Deep learning-based image quality assessment for optical coherence tomography macular scans: a multicentre study.

AIMS: To develop and externally test deep learning (DL) models for assessing the image quality of three-dimensional (3D) macular scans from Cirrus and Spectralis optical coherence tomography devices. METHODS: We retrospectively collected two data sets including 2277 Cirrus 3D scans and 1557 Spectralis 3D scans, respectively, for training (70%), fine-tuning (10%) and internal validation (20%) from electronic medical and research records at The Chinese University of Hong Kong Eye Centre and the Hong Kong Eye Hospital. Scans with various eye diseases (eg, diabetic macular oedema, age-related macular degeneration, polypoidal choroidal vasculopathy and pathological myopia), and scans of normal eyes from adults and children were included. Two graders labelled each 3D scan as gradable or ungradable, according to standardised criteria. We used a 3D version of the residual network (ResNet)-18 for Cirrus 3D scans and a multiple-instance learning pipline with ResNet-18 for Spectralis 3D scans. Two deep learning (DL) models were further tested via three unseen Cirrus data sets from Singapore and five unseen Spectralis data sets from India, Australia and Hong Kong, respectively. RESULTS: In the internal validation, the models achieved the area under curves (AUCs) of 0.930 (0.885-0.976) and 0.906 (0.863-0.948) for assessing the Cirrus 3D scans and Spectralis 3D scans, respectively. In the external testing, the models showed robust performance with AUCs ranging from 0.832 (0.730-0.934) to 0.930 (0.906-0.953) and 0.891 (0.836-0.945) to 0.962 (0.918-1.000), respectively. CONCLUSIONS: Our models could be used for filtering out ungradable 3D scans and further incorporated with a disease-detection DL model, allowing a fully automated eye disease detection workflow.

Integrated image-based deep learning and language models for primary diabetes care.

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.

Displacement of Submacular Hemorrhage Using Subretinal Cocktail Injection versus Pneumatic Displacement: A Real-World Comparative Study.

INTRODUCTION: The objective of this study was to compare the outcome of submacular hemorrhage (SMH) displacement using pneumatic displacement with intravitreal expansile gas versus pars plana vitrectomy (PPV) with subretinal injection of tissue plasminogen activator (tPA), anti-vascular endothelial growth factor (VEGF) agent, and air as primary surgery. METHODS: Retrospective interventional case series of 63 patients who underwent surgical displacement of SMH secondary to neovascular age-related macular degeneration (nAMD) or polypoidal choroidal vasculopathy (PCV) from May 1, 2015, to October 31, 2022. Medical records were reviewed for diagnosis, logMAR visual acuity (VA), central subfield thickness (CST), and postoperative displacement rates and complications up to 12 months after operation. RESULTS: The diagnosis was nAMD in 24 (38.1%) and PCV in 39 (61.9%) eyes. There were 40 (63.5%) eyes in the pneumatic displacement group (38 received C3F8, 2 received SF6) and 23 (36.5%) eyes in the subretinal cocktail injection. Mean baseline VA was 1.46 and 1.62, respectively (p = 0.404). The subretinal injection group had more extensive SMH (p = 0.005), thicker CST (1,006.6 µm vs. 780.2 µm, p = 0.012), and longer interval between symptom and operation (10.65 vs. 5.53 days, p < 0.001). The mean postoperative VA at 6 months was 0.67 and 0.91 (p = 0.180) for pneumatic displacement and subretinal injection groups, respectively, though VA was significantly better in the pneumatic group at 12-month visit (0.64 vs. 1.03, p = 0.040). At least 10 mean change in VA were >10 letters gain in both groups up to 12 months. Postoperative CST reduction was greater (625.1 µm vs. 326.5 µm, p = 0.008) and complete foveal displacement (87.0% vs. 37.5%), p < 0.001, odds ratio [OR] = 11.1) and displacement to arcade or beyond (52.5% vs. 17.5%, p = 0.009, OR = 5.15) were more frequent in the subretinal injection group. Two patients with failed pneumatic displacement were successfully treated with subretinal cocktail injection as a second operation. CONCLUSION: Surgical displacement of SMH leads to clinically meaningful improvement in VA. PPV with subretinal cocktail injection is more effective than pneumatic displacement in displacing SMH with similar safety profile despite longer interval before operation, higher CST, and more extensive SMH at baseline. Retinal surgeons could consider this novel technique in cases with thick and extensive SMH or as a rescue secondary operation in selected cases.

