Machine learning models for synthesizing actionable care decisions on lower extremity wounds

Nguyen, Holly; Agu, Emmanuel; Strong, Diane M.; Mombini, Haadi; Pedersen, Peder J.; Lindsay, Clifford; Dunn, Raymond M.; Loretz, Lorraine

doi:10.1016/j.smhl.2020.100139

Cited by 10 publications

(16 citation statements)

References 24 publications

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“…The use of machine learning for image classification is well-established; its application to aid in classifying diabetic foot images is already being advanced. [15][16][17][18][19][20] The performance of machine learning depends on the robustness of the training data set. Our pilot study is the first to demonstrate the feasibility of daily acquisition and transmission of diagnostic-quality images to generate such a robust training set.…”

Section: Discussionmentioning

confidence: 99%

Initial Clinical Experience with a Simple, Home System for Early Detection and Monitoring of Diabetic Foot Ulcers: The Foot Selfie

Swerdlow

Shin

D’Huyvetter

et al. 2021

J Diabetes Sci Technol

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Background: Diabetic foot ulcers (DFUs) are a leading cause of disability and morbidity. There is an unmet need for a simple, practical, home method to detect DFUs early and remotely monitor their healing. Method: We developed a simple, inexpensive, smartphone-based, “ Foot Selfie” system that enables patients to photograph the plantar surface of their feet without assistance and transmit images to a remote server. In a pilot study, patients from a limb-salvage clinic were asked to image their feet daily for six months and to evaluate the system by questionnaire at five time points. Transmitted results were reviewed weekly. Results: Fifteen patients (10 male) used the system after approximately 5 minutes of instruction. Participants uploaded images on a median of 76% of eligible study days. The system captured and transmitted diagnostic quality images of the entire plantar surface of both feet, permitting clinical-management decisions on a remote basis. We monitored 12 active wounds and 39 pre-ulcerative lesions (five wounds and 13 pre-ulcerative lesions at study outset); we observed healing of seven wounds and reversal of 20 pre-ulcerative lesions. Participants rated the system as useful, empowering, and preferable to their previous methods of foot screening. Conclusions: With minimal training, patients transmitted diagnostic-quality images from home on most days, allowing clinicians to review serial images. This system permits inexpensive home foot screening and monitoring of DFUs. Further studies are needed to determine whether it can reduce morbidity of DFUs and/or the associated cost of care. Artificial intelligence integration could improve scalability.

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Section: Discussionmentioning

confidence: 99%

Initial Clinical Experience with a Simple, Home System for Early Detection and Monitoring of Diabetic Foot Ulcers: The Foot Selfie

Swerdlow

Shin

D’Huyvetter

et al. 2021

J Diabetes Sci Technol

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“…18. Nguyen G, Agu E, Tulu B, Strong D, Mombini H, Pedersen P, et al (2020)/ EUA/Smart Health (30) Explorar classificadores de aprendizado de máquina para gerar decisões acionáveis no tratamento de feridas.…”

Section: Goulionis Je Vozikisunclassified

Sistemas de apoio à decisão clínica em úlceras de pé diabético:revisão de escopo

Garces,

Araújo,

Sousa

et al. 2023

Rev. esc. enferm. USP

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RESUMO Objetivo: Mapear as evidências científicas sobre uso de Sistemas de Apoio à Decisão Clínica no pé diabético. Método: Revisão de escopo fundamentada no JBI Manual for Evidence Synthesis e registrada na plataforma Open Science Framework. Realizaram-se buscas, em fontes primárias e secundárias, sobre protótipos e ferramentas informatizadas direcionadas à assistência ao paciente com pé diabético ou em risco de tê-lo, publicados em qualquer idioma ou período, em onze bases de dados e literatura cinza. Resultados: Foram identificados 710 estudos e, após critérios de elegibilidade, foram selecionados 23 que retratam o uso de sistemas de apoio à decisão no rastreio do pé diabético, predição do risco de úlceras e amputações, classificação do estágio de gravidade, decisão quanto ao plano de tratamento e avaliação da efetividade das intervenções, por meio do processamento de dados referentes a informações clínicas e sociodemográficas. Conclusão: Os sistemas especialistas destacam-se por resultados satisfatórios, com alta precisão e sensibilidade no que tange à orientação e qualificação do processo de tomada de decisão na prevenção e no cuidado ao pé diabético.

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“…On the one hand, various studies have shown that ML can be a practical tool in wound management, such as infection prevention and treatments 12–14 . However, to the best of our knowledge, there was a noticeable absence of thorough utilization of ML for the surveillance and prediction of surgical wound risks.…”

Section: Introductionmentioning

confidence: 99%

“…11 On the one hand, various studies have shown that ML can be a practical tool in wound management, such as infection prevention and treatments. [12][13][14] However, to the best of our knowledge, there was a noticeable absence of thorough utilization of ML for the surveillance and prediction of surgical wound risks. Consequently, a critical and imperative advancement in this field can be achieved by assessing the advantages and disadvantages of the model-building process to effectively summarize the applications of ML in the surveillance and prediction of surgical wound risks.…”

Section: Introductionmentioning

confidence: 99%

Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review

Zhang,

Zhao,

Farzan

et al. 2024

International Wound Journal

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Surgical wounds may arise due to harm inflicted upon soft tissue during surgical intervention, and many complications and injuries may accompany them. These complications can lead to prolonged hospitalization and poorer clinical outcomes. Also, Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in medical care and is increasingly used for diagnosis, complications, prognosis and recurrence prediction. This study aims to investigate surgical wound risk predictions and management using a ML algorithm by R programming language analysis. The systematic review, following PRISMA guidelines, spanned electronic databases using search terms like ‘machine learning’, ‘surgical’ and ‘wound’. Inclusion criteria covered experimental studies from 1990 to the present on ML's application in surgical wound evaluation. Exclusion criteria included studies lacking full text, focusing on ML in all surgeries, neglecting wound assessment and duplications. Two authors rigorously assessed titles, abstracts and full texts, excluding reviews and guidelines. Ultimately, relevant articles were then analysed. The present study identified nine articles employing ML for surgical wound management. The analysis encompassed various surgical procedures, including Cardiothoracic, Caesarean total abdominal colectomy, Burn plastic surgery, facial plastic surgery, laparotomy, minimal invasive surgery, hernia repair and unspecified surgeries. ML was skillful in evaluating surgical site infections (SSI) in seven studies, while two extended its use to burn‐grade diagnosis and wound classification. Support Vector Machine (SVM) and Convolutional Neural Network (CNN) were the most utilized algorithms. ANN achieved a 96% accuracy in facial plastic surgery wound management. CNN demonstrated commendable accuracies in various surgeries, and SVM exhibited high accuracy in multiple surgeries and burn plastic surgery. In sum, these findings underscore ML's potential for significant improvements in postoperative management and the development of enhanced care techniques, particularly in surgical wound management.

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Machine learning models for synthesizing actionable care decisions on lower extremity wounds

Cited by 10 publications

References 24 publications

Initial Clinical Experience with a Simple, Home System for Early Detection and Monitoring of Diabetic Foot Ulcers: The Foot Selfie

Initial Clinical Experience with a Simple, Home System for Early Detection and Monitoring of Diabetic Foot Ulcers: The Foot Selfie

Sistemas de apoio à decisão clínica em úlceras de pé diabético:revisão de escopo

Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review

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