No abstract

Precis: Pegasus outperformed 5 of the 6 ophthalmologists in terms of diagnostic performance, and there was no statistically significant difference between the deep learning system and the “best case” consensus between the ophthalmologists. The agreement between Pegasus and gold standard was 0.715, whereas the highest ophthalmologist agreement with the gold standard was 0.613. Furthermore, the high sensitivity of Pegasus makes it a valuable tool for screening patients with glaucomatous optic neuropathy. Purpose: The purpose of this study was to evaluate the performance of a deep learning system for the identification of glaucomatous optic neuropathy. Materials and Methods: Six ophthalmologists and the deep learning system, Pegasus, graded 110 color fundus photographs in this retrospective single-center study. Patient images were randomly sampled from the Singapore Malay Eye Study. Ophthalmologists and Pegasus were compared with each other and to the original clinical diagnosis given by the Singapore Malay Eye Study, which was defined as the gold standard. Pegasus’ performance was compared with the “best case” consensus scenario, which was the combination of ophthalmologists whose consensus opinion most closely matched the gold standard. The performance of the ophthalmologists and Pegasus, at the binary classification of nonglaucoma versus glaucoma from fundus photographs, was assessed in terms of sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC), and the intraobserver and interobserver agreements were determined. Results: Pegasus achieved an AUROC of 92.6% compared with ophthalmologist AUROCs that ranged from 69.6% to 84.9% and the “best case” consensus scenario AUROC of 89.1%. Pegasus had a sensitivity of 83.7% and a specificity of 88.2%, whereas the ophthalmologists’ sensitivity ranged from 61.3% to 81.6% and specificity ranged from 80.0% to 94.1%. The agreement between Pegasus and gold standard was 0.715, whereas the highest ophthalmologist agreement with the gold standard was 0.613. Intraobserver agreement ranged from 0.62 to 0.97 for ophthalmologists and was perfect (1.00) for Pegasus. The deep learning system took ∼10% of the time of the ophthalmologists in determining classification. Conclusions: Pegasus outperformed 5 of the 6 ophthalmologists in terms of diagnostic performance, and there was no statistically significant difference between the deep learning system and the “best case” consensus between the ophthalmologists. The high sensitivity of Pegasus makes it a valuable tool for screening patients with glaucomatous optic neuropathy. Future work will extend this study to a larger sample of patients.

Treatment of vertical osseous defects with nonporous or porous polytetrafluoroethylene membranes in combination with a xenograft resulted in statistically significant improvement in open and closed probing measurements, with no significant difference between treatment groups.

No abstract

No abstract

Purpose: To examine the benefits and feasibility of a mobile, real-time, community-based, teleophthalmology program for detecting eye diseases in the New York metro area. Design: Single site, nonrandomized, cross-sectional, teleophthalmologic study. Methods: Participants underwent a comprehensive evaluation in a Wi-Fi–equipped teleophthalmology mobile unit. The evaluation consisted of a basic anamnesis with a questionnaire form, brief systemic evaluations and an ophthalmologic evaluation that included visual field, intraocular pressure, pachymetry, anterior segment optical coherence tomography, posterior segment optical coherence tomography, and nonmydriatic fundus photography. The results were evaluated in real-time and follow-up calls were scheduled to complete a secondary questionnaire form. Risk factors were calculated for different types of ophthalmological referrals. Results: A total of 957 participants were screened. Out of 458 (48%) participants that have been referred, 305 (32%) had glaucoma, 136 (14%) had narrow-angle, 124 (13%) had cataract, 29 had (3%) diabetic retinopathy, 9 (1%) had macular degeneration, and 97 (10%) had other eye disease findings. Significant risk factors for ophthalmological referral consisted of older age, history of high blood pressure, diabetes mellitus, Hemoglobin Alc measurement of ≥6.5, and stage 2 hypertension. As for the ocular parameters, all but central corneal thickness were found to be significant, including having an intraocular pressure >21 mm Hg, vertical cup-to-disc ratio ≥0.5, visual field abnormalities, and retinal nerve fiber layer thinning. Conclusions: Mobile, real-time teleophthalmology is both workable and effective in increasing access to care and identifying the most common causes of blindness and their risk factors.

No abstract

Cutaneous sarcoidosis is rare in children. We report a case of a 5-year-old Bangladeshi girl who presented with fever, a papular eruption on the lower limbs and trunk, malaise, anorexia and weight loss. There was multisystem involvement with marked hepatosplenomegaly, generalized lymphadenopathy, parotid fullness and chronic uveitis. Pulmonary infiltrates were seen on the chest X-ray. Histology of a skin biopsy showed naked noncaseating granulomata and PCR for Mycobacterium tuberculosis was negative. A clinical diagnosis of sarcoidosis was made. The patient was treated with oral prednisolone (2 mg/kg per day). An excellent clinical response with resolution of the rash and improvement of extracutaneous signs was noted within 3 months and she remains well on low-dose prednisolone on alternate days. We discuss the presentation and management of sarcoidosis in children, and highlight the potential difficulty in differentiating this from disseminated tuberculosis.