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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Veröffentlicht in:Acta diabetologica Jg. 60; H. 8; S. 1083 - 1088
Hauptverfasser: Lupidi, Marco, Danieli, Luca, Fruttini, Daniela, Nicolai, Michele, Lassandro, Nicola, Chhablani, Jay, Mariotti, Cesare
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Milan Springer Milan 01.08.2023
Springer Nature B.V
Schlagworte:
Agreements
Algorithms
Archives & records
Artificial Intelligence
Automation
Cameras
Cataracts
Coronaviruses
COVID-19
Cross-Sectional Studies
Diabetes
Diabetes Mellitus
Diabetic retinopathy
Diabetic Retinopathy - diagnosis
Humans
Internal Medicine
Low income groups
Mass Screening - methods
Medicine
Medicine & Public Health
Metabolic Diseases
Original
Original Article
Patients
Photography
Retina
Retinopathy
Signs
Software
Statistical analysis
Finland
Handheld fundus cameras
Diabetic retinopathy
Screening
Artificial intelligence
ISSN:1432-5233, 0940-5429, 1432-5233
Online-Zugang:Volltext
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Zusammenfassung: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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This article belongs to the Topical Collection “Diabetic Eye Disease”, managed By Giuseppe Querques.
ISSN:1432-5233
0940-5429
1432-5233
DOI:10.1007/s00592-023-02104-0