Flicker Perimetry In Healthy Subjects: Influence Of Age And Gender, Learning Effect And Short-term Fluctuation.

To determine the mean critical fusion frequency and the short-term fluctuation, to analyze the influence of age, gender, and the learning effect in healthy subjects undergoing flicker perimetry. Study 1--95 healthy subjects underwent flicker perimetry once in one eye. Mean critical fusion frequency values were compared between genders, and the influence of age was evaluated using linear regression analysis. Study 2--20 healthy subjects underwent flicker perimetry 5 times in one eye. The first 3 sessions were separated by an interval of 1 to 30 days, whereas the last 3 sessions were performed within the same day. The first 3 sessions were used to investigate the presence of a learning effect, whereas the last 3 tests were used to calculate short-term fluctuation. Study 1--Linear regression analysis demonstrated that mean global, foveal, central, and critical fusion frequency per quadrant significantly decreased with age (p0.05), with the exception of the central area and inferonasal quadrant (p=0.049 and p=0.011, respectively), where the values were lower in females. Study 2--Mean global (p=0.014), central (p=0.008), and peripheral (p=0.03) critical fusion frequency were significantly lower in the first session compared to the second and third sessions. The mean global short-term fluctuation was 5.06 +/-1.13 Hz, the mean interindividual and intraindividual variabilities were 11.2+/-2.8% and 6.4+/-1.5%, respectively. This study suggests that, in healthy subjects, critical fusion frequency decreases with age, that flicker perimetry is associated with a learning effect, and that a moderately high short-term fluctuation is expected.7091-

Sensibilidade e especificidade dos classificadores de aprendizagem de máquina para o diagnóstico de glaucoma usando Spectral Domain OCT e perimetria automatizada acromática

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.OBJETIVO: Avaliar a sensibilidade e especificidade dos classificadores de aprendizagem de máquina no diagnóstico de glaucoma usando Spectral Domain OCT (SD-OCT) e perimetria automatizada acromática (PAA). MÉTODOS: Estudo transversal observacional. Sessenta e dois pacientes com glaucoma e 48 indivíduos normais foram incluídos. Todos os pacientes foram submetidos a exame oftalmológico completo, e perimetria automatizada acromática (24-2 SITA; Humphrey Field Analyzer II, Carl Zeiss Meditec, Inc., Dublin, CA) e exame de imagem da camada de fibras nervosas utilizando SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Curvas ROC (Receiver operator characteristic) foram obtidas para todos os parâmetros do SD-OCT e índices globais do campo visual (MD, PSD, GHT). Subsequentemente, os seguintes classificadores de aprendizagem de máquina (CAMs) foram testados usando parâmetros do OCT e CV: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA), Support Vector Machine Linear (SVML) e Support Vector Machine Gaussian (SVMG). Áreas abaixo da curva ROC (aROC) obtidas com os parâmetros isolados do campo visual (CV) e OCT foram comparados com os CAMs usando dados associados do OCT e CV. RESULTADOS: Combinando os dados do OCT e do CV, aROCs dos CAMs variaram entre 0,777(CTREE) e 0,946 (RAN). A maior aROC dos CAMs OCT+CV obtida com RAN (0,946) foi significativamente maior que o melhor parâmetro do OCT (p<0,05), mas não houve diferença estatística significativa com o melhor parâmetro do CV (p=0,19). CONCLUSÃO: Os classificadores de aprendizagem de máquina treinados com dados do OCT e CV podem discriminar entre olhos normais e glaucomatosos com sucesso. A combinação das medidas do OCT e CV melhoraram a acurácia diagnóstica comparados aos parâmetros do OCT.17017

Sensitivity And Specificity Of Machine Learning Classifiers For Glaucoma Diagnosis Using Spectral Domain Oct And Standard Automated Perimetry.

To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.76170-

Effects of Misalignments in the Retinal Nerve Fiber Layer Thickness Measurements with Spectral Domain Optical Coherence Tomography

Purpose. To investigate misalignments (MAs) on retinal nerve fiber layer thickness (RNFLT) measurements obtained with Cirrus© SD-OCT. Methods. This was a retrospective, observational, cross-sectional study. Twenty-seven healthy and 29 glaucomatous eyes of 56 individuals with one normal exam and another showing MA were included. MAs were defined as an improper alignment of vertical vessels in the en face image. MAs were classified in complete MA (CMA) and partial MA (PMA), according to their site: 1 (superior, outside the measurement ring (MR)), 2 (superior, within MR), 3 (inferior, within MR), and 4 (inferior, outside MR). We compared RNFLT measurements of aligned versus misaligned exams in all 4 sectors, in the superior area (sectors 1 + 2), inferior area (sectors 3 + 4), and within the measurement ring (sectors 2 + 3). Results. RNFLT measurements at 12 clock-hour of eyes with MAs in the superior area (sectors 1 + 2) were significantly lower than those obtained in the same eyes without MAs P=0.043. No significant difference was found in other areas (sectors 1 + 2 + 3 + 4, sectors 3 + 4, and sectors 2 + 3). Conclusion. SD-OCT scans with superior MAs may present lower superior RNFLT measurements compared to aligned exams

