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The subjectivity of objective evaluation of torsion on fundus photographs by practicing strabismologists.

ABSTRACT: To assess the variability of assessing the ocular torsion on fundus photographs among strabismus surgeons.This was a prospective, noninterventional, clinical trial involving 16 trained and experienced squint surgeons participated in the study. Two videos were prepared of a total of 10 fundus pictures with or without lines for disc foveal angle. The first video had a 4 s viewing time for each fundus image. The second video had the disc foveal lines drawn and a similar 4 s viewing time for each image. The participants were asked to grade the torsion. The primary outcome measure was to assess the agreement between the raters for ocular torsion measurement. Difference in the response of observers from the standard response was the secondary outcome measure.A 4 s viewing time was given to mimic the exposure time in the clinic while assessing torsion in a patient. Large variability was found among the responses. The kappa test was done for comparing the agreement between various observers which ranged from slight to fair (<0.40). There was no difference in torsion grading in 30.6% and 26.3% responses in the first and second video from the standard response, respectively. When a limit of ±1 grade was taken as acceptable for the responses, 66.2% for the first and 68.7% for the second video respectively were similar to standard response.There is wide variability in assessing ocular torsion by fundus photography. The level of accuracy does increase with marking the line on photographs but still remains unreliable.

SUBMITTER: Jethani J

PROVIDER: S-EPMC6113826 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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The subjectivity of objective evaluation of torsion on fundus photographs by practicing strabismologists.

Jethani Jitendra J Dave Paaraj P

Indian journal of ophthalmology 20180901 9

<h4>Purpose</h4>To assess the variability of assessing the ocular torsion on fundus photographs among strabismus surgeons.<h4>Methods</h4>This was a prospective, noninterventional, clinical trial involving 16 trained and experienced squint surgeons participated in the study. Two videos were prepared of a total of 10 fundus pictures with or without lines for disc foveal angle. The first video had a 4 s viewing time for each fundus image. The second video had the disc foveal lines drawn and a simi ...[more]

PMID: 30127145

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Project description:PurposePrevious approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative spectral-domain (SD) OCT data to train a DL algorithm to quantify glaucomatous structural damage on optic disc photographs.DesignCross-sectional study.ParticipantsA total of 32 820 pairs of optic disc photographs and SD OCT retinal nerve fiber layer (RNFL) scans from 2312 eyes of 1198 participants.MethodsThe sample was divided randomly into validation plus training (80%) and test (20%) sets, with randomization performed at the patient level. A DL convolutional neural network was trained to assess optic disc photographs and predict SD OCT average RNFL thickness.Main outcome measuresThe DL algorithm performance was evaluated in the test sample by evaluating correlation and agreement between the predictions and actual SD OCT measurements. We also assessed the ability to discriminate eyes with glaucomatous visual field loss from healthy eyes with area under the receiver operating characteristic (ROC) curves.ResultsThe mean prediction of average RNFL thickness from all 6292 optic disc photographs in the test set was 83.3±14.5 μm, whereas the mean average RNFL thickness from all corresponding SD OCT scans was 82.5±16.8 μm (P = 0.164). There was a very strong correlation between predicted and observed RNFL thickness values (Pearson r = 0.832; R2 = 69.3%; P < 0.001), with mean absolute error of the predictions of 7.39 μm. The area under the ROC curves for discriminating glaucomatous from healthy eyes with the DL predictions and actual SD OCT average RNFL thickness measurements were 0.944 (95% confidence interval [CI], 0.912-0.966) and 0.940 (95% CI, 0.902-0.966), respectively (P = 0.724).ConclusionsWe introduced a novel DL approach to assess fundus photographs and provide quantitative information about the amount of neural damage that can be used to diagnose and stage glaucoma. In addition, training neural networks to predict SD OCT data objectively represents a new approach that overcomes limitations of human labeling and could be useful in other areas of ophthalmology.

Project description:PURPOSE:To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs and to explore potential utility in detecting central GA (CGA). DESIGN:A deep learning model was developed to detect the presence of GA in color fundus photographs, and 2 additional models were developed to detect CGA in different scenarios. PARTICIPANTS:A total of 59?812 color fundus photographs from longitudinal follow-up of 4582 participants in the Age-Related Eye Disease Study (AREDS) dataset. Gold standard labels were from human expert reading center graders using a standardized protocol. METHODS:A deep learning model was trained to use color fundus photographs to predict GA presence from a population of eyes with no AMD to advanced AMD. A second model was trained to predict CGA presence from the same population. A third model was trained to predict CGA presence from the subset of eyes with GA. For training and testing, 5-fold cross-validation was used. For comparison with human clinician performance, model performance was compared with that of 88 retinal specialists. MAIN OUTCOME MEASURES:Area under the curve (AUC), accuracy, sensitivity, specificity, and precision. RESULTS:The deep learning models (GA detection, CGA detection from all eyes, and centrality detection from GA eyes) had AUCs of 0.933-0.976, 0.939-0.976, and 0.827-0.888, respectively. The GA detection model had accuracy, sensitivity, specificity, and precision of 0.965 (95% confidence interval [CI], 0.959-0.971), 0.692 (0.560-0.825), 0.978 (0.970-0.985), and 0.584 (0.491-0.676), respectively, compared with 0.975 (0.971-0.980), 0.588 (0.468-0.707), 0.982 (0.978-0.985), and 0.368 (0.230-0.505) for the retinal specialists. The CGA detection model had values of 0.966 (0.957-0.975), 0.763 (0.641-0.885), 0.971 (0.960-0.982), and 0.394 (0.341-0.448). The centrality detection model had values of 0.762 (0.725-0.799), 0.782 (0.618-0.945), 0.729 (0.543-0.916), and 0.799 (0.710-0.888). CONCLUSIONS:A deep learning model demonstrated high accuracy for the automated detection of GA. The AUC was noninferior to that of human retinal specialists. Deep learning approaches may also be applied to the identification of CGA. The code and pretrained models are publicly available at https://github.com/ncbi-nlp/DeepSeeNet.

Dataset Information

The subjectivity of objective evaluation of torsion on fundus photographs by practicing strabismologists.

Publications

The subjectivity of objective evaluation of torsion on fundus photographs by practicing strabismologists.

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