Background: In recent years, the flipped classroom method of teaching has received much attention in health sciences education. However, the application of flipped classrooms in ophthalmology education has not been well investigated. Objective: The goal of this study was to investigate the effectiveness and acceptability of the flipped classroom approach to teaching ophthalmology at the clerkship level. Design: Ninety-five fourth year medical students in an ophthalmology clerkship were randomly divided into two groups. An ocular trauma module was chosen for the content of this study. One group (FG (flipped group), n = 48) participated in flipped classroom instruction and was asked to watch a recorded lecture video and to read study materials before a face-to-face class meeting. They used the in-class time for discussion. The other group (TG (traditional group), n = 47) was assigned to traditional lecture-based instruction. These students attended a didactic lecture and completed assigned homework after the lecture. Feedback questionnaires were collected to compare students’ perspectives on the teaching approach they experienced and to evaluate students’ self-perceived competence and interest in ocular trauma. Pre- and post-tests were performed to assess student learning of the course materials. Results: More students in the FG agreed that the classroom helped to promote their learning motivation, improve their understanding of the course materials, and enhance their communication skill and clinical thinking. However, students in the FG did not show a preference for this method of teaching, and also reported more burden and pressure than those from the TG. Students from the FG performed better on the post test over the ocular trauma-related questions when compared to those from the TG. Conclusions: The flipped classroom approach shows promise in ophthalmology clerkship teaching. However, it has some drawbacks. Further evaluation and modifications are required before it can be widely accepted and implemented. Abbreviations FG: Flipped classroom group; TG: Traditional lecture-based classroom group; TBL: Team-based learning; PBL: Problem-based learning; ZOC: Zhongshan Ophthalmic Center

CC-Cruiser is an artificial intelligence (AI) platform developed for diagnosing childhood cataracts and providing risk stratification and treatment recommendations. The high accuracy of CC-Cruiser was previously validated using specific datasets. The objective of this study was to compare the diagnostic efficacy and treatment decision-making capacity between CC-Cruiser and ophthalmologists in real-world clinical settings. This multicentre randomized controlled trial was performed in five ophthalmic clinics in different areas across China. Pediatric patients (aged ≤ 14 years) without a definitive diagnosis of cataracts or history of previous eye surgery were randomized (1:1) to receive a diagnosis and treatment recommendation from either CC-Cruiser or senior consultants (with over 5 years of clinical experience in pediatric ophthalmology). The experts who provided a gold standard diagnosis, and the investigators who performed slit-lamp photography and data analysis were blinded to the group assignments. The primary outcome was the diagnostic performance for childhood cataracts with reference to cataract experts' standards. The secondary outcomes included the evaluation of disease severity and treatment determination, the time required for the diagnosis, and patient satisfaction, which was determined by the mean rating. This trial is registered with ClinicalTrials.gov (NCT03240848). Between August 9, 2017 and May 25, 2018, 350 participants (700 eyes) were randomly assigned for diagnosis by CC-Cruiser (350 eyes) or senior consultants (350 eyes). The accuracies of cataract diagnosis and treatment determination were 87.4% and 70.8%, respectively, for CC-Cruiser, which were significantly lower than 99.1% and 96.7%, respectively, for senior consultants (p < 0.001, OR = 0.06 [95% CI 0.02 to 0.19]; and p < 0.001, OR = 0.08 [95% CI 0.03 to 0.25], respectively). The mean time for receiving a diagnosis from CC-Cruiser was 2.79 min, which was significantly less than 8.53 min for senior consultants (p < 0.001, mean difference 5.74 [95% CI 5.43 to 6.05]). The patients were satisfied with the overall medical service quality provided by CC-Cruiser, typically with its time-saving feature in cataract diagnosis. CC-Cruiser exhibited less accurate performance comparing to senior consultants in diagnosing childhood cataracts and making treatment decisions. However, the medical service provided by CC-Cruiser was less time-consuming and achieved a high level of patient satisfaction. CC-Cruiser has the capacity to assist human doctors in clinical practice in its current state. National Key R&D Program of China (2018YFC0116500) and the Key Research Plan for the National Natural Science Foundation of China in Cultivation Project (91846109).

Background/aims To apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images. Methods In this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index. Results In the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps. Conclusions We used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

To compare the prediction accuracy of new intraocular lens (IOL) calculation formulas (Barrett Universal II (BUII), Emmetropia Verifying Optical (EVO), Kane and Ladas Super formula) and traditional formulas (Haigis, Hoffer Q, Holladay 1 and SRK/T) with Wang-Koch (WK) axial length (AL) adjustment in vitrectomized eyes. Retrospective consecutive case-series study. 111 eyes of 111 patients underwent uneventful phacoemulsification and enVista MX60 implantation following vitrectomy were enrolled and divided into four groups according to whether the vitreous cavity was filled with silicone oil. The performance of each formula was evaluated with or without lens constant optimization. Before lens constants optimization, the mean prediction errors (MEs) of all formulas were statistically different from zero (0.14-0.46D) in vitrectomized eyes, except for the Kane formula. The BUII, EVO, Kane, and Haigis had relatively lower mean absolute error (MAE) and median absolute error (MedAE) with optimized constants. No significant systemic bias was found in new formulas for vitrectomized eyes with AL over 26 mm (P>0.05). The Hoffer Q and Holladay 1 displayed significantly hyperopic shift (0.39 and 0.51D) for long eyes, which was corrected by the WK adjustment. There were no significant differences in the prediction accuracy of all formulas among 4 subgroups (P>0.05). The BUII, EVO, Kane, and Haigis displayed comparable performance in vitrectomized eyes with optimized constants. In vitrectomized highly myopic eyes, the new formulas and traditional formulas with WK adjustment exhibited satisfactory prediction accuracy. Silicone oil tamponade did not affect the prediction accuracy of formulas using IOLMaster 700.

Sorbitol dehydrogenase (SORD) deficiency has been identified as the most frequent autosomal recessive form of hereditary neuropathy. Loss of SORD causes high sorbitol levels in tissues due to the inability to convert sorbitol to fructose in the 2-step polyol pathway, leading to degenerative neuropathy. The underlying mechanisms of sorbitol-induced degeneration have not been fully elucidated, and no current FDA-approved therapeutic options are available to reduce sorbitol levels in the nervous system. Here, in a Drosophila model of SORD deficiency, we showed synaptic degeneration in the brain, neurotransmission defect, locomotor impairment, and structural abnormalities in the neuromuscular junctions. In addition, we found reduced ATP production in the brain and ROS accumulation in the CNS and muscle, indicating mitochondrial dysfunction. Applied Therapeutics has developed a CNS-penetrant next-generation aldose reductase inhibitor (ARI), AT-007 (govorestat), which inhibits the conversion of glucose to sorbitol. AT-007 significantly reduced sorbitol levels in patient-derived fibroblasts, induced pluripotent stem cell–derived (iPSC-derived) motor neurons, and Drosophila brains. AT-007 feeding in Sord-deficient Drosophila mitigated synaptic degeneration and significantly improved synaptic transduction, locomotor activity, and mitochondrial function. Moreover, AT-007 treatment significantly reduced ROS accumulation in Drosophila CNS, muscle, and patient-derived fibroblasts. These findings uncover the molecular and cellular pathophysiology of SORD neuropathy and provide a potential treatment strategy for patients with SORD deficiency.