Byeong Wook Yoo

Long-Term Reproducibility of Macular Ganglion Cell Analysis in Clinically Stable Glaucoma Patients.

PURPOSE To investigate the long-term reproducibility of macular ganglion cell analysis in clinically stable glaucoma patients using spectral-domain optical coherence tomography (SD-OCT). METHODS One hundred nine eyes of 109 clinically stable open-angle glaucoma patients with a localized retinal nerve fiber layer (RNFL) defect and a corresponding macular ganglion cell-inner plexiform layer (GCIPL) defect were included in this retrospective, longitudinal study. Clinical stability was defined as showing no change on serial structural (stereo-disc and RNFL photography) and functional (visual field progression analysis) assessments. Three serial SD-OCT (Cirrus-HD) peripapillary and macular scans taken at 6-month intervals were analyzed. Intraclass correlation coefficient (ICC), coefficient of variation (CV), test-retest standard deviation (TRTSD), and tolerance limit of area and angular width of GCIPL defect and GCIPL thickness measurements were assessed. RESULTS The ICC of the GCIPL thickness parameters ranged from 0.966 to 0.992, and the CV from 2.0% to 5.5%. The TRTSD was the lowest for the average GCIPL thickness (1.45 μm), the highest for the minimum GCIPL thickness (3.42 μm), and varied from 1.54 to 2.16 μm for the sectoral measurements. The ICC, CV, and TRTSD were 0.993, 3.9%, and 5.32° for angular width, and 0.930, 6.7%, and 0.27 mm2 for area of GCIPL defect. Measurement variances (TRTSD) for the GCIPL measurements showed no significant association with the glaucomatous severity. CONCLUSIONS The macular GCIPL thickness and deviation maps showed excellent long-term intervisit reproducibility. Macular ganglion cell analysis can be considered as an effective means of monitoring glaucomatous progression in macula.

Reproducibility of spectral-domain optical coherence tomography RNFL map for glaucomatous and fellow normal eyes in unilateral glaucoma.

Purpose:To compare the reproducibility of the optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) thickness map between glaucomatous and fellow normal eyes of unilateral glaucoma patients. Methods:In this prospective case-control study, Cirrus HD-OCT was performed for 79 unilateral glaucoma patients 3 times on the first visit and on 3 subsequent visits within a 2-month period. Test-retest standard deviation (TRT-SD) and tolerance limit based on the 1.645×√2×TRT-SD formula were derived for RNFL thicknesses at the respective superpixels of the RNFL thickness map. Results:The TRT-SDs and tolerance limits of the glaucomatous eyes (TRT-SD: 2.75 to 20.25 &mgr;m; tolerance limits: 6.40 and 47.11 &mgr;m) were significantly smaller than those of the fellow normal eyes (TRT-SD: 2.73 to 26.49 &mgr;m; tolerance limits: 6.35 and 61.63 &mgr;m) in the superotemporal, inferotemporal, and superonasal areas (P <0.05). The TRT-SDs in most areas showed a significant positive correlation with the RNFL thicknesses (P<0.05). Conclusions:The test-retest variabilities of the Cirrus HD-OCT RNFL thickness map of the glaucomatous eyes were lower than those of the fellow normal eyes, especially in areas of high diagnostic importance. Moreover, variability was positively correlated with the baseline RNFL thicknesses. Therefore, adjusting the tolerance limits on the basis of the baseline RNFL thickness values might help improve the ability to recognize progression. Further prospective studies on this issue are warranted.

Automated network analysis to measure brain effective connectivity estimated from EEG data of patients with alcoholism

OBJECTIVE Detection and diagnosis based on extracting features and classification using electroencephalography (EEG) signals are being studied vigorously. A network analysis of time series EEG signal data is one of many techniques that could help study brain functions. In this study, we analyze EEG to diagnose alcoholism. APPROACH We propose a novel methodology to estimate the differences in the status of the brain based on EEG data of normal subjects and data from alcoholics by computing many parameters stemming from effective network using Granger causality. MAIN RESULTS Among many parameters, only ten parameters were chosen as final candidates. By the combination of ten graph-based parameters, our results demonstrate predictable differences between alcoholics and normal subjects. A support vector machine classifier with best performance had 90% accuracy with sensitivity of 95.3%, and specificity of 82.4% for differentiating between the two groups.

