Conventional Visual Analysis Research Articles

Study of defects in PV generators using image analysis techniques with Matlab

It is evidence that the useful life of the photovoltaic generator, as its correct operation, define the production and the profitability of a photovoltaic installation. There are several aspects that cause descents in the level of production of the generator and therefore losses in the installation. Based on it, it has developed numerous of both corrective and preventive maintenance that avoid these circumstances. Along the document there are revised the possible defects that the photovoltaic generator can have and the techniques of detection of the same ones. Inside the techniques of analysis and examination [1] we will center on the very concrete one: the visual inspection and its development by image analysis tools with Matlab. The techniques used are part of the tool “Image processing toolbox” that provides a finished set of algorithms of standard reference and graphic hardware to the prosecution, the analysis and the visualization of images and the development of complementary algorithms. Across the employment and the adaptation of some of the included algorithms it will be claimed to approach a step more specializing in the conventional visual analysis used in the maintenance of the plants of photovoltaic solar energy.

Radiomics analysis enhances the diagnostic performance of CMR stress perfusion: a proof-of-concept study using the Dan-NICAD dataset.

To assess the feasibility of extracting radiomics signal intensity based features from the myocardium using cardiovascular magnetic resonance (CMR) imaging stress perfusion sequences. Furthermore, to compare the diagnostic performance of radiomics models against standard-of-care qualitative visual assessment of stress perfusion images, with the ground truth stenosis label being defined by invasive Fractional Flow Reserve (FFR) and quantitative coronary angiography. We used the Dan-NICAD 1 dataset, a multi-centre study with coronary computed tomography angiography, 1,5 T CMR stress perfusion, and invasive FFR available for a subset of 148 patients with suspected coronary artery disease. Image segmentation was performed by two independent readers. We used the Pyradiomics platform to extract radiomics first-order (n = 14) and texture (n = 75) features from the LV myocardium (basal, mid, apical) in rest and stress perfusion images. Overall, 92 patients (mean age 62 years, 56 men) were included in the study, 39 with positive FFR. We double-cross validated the model and, in each inner fold, we trained and validated a per territory model. The conventional analysis results reported sensitivity of 41% and specificity of 84%. Our final radiomics model demonstrated an improvement on these results with an average sensitivity of 53% and specificity of 86%. In this proof-of-concept study from the Dan-NICAD dataset, we demonstrate the feasibility of radiomics analysis applied to CMR perfusion images with a suggestion of superior diagnostic performance of radiomics models over conventional visual analysis of perfusion images in picking up perfusion defects defined by invasive coronary angiography.

Asymmetry index in anatomically symmetrized FDG-PET for improved epileptogenic focus detection in pharmacoresistant epilepsy.

Positron emission tomography (PET) imaging has assumed an essential role in the presurgical evaluation of epileptogenic foci in drug-resistant epilepsy by identifying the hypometabolic cerebral cortex. The authors herein designed a pilot study to test a novel technique of PET asymmetry after anatomical symmetrization coregistered to MRI (PASCOM), utilizing interhemispheric metabolic asymmetry on interictal fluorine 18-labeled fluorodeoxyglucose (FDG)-PET to better localize the epileptogenic zone. The authors analyzed interictal FDG-PET scans from 23 patients with drug-resistant epilepsy, mean (± SD) age 20.9 ± 13.1 years old, who had an Engel class I postsurgical outcome while followed up for > 12 months. T1-weighted and FLAIR MRI were used to create a patient-specific, structurally symmetrical template. The asymmetry index (AI) image was computed to detect the cerebral region of hypometabolism using different z-score threshold criteria to optimize sensitivity and specificity. The detected regions were compared with the resection cavity on postoperative MRI using predefined anatomical labels. PASCOM was compared with the visual analysis of FDG-PET by a nuclear medicine consultant blinded to other clinical data (VIS) and visual analysis during multidisciplinary team discussion (MDT). The efficacy of each technique was compared based on a performance score (S), sensitivity, specificity, and correct lateralization of epileptogenicity. The mean S was maximum (1.30 ± 1.23) for AI images when thresholded at z > 4 and retaining the cluster of more than 100 voxels containing the peak AI value (Z4C) with 73.03% sensitivity and 96.43% specificity. The mean S was minimum for VIS (0.27 ± 0.31). The mean sensitivity was maximum for MDT (85.04%) and minimum for Z5C (AI images thresholded at z > 5 and clustered; 59.47%), whereas the mean specificity was maximum for Z5C (97.77%) and minimum for VIS (64.60%). Z3C (AI images thresholded at z > 3 and clustered) and Z4C were able to correctly identify the side of epileptogenicity in all the patients. The PASCOM technique with a Z4C threshold had a maximum performance score with good sensitivity and specificity in localizing and lateralizing the epileptogenic zone. The described technique outperformed the conventional visual analysis of FDG-PET and hence warrants further prospective verification.