Light exposure therapy for myopia control: a systematic review and Bayesian network meta-analysis.

AIMS: To compare and rank the myopia control effects of different light wavelengths in children using a systematic review and Bayesian network meta-analysis (Bayesian NMA). METHODS: The review protocol was registered with PROSPERO. We searched PubMed, EMBASE and MEDLINE for relevant clinical and animal studies published as of 2 February 2023. We included studies comparing red, violet or full-spectrum light with controls. Data extracted included descriptive statistics and study outcomes (axial length (AL) elongation and progression of spherical equivalent (SE) refraction). After quality assessment, estimates of treatment effect outcomes (mean differences (MDs) and 95% CIs) were first pooled for the animal and clinical studies in a traditional meta-analysis. To compare and rank the different light wavelengths, the Bayesian NMA was then conducted for all the included clinical studies (12 studies) and separately for only randomised controlled trials (8 studies). MDs, 95% credible intervals (CrIs) and ranks of the various light wavelengths were estimated in the Bayesian NMA. RESULTS: When all clinical studies were included in the Bayesian NMA (12 studies), only red-light significantly slowed AL elongation, MD (95% CrI), -0.38 mm (-0.59 mm to -0.16 mm)/year and SE refraction progression, 0.72D (0.35D to 1.10D)/year compared with controls. It remained the only significant intervention when effect sizes from only RCTs (eight studies) were separately combined, (-0.28 mm (-0.40 mm to -0.15 mm)/year and 0.57D (0.22D to 0.92D)/year, for AL and SE refraction, respectively). CONCLUSION: Myopia control efficacy varied among different wavelengths of light, with red light ranked as the most effective. PROSPERO REGISTRATION NUMBER: Clinical studies: CRD42022368998; animal studies: CRD42022368671.

Performance of Artificial Intelligence in Detecting Diabetic Macular Edema From Fundus Photography and Optical Coherence Tomography Images: A Systematic Review and Meta-analysis.

BACKGROUND: Diabetic macular edema (DME) is the leading cause of vision loss in people with diabetes. Application of artificial intelligence (AI) in interpreting fundus photography (FP) and optical coherence tomography (OCT) images allows prompt detection and intervention. PURPOSE: To evaluate the performance of AI in detecting DME from FP or OCT images and identify potential factors affecting model performances. DATA SOURCES: We searched seven electronic libraries up to 12 February 2023. STUDY SELECTION: We included studies using AI to detect DME from FP or OCT images. DATA EXTRACTION: We extracted study characteristics and performance parameters. DATA SYNTHESIS: Fifty-three studies were included in the meta-analysis. FP-based algorithms of 25 studies yielded pooled area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of 0.964, 92.6%, and 91.1%, respectively. OCT-based algorithms of 28 studies yielded pooled AUROC, sensitivity, and specificity of 0.985, 95.9%, and 97.9%, respectively. Potential factors improving model performance included deep learning techniques, larger size, and more diversity in training data sets. Models demonstrated better performance when validated internally than externally, and those trained with multiple data sets showed better results upon external validation. LIMITATIONS: Analyses were limited by unstandardized algorithm outcomes and insufficient data in patient demographics, OCT volumetric scans, and external validation. CONCLUSIONS: This meta-analysis demonstrates satisfactory performance of AI in detecting DME from FP or OCT images. External validation is warranted for future studies to evaluate model generalizability. Further investigations may estimate optimal sample size, effect of class balance, patient demographics, and additional benefits of OCT volumetric scans.

Optical coherence tomography in the management of diabetic macular oedema.