Innovations in Glaucoma Surgery: Improving the Results

[no abstract available

Detecting retinal nerve fibre layer segmentation errors on spectral domain-optical coherence tomography with a deep learning algorithm

In this study we developed a deep learning (DL) algorithm that detects errors in retinal never fibre layer (RNFL) segmentation on spectral-domain optical coherence tomography (SDOCT) B-scans using human grades as the reference standard. A dataset of 25,250 SDOCT B-scans reviewed for segmentation errors by human graders was randomly divided into validation plus training (50%) and test (50%) sets. The performance of the DL algorithm was evaluated in the test sample by outputting a probability of having a segmentation error for each B-scan. The ability of the algorithm to detect segmentation errors was evaluated with the area under the receiver operating characteristic (ROC) curve. Mean DL probabilities of segmentation error in the test sample were 0.90 +/- 0.17 vs. 0.12 +/- 0.22 (P < 0.001) for scans with and without segmentation errors, respectively. The DL algorithm had an area under the ROC curve of 0.979 (95%CI: 0.974 to 0.984) and an overall accuracy of 92.4%. For the B-scans with severe segmentation errors in the test sample, the DL algorithm was 98.9% sensitive. This algorithm can help clinicians and researchers review images for artifacts in SDOCT tests in a timely manner and avoid inaccurate diagnostic interpretations9COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESSem informaçã

Avaliação de programa educativo para portadores de glaucoma

PURPOSE: To analyze the results of an educational program applied to glaucoma patients; to serve as a guideline for a definitive educational program to be developed at the Glaucoma Service of the University of Campinas. METHODS: Fifty glaucoma patients were given information about the disease and its treatment by an experienced nurse and answered the same questionnaire before and 2.7 months after the initial explanation. The technique of eyedrop instillation was compared before and after the educational program. RESULTS: There was no significant improvement concerning the knowledge of the identification of the ocular disease, the side effects, the meaning of glaucoma, the importance of treatment, the purpose of medications and the normal value of intraocular pressure. There was a significant improvement in all steps of eyedrop instillation (pOBJETIVO: Avaliar o resultado da implementação de um programa de educação à saúde a glaucomatosos; fornecer subsídios para a implantação de um programa de educação à saúde aos portadores de glaucoma atendidos em um serviço universitário. MÉTODOS: Foi realizada entrevista individual com 50 portadores de glaucoma, através de questionário, seguida de reunião na qual se aplicou o programa de educação à saúde. Após um intervalo médio de 2,7 meses, realizou-se nova entrevista onde os portadores de glaucoma foram submetidos ao mesmo questionário, com o acréscimo de questões sobre a importância atribuída às orientações recebidas e sobre as modificações no autocuidado. RESULTADOS: Não houve melhora significativa no conhecimento sobre a identificação da doença ocular, os efeitos colaterais, o significado do glaucoma, a importância do tratamento, a finalidade da terapêutica e o valor normal da pressão intra-ocular. Houve melhora significativa na técnica de instilação dos colírios e em relação ao conhecimento sobre a importância da hereditariedade e a finalidade da campimetria. CONCLUSÃO: Os autores concluem que o programa foi insuficiente para a apreensão de todo o seu conteúdo cognitivo. Os resultados podem ser conseqüentes ao tipo de processo ensino-aprendizagem, desenvolvido de forma vertical, que não atende à necessidade de informação dos glaucomatosos e à sua dificuldade de compreensão, decorrente do baixo nível cultural, de escolaridade e de fatores psicossociais relacionados à doença e condições de vida

Cost-effective approach to lung cancer risk for a radiological dispersal device (RDD) scenario

A release of radioactive material into the environment can lead to hazardous exposure of the population and serious future concerns about health issues such as an increased incidence of cancer. In this context, a practical methodology capable of providing useful basic information from the scenario can be valuable for immediate decisions and future risk assessment. For this work, the simulation of a radiological dispersal device (RDD) filled with americium-241 was considered. The radiation dose simulated by the HotSpot code was used as an input to the epidemiological equations from BEIR V producing the data used to assess the risk of lung cancer development. The methodology could be useful in providing training for responders aimed to the initial support addressed to decision-making for emergency response at the early phase of an RDD scenario. The results from the simulation allow estimating (a) the size of the potentially affected population, (b) the type of protection action considering gender and location of the individuals, (c) the absorbed doses, (d) the matrix of lung cancer incidence predictions over a period of 5 years, and (e) the cost-effectiveness in the initial decision environment