Search Results

Abstract

PURPOSE: The purpose of this study was to assess visual outcomes and patient satisfaction following cataract surgery involving the implantation of quad-loop intraocular lenses, including trifocal, bifocal, and toric variants.
METHODS: Information was obtained from both physical and electronic medical records of patients who underwent phacoemulsification cataract surgery with implantation of different intraocular lenses between January 1, 2022, and December 31, 2023. The study included individuals aged over 18 who received bilateral implantation of bifocal, trifocal, or monofocal toric intraocular lenses. Visual acuity was assessed at various postoperative time points using the logMAR scale. Quantitative variables were analyzed using mean and standard deviation.
RESULTS: A total of 92 eyes received premium intraocular lenses: 4 bifocal, 32 trifocal, 52 toric monofocal, and 4 trifocal toric lenses. The average preoperative corrected visual acuity was logMAR 0.478 ± 0.259. On the first postoperative day, the average uncorrected visual acuity was logMAR 0.301 ± 0.207. By day 30, 67.4% of eyes achieved uncorrected distance visual acuity of logMAR 0.2 or better. Patient satisfaction was high, with few reports of glare or halos.
CONCLUSION: Quad-loop intraocular lenses-including trifocal, bifocal, and toric models-demonstrated effective improvement in visual acuity and high levels of patient satisfaction. These lenses represent a suitable option for enhancing visual outcomes after cataract surgery. Additional studies with larger cohorts are recommended to confirm these results.

Keywords: Cataract extraction; Aberrometry/methods; Lenses, intraocular; Lens implantation, intraocular; Prosthesis design

Abstract

PURPOSE: This pilot study evaluated the diagnostic accuracy of a deep learning model for detecting pterygium in anterior segment photographs taken using smartphones in the Brazilian Amazon. The model’s performance was benchmarked against assessments made by experienced ophthalmologists, considered the clinical gold standard.
METHODS: In this cross-sectional study, 38 participants (76 eyes) from Barcelos, Brazil, were enrolled. Trained nonmedical health workers captured high-resolution anterior segment images using smartphones. These images were analyzed using a deep learning model based on the MobileNet-V2 convolutional neural network. Diagnostic metrics–including sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve–were calculated and compared with the ophthalmologists’ evaluations.
RESULTS: The deep learning model achieved a sensitivity of 91.43%, specificity of 90.24%, positive predictive value of 88.46%, negative predictive value of 92.79%, and an area under the curve of 0.91. Logistic regression revealed no statistically significant association between pterygium and demographic variables such as age or gender.
CONCLUSIONS: The deep learning model demonstrated high diagnostic performance in identifying pterygium in a remote Amazonian population. These preliminary findings support the potential use of artificial intelligence–based tools to facilitate early detection and screening in underserved regions, thereby enhancing access to ophthalmic care.

Keywords: Pterygium/diagnostic imaging; Smartphone; Diagnostic techniques, ophthalmological; Deep learning; Telemedicine; Artificial intelligence; Cross-sectional studies; Brazil/epidemiology