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The utility of measures of anterior segment parameters of a Pentacam Scheimpflug tomographer in discriminating high myopic astigmatism from keratoconus.

ABSTRACT: The study aimed to evaluate and compare anterior segment parameters between keratoconic eyes and eyes with high myopic astigmatism using Pentacam Scheimpflug tomography. This was a retrospective cross-sectional study that included sixty keratoconic eyes (thirty-two persons) and seventy-three eyes (forty-six persons) with high myopic astigmatism with mean ages 24.72 ± 11.65years and 26.60 ± 10.69years, respectively. Twenty-three parameters from the topographic map and fifteen parameters from the Belin-Ambrosió enhanced ectasia display map of the printouts of a Scheimpflug principle-based Pentacam tomographer were evaluated for their diagnostic accuracy using Receiver Operating Characteristic (ROC) curve. All parameters except cornea volume, anterior chamber volume, and anterior chamber angle indicated a significant difference between high myopic astigmatism and keratoconic eyes. The area under the receiver operating characteristic (AUROC) of eighteen Pentacam parameters was excellent (0.9-1.0) in discriminating keratoconus from high myopic astigmatism, out of which four {anterior minimum sagittal curvature (ant. Rmin), posterior minimum sagittal curvature (post. Rmin), maximum Ambrosió relational thickness (ART max) and total deviation value (D)} indicated excellent (>90%) sensitivity and specificity in addition to the excellent AUROC values. Topographic and Belin-Ambrosió enhanced ectasia display (BAD) maps of a Scheimpflug principle-based Pentacam tomographer are useful in enhancing the diagnosis of keratoconus and may also provide valuable information in effectively screening for keratoconus cases among refractive surgery candidates with high myopic astigmatism.

SUBMITTER: Zaabaar E

PROVIDER: S-EPMC8638937 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo develop and validate a deep learning algorithm that could automate the annotation of scleral spur (SS) and segmentation of anterior chamber (AC) structures for measurements of AC, iris, and angle width parameters in anterior segment OCT (ASOCT) scans.DesignCross-sectional study.SubjectsData from 2 population-based studies (i.e., the Singapore Chinese Eye Study and Singapore Malay Eye Study) and 1 clinical study on angle-closure disease were included in algorithm development. A separate clinical study on angle-closure disease was used for external validation.MethodImage contrast of ASOCT scans were first enhanced with CycleGAN. We utilized a heat map regression approach with coarse-to-fine framework for SS annotation. Then, an ensemble network of U-Net, full resolution residual network, and full resolution U-Net was used for structure segmentation. Measurements obtained from predicted SSs and structure segmentation were measured and compared with measurements obtained from manual SS annotation and structure segmentation (i.e., ground truth).Main outcome measuresWe measured Euclidean distance and intraclass correlation coefficients (ICC) to evaluate SS annotation and Dice similarity coefficient for structure segmentation. The ICC, Bland-Altman plot, and repeatability coefficient were used to evaluate agreement and precision of measurements.ResultsFor SS annotation, our algorithm achieved a Euclidean distance of 124.7 μm, ICC ≥ 0.95, and a 3.3% error rate. For structure segmentation, we obtained Dice similarity coefficient ≥ 0.91 for cornea, iris, and AC segmentation. For angle width measurements, ≥ 95% of data points were within the 95% limits-of-agreement in Bland-Altman plot with insignificant systematic bias (all P > 0.12). The ICC ranged from 0.71-0.87 for angle width measurements, 0.54 for IT750, 0.83-0.85 for other iris measurements, and 0.89-0.99 for AC measurements. Using the same SS coordinates from a human expert, measurements obtained from our algorithm were generally less variable than measurements obtained from a semiautomated angle assessment program.ConclusionWe developed a deep learning algorithm that could automate SS annotation and structure segmentation in ASOCT scans like human experts, in both open-angle and angle-closure eyes. This algorithm reduces the time needed and subjectivity in obtaining ASOCT measurements.Financial disclosuresThe author(s) have no proprietary or commercial interest in any materials discussed in this article.

Dataset Information

The utility of measures of anterior segment parameters of a Pentacam Scheimpflug tomographer in discriminating high myopic astigmatism from keratoconus.

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