ArticleJournal of diabetes science and technology2022
Predicting Diabetic Neuropathy Risk Level Using Artificial Neural Network and Clinical Parameters of Subjects With Diabetes.
Article in Journal of diabetes science and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 20 citations in OpenAlex.
- Screening for Diabetic Peripheral Neuropathy: Subjective Versus Objective Measures.The international journal of lower extremity wounds · 2026Article
- Innovative strategies for diabetic peripheral neuropathy: From clinical management to emerging bioengineering solutions.Bioactive materials · 2026Review
- Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2025Review
- Assessment of biomarkers associated with renal decline for prediction of incident distal sensory polyneuropathy in type 2 diabetes.Scientific reports · 2025Article
- Symptoms and objective signs of peripheral sensory neuropathy in POTS and correlations to gastrointestinal symptoms.PloS one · 2025Article
- Survival, incidence, and predictors of diabetic neuropathy among type 2 diabetic patients in hospitals of Addis Ababa.Frontiers in clinical diabetes and healthcare · 2024Article
- Early detection of diabetic neuropathy based on health belief model: a scoping review.Frontiers in endocrinology · 2024Article
- The foot in diabetes - a reminder of an ever-present risk.Clinical medicine (London, England) · 2023Review
- Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes.Journal of diabetes science and technology · 2023Article
- Serum lipidomic determinants of human diabetic neuropathy in type 2 diabetes.Annals of clinical and translational neurology · 2022Article
- Diabetic neuropathy: are we still barking up the wrong tree and is change finally in sight?Diabetologia · 2020Article
- Leveraging Data Science to Combat COVID-19: A Comprehensive Review.IEEE transactions on artificial intelligence · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA risk assessment tool has been developed for automated estimation of level of neuropathy based on the clinical characteristics of patients. The smart tool is based on risk factors for diabetic neuropathy, which utilizes vibration perception threshold (VPT) and a set of clinical variables as potential predictors.
methodsSignificant risk factors included age, height, weight, urine albumin-to-creatinine ratio, glycated hemoglobin, total cholesterol, and duration of diabetes. The continuous-scale VPT was recorded using a neurothesiometer and classified into three categories based on the clinical thresholds in volts (V): low risk (0-20.99 V), medium risk (21-30.99 V), and high risk (≥31 V).
resultsThe initial study had shown that by just using patient data (
conclusionIn the absence of any assessment devices or trained personnel, it is possible to establish with reasonable accuracy a diagnosis of diabetic neuropathy by means of the clinical parameters of the patient alone.
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