Search

To compare the accuracy of the Barrett Universal II, Haigis, and Olsen formulas in calculating intraocular lens (IOL) power in eyes with extreme myopia. Eye and Ear, Nose, and Throat Hospital, Fudan University, Shanghai, China. Prospective case series. Eyes were divided into 3 axial length (AL) groups as follows: 26.0 to 28.0 mm (control), 28.0 to 30.0 mm (extreme myopia 1), and 30.0 mm or more (extreme myopia 2). The mean error (ME) 1 month postoperatively was adjusted to zero by optimizing the lens factor; then, the median absolute errors (MedAEs) were compared between formulas. Factors associated with postoperative refractive errors were analyzed. After optimization, the MEs of the Barrett Universal II, Haigis, and Olsen formulas were 0.04 diopter (D) ± 0.48 (SD), 0.04 ± 0.66 D, and 0.04 ± 0.52 D, respectively, and the MedAEs were 0.37 D, 0.46 D, and 0.39 D, respectively (P =.044; Haigis versus Barrett: P =.038). In the extreme myopia 1 group, all 3 formulas produced small MedAEs (P =.662). In the extreme myopia 2 group, the Haigis formula produced a significantly greater MedAE than the Barrett Universal II formula (P =.007; Haigis versus Olsen: P =.055). The accuracy of the Haigis formula in myopic eyes was affected by the AL and keratometry value, whereas the accuracy of the Barrett Universal II and Olsen formulas was affected by the AL only. In eyes with an AL of 28.0 to 30.0 mm, all 3 formulas were accurate. In eyes with AL of 30.0 mm or more, the Barrett Universal II formula was better than the Haigis formula, possibly because there were fewer influencing factors. [ABSTRACT FROM AUTHOR]

An artificial intelligence-based and LOCS III-based automatic grading system of nuclear and cortical cataract was established, showing satisfactory grading reliability and referral capability. Purpose: To establish and validate an artificial intelligence (AI)-assisted automatic cataract grading program based on the Lens Opacities Classification System III (LOCS III). Setting: Eye and Ear, Nose, and Throat Hospital, Fudan University, Shanghai, China. Design: AI training. Methods: Advanced deep-learning algorithms, including Faster R-CNN and ResNet, were applied to the localization and analysis of the region of interest. An internal dataset from the EENT Hospital of Fudan University and an external dataset from the Pujiang Eye Study were used for AI training, validation, and testing. The datasets were automatically labeled on the AI platform regarding the capture mode and cataract grading based on the LOCS III. Results: The AI program showed reliable capture mode recognition, grading, and referral capability for nuclear and cortical cataract grading. In the internal and external datasets, 99.4% and 100% of automatic nuclear grading, respectively, had an absolute prediction error of ≤1.0, with a satisfactory referral capability (area under the curve [AUC]: 0.983 for the internal dataset; 0.977 for the external dataset); 75.0% (internal dataset) and 93.5% (external dataset) of the automatic cortical grades had an absolute prediction error of ≤1.0, with AUCs of 0.855 and 0.795 for referral, respectively. Good consistency was observed between automatic and manual grading when both nuclear and cortical cataracts were evaluated. However, automatic grading of posterior subcapsular cataracts was impractical. Conclusions: The AI program proposed in this study showed robust grading and diagnostic performance for both nuclear and cortical cataracts, based on LOCS III. [ABSTRACT FROM AUTHOR]

Purpose: To describe the distribution of angle [alpha] and angle [kappa] in a population with cataract in Shanghai. Setting: Eye and Ear, Nose, Throat Hospital, Fudan University, Shanghai, China. Design: Hospital-based, cross-sectional study. Methods: Angle [alpha], angle [kappa], and other ocular biometric parameters were determined by IOLMaster 700. The distributions of angle [alpha] and angle [kappa] and their associations with systemic and ocular parameters were assessed. Results: This study included 15 127 eyes of 15 127 cataract patients. The mean angle [alpha] and angle [kappa] values were 0.45 +/- 0.21 mm and 0.30 +/- 0.18 mm, respectively. Angle [alpha] and angle [kappa] were both predominantly located temporal to the visual axis. A greater angle [alpha] or angle [kappa] was associated with older age, lower corneal power, shorter white-to-white distance, and shallower anterior chamber depth (all P <.05). Angle [alpha] correlated positively with angle [kappa]. With increasing axial length (AL), angle [alpha] gradually decreased in a nonlinear way and shifted to the nasal side of the visual axis, whereas angle [kappa] decreased in eyes with AL less than 27.5 mm but increased again in eyes with longer AL. Conclusions: Angle [alpha] and angle [kappa], both predominantly located temporal to the visual axis, were influenced by multiple anterior segment parameters. As AL increased, the changes in angle [alpha] and angle [kappa] were nonlinear, and their locations gradually shifted from the temporal to the nasal side of the visual axis. [ABSTRACT FROM AUTHOR]