Dataset Information

Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris.

ABSTRACT:

Purpose

The purpose of this study was to evaluate the diagnostic performance of deep learning (DL) anterior segment optical coherence tomography (AS-OCT) as a plateau iris prediction model.

Design

We used a cross-sectional study of the development and validation of the DL system.

Methods

We conducted a collaboration between a referral eye center and an informative technology department. The study enrolled 179 eyes from 142 patients with primary angle closure disease (PACD). All patients had remaining appositional angle after iridotomy. Each eye was scanned in four quadrants for both AS-OCT and ultrasound biomicroscopy (UBM). A DL algorithm for plateau iris prediction of AS-OCT was developed from training datasets and was validated in test sets. Sensitivity, specificity, and area under the receiver operating characteristics curve (AUC-ROC) of the DL for predicting plateau iris were evaluated, using UBM as a reference standard.

Results

Total paired images of AS-OCT and UBM were from 716 quadrants. Plateau iris was observed with UBM in 276 (38.5%) quadrants. Trainings dataset with data augmentation were used to develop an algorithm from 2500 images, and the test set was validated from 160 images. AUC-ROC was 0.95 (95% confidence interval [CI] = 0.91 to 0.99), sensitivity was 87.9%, and specificity was 97.6%.

Conclusions

DL revealed a high performance in predicting plateau iris on the noncontact AS-OCT images.

Translational relevance

This work could potentially assist clinicians in more practically detecting this nonpupillary block mechanism of PACD.

SUBMITTER: Wanichwecharungruang B

PROVIDER: S-EPMC7794268 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris.

Wanichwecharungruang Boonsong B Kaothanthong Natsuda N Pattanapongpaiboon Warisara W Chantangphol Pantid P Seresirikachorn Kasem K Srisuwanporn Chaniya C Parivisutt Nucharee N Grzybowski Andrzej A Theeramunkong Thanaruk T Ruamviboonsuk Paisan P

Translational vision science & technology 20210106 1

<h4>Purpose</h4>The purpose of this study was to evaluate the diagnostic performance of deep learning (DL) anterior segment optical coherence tomography (AS-OCT) as a plateau iris prediction model.<h4>Design</h4>We used a cross-sectional study of the development and validation of the DL system.<h4>Methods</h4>We conducted a collaboration between a referral eye center and an informative technology department. The study enrolled 179 eyes from 142 patients with primary angle closure disease (PACD). ...[more]

PMID: 33505774

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Project description:Fuchs uveitis (FU) is a chronic and often unilateral ocular inflammation and characteristic iris atrophic changes, other than heterochromia, are common in FU and are key to the correct diagnosis in many cases. With the advent of anterior segment optical coherence tomography (AS-OCT), some investigators attempted to quantitatively study these atrophic changes; mostly by introducing various methods to measure iris thickness in AS-OCT images. We aimed to present an automated method in an observational case series to measure the smoothness index (SI) of the iris surface in AS-OCT images. The ratio of the length of the straight line connecting the most peripheral and central points of the anterior iris border (in nasal and temporal sides) to the actual length of this border on AS-OCT images, was defined as SI. In a uveitis referral center, twenty-two eyes of 11 patients with unilateral Fuchs uveitis (FU) (7 female) and 22 eyes of 11 healthy control subjects underwent AS-OCT imaging. Image J and a newly developed MATLAB algorithm were used for manual and automated SI measurements, respectively. Agreement between manual and automated measurements was evaluated with Bland-Altman analysis and interclass correlation coefficient. The inter-eye difference of SI was compared between the FU group and the control group. Automated mean overall SI was 0.868 ± 0.037 and 0.840 ± 0.039 in FU and healthy fellow eyes, respectively (estimated mean difference = - 0.028, 95% CI [- 0.038, - 0.018], p < 0.001). Bland- Altman plots showed good agreement between two methods in both healthy and FU eyes. The interclass correlation coefficient between the manual and automated measurements in the FU and healthy fellow eyes was 0.958 and 0.964, respectively. The inter-eye difference of overall SI was 0.029 ± 0.015 and 0.012 ± 0.008 in FU group and control group, respectively (p = 0.01). We concluded that the automated algorithm can rapidly and conveniently measure SI with results comparable to the manual method.

Dataset Information

Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris.

Purpose

Design

Methods

Results

Conclusions

Translational relevance

Publications

Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris.

Similar Datasets

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