Impact of lens autofluorescence and opacification on retinal imaging.
Imaging
Lens and zonules
Macula
Retina
Journal
BMJ open ophthalmology
ISSN: 2397-3269
Titre abrégé: BMJ Open Ophthalmol
Pays: England
ID NLM: 101714806
Informations de publication
Date de publication:
29 Apr 2024
29 Apr 2024
Historique:
received:
28
12
2023
accepted:
28
03
2024
medline:
30
4
2024
pubmed:
30
4
2024
entrez:
29
4
2024
Statut:
epublish
Résumé
Retinal imaging, including fundus autofluorescence (FAF), strongly depends on the clearness of the optical media. Lens status is crucial since the ageing lens has both light-blocking and autofluorescence (AF) properties that distort image analysis. Here, we report both lens opacification and AF metrics and the effect on automated image quality assessment. 227 subjects (range: 19-89 years old) received quantitative AF of the lens (LQAF), Scheimpflug, anterior chamber optical coherence tomography as well as blue/green FAF (BAF/GAF), and infrared (IR) imaging. LQAF values, the Pentacam Nucleus Staging score and the relative lens reflectivity were extracted to estimate lens opacification. Mean opinion scores of FAF and IR image quality were compiled by medical readers. A regression model for predicting image quality was developed using a convolutional neural network (CNN). Correlation analysis was conducted to assess the association of lens scores, with retinal image quality derived from human or CNN annotations. Retinal image quality was generally high across all imaging modalities (IR (8.25±1.99) >GAF >BAF (6.6±3.13)). CNN image quality prediction was excellent (average mean absolute error (MAE) 0.9). Predictions were comparable to human grading. Overall, LQAF showed the highest correlation with image quality grading criteria for all imaging modalities (eg, Pearson correlation±CI -0.35 (-0.50 to 0.18) for BAF/LQAF). BAF image quality was most vulnerable to an increase in lenticular metrics, while IR (-0.19 (-0.38 to 0.01)) demonstrated the highest resilience. The use of CNN-based retinal image quality assessment achieved excellent results. The study highlights the vulnerability of BAF to lenticular remodelling. These results can aid in the development of cut-off values for clinical studies, ensuring reliable data collection for the monitoring of retinal diseases.
Sections du résumé
BACKGROUND
BACKGROUND
Retinal imaging, including fundus autofluorescence (FAF), strongly depends on the clearness of the optical media. Lens status is crucial since the ageing lens has both light-blocking and autofluorescence (AF) properties that distort image analysis. Here, we report both lens opacification and AF metrics and the effect on automated image quality assessment.
METHODS
METHODS
227 subjects (range: 19-89 years old) received quantitative AF of the lens (LQAF), Scheimpflug, anterior chamber optical coherence tomography as well as blue/green FAF (BAF/GAF), and infrared (IR) imaging. LQAF values, the Pentacam Nucleus Staging score and the relative lens reflectivity were extracted to estimate lens opacification. Mean opinion scores of FAF and IR image quality were compiled by medical readers. A regression model for predicting image quality was developed using a convolutional neural network (CNN). Correlation analysis was conducted to assess the association of lens scores, with retinal image quality derived from human or CNN annotations.
RESULTS
RESULTS
Retinal image quality was generally high across all imaging modalities (IR (8.25±1.99) >GAF >BAF (6.6±3.13)). CNN image quality prediction was excellent (average mean absolute error (MAE) 0.9). Predictions were comparable to human grading. Overall, LQAF showed the highest correlation with image quality grading criteria for all imaging modalities (eg, Pearson correlation±CI -0.35 (-0.50 to 0.18) for BAF/LQAF). BAF image quality was most vulnerable to an increase in lenticular metrics, while IR (-0.19 (-0.38 to 0.01)) demonstrated the highest resilience.
CONCLUSION
CONCLUSIONS
The use of CNN-based retinal image quality assessment achieved excellent results. The study highlights the vulnerability of BAF to lenticular remodelling. These results can aid in the development of cut-off values for clinical studies, ensuring reliable data collection for the monitoring of retinal diseases.
Identifiants
pubmed: 38684375
pii: bmjophth-2023-001628
doi: 10.1136/bmjophth-2023-001628
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: LvdE: Heidelberg Engineering (R). GCR: BONFOR (O-137.0031, Faculty of Medicine, University of Bonn, Bonn, Germany). MV: None. JH: None. MS: Gerok Research Grant (BONFOR O-137.0030, Faculty of Medicine, University of Bonn, Bonn, Germany), Carl Zeiss MedicTec AG (F), CenterVue (F), Heidelberg Engineering (F). MP: Appellis (C), Boehringer Ingelheim (C), Daiichi Sankyo (C). MF: none. KRS: none. FGH: Acucela (C,F), Allergan (F), Apellis (C, F), Bayer (C, F), Boehringer-Ingelheim (C), Bioeq/Formycon (F,C), CenterVue (F), Ellex (F), Roche/Genentech (C,F), Geuder (C,F), Graybug (C), Gyroscope (C), Heidelberg Engineering (C,F), IvericBio (C, F), Kanghong (C,F), LinBioscience (C), NightStarX (F), Novartis (C,F), Optos (F), Oxurion (C), Pixium Vision (C,F), Oxurion (C), Stealth BioTherapeutics (C), Zeiss (F,C). TA: Apellis (C), Heidelberg Engineering (R), Roche (C), Novartis (C), Novartis (R), Bayer (C), Nidek (R).