ABSTRACT
PURPOSE: This study was aimed to evaluate the agreement between the swept-source optical coherence tomography (SS-OCT)-based biometry, fundus photographs, and their combination, in comparison to the gold standard spectral-domain optical coherence tomography (SD-OCT) for the detection of center-involving diabetic macular edema (CI-DME). METHODS: We conducted a retrospective cross-sectional study involving 55 subjects (78 eyes) diagnosed with diabetic macular edema (DME) detected clinically and on SD-OCT (Carl Zeiss Meditec AG). Post-mydriatic 45-degree color fundus photograph (Crystal-Vue NFC-700), 1 mm macular scan obtained from SS-OCT-based biometry (IOL-Master 700), and macula cube scan obtained from SD-OCT was used to detect and grade DME into CI-DME and NCI-DME. RESULTS: Our findings revealed that SS-OCT-based biometry was noted to have a high sensitivity of 1 (0.94-1.00) and a specificity of 0.63 (0.31-0.89) in detecting CI-DME compared to the gold standard (SD-OCT). When combined with data from fundus photographs, specificity decreased to 0.32 (0.15-0.53). Fundus photographs alone exhibited a low sensitivity of 0.52 (0.38-0.64) and a specificity of 0.45 (0.16-0.76) in CI-DME detection. CONCLUSION: In conclusion, SS-OCT-based biometry can be used as an effective tool for the detection of CI-DME in diabetic patients undergoing cataract surgery and can serve as a screening tool in centers without SD-OCT facilities.
Diabetic Macular Edema (DME); Center Involving Diabetic Macular Edema (CI-DME); Non-Center Involving Diabetic Macular Edema (NCI-DME); Swept-Source Optical Coherence Tomography (SS-OCT); Spectral-Domain Optical Coherence Tomography (SD-OCT); Anti-Vascular Endothelial Growth Factor (Anti-VEGF); Central Retinal Thickness (CRT); Intra Retinal Fluid (IRF); Sub Retinal Fluid (SRF); Diabetic Retinopathy (DR); Non Proliferative Diabetic Retinopathy (NPDR); Proliferative Diabetic Retinopathy (PDR); Best Corrected Visual Acuity (BCVA); Glycosylated hemoglobin (HbA1c); Mean Spherical Error (MSE); Standard Deviation (SD); Positive Predictive value (PPV); Predictive value (PPV); Negative predictive value (NPV); Area under the Curve (AUC).
ABSTRACT
Purpose: To evaluate the ability of an artificial intelligence (AI) model, ChatGPT, in predicting the diabetic retinopathy (DR) risk. Methods: This retrospective observational study utilized an anonymized dataset of 111 patients with diabetes who underwent a comprehensive eye examination along with clinical and biochemical assessments. Clinical and biochemical data along with and without central subfield thickness (CST) values of the macula from OCT were uploaded to ChatGPT-4, and the response from the ChatGPT was compared to the clinical DR diagnosis made by an ophthalmologist. Results: The study assessed the consistency of responses provided by ChatGPT, yielding an Intraclass Correlation Coefficient (ICC) value of 0.936 (95% CI, 0.913-0.954, p < 0.001) (with CST) and 0.915 (95% CI, 0.706-0.846, p < 0.001) (without CST), both situations indicated excellent reliability. The sensitivity and specificity of ChatGPT in predicting the DR cases were evaluated. The results revealed a sensitivity of 67% with CST and 73% without CST. The specificity was 68% with CST and 54% without CST. However, Cohen's kappa revealed only a fair agreement between ChatGPT predictions and clinical DR status in both situations, with CST (kappa = 0.263, p = 0.005) and without CST (kappa = 0.351, p < 0.001). Conclusion: This study suggests that ChatGPT has the potential of a preliminary DR screening tool with further optimization needed for clinical use.
