OBJECTIVE

To assess the diagnostic accuracy of point-of-care screening using Medios™ Artificial Intelligence (AI) in the diagnosis of diabetic retinopathy (DR).

METHODS

This is a multi-center, cross-sectional, instrument validation study among adult Filipinos with Type 2 diabetes seen at Endocrine specialty clinics from May to November 2021. Retinal images were captured by a minimally trained nurse using the Remedio Fundus on Phone (FOP). Images were interpreted separately by the Medios™ AI and three retina specialists. The primary outcome measure is the accuracy of Medios™™ AI in diagnosing DR compared to retina specialists’ findings using sensitivity and specificity, predictive values, and likelihood ratios.

RESULTS

A total of 182 subjects with Type 2 diabetes were included in the study. The sensitivity and specificity of the Medios™ AI in diagnosing any DR were 73.68% (95%CI, 57.99-85.03) and 83.74% (95%CI, 79.35-87.35), respectively, compared with the retinal specialists’ findings using the same images. The positive and negative predictive values were 34.57% (95%CI, 25.12-45.41) and 96.47% (95%CI, 93.62-98.07). The positive and negative likelihood ratios were 4.53 (95%CI, 4.26 4.82) and 0.31 (95%CI, 0.26-0.38). The overall diagnostic accuracy of Medios™ AI in detecting DR was 82.69% (95%CI, 78.47-86.23).

CONCLUSION

The Medios™ AI system showed acceptable diagnostic accuracy when used as a point-of-care screening tool in detecting DR among patients with Type 2 diabetes seen at Endocrine specialty clinics. This technology can be a useful screening tool for endocrinologists as it is relatively inexpensive, safe, and easily performed. It can also shorten the lead time from screening to referral and intervention.

Human ; Diabetes Mellitus ; Diabetic Retinopathy

Objective: To identify unique immunogenic epitopes of Zika virus non-structural 1 (NS1) antigen and produce immunoglobulin Y (IgY) for potential use in he diagnosis of of Zika virus infection. Methods: Immunogenic epitopes were identified using in silico B-cell epitope prediction. A synthetic peptide analog of the predicted epitope was used to induce antipeptide IgY production in hens which was purified using affinity chromatography. Presence of purified IgY and its binding specificity were performed by gel electrophoresis and ELISA, respectively. Results: Out of the nine continuous epitopes identified, the sequence at position 193-208 (LKVREDYSLECDPAVI) was selected and used to produce anti-peptide IgY. The produced IgY was found to bind to the synthetic analog of the Zika virus NS1 immunogenic epitope but not to other flaviviruses and random peptides from other pathogens. Conclusions: In this study, we identified an immunogenic epitope unique to Zika virus that can be used to develop a serodiagnostic tool that specifically detect Zika virus infection.