Comparison of cataract patients with regular corneal astigmatism after implantation of extended range-of-vision and bifocal toric intraocular lenses.

Purpose: To compare the postoperative visual acuity and visual quality between extended range-of-vision and multifocal toric intraocular lens (IOLs) after implantation in cataract patients with regular corneal astigmatism. Setting: Department of Ophthalmology, the Second Hospital of Jilin University, Changchun, Jilin Province, China. Design: Retrospective and single-center study. Methods: The study involved implanting the Tecnis Symphony (ZXR00IOL) or the bifocal toric (ZMTIOL) in patients undergoing cataract surgery. Three months after surgery, lens performance was evaluated using distance, intermediate, and near visual acuity tests, defocus curves, the modulation transfer function (MTF), a visual function index questionnaire (VF-14), and the adverse optical interference phenomena. Results: The 3-month postoperative follow-up found that both groups had good corrected distance vision. The ZMT group had better-uncorrected distance visual acuity and near visual acuity (p < 0.05). However, the ZXR group showed better uncorrected intermediate visual acuity (p < 0.05) and visual continuity. Overall astigmatism in the postoperative ZMT group was significantly lower than that in the pre-operative group (p < 0.05). The ZMT group had lower total high-order aberrations (tHOs), higher MTF values, and higher VF-14 scores (p < 0.05). Finally, the ZXR group exhibited reduced halo and glare phenomena (p < 0.05). Conclusion: We found that ZMT can effectively correct a corneal astigmatism of 1.0-1.5 D and ZXR can improve patient outcomes regarding subjective optical quality and range of vision. These findings have the potential to improve future astigmatism treatment options.

Automatic pulmonary auscultation grading diagnosis of Coronavirus Disease 2019 in China with artificial intelligence algorithms: A cohort study.

BACKGROUND AND OBJECTIVE: Research on automatic auscultation diagnosis of COVID-19 has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated grading diagnosis of COVID-19 by pulmonary auscultation analysis. METHODS: 172 confirmed cases of COVID-19 in Tongji Hospital were divided into moderate, severe and critical group. Pulmonary auscultation were recorded in 6-10 sites per patient through 3M littmann stethoscope and the data were transferred to computer to construct the dataset. Convolutional neural network (CNN) were designed to generate classifications of the auscultation. F1 score, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity and specificity were quantified. Another 45 normal patients were served as control group. RESULTS: There are about 56.52%, 59.46% and 78.85% abnormal auscultation in the moderate, severe and critical groups respectively. The model showed promising performance with an averaged F1 scores (0.9938 95% CI 0.9923-0.9952), AUC ROC score (0.9999 95% CI 0.9998-1.0000), sensitivity (0.9938 95% CI 0.9910-0.9965) and specificity (0.9979 95% CI 0.9970-0.9988) in identifying the COVID-19 patients among normal, moderate, severe and critical group. It is capable in identifying crackles, wheezes, phlegm sounds with an averaged F1 scores (0.9475 95% CI 0.9440-0.9508), AUC ROC score (0.9762 95% CI 0.9848-0.9865), sensitivity (0.9482 95% CI 0.9393-0.9578) and specificity (0.9835 95% CI 0.9806-0.9863). CONCLUSIONS: Our model is accurate and efficient in automatically diagnosing COVID-19 according to different categories, laying a promising foundation for AI-enabled auscultation diagnosing systems for lung diseases in clinical applications.