Grading of soft tissues sarcomas using radiomics models: Choice of imaging methods and comparison with conventional visual analysis.

To determine which combination of imaging modalities/contrast, radiomics models, and how many features provides the best diagnostic performance for the differentiation between low- and high-grade soft tissue sarcomas (STS) using a radiomics approach. MRI and CT from 39 patients with a histologically confirmed STS were prospectively analyzed. Images were evaluated both quantitatively by radiomics models and qualitatively by visual evaluation (used as reference) for grading (low-grade vs high-grade). In radiomics analysis, 120 radiomic features were extracted and contributed into three models: least absolute shrinkage and selection operator with logistic regression(LASSO-LR), recursive feature elimination and cross-validation (RFECV-SVC) and analysis of variance with SVC (ANOVA-SVC). Those were applied to different combinations of imaging modalities acquisition, with and without contrast medium administration, as well as selected number of features. Fat-saturated T2w (FS-T2w) MR images using RFECV-SVC radiomic models involving five features yielded the best results with mean sensitivity, specificity, and accuracy of 92% ± 10%, 78% ± 30%, and 89% ± 12%, respectively. The performance of radiomics was better than that of conventional analysis (67% accuracy) for STS grading. Combination of multiple contrast or imaging modalities did not increase the diagnostic performance. FS-T2w MR images alone with a five-feature radiomics analysis usingh REFCV-SVC model may be able to provide sufficient diagnositic performance compared to conventional visual evaluation with multiple MRI contrast and CT imaging.

Confidence Level Estimation for Advanced Decision-Making in Transformer Short-circuit Fault Diagnosis

Frequency response analysis (FRA) is almost certainly the most efficient and well-established method for evaluating the mechanical integrity of transformer active part. FRA has a solid background in both industrial practice and academic research. Besides conventional visual analysis, a statistical analysis has been applied to the FRA data interpretation. The existing standards and recent studies have considered different statistical indicators (SIs) on an individual basis. However, the utility of each SI in its “silo” may lead to different and possibly contradictory decisions. Inspired by the bolstered error estimation technique used in pattern recognition, this article presents a new method that can utilize multiple SIs obtained from the FRA data to classify transformer operating conditions and estimate the level of confidence in decisions made. The probabilistic method proposed herein is an attempt to bridge the gap between these decision-making silos. The practical implementation of the proposed technique on distribution and power transformers revealed a reliable interpretation and classification results. At the same time, having an estimate for the level of confidence in the decision made by the method further helps engineers and utility operators make informed decisions and have a better understanding of the level of the transformer fault severity.

Automatic analysis of integrated magnetic resonance and positron emission tomography images improves the accuracy of detection of focal cortical dysplasia type IIb lesions.