Diabetic macular oedema (DMO) is the major cause of visual impairment in people with diabetes. Optical coherence tomography (OCT) is now the most widely used modality to assess presence and severity of DMO. DMO is currently broadly classified based on the involvement to the central 1 mm of the macula into non-centre or centre involved DMO (CI-DMO) and DMO can occur with or without visual acuity (VA) loss. This classification forms the basis of management strategies of DMO. Despite years of research on quantitative and qualitative DMO related features assessed by OCT, these do not fully inform physicians of the prognosis and severity of DMO relative to visual function. Having said that, recent research on novel OCT biomarkers development and re-defined classification of DMO show better correlation with visual function and treatment response. This review summarises the current evidence of the association of OCT biomarkers in DMO management and its potential clinical importance in predicting VA and anatomical treatment response. The review also discusses some future directions in this field, such as the use of artificial intelligence to quantify and monitor OCT biomarkers and retinal fluid and identify phenotypes of DMO, and the need for standardisation and classification of OCT biomarkers to use in future clinical trials and clinical practice settings as prognostic markers and secondary treatment outcome measures in the management of DMO.

What can GPT-4 do for Diagnosing Rare Eye Diseases? A Pilot Study.

INTRODUCTION: Generative pretrained transformer-4 (GPT-4) has gained widespread attention from society, and its potential has been extensively evaluated in many areas. However, investigation of GPT-4's use in medicine, especially in the ophthalmology field, is still limited. This study aims to evaluate GPT-4's capability to identify rare ophthalmic diseases in three simulated scenarios for different end-users, including patients, family physicians, and junior ophthalmologists. METHODS: We selected ten treatable rare ophthalmic disease cases from the publicly available EyeRounds service. We gradually increased the amount of information fed into GPT-4 to simulate the scenarios of patient, family physician, and junior ophthalmologist using GPT-4. GPT-4's responses were evaluated from two aspects: suitability (appropriate or inappropriate) and accuracy (right or wrong) by senior ophthalmologists (> 10 years' experiences). RESULTS: Among the 30 responses, 83.3% were considered "appropriate" by senior ophthalmologists. In the scenarios of simulated patient, family physician, and junior ophthalmologist, seven (70%), ten (100%), and eight (80%) responses were graded as "appropriate" by senior ophthalmologists. However, compared to the ground truth, GPT-4 could only output several possible diseases generally without "right" responses in the simulated patient scenarios. In contrast, in the simulated family physician scenario, 50% of GPT-4's responses were "right," and in the simulated junior ophthalmologist scenario, the model achieved a higher "right" rate of 90%. CONCLUSION: To our knowledge, this is the first proof-of-concept study that evaluates GPT-4's capacity to identify rare eye diseases in simulated scenarios involving patients, family physicians, and junior ophthalmologists. The results indicate that GPT-4 has the potential to serve as a consultation assisting tool for patients and family physicians to receive referral suggestions and an assisting tool for junior ophthalmologists to diagnose rare eye diseases. However, it is important to approach GPT-4 with caution and acknowledge the need for verification and careful referrals in clinical settings.

Pathogenesis of myopic choroidal neovascularization: A systematic review and meta-analysis.

Myopic choroidal neovascularization (CNV) is a vision-threatening complication of high myopia. Here, we systematically review cohort, case-control, and cross-sectional studies in PubMed, Embase, and Web of Science, and summarize the associated factors of myopic CNV using meta-analysis where applicable. Among 1,333 records assessed, 50 were found eligible, all having a low-to-moderate risk of bias. Highly myopic eyes with CNV had a higher risk of lacquer cracks (odds ratio = 2.88) and patchy chorioretinal atrophy (odds ratio = 3.43) than those without. The mean posterior staphyloma height (µm) was greater in myopic CNV eyes than in highly myopic eyes without CNV (mean difference = 82.03). The thinning of choroidal thickness (µm) between myopic eyes with and without CNV differed significantly (mean difference = -47.76). The level of vascular endothelial growth factor (pg/ml) in the aqueous humor of myopic CNV eyes was significantly higher than in highly myopic eyes without CNV (mean difference = 24.98), the same as interleukin-8 (IL-8) (pg/ml, mean difference = 7.73). Single-nucleotide polymorphisms in the vascular endothelial growth factor, complement factor I, and collagen type VIII alpha 1 genes were associated with myopic CNV. We found that myopic CNV eyes have a higher ratio of lacquer cracks and patchy chorioretinal atrophy, thinner choroid, greater posterior staphyloma height, and a higher level of vascular endothelial growth factor and IL-8 in aqueous. Structural predisposing lesions, hemodynamic, genetic, and systemic factors are also associated with myopic CNV.

