Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting
Aim Diabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Auro...
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| Vydáno v: | Acta diabetologica Ročník 60; číslo 8; s. 1083 - 1088 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Milan
Springer Milan
01.08.2023
Springer Nature B.V |
| Témata: | |
| ISSN: | 1432-5233, 0940-5429, 1432-5233 |
| On-line přístup: | Získat plný text |
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| Abstract | Aim
Diabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Aurora, Optomed, Oulu, Finland) in a first line screening of a real-world clinical setting.
Methods
It was an observational cross-sectional study including 256 eyes of 256 consecutive patients. The sample included both diabetic and non-diabetic patients. Each patient received a 50°, macula centered, non-mydriatic fundus photography and, after pupil dilation, a complete fundus examination by an experienced retina specialist. All images were after analyzed by a skilled operator and by the AI algorithm. The results of the three procedures were then compared.
Results
The agreement between the operator-based fundus analysis in bio-microscopy and the fundus photographs was of 100%. Among the DR patients the AI algorithm revealed signs of DR in 121 out of 125 subjects (96.8%) and no signs of DR 122 of the 126 non-diabetic patients (96.8%). The sensitivity of the AI algorithm was 96.8% and the specificity 96.8%. The overall concordance coefficient k (95% CI) between AI-based assessment and fundus biomicroscopy was 0.935 (0.891–0.979).
Conclusions
The Aurora fundus camera is effective in a first line screening of DR. Its in-built AI software can be considered a reliable tool to automatically identify the presence of signs of DR and therefore employed as a promising resource in large screening campaigns. |
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| AbstractList | AimDiabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Aurora, Optomed, Oulu, Finland) in a first line screening of a real-world clinical setting.MethodsIt was an observational cross-sectional study including 256 eyes of 256 consecutive patients. The sample included both diabetic and non-diabetic patients. Each patient received a 50°, macula centered, non-mydriatic fundus photography and, after pupil dilation, a complete fundus examination by an experienced retina specialist. All images were after analyzed by a skilled operator and by the AI algorithm. The results of the three procedures were then compared.ResultsThe agreement between the operator-based fundus analysis in bio-microscopy and the fundus photographs was of 100%. Among the DR patients the AI algorithm revealed signs of DR in 121 out of 125 subjects (96.8%) and no signs of DR 122 of the 126 non-diabetic patients (96.8%). The sensitivity of the AI algorithm was 96.8% and the specificity 96.8%. The overall concordance coefficient k (95% CI) between AI-based assessment and fundus biomicroscopy was 0.935 (0.891–0.979).ConclusionsThe Aurora fundus camera is effective in a first line screening of DR. Its in-built AI software can be considered a reliable tool to automatically identify the presence of signs of DR and therefore employed as a promising resource in large screening campaigns. Diabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Aurora, Optomed, Oulu, Finland) in a first line screening of a real-world clinical setting. It was an observational cross-sectional study including 256 eyes of 256 consecutive patients. The sample included both diabetic and non-diabetic patients. Each patient received a 50°, macula centered, non-mydriatic fundus photography and, after pupil dilation, a complete fundus examination by an experienced retina specialist. All images were after analyzed by a skilled operator and by the AI algorithm. The results of the three procedures were then compared. The agreement between the operator-based fundus analysis in bio-microscopy and the fundus photographs was of 100%. Among the DR patients the AI algorithm revealed signs of DR in 121 out of 125 subjects (96.8%) and no signs of DR 122 of the 126 non-diabetic patients (96.8%). The sensitivity of the AI algorithm was 96.8% and the specificity 96.8%. The overall concordance coefficient k (95% CI) between AI-based assessment and fundus biomicroscopy was 0.935 (0.891-0.979). The Aurora fundus camera is effective in a first line screening of DR. Its in-built AI software can be considered a reliable tool