Quality assessment of anterior segment OCT images: Development and validation of quality criteria.

Artefact Image quality Optical coherence tomography

Journal

Photodiagnosis and photodynamic therapy
ISSN: 1873-1597
Titre abrégé: Photodiagnosis Photodyn Ther
Pays: Netherlands
ID NLM: 101226123

Informations de publication

Date de publication:
11 Nov 2023
Historique:
received: 06 10 2023
revised: 27 10 2023
accepted: 03 11 2023
pubmed: 13 11 2023
medline: 13 11 2023
entrez: 12 11 2023
Statut: aheadofprint

Résumé

The utility of medical imaging is dependant on image quality. We aimed to develop and validate quality criteria for ocular anterior segment optical coherence tomography (AS-OCT) images. We undertook a cross-sectional study using AS-OCT images from patients aged 6-16. A novel three-level grading system (good, limited or poor) was developed based on the presence of image artefact (categorised as lid, eyelash, cropping, glare, or movement artefact). Three independent experts graded 2825 images, with agreement assessed using confusion matrices and intraclass correlation coefficients (ICC) for each parameter. There was very good inter-grader IQA agreement assessing image quality with ICC 0.85 (95 %CI: 0.84-0.87). The most commonly occurring artefact was eyelash artefact (1008/2825 images, 36 %). Graders labelled 621/2825 (22 %) images as good and 384 (14 %) as poor. There was complete agreement at either end of the confusion matrix with no 'good' images labelled as 'poor' by other graders, and vice versa. Similarly, there was very good agreement when assessing presence of lash (0.96,0.94-0.98), movement (0.97,0.96-0.99), glare (0.82,0.80-0.84) and cropping (0.90,0.88-0.92). The novel image quality assessment criteria (IQAC) described here have good interobserver agreement overall, and excellent agreement on the differentiation between 'good' and 'poor' quality images. The large proportion of images graded as 'limited' suggests the need for refine this classification, using the specific IQAC features, for which we also report high interobserver agreement. These findings support the future potential for wider clinical and community care implementation of AS-OCT for the diagnosis and monitoring of ocular disease.

Sections du résumé

BACKGROUND BACKGROUND
The utility of medical imaging is dependant on image quality. We aimed to develop and validate quality criteria for ocular anterior segment optical coherence tomography (AS-OCT) images.
METHODS METHODS
We undertook a cross-sectional study using AS-OCT images from patients aged 6-16. A novel three-level grading system (good, limited or poor) was developed based on the presence of image artefact (categorised as lid, eyelash, cropping, glare, or movement artefact). Three independent experts graded 2825 images, with agreement assessed using confusion matrices and intraclass correlation coefficients (ICC) for each parameter.
RESULTS RESULTS
There was very good inter-grader IQA agreement assessing image quality with ICC 0.85 (95 %CI: 0.84-0.87). The most commonly occurring artefact was eyelash artefact (1008/2825 images, 36 %). Graders labelled 621/2825 (22 %) images as good and 384 (14 %) as poor. There was complete agreement at either end of the confusion matrix with no 'good' images labelled as 'poor' by other graders, and vice versa. Similarly, there was very good agreement when assessing presence of lash (0.96,0.94-0.98), movement (0.97,0.96-0.99), glare (0.82,0.80-0.84) and cropping (0.90,0.88-0.92).
CONCLUSIONS CONCLUSIONS
The novel image quality assessment criteria (IQAC) described here have good interobserver agreement overall, and excellent agreement on the differentiation between 'good' and 'poor' quality images. The large proportion of images graded as 'limited' suggests the need for refine this classification, using the specific IQAC features, for which we also report high interobserver agreement. These findings support the future potential for wider clinical and community care implementation of AS-OCT for the diagnosis and monitoring of ocular disease.

Identifiants

pubmed: 37952811
pii: S1572-1000(23)00613-0
doi: 10.1016/j.pdpdt.2023.103886
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103886

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest No conflicting relationship exists for any author.

Auteurs

Radhika Pooja Patel (RP)

Imperial College London, United Kingdom; Population, Policy and Practice Department, UCL Great Ormond Street Institute of Child Health, London, United Kingdom.

Harry Petrushkin (H)

Moorfields Eye Hospital, London, United Kingdom; Great Ormond Street Hospital, London, United Kingdom.

Katie Etherton (K)

Moorfields Eye Hospital, London, United Kingdom.

Katherine Terence (K)

Population, Policy and Practice Department, UCL Great Ormond Street Institute of Child Health, London, United Kingdom.

Andrew D Dick (AD)

UCL Institute of Ophthalmology, London, United Kingdom; Bristol University, Bristol, United Kingdom; NIHR Moorfields Biomedical Research Centre, London, United Kingdom.

Jugnoo S Rahi (JS)

Population, Policy and Practice Department, UCL Great Ormond Street Institute of Child Health, London, United Kingdom; Moorfields Eye Hospital, London, United Kingdom; Great Ormond Street Hospital, London, United Kingdom; NIHR Great Ormond Street Hospital Biomedical Research, London, United Kingdom; UCL Institute of Ophthalmology, London, United Kingdom; NIHR Moorfields Biomedical Research Centre, London, United Kingdom; Ulverscroft Vision Research Group, London, United Kingdom.

Ameenat Lola Solebo (AL)

Population, Policy and Practice Department, UCL Great Ormond Street Institute of Child Health, London, United Kingdom; Great Ormond Street Hospital, London, United Kingdom; NIHR Great Ormond Street Hospital Biomedical Research, London, United Kingdom; NIHR Moorfields Biomedical Research Centre, London, United Kingdom. Electronic address: a.solebo@ucl.ac.uk.

Classifications MeSH