Sensitivity and specificity of automated analysis of single-field non-mydriatic fundus photographs by Bosch DR Algorithm—Comparison with mydriatic fundus photography (ETDRS) for screening in undiagnosed diabetic retinopathy
Diabetic retinopathy (DR) is a leading cause of blindness among working-age adults. Early diagnosis through effective screening programs is likely to improve vision outcomes. The ETDRS seven-standard-field 35-mm stereoscopic color retinal imaging (ETDRS) of the dilated eye is elaborate and requires...
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Published in | PloS one Vol. 12; no. 12; p. e0189854 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
27.12.2017
Public Library of Science (PLoS) |
Subjects | |
Online Access | Get full text |
ISSN | 1932-6203 1932-6203 |
DOI | 10.1371/journal.pone.0189854 |
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Abstract | Diabetic retinopathy (DR) is a leading cause of blindness among working-age adults. Early diagnosis through effective screening programs is likely to improve vision outcomes. The ETDRS seven-standard-field 35-mm stereoscopic color retinal imaging (ETDRS) of the dilated eye is elaborate and requires mydriasis, and is unsuitable for screening. We evaluated an image analysis application for the automated diagnosis of DR from non-mydriatic single-field images. Patients suffering from diabetes for at least 5 years were included if they were 18 years or older. Patients already diagnosed with DR were excluded. Physiologic mydriasis was achieved by placing the subjects in a dark room. Images were captured using a Bosch Mobile Eye Care fundus camera. The images were analyzed by the Retinal Imaging Bosch DR Algorithm for the diagnosis of DR. All subjects also subsequently underwent pharmacological mydriasis and ETDRS imaging. Non-mydriatic and mydriatic images were read by ophthalmologists. The ETDRS readings were used as the gold standard for calculating the sensitivity and specificity for the software. 564 consecutive subjects (1128 eyes) were recruited from six centers in India. Each subject was evaluated at a single outpatient visit. Forty-four of 1128 images (3.9%) could not be read by the algorithm, and were categorized as inconclusive. In four subjects, neither eye provided an acceptable image: these four subjects were excluded from the analysis. This left 560 subjects for analysis (1084 eyes). The algorithm correctly diagnosed 531 of 560 cases. The sensitivity, specificity, and positive and negative predictive values were 91%, 97%, 94%, and 95% respectively. The Bosch DR Algorithm shows favorable sensitivity and specificity in diagnosing DR from non-mydriatic images, and can greatly simplify screening for DR. This also has major implications for telemedicine in the use of screening for retinopathy in patients with diabetes mellitus. |
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AbstractList | Diabetic retinopathy (DR) is a leading cause of blindness among working-age adults. Early diagnosis through effective screening programs is likely to improve vision outcomes. The ETDRS seven-standard-field 35-mm stereoscopic color retinal imaging (ETDRS) of the dilated eye is elaborate and requires mydriasis, and is unsuitable for screening. We evaluated an image analysis application for the automated diagnosis of DR from non-mydriatic single-field images. Patients suffering from diabetes for at least 5 years were included if they were 18 years or older. Patients already diagnosed with DR were excluded. Physiologic mydriasis was achieved by placing the subjects in a dark room. Images were captured using a Bosch Mobile Eye Care fundus camera. The images were analyzed by the Retinal Imaging Bosch DR Algorithm for the diagnosis of DR. All subjects also subsequently underwent pharmacological mydriasis and ETDRS imaging. Non-mydriatic and mydriatic images were read by ophthalmologists. The ETDRS readings were used as the gold standard for calculating the sensitivity and specificity for the software. 