to automatically identify the presence of signs of DR and therefore employed as a promising resource in large screening campaigns. Aim Diabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Aurora, Optomed, Oulu, Finland) in a first line screening of a real-world clinical setting. Methods It was an observational cross-sectional study including 256 eyes of 256 consecutive patients. The sample included both diabetic and non-diabetic patients. Each patient received a 50°, macula centered, non-mydriatic fundus photography and, after pupil dilation, a complete fundus examination by an experienced retina specialist. All images were after analyzed by a skilled operator and by the AI algorithm. The results of the three procedures were then compared. Results The agreement between the operator-based fundus analysis in bio-microscopy and the fundus photographs was of 100%. Among the DR patients the AI algorithm revealed signs of DR in 121 out of 125 subjects (96.8%) and no signs of DR 122 of the 126 non-diabetic patients (96.8%). The sensitivity of the AI algorithm was 96.8% and the specificity 96.8%. The overall concordance coefficient k (95% CI) between AI-based assessment and fundus biomicroscopy was 0.935 (0.891–0.979). Conclusions The Aurora fundus camera is effective in a first line screening of DR. Its in-built AI software can be considered a reliable tool to automatically identify the presence of signs of DR and therefore employed as a promising resource in large screening campaigns. Diabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Aurora, Optomed, Oulu, Finland) in a first line screening of a real-world clinical setting.AIMDiabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Aurora, Optomed, Oulu, Finland) in a first line screening of a real-world clinical setting.It was an observational cross-sectional study including 256 eyes of 256 consecutive patients. The sample included both diabetic and non-diabetic patients. Each patient received a 50°, macula centered, non-mydriatic fundus photography and, after pupil dilation, a complete fundus examination by an experienced retina specialist. All images were after analyzed by a skilled operator and by the AI algorithm. The results of the three procedures were then compared.METHODSIt was an observational cross-sectional study including 256 eyes of 256 consecutive patients. The sample included both diabetic and non-diabetic patients. Each patient received a 50°, macula centered, non-mydriatic fundus photography and, after pupil dilation, a complete fundus examination by an experienced retina specialist. All images were after analyzed by a skilled operator and by the AI algorithm. The results of the three procedures were then compared.The agreement between the operator-based fundus analysis in bio-microscopy and the fundus photographs was of 100%. Among the DR patients the AI algorithm revealed signs of DR in 121 out of 125 subjects (96.8%) and no signs of DR 122 of the 126 non-diabetic patients (96.8%). The sensitivity of the AI algorithm was 96.8% and the specificity 96.8%. The overall concordance coefficient k (95% CI) between AI-based assessment and fundus biomicroscopy was 0.935 (0.891-0.979).RESULTSThe agreement between the operator-based fundus analysis in bio-microscopy and the fundus photographs was of 100%. Among the DR patients the AI algorithm revealed signs of DR in 121 out of 125 subjects (96.8%) and no signs of DR 122 of the 126 non-diabetic patients (96.8%). The sensitivity of the AI algorithm was 96.8% and the specificity 96.8%. The overall concordance coefficient k (95% CI) between AI-based assessment and fundus biomicroscopy was 0.935 (0.891-0.979).The Aurora fundus camera is effective in a first line screening of DR. Its in-built AI software can be considered a reliable tool to automatically identify the presence of signs of DR and therefore employed as a promising resource in large screening campaigns.CONCLUSIONSThe Aurora fundus camera is effective in a first line screening of DR. Its in-built AI software can be considered a reliable tool to automatically identify the presence of signs of DR and therefore employed as a promising resource in large screening campaigns. |
| Author | Mariotti, Cesare Fruttini, Daniela Chhablani, Jay Nicolai, Michele Lupidi, Marco Danieli, Luca Lassandro, Nicola |