Assessment of Parafoveal Diabetic Macular Ischemia on Optical Coherence Tomography Angiography Images to Predict Diabetic Retinal Disease Progression and Visual Acuity Deterioration.

Importance: The presence of diabetic macular ischemia (DMI) on optical coherence tomography angiography (OCTA) images predicts diabetic retinal disease progression and visual acuity (VA) deterioration, suggesting an OCTA-based DMI evaluation can further enhance diabetic retinopathy (DR) management. Objective: To investigate whether an automated binary DMI algorithm using OCTA images provides prognostic value on DR progression, diabetic macular edema (DME) development, and VA deterioration in a cohort of patients with diabetes. Design, Setting, and Participants: In this cohort study, DMI assessment of superficial capillary plexus and deep capillary plexus OCTA images was performed by a previously developed deep learning algorithm. The presence of DMI was defined as images exhibiting disruption of fovea avascular zone with or without additional areas of capillary loss, while absence of DMI was defined as images presented with intact fovea avascular zone outline and normal distribution of vasculature. Patients with diabetes were recruited starting in July 2015 and were followed up for at least 4 years. Cox proportional hazards models were used to evaluate the association of the presence of DMI with DR progression, DME development, and VA deterioration. Analysis took place between June and December 2022. Main Outcomes and Measures: DR progression, DME development, and VA deterioration. Results: A total of 321 eyes from 178 patients were included for analysis (85 [47.75%] female; mean [SD] age, 63.39 [11.04] years). Over a median (IQR) follow-up of 50.41 (48.16-56.48) months, 105 eyes (32.71%) had DR progression, 33 eyes (10.28%) developed DME, and 68 eyes (21.18%) had VA deterioration. Presence of superficial capillary plexus-DMI (hazard ratio [HR], 2.69; 95% CI, 1.64-4.43; P < .001) and deep capillary plexus-DMI (HR, 3.21; 95% CI, 1.94-5.30; P < .001) at baseline were significantly associated with DR progression, whereas presence of deep capillary plexus-DMI was also associated with DME development (HR, 4.60; 95% CI, 1.15-8.20; P = .003) and VA deterioration (HR, 2.12; 95% CI, 1.01-5.22; P = .04) after adjusting for age, duration of diabetes, fasting glucose, glycated hemoglobin, mean arterial blood pressure, DR severity, ganglion cell-inner plexiform layer thickness, axial length, and smoking at baseline. Conclusions and Relevance: In this study, the presence of DMI on OCTA images demonstrates prognostic value for DR progression, DME development, and VA deterioration.

Recent Advances in Clinical Applications of Imaging in Retinal Diseases.

Many diseases that cause visual impairment, as well as systemic conditions, manifest in the posterior segment of the eye. With the advent of high-speed, high-resolution, reliable, and noninvasive imaging techniques, ophthalmologists are becoming more dependent on ocular imaging for disease diagnosis, classification, and management in clinical practice. There are rapid advances on the indications of multimodal retinal imaging techniques, including the application of ultra-widefield fundus angiography, fundus autofluorescence, optical coherence tomography, as well as optical coherence tomography angiography. This review summarizes and highlights the clinical applications, latest indications, and interpretations of multimodal imaging in age-related macular degeneration, polypoidal choroidal vasculopathy, diabetic macular edema, central serous chorioretinopathy, diabetic retinopathy, retinal vein occlusion, and uveitis.