foot infection. In assessing diabetic foot ulcers, the infection has significant clinical implications in predicting the likelihood of amputation. In this work, a diabetic foot infection network (DFINET) is proposed to assess infection and no infection from diabetic foot ulcer images. A DFINET consists of 22 layers with a unique parallel convolution layer with ReLU, a normalization layer, and a fully connected layer with a dropout connection. Experiments have shown that the DFINET, when combined with this technique and improved image augmentation, should yield promising results in infection recognition, with an accuracy of 91.98%, and a Matthews correlation coefficient of 0.84 on binary classification. Such enhancements to existing methods shows that the suggested approach can assist medical experts in automated detection of DFI.

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Utilization of smartphone and tablet camera photographs to predict healing of diabetes-related foot ulcers

Cited by 24 publications

References 20 publications

Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review

Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review

Sistemas Computacionais Para Auxílio Na Cicatrização De Feridas: Revisão De Escopo

Automated Detection of Infection in Diabetic Foot Ulcer Images Using Convolutional Neural Network

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