564 consecutive subjects (1128 eyes) were recruited from six centers in India. Each subject was evaluated at a single outpatient visit. Forty-four of 1128 images (3.9%) could not be read by the algorithm, and were categorized as inconclusive. In four subjects, neither eye provided an acceptable image: these four subjects were excluded from the analysis. This left 560 subjects for analysis (1084 eyes). The algorithm correctly diagnosed 531 of 560 cases. The sensitivity, specificity, and positive and negative predictive values were 91%, 97%, 94%, and 95% respectively. The Bosch DR Algorithm shows favorable sensitivity and specificity in diagnosing DR from non-mydriatic images, and can greatly simplify screening for DR. This also has major implications for telemedicine in the use of screening for retinopathy in patients with diabetes mellitus.Diabetic retinopathy (DR) is a leading cause of blindness among working-age adults. Early diagnosis through effective screening programs is likely to improve vision outcomes. The ETDRS seven-standard-field 35-mm stereoscopic color retinal imaging (ETDRS) of the dilated eye is elaborate and requires mydriasis, and is unsuitable for screening. We evaluated an image analysis application for the automated diagnosis of DR from non-mydriatic single-field images. Patients suffering from diabetes for at least 5 years were included if they were 18 years or older. Patients already diagnosed with DR were excluded. Physiologic mydriasis was achieved by placing the subjects in a dark room. Images were captured using a Bosch Mobile Eye Care fundus camera. The images were analyzed by the Retinal Imaging Bosch DR Algorithm for the diagnosis of DR. All subjects also subsequently underwent pharmacological mydriasis and ETDRS imaging. Non-mydriatic and mydriatic images were read by ophthalmologists. The ETDRS readings were used as the gold standard for calculating the sensitivity and specificity for the software. 564 consecutive subjects (1128 eyes) were recruited from six centers in India. Each subject was evaluated at a single outpatient visit. Forty-four of 1128 images (3.9%) could not be read by the algorithm, and were categorized as inconclusive. In four subjects, neither eye provided an acceptable image: these four subjects were excluded from the analysis. This left 560 subjects for analysis (1084 eyes). The algorithm correctly diagnosed 531 of 560 cases. The sensitivity, specificity, and positive and negative predictive values were 91%, 97%, 94%, and 95% respectively. The Bosch DR Algorithm shows favorable sensitivity and specificity in diagnosing DR from non-mydriatic images, and can greatly simplify screening for DR. This also has major implications for telemedicine in the use of screening for retinopathy in patients with diabetes mellitus. Diabetic retinopathy (DR) is a leading cause of blindness among working-age adults. Early diagnosis through effective screening programs is likely to improve vision outcomes. The ETDRS seven-standard-field 35-mm stereoscopic color retinal imaging (ETDRS) of the dilated eye is elaborate and requires mydriasis, and is unsuitable for screening. We evaluated an image analysis application for the automated diagnosis of DR from non-mydriatic single-field images. Patients suffering from diabetes for at least 5 years were included if they were 18 years or older. Patients already diagnosed with DR were excluded. Physiologic mydriasis was achieved by placing the subjects in a dark room. Images were captured using a Bosch Mobile Eye Care fundus camera. The images were analyzed by the Retinal Imaging Bosch DR Algorithm for the diagnosis of DR. All subjects also subsequently underwent pharmacological mydriasis and ETDRS imaging. Non-mydriatic and mydriatic images were read by ophthalmologists. The ETDRS readings were used as the gold standard for calculating the sensitivity and specificity for the software. 564 consecutive subjects (1128 eyes) were recruited from six centers in India. Each subject was evaluated at a single outpatient visit. Forty-four of 1128 images (3.9%) could not be read by the algorithm, and were categorized as inconclusive. In four subjects, neither eye provided an acceptable image: these four subjects were excluded from the analysis. This left 560 subjects for analysis (1084 eyes). The algorithm correctly diagnosed 531 of 560 cases. The sensitivity, specificity, and positive and negative predictive values were 91%, 97%, 94%, and 95% respectively. The Bosch DR Algorithm shows favorable sensitivity and specificity in diagnosing DR from non-mydriatic images, and can greatly simplify screening for DR. This also has major implications for telemedicine in the use of screening for retinopathy in patients with diabetes mellitus. |
