Characterization of Errors in Retinopathy of Prematurity Diagnosis by Ophthalmologists-in-Training in the United States and Canada

PURPOSE: To identify the prominent factors that lead to misdiagnosis of retinopathy of prematurity (ROP) by ophthalmologists-in-training in the United States and Canada. METHODS: This prospective cohort study included 32 ophthalmologists-in-training at six ophthalmology training programs in the United States and Canada. Twenty web-based cases of ROP using wide-field retinal images were presented, and ophthalmologists-in-training were asked to diagnose plus disease, zone, stage, and category for each eye. Responses were compared to a consensus reference standard diagnosis for accuracy, which was established by combining the clinical diagnosis and the image-based diagnosis by multiple experts. The types of diagnostic errors that occurred were analyzed with descriptive and chi-squared analysis. Main outcome measures were frequency of types (category, zone, stage, plus disease) of diagnostic errors; association of errors in zone, stage, and plus disease diagnosis with incorrectly identified category; and performance of ophthalmologists-in-training across postgraduate years. RESULTS: Category of ROP was misdiagnosed at a rate of 48%. Errors in classification of plus disease were most commonly associated with misdiagnosis of treatment-requiring (plus error rate = 16% when treatment-requiring was correctly diagnosed vs 81% when underdiagnosed as type 2 or pre-plus; mean difference: 64.3; 95% CI: 51.9 to 76.7; CONCLUSIONS: Ophthalmologists-in-training in the United States and Canada misdiagnosed ROP nearly half of the time, with incorrect identification of plus disease as a leading cause. Integration of structured learning for ROP in residency education may improve diagnostic competency

Symptom burden of patients with dry eye disease: a four domain analysis.

To determine which sensory (symptom persistence and intensity) and reactive (activity and affective interference) domains of symptom analysis are essential for assessing symptom burden in dry eye disease (DED) patients.A symptom domain tool was developed to investigate all four symptom domains in DED. In a cross-sectional pilot study, we administered the symptom burden tool and the Ocular Surface Disease Index (OSDI) questionnaire to 48 DED patients. Total and domain scores from the symptom burden tool and the OSDI were normalized to achieve comparability. Spearman correlation coefficients were calculated to measure the relationship between domains and subscales. Agreement between the symptom burden tool and OSDI was assessed by Bland-Altman plot. Assigned treatments were compared by symptom burden to determine whether treatment aggressiveness is linked to symptom intensity.There was high agreement between the symptom burden tool and the OSDI. Symptom persistence had a stronger correlation with affective interference (r  =  0.62 for the symptom burden tool and r = 0.73 for the OSDI) than activity interference (r = 0.58 for the symptom burden tool and r = 0.60 for the OSDI). Symptom intensity correlated weakly with affective interference (r = 0.38) and activity interference (r = 0.37) in the symptom burden tool (OSDI does not have a subscale for intensity). In patients with equal persistence of symptoms, those having high symptom intensity were receiving more aggressive treatment (66.7%) than those with lower symptom intensity (33.3%).Persistence of symptoms correlates better with affective interference than activity interference. Intensity of symptoms may be important for treatment decisions

Dry Eye Symptom Burden Tool consisting of four domains: Symptom persistence, symptom intensity, activity interference, and affective interference.

<p>Dry Eye Symptom Burden Tool consisting of four domains: Symptom persistence, symptom intensity, activity interference, and affective interference.</p

Symptom burden domains and measurement scales.

<p>(A). Domains of Symptom Burden of Dry Eye Disease. The symptom burden of dry eye disease is divided into two main dimensions: sensory dimension and reactive dimension. The sensory dimension is divided into two domains, symptom persistency and symptom intensity, while the reactive dimension is divided into activity interference and affective interference [Adapted from reference # 7]. (B). Scales for Measuring Symptoms. The visual analog and numerical scales are used to measure intensity of symptoms, whereas the visual rating scale is used to measure persistence of symptoms.</p

Scatter Plots Between Persistence Domain in the Symptom Burden Tool or Persistence Subscale in the OSDI Questionnaire versus Affective and Activity Interference.

<p>Persistence calculated from the OSDI subscale A plotted with affective interference shows the highest linear agreement with a coefficient of determination R² of 0.49. The coefficient is comparable to that of persistence from the symptom burden tool and its relationship with affective interference (R²  =  0.44). Activity interference showed lower coefficients of determination R² of 0.34 and 0.35 for symptom persistence calculated from the symptom burden tool and symptom persistence from the OSDI subscale.</p

Automated Region of Interest Selection Improves Deep Learning-Based Segmentation of Hyper-Reflective Foci in Optical Coherence Tomography Images

Hyperreflective foci (HRF) have been associated with retinal disease progression and demonstrated as a negative prognostic biomarker for visual function. Automated segmentation of HRF in retinal optical coherence tomography (OCT) scans can be beneficial to identify the formation and movement of the HRF biomarker as a retinal disease progresses and can serve as the first step in understanding the nature and severity of the disease. In this paper, we propose a fully automated deep neural network based HRF segmentation model in OCT images. We enhance the model&rsquo;s performance by using a patch-based strategy that increases the model&rsquo;s compute on the HRF pixels. The patch-based strategy is evaluated against state of the art HRF segmentation pipelines on clinical retinal image data. Our results shows that the patch-based approach demonstrates a high precision score and intersection over union (IOU) using a ResNet34 segmentation model with Binary Cross Entropy loss function. The HRF segmentation pipeline can be used for analyzing HRF biomarkers for different retinopathies