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Yang Lowry posted an update 6 months ago
SS level worsening. Finally, these findings reaffirm the fact that close clinical follow-up is important even among eyes that achieve substantial DRSS improvements with apparently quiescent disease.
To compare the outcomes of penetrating keratoplasty (PK) and deep anterior lamellar keratoplasty (DALK) for pediatric keratoconus.
Retrospective comparative interventional case series.
This study included consecutive pediatric keratoconus cases (≤18 years of age) who received PK (n=45) or DALK (n=54) in 2 different time periods. Postoperative best spectacle-corrected visual acuity (BSCVA), refraction, and complications were compared between the study groups.
The mean follow-up was 83.3±46.1 and 63.3±45.6 months in the PK and DALK groups, respectively (P=.10). Postoperatively, BSCVA was 0.20±0.19 logMAR in the PK group and 0.26±0.19 logMAR in the DALK group (P=.11), with a BSCVA of ≥20/40 in 91.1% and 83.3% of eyes, respectively (P=.25). Two groups were comparable regarding postoperative refractive outcomes. Graft epitheliopathy and suture-associated complications were more commonly encountered after DALK, which was attributable to the effect of low-quality grafts on the clinical outcomes of DALK. Ten PK eyes (22.2%) and 9 DALK eyes (16.7%) experienced at least 1 episode of graft rejection within 5 years of corneal transplantation (P=.49). Rejection was reversible in 93.1% and 100% of episodes in the PK and DALK groups, respectively (P=.63). At the postoperative year 5, 95.6% of grafts in the PK group and 98.2% in the DALK group remained clear (P=.45).
No significant difference was observed in the outcomes between PK and DALK in pediatric keratoconus. Low-quality donor tissues in DALK increased the incidence of graft epithelial problems and suture-related complications as compared to PK.
No significant difference was observed in the outcomes between PK and DALK in pediatric keratoconus. Low-quality donor tissues in DALK increased the incidence of graft epithelial problems and suture-related complications as compared to PK.
To compare the differences among clinical, demographic, and multimodal imaging features of choroidal granulomas associated with tuberculosis and sarcoidosis.
Retrospective comparative case series.
Clinical features and fundus imaging, including fluorescein and indocyanine green angiography and optical coherence tomography of patients with tuberculomas and sarcoid choroidal granulomas seen at 3 tertiary care centers, were reviewed. selleckchem The differences among clinical appearances, including morphology of the lesions (size, shape, extent), vascularity, and multimodal imaging features, were compared. Repeated logistic regression measurements with a multilevel random effects model was used to assess characteristics of individual granulomas that could predict the underlying cause.
The study included 47 eyes of 38 patients (22 with tuberculomas and 16 with sarcoid granulomas; total of 138 granulomas). Patients with tuberculomas were significantly younger (33.8 ± 10.1 vs. 48.6 ± 14.3 years, respectively; P=.002), al granulomas have various clinical and imaging features that help differentiate between the 2 entities with high predictability and can supplement immunological and radiological tests in a diagnosis.
To test the hypothesis that visual field (VF) progression can be predicted from baseline and longitudinal optical coherence tomography (OCT) structural measurements.
Prospective cohort study.
A total of 104 eyes (104 patients) with ≥3 years of follow-up and ≥5 VF examinations were enrolled. We defined VF progression based on pointwise linear regression on 24-2 VF (≥3 locations with slope less than or equal to -1.0 dB/year and P < .01). We used elastic net logistic regression (ENR) and machine learning to predict VF progression with demographics, baseline circumpapillary retinal nerve fiber layer (RNFL), macular ganglion cell/inner plexiform layer (GCIPL) thickness, and RNFL and GCIPL change rates at central 24 superpixels and 3 eccentricities, 3.4°, 5.5°, and 6.8°, from fovea and hemimaculas. Areas-under-ROC curves (AUC) were used to compare models.
Average ± SD follow-up and VF examinations were 4.5 ± 0.9 years and 8.7 ± 1.6, respectively. VF progression was detected in 23 eyes (22%). ENR selected rates of change of superotemporal RNFL sector and GCIPL change rates in 5 central superpixels and at 3.4°and 5.6° eccentricities as the best predictor subset (AUC=0.79 ± 0.12). Best machine learning predictors consisted of baseline superior hemimacular GCIPL thickness and GCIPL change rates at 3.4° eccentricity and 3 central superpixels (AUC=0.81 ± 0.10). Models using GCIPL-only structural variables performed better than RNFL-only models.
VF progression can be predicted with clinically relevant accuracy from baseline and longitudinal structural data. Further refinement of proposed models would assist clinicians with timely prediction of functional glaucoma progression and clinical decision making.
VF progression can be predicted with clinically relevant accuracy from baseline and longitudinal structural data. Further refinement of proposed models would assist clinicians with timely prediction of functional glaucoma progression and clinical decision making.
To report a multidisease deep learning diagnostic network (MDDN) of common corneal diseases dry eye syndrome (DES), Fuchs endothelial dystrophy (FED), and keratoconus (KCN) using anterior segment optical coherence tomography (AS-OCT) images.
Development of a deep learning neural network diagnosis algorithm.
A total of 158,220 AS-OCT images from 879 eyes of 478 subjects were used to develop and validate a classification deep network. After a quality check, the network was trained and validated using 134,460 images. We tested the network using a test set of consecutive patients involving 23,760 AS-OCT images of 132 eyes of 69 patients. The area under receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), and F1 score and 95% confidence intervals (CIs) were computed.
The MDDN achieved eye-level AUROCs >0.99 (95% CI 0.90, 1.0), AUPRCs > 0.96 (95% CI 0.90, 1.0), and F1 scores > 0.90 (95% CI 0.81, 1.0) for DES, FED, and KCN, respectively.
MDDN is a novel diagnostic tool for corneal diseases that can be used to automatically diagnose KCN, FED, and DES using only AS-OCT images.
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