White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks

The white blood cell differential count of the bone marrow provides information concerning the distribution of immature and mature cells within maturation stages. The results of such examinations are important for the diagnosis of various diseases and for follow-up care after chemotherapy. However, manual, labor-intensive methods to determine the differential count lead to inter- and intra-variations among the results obtained by hematologists. Therefore, an automated system to conduct the white blood cell differential count is highly desirable, but several difficulties hinder progress. There are variations in the white blood cells of each maturation stage, small inter-class differences within each stage, and variations in images because of the different acquisition and staining processes. Moreover, a large number of classes need to be classified for bone marrow smear analysis, and the high density of touching cells in bone marrow smears renders difficult the segmentation of single cells, which is crucial to traditional image processing and machine learning. Few studies have attempted to discriminate bone marrow cells, and even these have either discriminated only a few classes or yielded insufficient performance. In this study, we propose an automated white blood cell differential counting system from bone marrow smear images using a dual-stage convolutional neural network (CNN). A total of 2,174 patch images were collected for training and testing. The dual-stage CNN classified images into 10 classes of the myeloid and erythroid maturation series, and achieved an accuracy of 97.06%, a precision of 97.13%, a recall of 97.06%, and an F-1 score of 97.1%. The proposed method not only showed high classification performance, but also successfully classified raw images without single cell segmentation and manual feature extraction by implementing CNN. Moreover, it demonstrated rotation and location invariance. These results highlight the promise of the proposed method as an automated white blood cell differential count system.

Application of Robot Manipulator for Cardiopulmonary Resuscitation

This paper presents an application of a robot manipulator to perform Cardiopulmonary resuscitation(CPR) in emergency situations. CPR is one of the most important treatments which serves to save patients in cardiac arrest. The proposed robot CPR system attempts to overcome the limitations of current CPR methods in two aspects. First, it can provide much more consistent CPR than humans in terms of strength and timing. Second, biological data of a patient can be used to determine the best compression point during CPR. The feasibility of the proposed system is demonstrated through experiments: one simulation on a mannequin and two animal tests. It is also expected that this robotic CPR system can be a good platform to investigate many aspects of CPR methods and guidelines with accurate measurements and actions.

Comparison of changes of macular ganglion cell-inner plexiform layer defect between stable group and progression group in primary open-angle glaucoma

AbstractPurposeTo compare the changes in macular ganglion cell-inner plexiform layer (GCIPL) defect between stable and progression primary open-angle glaucoma (POAG) groups.Study designRetrospective observational study.MethodsA total of 100 POAG eyes with localized retinal nerve fiber layer (RNFL) defect and corresponding macular GCIPL defect were selected for this study. Glaucoma progression was defined by either structural or functional deterioration. The number of abnormal superpixels on macular GCIPL deviation maps was calculated using a customized MATLAB program. GCIPL defect change was evaluated in two aspects: increased angular width and increased area. The defect patterns were categorized and compared between the stable and progression groups.ResultsThe increase rate of angular width of GCIPL defect was higher in the progression group than in the stable group (P = 0.029). In respect to the area of GCIPL defect, there was no statistically significant differences between the groups (P = 0.227). Twenty-seven (27) of 100 (27.0%) eyes showed increased angular width of GCIPL defect. It was more frequent in the progression group than in the stable group (P = 0.043). Seventeen (17) of 27 (63.0%) eyes showed the away from the horizontal temporal raphe type progression and it was the most common change pattern of angular width of GCIPL defect.ConclusionsIncreased angular width of GCIPL defect was the more prominent feature of change, and was more frequent in the progression group than in the stable group. Among the types of GCIPL defect classified, the away from the horizontal temporal raphe type was the most common.