| Author_xml | – sequence: 1 givenname: Marco orcidid: 0000-0002-6817-2488 surname: Lupidi fullname: Lupidi, Marco email: marcomed2@gmail.com organization: Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche, Fondazione per la Macula Onlus, Dipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno-Infantili (DINOGMI), University Eye Clinic – sequence: 2 givenname: Luca surname: Danieli fullname: Danieli, Luca organization: IRCCS - Fondazione Bietti – sequence: 3 givenname: Daniela surname: Fruttini fullname: Fruttini, Daniela organization: Department of Medicine and Surgery, University of Perugia, S. Maria della Misericordia Hospital – sequence: 4 givenname: Michele surname: Nicolai fullname: Nicolai, Michele organization: Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche – sequence: 5 givenname: Nicola surname: Lassandro fullname: Lassandro, Nicola organization: Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche – sequence: 6 givenname: Jay surname: Chhablani fullname: Chhablani, Jay organization: Department of Ophthalmology, UPMC Eye Center, University of Pittsburgh – sequence: 7 givenname: Cesare surname: Mariotti fullname: Mariotti, Cesare organization: Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37154944$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1080_17434440_2025_2508877 crossref_primary_10_3390_diagnostics14171846 crossref_primary_10_1016_j_pdpdt_2024_103965 crossref_primary_10_3389_fmed_2024_1481088 crossref_primary_10_1080_07853890_2024_2352018 crossref_primary_10_1007_s11042_024_18309_6 crossref_primary_10_1097_ICU_0000000000001084 crossref_primary_10_1089_dia_2024_2505 crossref_primary_10_1186_s40662_024_00389_y crossref_primary_10_1055_a_2620_1956 crossref_primary_10_1080_17469899_2025_2503792 crossref_primary_10_3389_fmed_2025_1519768 crossref_primary_10_1038_s41433_024_03354_0 |
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| Issue | 8 |
| Keywords | Handheld fundus cameras Diabetic retinopathy Screening Artificial intelligence |
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| References_xml | – volume: 8 start-page: 337 issue: 4 year: 2020 end-page: 347 ident: CR5 article-title: Screening for diabetic retinopathy: new perspectives and challenges publication-title: Lancet Diabetes Endocrinol doi: 10.1016/S2213-8587(19)30411-5 – volume: 15 start-page: 620 issue: 4 year: 2022 end-page: 627 ident: CR19 article-title: A new handheld fundus camera combined with visual artificial intelligence facilitates diabetic retinopathy screening publication-title: Int J Ophthalmol doi: 10.18240/ijo.2022.04.16 – year: 2014 ident: CR17 article-title: Screening for diabetic retinopathy in the central Region of Portugal. Added Value of Automated 'Disease/No Disease' grading publication-title: Ophthalmologica doi: 10.1159/000368426 – volume: 11 start-page: 2352 issue: 9 year: 2022 ident: CR14 article-title: Handheld fundus camera for diabetic retinopathy screening: a comparison study with table-top fundus camera in real-life setting publication-title: J Clin Med doi: 10.3390/jcm11092352 – volume: 30 start-page: 17 issue: 2 year: 2015 ident: CR8 article-title: Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss publication-title: Eye Vis doi: 10.1186/s40662-015-0026-2 – volume: 57 start-page: 5200 issue: 13 year: 2016 end-page: 5206 ident: CR16 article-title: Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning publication-title: Invest Ophthalmol Vis Sci doi: 10.1167/iovs.16-19964 – volume: 67 start-page: 1004 issue: 7 year: 2019 end-page: 1009 ident: CR20 article-title: Artificial intelligence in diabetic retinopathy: a natural step to the future publication-title: Indian J Ophthalmol doi: 10.4103/ijo.IJO_1989_18 – volume: 148 start-page: 111 issue: 1 year: 2009 end-page: 118 ident: CR9 article-title: Screening for diabetic retinopathy: 1 and 3 nonmydriatic 45-degree digital fundus photographs vs 7 standard early treatment diabetic retinopathy study fields publication-title: Am J Ophthalmol doi: 10.1016/j.ajo.2009.02.031 – volume: 21 start-page: 635 issue: 11 year: 2019 end-page: 643 ident: CR18 article-title: The value of automated diabetic retinopathy screening with the EyeArt system: a study of more than 100,000 consecutive encounters from people with diabetes publication-title: Diabetes Technol Ther doi: 10.1089/dia.2019.0164 – volume: 72 start-page: 100759 year: 2019 ident: CR21 article-title: Deep learning in ophthalmology: The technical and clinical considerations publication-title: Prog Retin Eye Res doi: 10.1016/j.preteyeres.2019.04.003 – volume: 376 start-page: 124 issue: 9735 year: 2010 end-page: 136 ident: CR1 article-title: Diabetic retinopathy publication-title: Lancet doi: 10.1016/S0140-6736(09)62124-3 – volume: 238 start-page: 89 issue: 1–2 year: 2017 end-page: 99 ident: CR12 article-title: Predictors of photographic quality with a handheld nonmydriatic fundus camera used for screening of vision-threatening diabetic retinopathy publication-title: Ophthalmologica doi: 10.1159/000475773 – volume: 35 start-page: 11 issue: 1 year: 2020 end-page: 23 ident: CR3 article-title: Prevalence, incidence and future projection of diabetic eye disease in Europe: a systematic review and meta-analysis publication-title: Eur J Epidemiol doi: 10.1007/s10654-019-00560-z – volume: 33 start-page: 159 issue: 1 year: 1977 end-page: 174 ident: CR7 article-title: The measurement of observer agreement for categorical data publication-title: Biometrics doi: 10.2307/2529310 – year: 2019 ident: CR11 article-title: Artificial intelligence in diabetic eye disease 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handheld fundus cameras for eye disease: a systematic review and pooled analysis publication-title: Surv Ophthalmol doi: 10.1016/j.survophthal.2021.11.006 – volume: 110 start-page: 1677 issue: 9 year: 2003 end-page: 1682 ident: CR6 article-title: Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales publication-title: Ophthalmology doi: 10.1016/S0161-6420(03)00475-5 – volume: 99 start-page: e1415 issue: 8 year: 2021 end-page: e1420 ident: CR13 article-title: Handheld fundus camera performance, image quality and outcomes of diabetic retinopathy grading in a pilot screening study publication-title: Acta Ophthalmol doi: 10.1111/aos.14850 – volume: 35 start-page: 11 issue: 1 year: 2020 ident: 2104_CR3 publication-title: Eur J Epidemiol doi: 10.1007/s10654-019-00560-z – volume: 72 start-page: 100759 year: 2019 ident: 2104_CR21 publication-title: Prog Retin Eye Res doi: 10.1016/j.preteyeres.2019.04.003 – volume: 8 start-page: 337 issue: 4 year: 2020 ident: 2104_CR5 publication-title: Lancet Diabetes Endocrinol doi: 10.1016/S2213-8587(19)30411-5 – year: 2019 ident: 2104_CR11 publication-title: Asia Pac J Ophthalmol doi: 10.22608/APO.201976 – volume: 67 start-page: 1531 issue: 5 year: 2022 ident: 2104_CR4 publication-title: Surv Ophthalmol doi: 10.1016/j.survophthal.2021.11.006 – volume: 30 start-page: 17 issue: 2 year: 2015 ident: 2104_CR8 publication-title: Eye Vis doi: 10.1186/s40662-015-0026-2 – year: 2014 ident: 2104_CR17 publication-title: Ophthalmologica doi: 10.1159/000368426 – volume: 111 start-page: 1055 issue: 5 year: 2004 ident: 2104_CR10 publication-title: Ophthalmology doi: 10.1016/j.ophtha.2004.02.004 – volume: 57 start-page: 5200 issue: 13 year: 2016 ident: 2104_CR16 publication-title: Invest Ophthalmol Vis Sci doi: 10.1167/iovs.16-19964 – volume: 99 start-page: e1415 issue: 8 year: 2021 ident: 2104_CR13 publication-title: Acta Ophthalmol doi: 10.1111/aos.14850 – volume: 131 start-page: 351 issue: 3 year: 2013 ident: 2104_CR15 publication-title: JAMA Ophthalmol doi: 10.1001/jamaophthalmol.2013.1743 – volume: 67 start-page: 1004 issue: 7 year: 2019 ident: 2104_CR20 publication-title: Indian J Ophthalmol doi: 10.4103/ijo.IJO_1989_18 – volume: 11 start-page: 2352 issue: 9 year: 2022 ident: 2104_CR14 publication-title: J Clin Med doi: 10.3390/jcm11092352 – volume: 21 start-page: 635 issue: 11 year: 2019 ident: 2104_CR18 publication-title: Diabetes Technol Ther doi: 10.1089/dia.2019.0164 – volume: 15 start-page: 620 issue: 4 year: 2022 ident: 2104_CR19 publication-title: Int J Ophthalmol doi: 10.18240/ijo.2022.04.16 – volume: 148 start-page: 111 issue: 1 year: 2009 ident: 2104_CR9 publication-title: Am J Ophthalmol doi: 10.1016/j.ajo.2009.02.031 – volume: 376 start-page: 124 issue: 9735 year: 2010 ident: 2104_CR1 publication-title: Lancet doi: 10.1016/S0140-6736(09)62124-3 – volume: 110 start-page: 1677 issue: 9 year: 2003 ident: 2104_CR6 publication-title: Ophthalmology doi: 10.1016/S0161-6420(03)00475-5 – volume: 238 start-page: 89 issue: 1–2 year: 2017 ident: 2104_CR12 publication-title: Ophthalmologica doi: 10.1159/000475773 – volume: 5 start-page: e1221 issue: 12 year: 2017 ident: 2104_CR2 publication-title: Lancet Glob Health doi: 10.1016/S2214-109X(17)30393-5 – volume: 33 start-page: 159 issue: 1 year: 1977 ident: 2104_CR7 publication-title: Biometrics doi: 10.2307/2529310 |
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Diabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst... Diabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst... AimDiabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst... |
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