Assessment of Scleral Contour Changes in High Myopia by Optical Coherence Tomography.

PURPOSE: This study aimed to establish and validate optical coherence tomography (OCT) based diagnostic criteria of high myopia. METHODS: This was a cross-sectional study including 100 eyes of high myopia with axial length larger than 26.5 mm and 100 control eyes, which were examined by spectral-domain OCT. Vertical and horizontal OCT of 9 mm scanning across fovea were analyzed. OCT characteristics including mirror artifact, scleral sink due to steep inclination of posterior sclera, scleral visibility, abnormality of outer retinal layer, foveoschisis, and dome-shaped macula were assessed in each group. RESULTS: The mean axial length was 28.65 ± 2.07 mm (range, 26.51-34.59 mm) in high myopia group and 23.79 ± 0.99 mm (range, 21.26-25.94 mm) in control group. Among the OCT characteristics noted at high frequency in the eyes with high myopia, three criteria achieving high sensitivity and specificity were determined: scleral sink over 500 µm, scleral visibility over 100 µm, and dome-shaped macula. Under conditions of presence of any of three criteria in either horizontal or vertical OCT scanning, the diagnostic sensitivity, specificity, positive predictive value, and negative predictive value for high myopia were found to be 95.0%, 98.0%, 98.0%, and 95.1%, respectively. In a new set of OCT images from 50 eyes with high myopia and 50 eyes of nonhigh myopia cases, the OCT-based criteria also proved similar level of diagnostic validity. CONCLUSIONS: The OCT-based criteria, which directly addressed posterior scleral contour changes, may lead to an intuitive and accurate diagnosis of high myopia. Also, the criteria may contribute to early detection and monitoring of eyes that cannot be defined as high myopia but can progress. OCT may be useful for monitoring high myopia patients as OCT can detect myopia-associated retinal pathologies as well as scleral contour changes.

Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks.

PURPOSE: Accurate image-based ophthalmic diagnosis relies on fundus image clarity. This has important implications for the quality of ophthalmic diagnoses and for emerging methods such as telemedicine and computer-based image analysis. The purpose of this study was to implement a deep convolutional neural network (CNN) for automated assessment of fundus image quality in retinopathy of prematurity (ROP). DESIGN: Experimental study. PARTICIPANTS: Retinal fundus images were collected from preterm infants during routine ROP screenings. METHODS: Six thousand one hundred thirty-nine retinal fundus images were collected from 9 academic institutions. Each image was graded for quality (acceptable quality [AQ], possibly acceptable quality [PAQ], or not acceptable quality [NAQ]) by 3 independent experts. Quality was defined as the ability to assess an image confidently for the presence of ROP. Of the 6139 images, NAQ, PAQ, and AQ images represented 5.6%, 43.6%, and 50.8% of the image set, respectively. Because of low representation of NAQ images in the data set, images labeled NAQ were grouped into the PAQ category, and a binary CNN classifier was trained using 5-fold cross-validation on 4000 images. A test set of 2109 images was held out for final model evaluation. Additionally, 30 images were ranked from worst to best quality by 6 experts via pairwise comparisons, and the CNN's ability to rank quality, regardless of quality classification, was assessed. MAIN OUTCOME MEASURES: The CNN performance was evaluated using area under the receiver operating characteristic curve (AUC). A Spearman's rank correlation was calculated to evaluate the overall ability of the CNN to rank images from worst to best quality as compared with experts. RESULTS: The mean AUC for 5-fold cross-validation was 0.958 (standard deviation, 0.005) for the diagnosis of AQ versus PAQ images. The AUC was 0.965 for the test set. The Spearman's rank correlation coefficient on the set of 30 images was 0.90 as compared with the overall expert consensus ranking. CONCLUSIONS: This model accurately assessed retinal fundus image quality in a comparable manner with that of experts. This fully automated model has potential for application in clinical settings, telemedicine, and computer-based image analysis in ROP and for generalizability to other ophthalmic diseases.

Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity.

BACKGROUND: Prior work has demonstrated the near-perfect accuracy of a deep learning retinal image analysis system for diagnosing plus disease in retinopathy of prematurity (ROP). Here we assess the screening potential of this scoring system by determining its ability to detect all components of ROP diagnosis. METHODS: Clinical examination and fundus photography were performed at seven participating centres. A deep learning system was trained to detect plus disease, generating a quantitative assessment of retinal vascular abnormality (the i-ROP plus score) on a 1-9 scale. Overall ROP disease category was established using a consensus reference standard diagnosis combining clinical and image-based diagnosis. Experts then ranked ordered a second data set of 100 posterior images according to overall ROP severity. RESULTS: 4861 examinations from 870 infants were analysed. 155 examinations (3%) had a reference standard diagnosis of type 1 ROP. The i-ROP deep learning (DL) vascular severity score had an area under the receiver operating curve of 0.960 for detecting type 1 ROP. Establishing a threshold i-ROP DL score of 3 conferred 94% sensitivity, 79% specificity, 13% positive predictive value and 99.7% negative predictive value for type 1 ROP. There was strong correlation between expert rank ordering of overall ROP severity and the i-ROP DL vascular severity score (Spearman correlation coefficient=0.93; p<0.0001). CONCLUSION: The i-ROP DL system accurately identifies diagnostic categories and overall disease severity in an automated fashion, after being trained only on posterior pole vascular morphology. These data provide proof of concept that a deep learning screening platform could improve objectivity of ROP diagnosis and accessibility of screening.