We aimed to develop an efficient and objective pre-evaluation method to identify the precise location of a focal cortical dysplasia lesion before surgical resection to reduce medication use and decrease the post-operative frequency of seizure attacks. We developed a novel machine learning-based approach using cortical surface-based features by integrating MRI and metabolic PET to identify focal cortical dysplasia lesions. Significant surface-based features of 22 patients with histopathologically proven FCD IIb lesions were extracted from PET and MRI images using FreeSurfer. We modified significant parameters, trained and tested the XGBoost model using these surface-based features, and made predictions. We detected lesions in all 20 patients using the XGBoost model, with an accuracy of 91%. We used one-way chi-squared test to test the null hypothesis that the population proportion was 50% (p=0.0001), indicating that our classification of the algorithm was statistically significant. The sensitivity, specificity, and false-positive rates were 93%, 91%, and 9%, respectively. We developed an objective, quantitative XGBoost classifier that combined MRI and PET imaging features to locate focal cortical dysplasia. This automated method yielded better outcomes than conventional visual analysis and single modality quantitative analysis for surgical pre-evaluation, especially in subtle or visually unidentifiable FCD lesions. This time-efficient method would also help doctors identify otherwise overlooked details.

99mTc-white blood cell SPECT/CT to assess diabetic foot osteomyelitis remission: contribution of semi-quantitative scoring system.

We previously reported that 99mTc-White blood cell (WBC) single-photon emission computed tomography (SPECT/CT) could be a useful tool to assess diabetic foot osteomyelitis (DFO) remission and guide the duration of antibiotic treatment. The aim of the present study was to evaluate the performance and reproducibility of two analysis methods to diagnose DFO remission using 99mTc-WBC-SPECT/CT. 99mTc-WBC-SPECT/CT performed for patients with DFO at the end of antibiotic treatment were retrospectively read by two nuclear physicians (one senior and one junior). Assessment by conventional visual analysis and by the use of a semi-quantitative scoring system, the composite score index (CSI), was performed. The performance and reproducibility of methods were compared between the two nuclear physicians. Successful treatment of DFO was defined by the absence of DFO relapse at the same site within 1 year. A total of 68 patients with 74 DFO were included. Three were excluded from the analysis due to the low quality of SPECT/CT; among the 71 DFO analyzed, 11 (15.5%) had a relapse during follow-up. Performances of 99mTc-WBC-SPECT/CT to predict DFO remission with conventional visual assessment were significantly lower for junior than for senior nuclear physician with moderate inter-rater agreement (Kappa: 0.417). Performances with the use of CSI were similar between the two readers with good inter-rater agreement (Kappa: 0.756). The study found that conventional visual assessment of 99mTc-WBC-SPECT/CT to assess DFO remission requires experience, and supported that CSI could be useful for junior nuclear physician to discriminate residual infections and inflammatory post-treatment uptake.

Brain MRI post-processing with MAP07 in the preoperative evaluation of patients with pharmacoresistant epilepsy – Croatian single centre experience

ObjectiveThis study aimed to determine the role of brain MRI post-processing method MAP07 (Morphometric Analysis Program) in detecting epileptogenic brain lesions in patients with pharmacoresistant epilepsy (PE). MAP07 is a sophisticated diagnostic program that offers several morphometric maps and facilitates the detection and localization of hippocampal sclerosis (HS), focal cortical dysplasias (FCD), and other types of cortical malformations, which could be undetected by conventional visual MRI analysis (CVA). Methods120 patients aged > 16 years with PE have been recruited. 3 T MRI was performed according to epilepsy imaging protocol followed by image postprocessing with a fully automated MATLAB script, MAP07, by applying SPM5 algorithms. Statistical analysis was performed in IBM SPSS Statistics, version 25.0. ResultsAnalysis in our patients showed a high sensitivity of MAP07 with low specificity and with a high proportion of false-positive patients. After MRI analysis, out of 120 patients, 32 were found to have no structural abnormalities by conventional visual analysis in whom after MAP07 in 5 patients structural lesions were found (in one HS, in one FCD, in two perinatal vascular lesions, and in one hippocampal hyperintensity). There was a quite high overall coincidence of the findings of MAP07 and MRI for the detection of FCD, HS, perinatal ischemia/chronic vascular lesions, heterotopias, and polymicrogyria (kappa coefficient above 0.700). ConclusionsMAP07 analysis is a useful, additional, and automated method that may guide re-evaluation of MRI by highlighting suspicious cortical regions, as a complementary method to CVA, by enhancing the visualization of cortical malformations and lesions.

