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Purpose: Community Eye Clinics (CEC) increase accessibility of specialist ophthalmic services in the community, reducing demand for tertiary eye services. This paper aims to evaluate the impact of CEC on first-visit referrals from Hougang Polyclinic (HOU) to Tan Tock Seng Hospital Ophthalmology Specialist Outpatient Clinic (SOC)., Methods: A retrospective analysis was performed on first-visit referrals from Hougang Polyclinic (HOU) to Tan Tock Seng Hospital Ophthalmology Specialist Outpatient Clinic (SOC) over a similar 3-months period before and after the introduction of CEC in August 2018 (1 January to 31 March in 2018 and 2019, respectively). Data pertaining to patients' presenting complaints, referral reasons, final diagnoses, follow-up plans, and need for ophthalmic intervention were obtained., Results: We included 978 patients in our study. There was a 27.5% reduction in the number of first-visit referrals seen at SOC after the establishment of CEC. Patients were more likely to be referred on to sub-specialty eye clinics (10.8% vs. 12.9%, p = p  = .304) and receive more ophthalmic interventions (15% vs. 16.3%, p  = .066) than prior to CEC., Conclusion: The CEC provides greater accessibility to eye care within the community. Optometrists are upskilled to manage patients with stable eye conditions, whilst eye specialists can provide timely care to the SOC for patients with more severe eye conditions.

Purpose: To develop and validate a deep learning system (DLS) for estimation of vertical cup-to-disc ratio (vCDR) in ultra-widefield (UWF) and smartphone-based fundus images., Methods: A DLS consisting of two sequential convolutional neural networks (CNNs) to delineate optic disc (OD) and optic cup (OC) boundaries was developed using 800 standard fundus images from the public REFUGE data set. The CNNs were tested on 400 test images from the REFUGE data set and 296 UWF and 300 smartphone-based images from a teleophthalmology clinic. vCDRs derived from the delineated OD/OC boundaries were compared with optometrists' annotations using mean absolute error (MAE). Subgroup analysis was conducted to study the impact of peripapillary atrophy (PPA), and correlation study was performed to investigate potential correlations between sectoral CDR (sCDR) and retinal nerve fiber layer (RNFL) thickness., Results: The system achieved MAEs of 0.040 (95% CI, 0.037-0.043) in the REFUGE test images, 0.068 (95% CI, 0.061-0.075) in the UWF images, and 0.084 (95% CI, 0.075-0.092) in the smartphone-based images. There was no statistical significance in differences between PPA and non-PPA images. Weak correlation (r = -0.4046, P < 0.05) between sCDR and RNFL thickness was found only in the superior sector., Conclusions: We developed a deep learning system that estimates vCDR from standard, UWF, and smartphone-based images. We also described anatomic peripapillary adversarial lesion and its potential impact on OD/OC delineation., Translational Relevance: Artificial intelligence can estimate vCDR from different types of fundus images and may be used as a general and interpretable screening tool to improve community reach for diagnosis and management of glaucoma.