Deep Learning for Image Quality Assessment of Fundus Images in Retinopathy of Prematurity.

Accurate image-based medical diagnosis relies upon adequate image quality and clarity. This has important implications for clinical diagnosis, and for emerging methods such as telemedicine and computer-based image analysis. In this study, we trained a convolutional neural network (CNN) to automatically assess the quality of retinal fundus images in a representative ophthalmic disease, retinopathy of prematurity (ROP). 6,043 wide-angle fundus images were collected from preterm infants during routine ROP screening examinations. Images were assessed by clinical experts for quality regarding ability to diagnose ROP accurately, and were labeled "acceptable" or "not acceptable." The CNN training, validation and test sets consisted of 2,770 images, 200 images, and 3,073 images, respectively. Test set accuracy was 89.1%, with area under the receiver operating curve equal to 0.964, and area under the precision-recall curve equal to 0.966. Taken together, our CNN shows promise as a useful prescreening method for telemedicine and computer-based image analysis applications. We feel this methodology is generalizable to all clinical domains involving image-based diagnosis.

Erratum to: The KMDS-NATION Study: Korean Movement Disorders Society Multicenter Assessment of Non-Motor Symptoms and Quality of Life in Parkinson's Disease NATION Study Group.

This corrects the article on p. 393 in vol. 12, PMID: 27819413.

The KMDS-NATION Study: Korean Movement Disorders Society Multicenter Assessment of Non-Motor Symptoms and Quality of Life in Parkinson's Disease NATION Study Group.

BACKGROUND AND PURPOSE: Nonmotor symptoms (NMS) in Parkinson's disease (PD) have multisystem origins with heterogeneous manifestations that develop throughout the course of PD. NMS are increasingly recognized as having a significant impact on the health-related quality of life (HrQoL). We aimed to determine the NMS presentation according to PD status, and the associations of NMS with other clinical variables and the HrQoL of Korean PD patients. METHODS: We surveyed patients in 37 movement-disorders clinics throughout Korea. In total, 323 PD patients were recruited for assessment of disease severity and duration, NMS, HrQoL, and other clinical variables including demographics, cognition, sleep scale, fatigability, and symptoms. RESULTS: In total, 98.1% of enrolled PD subjects suffered from various kinds of NMS. The prevalence of NMS and scores in each NMS domain were significantly higher in the PD group, and the NMS worsened as the disease progressed. Among clinical variables, disease duration and depressive mood showed significant correlations with all NMS domains (p<0.001). NMS status impacted HrQoL in PD (rS=0.329, p<0.01), and the association patterns differed with the disease stage. CONCLUSIONS: The results of our survey suggest that NMS in PD are not simply isolated symptoms of degenerative disease, but rather exert significant influences throughout the disease course. A novel clinical approach focused on NMS to develop tailored management strategies is warranted to improve the HrQoL in PD patients.

A comparison of montreal cognitive assessment between patients with visual hallucinations and without visual hallucinations in Parkinson's disease.

BACKGROUND: Visual hallucination (VH) is closely associated with cognitive impairment in Parkinson's disease (PD). The aim of this study is to analyze the domain of cognitive impairment related to the VH by analyzing cognition in PD. METHOD: Twenty-six of 58 patients had VH (PD-VH), whereas 32 patients had no hallucinations (PD-NH); patients assigned to the PD-VH on the basis of having at least one VH per week for one month before participation in the study. All of the patients were administered the Montreal Cognitive Assessment-Korean test (MoCA-K). We analyzed the differences in cognition between PD-VH and PD-NH. RESULTS: The Hoehn-Yahr (HY) stage and the Beck Depression Inventory version I (BDI) scores in PD-VH were significantly different from those in PD-NH. After adjusting for the HY stage and BDI, the language domain of the MoCA-K cognitive domains presents a significant difference between PD-VH and PD-NH. CONCLUSION: Sentence processing and naming as the composition of the language domain in the MoCA-K have been associated in common with posterior cortical dysfunction. Although our result shows that the language domain of MoCA-K is sensitive to cognitive deficit in PD-VH patients, it needs further neuroimaging analysis for certifying the relationship between language impairment and posterior cortical deficit as the pathophysiology of PD-VH.

Synthesis of low-cost, rubbery amphiphilic comb-like copolymers and their use in the templated synthesis of mesoporous TiO2 films for solid-state dye-sensitized solar cells.