EEG markers predictive of epilepsy risk in pediatric cerebral malaria – A feasibility study

ObjectiveCerebral malaria (CM) affects 500,000 million children annually, 10% whom develop epilepsy within two years. Acute identification of biomarkers for post-CM epilepsy would allow for follow-up of the highest risk populations in resource-limited regions. We investigated the utility of electroencephalogram (EEG) and clinical metrics obtained during acute CM infection for predicting epilepsy. MethodsWe analyzed 70 EEGs recorded within 24 h of admission for CM hospitalization obtained during the Blantyre Malaria Project Epilepsy Study (2005–2007), a prospective cohort study of pediatric CM survivors. While all studies underwent spectral analyses for comparisons of mean power band frequencies, a subset of EEGs from the 10 subjects who developed epilepsy and 10 age- and sex-matched controls underwent conventional visual analysis. Findings were tested for relationships to epilepsy outcomes. ResultsTen of the 70 subjects developed epilepsy. There were no significant differences between groups that were analyzed via visual EEG review; however, spectral EEG analyses revealed a significantly higher gamma-delta power ratio in CM survivors who developed epilepsy (0.23 ± 0.10) than in those who did not (0.16 ± 0.06), p = 0.003. Excluding potential confounders, multivariable logistic-regression analyses found relative gamma power (p = 0.003) and maximum temperature during admission (p = 0.03) significant and independent predictors of post-CM epilepsy, with area under receiver operating characteristics (AUROC) curve of 0.854. ConclusionsWe found that clinical and EEG metrics acquired during acute CM presentation confer risk of post-CM epilepsy. Further studies are required to investigate the utility of gamma activity as a potential biomarker of epileptogenesis and study this process over time. Additionally, resource limitations currently prevent follow-up of all CM cases to surveil for epilepsy, and identification of acute biomarkers in this population would offer the opportunity to allocate resources more efficiently.

Prognostic Value and Reproducibility of AI-assisted Analysis of Lung Involvement in COVID-19 on Low-Dose Submillisievert Chest CT: Sample Size Implications for Clinical Trials.

PurposeTo compare the prognostic value and reproducibility of visual versus AI-assisted analysis of lung involvement on submillisievert low-dose chest CT in COVID-19 patients.Materials and MethodsThis was a HIPAA-compliant, institutional review board-approved retrospective study. From March 15 to June 1, 2020, 250 RT-PCR confirmed COVID-19 patients were studied with low-dose chest CT at admission. Visual and AI-assisted analysis of lung involvement was performed by using a semi-quantitative CT score and a quantitative percentage of lung involvement. Adverse outcome was defined as intensive care unit (ICU) admission or death. Cox regression analysis, Kaplan-Meier curves, and cross-validated receiver operating characteristic curve with area under the curve (AUROC) analysis was performed to compare model performance. Intraclass correlation coefficients (ICCs) and Bland- Altman analysis was used to assess intra- and interreader reproducibility.ResultsAdverse outcome occurred in 39 patients (11 deaths, 28 ICU admissions). AUC values from AI-assisted analysis were significantly higher than those from visual analysis for both semi-quantitative CT scores and percentages of lung involvement (all P<0.001). Intrareader and interreader agreement rates were significantly higher for AI-assisted analysis than visual analysis (all ICC ≥0.960 versus ≥0.885). AI-assisted variability for quantitative percentage of lung involvement was 17.2% (coefficient of variation) versus 34.7% for visual analysis. The sample size to detect a 5% change in lung involvement with 90% power and an α error of 0.05 was 250 patients with AI-assisted analysis and 1014 patients with visual analysis.ConclusionAI-assisted analysis of lung involvement on submillisievert low-dose chest CT outperformed conventional visual analysis in predicting outcome in COVID-19 patients while reducing CT variability. Lung involvement on chest CT could be used as a reliable metric in future clinical trials.