Audience | Academic |
Author | K., S. Smitha Palsule, Aratee Kumar, Devesh Chandel, Shailja Bawankar, Pritam Shanbhag, Nita Sood, Suneet Dhawan, Bodhraj |
AuthorAffiliation | 5 Deenanath Mangeshkar Hospital, Pune, Maharashtra, India Soochow University Medical College, CHINA 2 Department of Ophthalmology, Dr. D.Y Patil Hospital & Research Centre, Mumbai, India 7 Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Johor Bahru, Malaysia 6 Think-i, Noida, Uttar Pradesh, India 4 NKP Salve Institute of Medical Sciences and Research Center, Nagpur, Maharashtra, India 3 KLES Dr. Prabhakar Kore Hospital & Research Centre, Belgavi, Karnataka, India 1 Sri Sankaradeva Nethralaya, Guwahati, India |
AuthorAffiliation_xml | – name: 5 Deenanath Mangeshkar Hospital, Pune, Maharashtra, India – name: 2 Department of Ophthalmology, Dr. D.Y Patil Hospital & Research Centre, Mumbai, India – name: 6 Think-i, Noida, Uttar Pradesh, India – name: 3 KLES Dr. Prabhakar Kore Hospital & Research Centre, Belgavi, Karnataka, India – name: 1 Sri Sankaradeva Nethralaya, Guwahati, India – name: Soochow University Medical College, CHINA – name: 7 Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Johor Bahru, Malaysia – name: 4 NKP Salve Institute of Medical Sciences and Research Center, Nagpur, Maharashtra, India |
Author_xml | – sequence: 1 givenname: Pritam surname: Bawankar fullname: Bawankar, Pritam – sequence: 2 givenname: Nita surname: Shanbhag fullname: Shanbhag, Nita – sequence: 3 givenname: S. Smitha surname: K. fullname: K., S. Smitha – sequence: 4 givenname: Bodhraj surname: Dhawan fullname: Dhawan, Bodhraj – sequence: 5 givenname: Aratee surname: Palsule fullname: Palsule, Aratee – sequence: 6 givenname: Devesh surname: Kumar fullname: Kumar, Devesh – sequence: 7 givenname: Shailja surname: Chandel fullname: Chandel, Shailja – sequence: 8 givenname: Suneet orcidid: 0000-0003-1125-1721 surname: Sood fullname: Sood, Suneet |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29281690$$D View this record in MEDLINE/PubMed |
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Copyright | COPYRIGHT 2017 Public Library of Science 2017 Bawankar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2017 Bawankar et al 2017 Bawankar et al |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: Investigators were given grant per completed subject by Think-i, Unit of Tenet Health Edutech Pvt. Ltd., the CRO conducting the study. DK and SC are employees of Think-i Unit of Tenet Health Edutech Pvt. Ltd., the CRO that was responsible for conducting the research on behalf of Robert Bosch Engineering and Business Solutions Private Limited. SS is a consultant for Cliniminds India, an affiliate of Think-i. The authors declare that this does not alter their adherence to PLOS ONE policies on sharing data and materials. Robert Bosch Engineering and Business Solutions Private Limited has applied for the patent for the product. None of the authors of this paper has any financial interest in any part of the company, the algorithm, or the product. |
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Snippet | Diabetic retinopathy (DR) is a leading cause of blindness among working-age adults. Early diagnosis through effective screening programs is likely to improve... |
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SubjectTerms | Adults Algorithms Automation Biology and Life Sciences Blindness Cameras Care and treatment Color vision Cost analysis Diabetes Diabetes mellitus Diabetic retinopathy Diagnosis Engineering and Technology Eye Eye (anatomy) Health sciences Hospitals Image analysis Image processing Medical personnel Medical tests Medicine and Health Sciences Patients Pharmacology Photography Physical Sciences Research and Analysis Methods Retina Retinal images Retinopathy Screening Sensitivity Sensitivity analysis Stereoscopy Telemedicine Testing |
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Title | Sensitivity and specificity of automated analysis of single-field non-mydriatic fundus photographs by Bosch DR Algorithm—Comparison with mydriatic fundus photography (ETDRS) for screening in undiagnosed diabetic retinopathy |
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