Low-cost, rubbery amphiphilic comb-like copolymers consisting of hydrophobic poly(lauryl methacrylate) (PLMA) and hydrophilic poly(oxyethylene methacrylate) (POEM) were synthesized via one-step free radical polymerization. The synthesis of PLMA-POEM copolymers was confirmed using Fourier transform infra-red spectroscopy (FT-IR), (1)H-nuclear magnetic resonance ((1)H-NMR) and gel permeation spectroscopy (GPC). The PLMA-POEM copolymers were used as a structure-directing agent for the formation of anatase mesoporous TiO2 films. Careful adjustment of the precursor and polymer molecular weight (MW) was made to systematically vary the TiO2 structure and its effect on the performances of solid-state dye-sensitized solar cells (ssDSSCs). The use of a low MW polymer resulted in a worm-like structure with smaller pores, whereas an aggregated honeycomb-like structure with bimodal pores was obtained for the high MW system, as characterized by scanning electron microscopy (SEM), grazing incidence small-angle X-ray scattering (GI-SAXS) and N2 adsorption-desorption measurement. An efficiency of 4.2% was obtained at 100 mW cm(-2) when using 2 µm-thick TiO2 film prepared with a high MW copolymer. The higher efficiency was due to better pore filling of the solid electrolyte and improved light scattering properties. By using a layer-by-layer method, the efficiency was further improved to 5.0% at 7 µm thickness, which was greater than that of commercially available paste (3.9%).

Assessment of the right ventricular function and mass using cardiac multi-detector computed tomography in patients with chronic obstructive pulmonary disease.

OBJECTIVE: We wanted to assess the relationship between measurements of the right ventricular (RV) function and mass, with using cardiac multi-detector computed tomography (MDCT) and the severity of chronic obstructive pulmonary disease (COPD) as determined by the pulmonary function test (PFT). MATERIALS AND METHODS: Measurements of PFT and cardiac MDCT were obtained in 33 COPD patients. Using the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification, the patients were divided into three groups according to the severity of the disease: stage I (mild, n = 4), stage II (moderate, n = 15) and stage III (severe, n = 14). The RV function and the wall mass were obtained by cardiac MDCT. The results were compared among the groups using the Student-Newman-Keuls method. Pearson's correlation was used to evaluate the relationship between the right ventricular ejection fraction (RVEF) and the wall mass results with the PFT results. P-values less than 0.05 were considered statistically significant. RESULTS: The RVEF and mass were 47+/-3% and 41+/-2 g in stage I, 46+/-6% and 46+/-5 g in stage II, and 35+/-5% and 55+/-6 g in stage III, respectively. The RVEF was significantly lower in stage III than in stage I and II (p < 0.01). The RV mass was significantly different among the three stages, according to the disease severity of COPD (p < 0.05). The correlation was excellent between the MDCT results and forced expiratory volume in 1 sec (r = 0.797 for RVEF and r = -0.769 for RV mass) and forced expiratory volume in 1 sec to the forced vital capacity (r = 0.745 for RVEF and r = -0.718 for RV mass). CONCLUSION: Our study shows that the mean RV wall mass as measured by cardiac MDCT correlates well with the COPD disease severity as determined by PFT.

Two-phase reconstruction for the assessment of left ventricular volume and function using retrospective ECG-gated MDCT: comparison with echocardiography.

OBJECTIVE: The aims of our study were to investigate the clinical feasibility of a two-phase reconstruction method based on ECG to evaluate left ventricular (LV) volume and function using cardiac MDCT and to compare these results with those from echocardiography. SUBJECTS AND METHODS: The LV end-diastolic and end-systolic volumes, stroke volume, and ejection fraction were measured using two different methods of cardiac MDCT in 19 patients who had undergone cardiac MDCT and echocardiography. The first was a two-phase reconstruction method based on retrospective ECG-triggering: The end-systolic phase was reconstructed when the reconstruction window was located halfway in the ascending T wave on ECG, and the end-diastolic phase was reconstructed when the reconstruction window was located at the starting point of the QRS complex on ECG. The second was a multiphase reconstruction method: 20 series of images were reconstructed at every 5% throughout the cardiac cycle. The LV volumes and function determined by the two reconstruction methods were compared. The results measured by cardiac MDCT were compared with those obtained by echocardiography. RESULTS: The LV end-diastolic and end-systolic volumes, stroke volume, and ejection fraction measured by the two-phase reconstruction method correlated well with those measured by the multiphase reconstruction method (r = 0.984, 0.978, 0.969, 0.969, respectively). There were no significant differences between the results of the two different reconstruction methods (p > 0.05). The LV volumes showed moderate to good correlation between cardiac MDCT and echocardiography (0.766 < r < 0.940). Ejection fraction measured by cardiac MDCT yielded a significant overestimation of 2.9% +/- 8.7% (mean +/- SD) compared with that measured by echocardiography. CONCLUSION: A two-phase reconstruction method on cardiac MDCT is relatively simple and can provide an objective standard for reconstructing the appropriate image sets for end-diastole and end-systole without the need to review serial preview images.