Focal cortical dysplasia II-related seizures originate from the bottom of the dysplastic sulcus: A stereoelectroencephalography study

ObjectivesFocal cortical dysplasia (FCD) II is a frequently observed histopathological substrate in epilepsy surgery. In the present study, we explored the spatial distribution of epileptogenic activities across FCD II lesions using stereoelectroencephalography. MethodsPatients with histopathologically confirmed type II FCDs and who had at least one depth electrode that go through the wall of the dysplastic sulcus from the surface to the bottom were included. The dysplastic sulci were divided into the bottom and non-bottom parts manually, and contacts were defined as bottom or non-bottom contacts according to their locations. Factors (bottom location, pathological subtype, magnetic resonance imaging manifestation, and presence of bottom-of-sulcus dysplasia) potentially associated with earliest onset identified by conventional visual analysis, epileptogenicity index (EI), and standardized number of high-frequency oscillations (HFOs) were analyzed. Linear regression analyses between distance (from the location of the analyzed contact to the bottom of the sulcus) and EI value and HFO number were performed. ResultsSixteen patients with 19 depth electrodes containing 112 valid contacts were included. Bottom location was the sole factor significantly associated with earliest onset (P < 0.001), EI value (P < 0.001), and HFO number (P < 0.001). Most earliest onsets were recorded by the bottom contacts, bottom contacts had higher EI value (0.81 ± 0.28 vs. 0.31 ± 0.24, P < 0.001) and more HFOs (0.78 ± 0.28 vs. 0.35 ± 0.31, P < 0.001) than non-bottom contacts. Moreover, the EI value (R = −0.72, P < 0.001) and HFO number (R = −0.64, P < 0.001) were significantly negatively correlated with distance, regardless of histopathological subtype, MRI manifestation, or absence of bottom-of-sulcus dysplasia. ConclusionSeizure onsets and interictal HFOs most often arise from the bottom part of a sulcus with type II FCD. SignificanceThe findings of the present study contribute to intracranial electrode selection, trajectory planning, and, later on, resection of this kind of malformation.

Clinical Value of Machine Learning in the Automated Detection of Focal Cortical Dysplasia Using Quantitative Multimodal Surface-Based Features.

Objective: To automatically detect focal cortical dysplasia (FCD) lesion by combining quantitative multimodal surface-based features with machine learning and to assess its clinical value.Methods: Neuroimaging data and clinical information for 74 participants (40 with histologically proven FCD type II) was retrospectively included. The morphology, intensity and function-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface and fed to an artificial neural network. The classifier performance was quantitatively and qualitatively assessed by performing statistical analysis and conventional visual analysis.Results: The accuracy, sensitivity, specificity of the neural network classifier based on multimodal surface-based features were 70.5%, 70.0%, and 69.9%, respectively, which outperformed the unimodal classifier. There was no significant difference in the detection rate of FCD subtypes (Pearson’s Chi-Square = 0.001, p = 0.970). Cohen’s kappa score between automated detection outcomes and post-surgical resection region was 0.385 (considered as fair).Conclusion: Automated machine learning with multimodal surface features can provide objective and intelligent detection of FCD lesion in pre-surgical evaluation and can assist the surgical strategy. Furthermore, the optimal parameters, appropriate surface features and efficient algorithm are worth exploring.

