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DOI: 10.1055/a-1484-9678
Evaluation der Implementierung eines zugelassenen Künstliche-Intelligenz-Systems zur Erkennung der diabetischen Retinopathie
Evaluation of the implementation of an approved artificial intelligence system for the detection of diabetic retinopathy
Zusammenfassung
Einleitung Ziel der Studie war die Evaluation der Genauigkeit einer auf einem Künstliche-Intelligenz-System (KI) basierenden Bewertung von Fundusfotografien im Vergleich zum Augenarzt in Bezug auf das diabetische Retinopathie-Screening in einer internistisch geführten Klinik. Zudem erfolgte die Erhebung der Gesamtuntersuchungsdauer wie auch der Patienten- und Untersucherzufriedenheit.
Methoden Im Rahmen der Studie erhielten 112 ambulante Patienten eine Fundusfotografie mit automatisierter Diagnose der diabetischen Retinopathie (DR) über das IDx-DR-System (Digital Diagnostics). Die Aufnahmen erfolgten mit der Kamera Topcon TRC-NW400 (Topcon Corp. Japan). Einschlusskriterium war die Diagnose eines Diabetes mellitus Typ 1, 2 oder 3. Bei Patienten, bei denen keine Aufnahme mit ausreichender Qualität in Miosis durchgeführt werden konnte, erfolgte die Aufnahme in Mydriasis.
Ergebnisse Von 112 Patienten konnte bei 107 Patienten (95,5 %) durch das Grading mittels IDx-DR, anhand der Fundusaufnahmen, eine Analyse durchgeführt werden – vs. bei 103 Patienten (91,9 %) durch das Grading derselben, hochauflösenden Fundusaufnahmen durch Augenärzte. Bei den verbleibenden Patienten war eine Beurteilung allein durch die Funduskopie in Mydriasis möglich. Es zeigte sich eine hochsignifikante Korrelation bezüglich der Einschätzung der Schwere der diabetischen Retinopathie zwischen Untersucher und dem IDx-DR-System (Correlation coefficient (r) = 0,8738; p < 0,0001). Die Patientenzufriedenheit lag bei 4,5 ± 0,6 [1–5], die Gesamtdauer der Untersuchung in Miosis lag im Mittel bei 3:04 ± 0:28 [min:sek].
Schlussfolgerung Das Retinopathiescreening mittels IDx-DR ermöglicht die automatisierte, zeitnahe und zuverlässige Beurteilung bzgl. des Vorliegens einer diabetischen Retinopathie mit einem robusten technischen und klinischen Arbeitsfluss, der mit einer hohen Patientenzufriedenheit einhergeht.
Abstract
Introduction The aim of this study was to evaluate the accuracy of an artificial intelligence (AI)-based analysis of fundus photographs compared to ophthalmologists in diabetic retinopathy screening in an internal medicine clinic. In addition, the total examination time as well as the patient and examiner satisfaction were surveyed.
Methods In the study, 112 outpatients received fundus photography with automated diagnosis of diabetic retinopathy (DR) via the IDx-DR system (Digital Diagnostics). The images were taken with the Topcon TRC-NW400 camera (Topcon Corp. Japan). Inclusion criterion was a diagnosis of diabetes mellitus type 1, 2, or 3. Patients who could not be imaged with sufficient quality in miosis were imaged in mydriasis.
Results Of 112 patients, analysis was possible in 107 patients (95.5 %) by grading by IDx-DR, based on fundus images – vs. 103 patients (91.9 %) by grading by ophthalmologists, based on the same, high-resolution fundus images. In the remaining patients, grading was possible by funduscopy alone. There was a highly significant correlation regarding the assessment of the severity of diabetic retinopathy between the examiner and the IDx-DR system (Correlation coefficient (r) = 0.8738; p < 0.0001). Patient satisfaction was 4.5 ± 0.6 [1–5], and total examination time in miosis averaged 3:04 ± 0:28 [min:sec].
Conclusion Retinopathy screening using IDx-DR enables automated diagnosis based on fundus photographs with a robust, technical and clinical workflow that allows timely and reliable assessment of diabetic retinopathy and is associated with high patient satisfaction.
Publication History
Received: 09 December 2020
Accepted: 16 April 2021
Article published online:
28 May 2021
© 2021. Thieme. All rights reserved.
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