We investigated using ultrawide-field fundus images with a deep convolutional neural network (DCNN), which is a machine learning technology, to detect treatment-naïve proliferative diabetic retinopathy (PDR).

We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep learning, which is a machine learning technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images). We conducted training with a deep convolutional neural network (CNN) using the images and constructed a deep-learning model. The CNN exhibited high sensitivity of 100% (95% confidence interval CI [93.5–100%]) and high specificity of 99.5% (95% CI [97.1–99.9%]). The area under the curve was 0.9993 (95% CI [0.9993–0.9994]). Our findings suggest that MHs could be diagnosed using an approach involving wide angle camera images and deep learning.

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.

Rationale: In this paper, we report on 2 patients who developed branch retinal vein occlusion (BRVO) exacerbation 1 day after administration of the BNT162b2 (Pfizer-BioNTech) SARS-CoV-2 vaccine. Patient concerns: Case 1: A 71 year-old female developed vision loss in her left eye 1 day after receiving a second dose of the SARS-CoV-2 mRNA vaccine. This patient was diagnosed with temporal inferior BRVO and secondary macular edema (ME) in her left eye. ME resolved after 3 doses of intravitreal aflibercept (IVA). After treatment, no recurrence of ME was observed. Case 2: A 72 year-old man developed vision loss in his right eye 1 day after receiving the first dose of the SARS-CoV-2 mRNA vaccine. This patient was diagnosed with temporal superior BRVO in the right eye without ME. The patient was followed up and did not undergo any additional treatment. Diagnoses: Case1: Temporal superior BRVO and secondary ME were observed in the left eye. Her best-corrected visual acuity (BCVA) was 20/30. Case2: Temporal superior BRVO recurrence and secondary ME were observed in the right eye. BCVA was 20/25. Interventions: Case1: Additional dose of IVA was administered. Case2: Two times of Intravitreal ranibizumab was administered twice. Outcomes: Case1: Subsequently, ME resolved BCVA was 20/20. Case2: Subsequently, ME resolved BCVA was 20/25. Lessons: Both cases showed a possible association between SARS-CoV-2 vaccination and the exacerbation of BRVO.

Background and Objectives: The present study evaluated the detection of diabetic retinopathy (DR) using an automated fundus camera focusing exclusively on retinal hemorrhage (RH) using a deep convolutional neural network, which is a machine-learning technology. Materials and Methods: This investigation was conducted via a prospective and observational study. The study included 89 fundus ophthalmoscopy images. Seventy images passed an image quality review and were graded as showing no apparent DR (n = 51), mild nonproliferative DR (NPDR; n = 16), moderate NPDR (n = 1), severe NPDR (n = 1), and proliferative DR (n = 1) by three retinal experts according to the International Clinical Diabetic Retinopathy Severity scale. The RH numbers and areas were automatically detected and the results of two tests—the detection of mild-or-worse NPDR and the detection of moderate-or-worse NPDR—were examined. Results: The detection of mild-or-worse DR showed a sensitivity of 0.812 (95% confidence interval: 0.680–0.945), specificity of 0.888, and area under the curve (AUC) of 0.884, whereas the detection of moderate-or-worse DR showed a sensitivity of 1.0, specificity of 1.0, and AUC of 1.0. Conclusions: Automated diagnosis using artificial intelligence focusing exclusively on RH could be used to diagnose DR requiring ophthalmologist intervention.

To determine the correlation between the optic nerve head (ONH) circulation determined by laser speckle flowgraphy and the best-corrected visual acuity or retinal sensitivity before and after intravitreal bevacizumab or ranibizumab for central retinal vein occlusion. Thirty-one eyes of 31 patients were treated with intravitreal bevacizumab or ranibizumab for macular edema due to a central retinal vein occlusion. The blood flow in the large vessels on the ONH, the best-corrected visual acuity, and retinal sensitivity were measured at the baseline, and at 1, 3, and 6 months after treatment. The arteriovenous passage time on fluorescein angiography was determined. The venous tortuosity index was calculated on color fundus photograph by dividing the length of the tortuous retinal vein by the chord length of the same segment. The blood flow was represented by the mean blur rate (MBR) determined by laser speckle flowgraphy. To exclude the influence of systemic circulation and blood flow in the ONH tissue, the corrected MBR was calculated as MBR of ONH vessel area – MBR of ONH tissue area in the affected eye divided by the vascular MBR – tissue MBR in the unaffected eye. Pearson’s correlation tests were used to determine the significance of correlations between the MBR and the best-corrected visual acuity, retinal sensitivity, arteriovenous passage time, or venous tortuosity index. At the baseline, the corrected MBR was significantly correlated with the arteriovenous passage time and venous tortuosity index (r = -0.807, P < 0.001; r = -0.716, P < 0.001; respectively). The corrected MBR was significantly correlated with the best-corrected visual acuity and retinal sensitivity at the baseline, and at 1, 3, and 6 months (all P < 0.050). The corrected MBR at the baseline was significantly correlated with the best-corrected visual acuity at 6 months (r = -0.651, P < 0.001) and retinal sensitivity at 6 months (r = 0.485, P = 0.005). The pre-treatment blood flow velocity of ONH can be used as a predictive factor for the best-corrected visual acuity and retinal sensitivity after anti-VEGF therapy for central retinal vein occlusion. Trial Registration number: UMIN000009072 . Date of registration: 10/15/2012.