HR-EEG Source localization

High-resolution EEG (HR-EEG) allows the recording of electric brain activities with excellent temporal resolution. Data have to be recorded with high temporal resolution (sampling rate) and high spatial resolution (number of channels). Data analyses are based on several steps including interictal and/or ictal selections, head model elaboration and use of several algorithms in order to solve the inverse problem. These tools now represent real methods of Electric Source Imaging. HR-EEG constitutes non-invasive and complementary examination with Magnetoencephalography (MEG), characterized by distinct sensitivities according to the location and orientation of intracerebral generators. In the presurgical assessment of drug-resistant partial epilepsies, HR-EEG can characterize and localize interictal activities and thus the irritative zone. HR-EEG often yields significant additional data that are complementary to other presurgical investigations. In some lesional epilepsy cases as epilepsies related to focal cortical dysplasia, localization of the irritative zone using HR-EEG is particularly relevant in presurgical evaluation, epileptogenic and irritative zones being characterized by an overlap. It has been demonstrated that HR-EEG more accurately estimated the epileptogenic zone in subgroups of patients who are often the most difficult cases in epilepsy surgery: frontal lobe epilepsy, negative MRI and the presence of malformation of cortical development [1] . Moreover, source localization results may help in identifying subtle focal cortical dysplasia abnormalities and increasing the diagnostic yield of conventional MRI visual analysis [3] . During presurgical evaluation of pharmacoresistant partial epilepsies, stereoelectroencephalography records interictal and ictal activities directly but is inherently limited in spatial sampling. In contrast, scalp-EEG and MEG are less sensitive but provide a global view on brain activity. Therefore, recording simultaneously these three modalities [2] should provide a better understanding of the underlying brain sources by taking advantage of the different sensitivities of the three recording techniques.

Performance of Spectrogram-Based Seizure Identification of Adult EEGs by Critical Care Nurses and Neurophysiologists.

Continuous EEG screening using spectrograms or compressed spectral arrays (CSAs) by neurophysiologists has shorter review times with minimal loss of sensitivity for seizure detection when compared with visual analysis of raw EEG. Limited data are available on the performance characteristics of CSA-based seizure detection by neurocritical care nurses. This is a prospective cross-sectional study that was conducted in two academic neurocritical care units and involved 33 neurointensive care unit nurses and four neurophysiologists. All nurses underwent a brief training session before testing. Forty two-hour CSA segments of continuous EEG were reviewed and rated for the presence of seizures. Two experienced clinical neurophysiologists masked to the CSA data performed conventional visual analysis of the raw EEG and served as the gold standard. The overall accuracy was 55.7% among nurses and 67.5% among neurophysiologists. Nurse seizure detection sensitivity was 73.8%, and the false-positive rate was 1-per-3.2 hours. Sensitivity and false-alarm rate for the neurophysiologists was 66.3% and 1-per-6.4 hours, respectively. Interrater agreement for seizure screening was fair for nurses (Gwet AC1 statistic: 43.4%) and neurophysiologists (AC1: 46.3%). Training nurses to perform seizure screening utilizing continuous EEG CSA displays is feasible and associated with moderate sensitivity. Nurses and neurophysiologists had comparable sensitivities, but nurses had a higher false-positive rate. Further work is needed to improve sensitivity and reduce false-alarm rates.

Accuracy of Limited-Montage Electroencephalography in Monitoring Postanoxic Comatose Patients.

