Literature DB >> 1832355

Detection of diabetic retinopathy in the community using a non-mydriatic camera.

E R Higgs1, B A Harney, A Kelleher, J P Reckless.   

Abstract

The role of the non-mydriatic fundus camera in detection of diabetic retinopathy was evaluated as part of a comprehensive screening programme for diabetic complications offered to all diabetic patients in a rural town. Retinopathy was demonstrated in 124/358 (35%) of patients screened. Forty-eight patients (13%) were judged to have sight-threatening retinopathy, of whom 29 patients (8% of the total) were not already under the care of an ophthalmologist. However, in only 66% of patients were photographs of both eyes of adequate quality to assess for retinopathy. The percentage of poor quality photographs increased with age in those aged greater than 50 years. It is concluded that the non-mydriatic camera can increase the detection of sight-threatening retinopathy in the community. Although this method of screening is not perfect, because of the number of poor quality photographs, it may be as good as or better than existing screening practices in unselected diabetic populations.

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Year:  1991        PMID: 1832355     DOI: 10.1111/j.1464-5491.1991.tb01650.x

Source DB:  PubMed          Journal:  Diabet Med        ISSN: 0742-3071            Impact factor:   4.359


  7 in total

1.  Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images.

Authors:  C Sinthanayothin; J F Boyce; H L Cook; T H Williamson
Journal:  Br J Ophthalmol       Date:  1999-08       Impact factor: 4.638

2.  Evaluation of diabetic retinopathy screening using a non-mydriatic retinal digital camera in primary care settings in south Israel.

Authors:  Yossi Mizrachi; Boris Knyazer; Sara Guigui; Shirley Rosen; Tova Lifshitz; Nadav Belfair; Itamar Klemperer; Marina Schneck; Jaime Levy
Journal:  Int Ophthalmol       Date:  2013-12-01       Impact factor: 2.031

3.  Use of mobile screening unit for diabetic retinopathy in rural and urban areas.

Authors:  G P Leese; S Ahmed; R W Newton; R T Jung; A Ellingford; P Baines; S Roxburgh; J Coleiro
Journal:  BMJ       Date:  1993-01-16

4.  Automated identification of diabetic retinopathy stages using digital fundus images.

Authors:  Jagadish Nayak; P Subbanna Bhat; Rajendra Acharya; C M Lim; Manjunath Kagathi
Journal:  J Med Syst       Date:  2008-04       Impact factor: 4.460

5.  Accuracy of primary care clinicians in screening for diabetic retinopathy using single-image retinal photography.

Authors:  Tillman F Farley; Naresh Mandava; F Ryan Prall; Cece Carsky
Journal:  Ann Fam Med       Date:  2008 Sep-Oct       Impact factor: 5.166

6.  A Comparison of Artificial Intelligence and Human Diabetic Retinal Image Interpretation in an Urban Health System.

Authors:  Nikita Mokhashi; Julia Grachevskaya; Lorrie Cheng; Daohai Yu; Xiaoning Lu; Yi Zhang; Jeffrey D Henderer
Journal:  J Diabetes Sci Technol       Date:  2021-03-10

7.  Sensitivity and specificity of nonmydriatic digital imaging in screening diabetic retinopathy in Indian eyes.

Authors:  Vishali Gupta; Reema Bansal; Amod Gupta; Anil Bhansali
Journal:  Indian J Ophthalmol       Date:  2014-08       Impact factor: 1.848

  7 in total

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