Effect of simulated cataract on the accuracy of artificial intelligence in detecting diabetic retinopathy in color fundus photos

Purpose: Artificial intelligence (AI) is often trained on images without ocular co-morbidities, limiting its generalizability. This study aims to evaluate the accuracy of a convolutional neural network (CNN) applied to color fundus photos (CFPs) with simulated cataracts (SCs) in detecting diabetic retinopathy (DR). Methods: A database of 3662 CFPs (from Asia Pacific Tele-Ophthalmology Society (APTOS) 2019) was used. Using transfer learning, a CNN was trained to classify the training images as either DR or non-DR. The CNN was then applied to classify the testing images after an SC was applied, using varying degrees of Gaussian blur. Results: Accuracy without SC was 97.0%, sensitivity (Sn) 95.7%, specificity (Sp) 98.3%. For mild SC, accuracy was 93.1%, Sn 91.8%, Sp 94.3%. For moderate SC, accuracy was 62.8%, Sn 31.4%, Sp 95.2%. For severe SC, accuracy was 53.5%, Sn 11.8%, Sp 96.5%. Conclusion: SCs significantly impaired AI accuracy. To prepare AI for clinical use, cataracts and other real-world clinical challenges affecting image quality must be accounted for

Disparities in lung cancer short- and long-term outcomes after surgery: Analysis from the national cancer database✰

Introduction: Social determinants of health are particularly important in lung cancer epidemiology. Previous studies have primarily associated social determinants with long-term outcomes, such as survival, but fail to include short-term outcomes after surgery. The National Cancer Database (NCDB) was used to draw associations between social factors of patients with lung cancer and short-term post-surgical outcomes, while comparing them to prognostic factors, including stage at diagnosis and survival. Methods: The 2004–17 NCDB was queried for patients with primary epithelial tumor, squamous cell carcinoma, or adenocarcinoma of the lung treated with curative intent. Linear, binary logistic, Kaplan-Meier, and Cox proportional hazards regression models were utilized. Results: On logistic regression modeling, male gender, low income, lacking insurance, and facility in the central United States were associated with poor short-term outcomes (<0.05). Increased age, White race, and Black race were associated with increased length of hospital stay and mortality, but negatively correlated with readmission rates (<0.05). Medicare and Medicaid were associated with increased length of stay and mortality, respectively (<0.05). Similar patterns were observed for higher stage at diagnosis (<0.05). Hazard ratios were elevated with increased age, male gender, White race, lacking insurance, Medicaid, and facility in the central United States (<0.05). Conclusion: Many social factors previously associated with poor prognosis after lung cancer diagnosis are also associated with poor short-term outcomes after surgery. This study implies that healthcare providers treating lung cancer should proceed with care while aware that patients with the discussed social factors are predisposed to complicated recoveries

Impact of Gap Years Following Medical School Graduation on Resident Research Productivity in Ophthalmology

Background Gap years following medical school graduation have become more common, but research into their tangible career benefit is lacking. Examining the impact of gap years on resident scholarly productivity in ophthalmology may provide insight generalizable to all specialties