The Advent of Artificial Intelligence in Diabetic Foot Medicine: A New Horizon, a New Order, or a False Dawn?

Howard, Theodore; Ahluwalia, Raju; Παπάνας, Νικόλαος

doi:10.1177/15347346211041866

Cited by 11 publications

(4 citation statements)

References 36 publications

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“…On the other hand, one review focused on the diabetic foot, but artificial intelligence methodologies were only covered in a small part of the review [ 61 ]. Another review study focused on artificial intelligence in diabetic foot [ 62 ], but it did not report details either on the physiological/clinical study outcomes or on the specific artificial intelligence techniques used in each of the analyzed studies. In contrast, another recent review study presented with care and in detail some artificial intelligence-based studies on the diabetic foot [ 63 ], but the addressed studies were limited to those based on diabetic foot imaging and analyzed with deep learning methodologies.…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…The diabetic foot must be examined for any lesions and screened for peripheral neuropathy and peripheral vascular disease, both of which can cause wounds or ulcers. Additionally, prompt referral of patients with foot ulcers and signs of infection, foot and systemic sepsis, or ischemia to a clinic specifically for diabetic feet will lower morbidity and mortality ( Howard et al, 2021 ).…”

Section: Review Of Literaturementioning

confidence: 99%

Prediction of Foot Ulcers Using Artificial Intelligence for Diabetic Patients at Cairo University Hospital, Egypt

Mousa

Mohamed

et al. 2023

SAGE Open Nursing

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Introduction In Egypt, diabetic foot ulcers markedly contribute to the morbidity and mortality of diabetic patients. Accurately predicting the risk of diabetic foot ulcers could dramatically reduce the enormous burden of amputation. Objective The aim of this study is to design an artificial intelligence-based artificial neural network and decision tree algorithms for the prediction of diabetic foot ulcers. Methods A case–control study design was utilized to fulfill the aim of this study. The study was conducted at the National Institute of Diabetes and Endocrine Glands, Cairo University Hospital, Egypt. A purposive sample of 200 patients was included. The tool developed and used by the researchers was a structured interview questionnaire including three parts: Part I: demographic characteristics; Part II: medical data; and Part III: in vivo measurements. Artificial intelligence methods were used to achieve the aim of this study. Results The researchers used 19 significant attributes based on medical history and foot images that affect diabetic foot ulcers and then proposed two classifiers to predict the foot ulcer: a feedforward neural network and a decision tree. Finally, the researchers compared the results between the two classifiers, and the experimental results showed that the proposed artificial neural network outperformed a decision tree, achieving an accuracy of 97% in the automated prediction of diabetic foot ulcers. Conclusion Artificial intelligence methods can be used to predict diabetic foot ulcers with high accuracy. The proposed technique utilizes two methods to predict the foot ulcer; after evaluating the two methods, the artificial neural network showed a higher improvement in performance than the decision tree algorithm. It is recommended that diabetic outpatient clinics develop health education and follow-up programs to prevent complications from diabetes.

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“…Furthermore, technology has introduced new horizons for patient education [62,63]. Digital health tools, encompassing mobile applications and telehealth services, present innovative avenues for delivering diabetes education [64].…”

Section: Knowledge Levels and Medication Non-adherencementioning

confidence: 99%

Patient-Related Factors Affecting Type 2 Diabetes Medication Non-Adherence: A Comprehensive Review of BMI, Knowledge Levels, Treatment Concerns, and Self-Efficacy

Milla-Amekor,

Ewusie

2023

AJMAH

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The persistent issue of non-adherence to medications in patients diagnosed with type 2 diabetes has far-reaching implications, affecting not only the health outcomes for individual patients but also exacerbating the overall financial burden of healthcare systems. This article aims to provide a comprehensive review of a spectrum of factors that influence adherence to diabetic medications, thus extending the discourse beyond a mere attribution of responsibility to patients. In particular, the study looks at how factors like BMI, disease knowledge, treatment worries, and self-efficacy are linked to non-adherence. The review substantiates that deficits in disease-specific knowledge and lower levels of self-efficacy are significantly correlated with medication non-adherence. Concurrently, elevated BMI values and apprehensions regarding treatment were also found to have an adverse effect on adherence, although the magnitude of their influence was comparatively less pronounced. Consequently, the incorporation of these identified variables into customised treatment regimens is strongly advocated. Interventional strategies, such as educational modules designed to enhance the understanding of the disease, coupled with psychological support mechanisms aimed at elevating self-efficacy, emerged as viable solutions to the persistent issue of medication non-adherence. Moreover, pre-emptively addressing concerns related to treatment options and accounting for BMI when prescribing medications offer additional pathways to improve medication compliance rates. The paper reinforces the imperative for a multidimensional patient-centered healthcare model. By meticulously identifying and addressing the multifaceted factors that contribute to medication non-adherence, healthcare practitioners could optimise health outcomes, mitigate complications, and thus effectuate substantial reductions in healthcare costs associated with type 2 diabetes.

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The Advent of Artificial Intelligence in Diabetic Foot Medicine: A New Horizon, a New Order, or a False Dawn?

Cited by 11 publications

References 36 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

Prediction of Foot Ulcers Using Artificial Intelligence for Diabetic Patients at Cairo University Hospital, Egypt

Patient-Related Factors Affecting Type 2 Diabetes Medication Non-Adherence: A Comprehensive Review of BMI, Knowledge Levels, Treatment Concerns, and Self-Efficacy

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