To compare the visual performance of multifocal intraocular lenses (IOLs) and monofocal IOLs made of the same material. The subjects included patients implanted with either Tecnis® monofocal IOLs (ZA9003 or ZCB00) or Tecnis® multifocal IOLs (ZMA00 or ZMB00) bilaterally. We conducted a retrospective study comparing the two types of IOLs. The multifocal group included 46 patients who were implanted with Tecnis® multifocal IOLs bilaterally. The monofocal group was an age- and sex-matched control group, and included 85 patients who were implanted with Tecnis® monofocal IOLs bilaterally. Lens opacity grading, the radius of corneal curvature, corneal astigmatism, axial length and the refractive status were measured preoperatively. Pupil size, ocular aberrometry, distance, intermediate and near visual acuity, contrast sensitivity with and without glare and the responses to a quality-of-vision questionnaire were evaluated pre- and postoperatively. The uncorrected near visual acuity was significantly better in the multifocal group, whereas both the corrected intermediate and near visual acuity were better in the monofocal group. Contrast sensitivity (with and without glare) was significantly better in the monofocal group. The rate of spectacle dependency was significantly lower in the multifocal group. There were no significant differences between the two groups regarding most items of the postoperative quality-of-vision questionnaire (VFQ-25), with the exception that the patients in the monofocal group reported fewer problems with nighttime driving. The multifocal IOLs used in this study reduced spectacle dependency more so than monofocal IOLs and did not compromise the subjective visual function, with the exception of nighttime driving.

Background Artificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on many images while preserving patient privacy. Methods A web-based course was designed using 600 synthetic ultra-widefield (UWF) retinal images to teach students to detect disease in these images. The images were generated by stable diffusion, a large generative foundation model, which we fine-tuned with 6285 real UWF images from six categories: five retinal diseases (age-related macular degeneration, glaucoma, diabetic retinopathy, retinal detachment and retinal vein occlusion) and normal. 161 trainee orthoptists took the course. They were evaluated with two tests: one consisting of UWF images and another of standard field (SF) images, which the students had not encountered in the course. Both tests contained 120 real patient images, 20 per category. The students took both tests once before and after training, with a cool-off period in between. Results On average, students completed the course in 53 min, significantly improving their diagnostic accuracy. For UWF images, student accuracy increased from 43.6% to 74.1% (p<0.0001 by paired t-test), nearly matching the previously published state-of-the-art AI model’s accuracy of 73.3%. For SF images, student accuracy rose from 42.7% to 68.7% (p<0.0001), surpassing the state-of-the-art AI model’s 40%. Conclusion Synthetic images can be used effectively in medical education. We also found that humans are more robust to novel situations than AI models, thus showcasing human judgement’s essential role in medical diagnosis.

The present study aims to describe the use of machine learning (ML) in predicting the occurrence of postoperative refraction after cataract surgery and compares the accuracy of this method to conventional intraocular lens (IOL) power calculation formulas. In total, 3331 eyes from 2010 patients were assessed. The objects were divided into training data and test data. The constants for the IOL power calculation formulas and model training for ML were optimized using training data. Then, the occurrence of postoperative refraction was predicted using conventional formulas, or ML models were calculated using the test data. We evaluated the SRK/T formula, Haigis formula, Holladay 1 formula, Hoffer Q formula, and Barrett Universal II formula (BU-II); similar to ML methods, we assessed support vector regression (SVR), random forest regression (RFR), gradient boosting regression (GBR), and neural network (NN). Among the conventional formulas, BU-II had the lowest mean and median absolute error of prediction. Therefore, we compared the accuracy of our method with that of BU-II. The absolute errors of some ML methods were lower than those of BU-II. However, no statistically significant difference was observed. Thus, the accuracy of our method was not inferior to that of BU-II.

A 79-year-old man receiving regular, once-monthly ophthalmologic conservative treatment for left chronic dacryocystitis had a normal cornea on 10 March 2020. On 5 April, he noticed decreasing left visual acuity. Using telephone consultation, the patient’s doctor explained to him that he should visit him on 7 April. However, owing to the fear of contracting coronavirus disease 2019, he cancelled his scheduled appointment on 7 April. On 11 May, he noticed significant left visual impairment with severe pain and promptly visited an ophthalmology clinic. As left corneal perforation was observed, he was urgently referred to our hospital. His left cornea had melted and was perforated from the centre to the lower part, exposing the intraocular lens and iris ( Fig. 1). We made the working diagnosis of corneal melting and perforation secondary to chronic dacryocystitis. Methicillin-sensitive Staphylococcus aureus (MSSA) was detected in culture and sensitivity tests. Emergency evisceration was performed. Fluoroquinolone antibacterial eye drops, commonly used as antibacterial agents, have strong antibacterial activity against pathogenic bacteria causing various bacterial infections, including MSSA [ 1, 2] Regardless of the underlying aetiology, breakdown of the corneal epithelium is the main pathologic mechanism of corneal ulceration. If the integrity of the corneal epithelium is compromised by any means, it can result in corneal ulcer and perforation [ 3]. If the patient had attended his scheduled appointment on 7 April after noticing decreasing left visual acuity and had received routine ophthalmologic examination and infection treatment with a sensitive drug, even if a corneal ulcer would have developed, it is highly possible that the serious complication of corneal perforation could have been prevented.

Aim: We compared the visual performance of toric intraocular lenses (IOLs) and non-toric IOLs made of the same material. Patients and materials: The subjects included patients implanted with either Acrysof IQ^® toric IOLs (SN6AT3-9) or Acrysof IQ^® IOLs (SN60WF) bilaterally. The toric group included 103 patients who were implanted with Acrysof IQ toric IOLs bilaterally. The non-toric group was a corneal astigmatism-matched control group and included 103 patients who were implanted with Acrysof IQ IOLs bilaterally. Results: The uncorrected distance visual acuity was significantly better in the toric group, whereas the uncorrected 50 cm visual acuity was better in the non-toric group. There was no significant difference in contrast sensitivity (with and without glare) between both the groups. The rate of spectacle dependency for distance vision was significantly lower in the toric group. There were no significant differences between the two groups in all items of the postoperative quality-of-vision questionnaire (25-item Visual Function Questionnaire). Conclusion: The toric IOLs used in this study reduced spectacle dependency more than the non-toric IOLs and did not compromise the subjective visual function, but the uncorrected 50 cm vision was worse in toric IOL implanted eyes.

This retrospective study examined the effect of the size of training data on the accuracy of machine learning-assisted SRK/T power calculation. Clinical records of 4800 eyes of 4800 Japanese patients with intraocular lenses (IOLs) were reviewed. A support vector regressor (SVR) was used for refining the SRK/T formula, and dataset sizes for training and evaluation were reduced from full to 1/64. The prediction errors from the postoperative refractions were calculated, and the proportion within ± 0.25 D, ± 0.50 D, and ± 1.00 D of errors were compared with those using full data. The influence of the difference in A-constant was also evaluated. Prediction errors within ± 0.50 D in the use of full data were obtained with the dataset of ≥ 150 eyes (P = 0.016), whereas the datasets of ≥ 300 eyes were required for the error within ± 0.25 D (P < 0.030). The prediction errors did not alter with the A-constant values among IOLs with open-loop haptics, except for IOLs with plated haptics. In conclusion, the accuracy of SVR-assisted SRK/T could be achieved with the training dataset of ≥ 150 eyes for the Japanese population, and the calculation was versatile for any open-looped IOLs.