1467-P: Retinal Vascular Biomarkers in Diabetic Peripheral Neuropathy
| Autor: | Jeremy Benson, Mark R. Burge, Peter Soliz, Vinayak S Joshi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
medicine.diagnostic_test business.industry Endocrinology Diabetes and Metabolism Medical record Retinal Physical examination medicine.disease chemistry.chemical_compound medicine.anatomical_structure Peripheral neuropathy chemistry Peripheral nerve Diabetes mellitus Ophthalmology Cohort Internal Medicine medicine business Artery |
| Zdroj: | Diabetes. 69 |
| ISSN: | 1939-327X 0012-1797 |
| DOI: | 10.2337/db20-1467-p |
| Popis: | Purpose: Studies have shown that individuals with retinal blood vessel abnormalities often present with other biomarkers associated with diabetic peripheral neuropathy (DPN). Existing software requires extensive manual intervention to measure structures in the retinal vasculature (e.g., arterial caliber, artery/vein ratios, fractals, etc.) for the purpose of studying the relationship of these structures to DPN. The goal of this study was to demonstrate an artificial intelligence (AI) software system for determining DPN risk based on retinal vascular abnormalities. Our approach circumvented the need to explicitly measure vessel characteristics by using AI as applied to a state-of-the-art methodology known as “convolutional neural networks” (CNN) to characterize the retinal vascular structures. CNNs have been demonstrated to be effective in a number of medical applications. This study is the first time CNNs have been used to extract retinal vascular features for DPN risk assessment. Methods: From a database of 25,000 cases previously labeled as healthy (controls), unaffected diabetes (DM), or DPN, we have identified 331 cases that fit the requirements for this study. Controls (N=103) had monofilament and vibration tests to confirm normal peripheral nerve function. DM patients (N=163) were classified based on their medical records and monofilament and vibration tests. DPN patients (N=65) were confirmed by clinical examination and their medical record. With these cases, an AI classifier was trained to identify individuals with DPN biomarkers based on spatial features extracted from the retinal abnormalities. Results: 80% of cohort data were used for training and 20% was used for testing. The resulting AI tool demonstrated 95% specificity and 78% sensitivity when using the clinical exam as the reference standard. Conclusions: AI-based CNN analysis of retinal vascular features show promise for identifying subjects with DPN and reduced human manipulation and measurement of tedious vessel geometries. Disclosure M.R. Burge: None. P. Soliz: None. J. Benson: None. V. Joshi: None. Funding National Institutes of Health (DK104578) |
| Databáze: | OpenAIRE |
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