Continuous EEG (cEEG) monitoring may help to identify the small percentage of adults with hypoxic-ischemic encephalopathy (HIE) who will regain consciousness if allowed sufficient time. However, the limited yield in this population has led some to question the cost-effectiveness cEEG monitoring in this population. We hypothesized that limited-montage cEEG could provide essentially the same neurophysiologic information at lower cost. In this proof of concept study, we aim to demonstrate the potentials of limited channel EEG in prognostication in postanoxic patients. We retrospectively reviewed cEEG data from cases monitored at our institution with conventional 21-channel EEG over a 6-month period. Twenty-eight cases were identified in which patients with HIE underwent cEEG for at least 24 hours. Gold-standard findings were determined by conventional visual analysis of the full cEEG, and 2 independent electroencephalographers scored the same data using only limited-montage (4-channel) views. The sensitivity and specificity of limited-montage cEEG review were compared with conventional analysis. We also compared the relative costs of conventional and limited-montage EEG. Using 4-channel limited montage cEEG, reviewers were able to classify accurately background continuity (in 88%), background amplitude (in 81%), maximum background frequency (in 70%), periodic epileptiform discharges, including a seizure (in 92%) and sporadic discharges (in 91%). All epileptiform features were detected with greater than 90% sensitivity and specificity. Eye movement artifact seen over bifrontal electrodes gave false positive detections of periodic epileptiform discharges in 31% of cases. Limited-channel continuous EEG monitoring can provide meaningful electrophysiological data that can be used for prognostication in postanoxic comatose patients. Limited channel EEG can be a cost-effective alternative to conventional EEG monitoring in post-anoxic comatose patients.

Rewarming affects EEG background in term newborns with hypoxic-ischemic encephalopathy undergoing therapeutic hypothermia.

To investigate how rewarming impacts the evolution of EEG background in neonates with hypoxic-ischemic encephalopathy (HIE) undergoing therapeutic hypothermia (TH). We recruited a retrospective cohort of 15 consecutive newborns with moderate (9) and severe (6) HIE monitored with a continuous EEG during TH and at least 12h after its end. EEG background was analyzed using conventional visual and quantitative EEG analysis methods including EEG discontinuity, absolute and relative spectral magnitudes. One patient with seizures on rewarming was excluded from analyses. Visual and quantitative analyses demonstrated significant changes in EEG background from pre- to post-rewarming, characterized by an increased EEG discontinuity, more pronounced in newborns with severe compared to moderate HIE. Neonates with moderate HIE also had an increase in the relative magnitude of slower delta and a decrease in higher frequency theta and alpha waves with rewarming. Rewarming affects EEG background in HIE newborns undergoing TH, which may represent a transient adaptive response or reflect an evolving brain injury. EEG background impairment induced by rewarming may represent a biomarker of evolving encephalopathy in HIE newborns undergoing TH and underscores the importance of continuously monitoring the brain health in critically ill neonates.

Quantitative Dobutamine Stress Echocardiography Using Speckle-Tracking Analysis versus Conventional Visual Analysis for Detection of Significant Coronary Artery Disease after ST-Segment Elevation Myocardial Infarction.

Residual ischemia detection after ST-segment elevation myocardial infarction (STEMI) during dobutamine stress echocardiography (DSE) using visual analysis is challenging. The aim of the present study was to investigate the feasibility and accuracy of two-dimensional speckle-tracking strain DSE to detect significant coronary artery disease (CAD) after STEMI. First STEMI patients (n = 105; mean age, 60 ± 11 years; 86% men) treated with primary percutaneous coronary intervention undergoing full-protocol DSE at 3 months and repeat coronary angiography within 1 year were retrospectively included. Using two-dimensional speckle-tracking echocardiography, segmental and global left ventricular peak longitudinal systolic strain (PLSS) at rest and peak stress and change (Δ) in PLSS were measured. Significant CAD was defined as detection of >70% diameter stenosis at coronary angiography. In total, 1,653 (93%) and 1,645 (92%) segments were analyzable at rest and peak stress, respectively. At follow-up, 38 patients (36%) showed significant angiographic CAD. These patients demonstrated greater worsening in global PLSS from rest to peak (-16.8 ± 0.5% to -12.6 ± 0.5%) compared with patients without significant CAD (-16.6 ± 0.4% to -14.3 ± 0.3%; group-stage interaction P < .001). The optimal cutoff of ΔPLSS for the detection of significant CAD on receiver operating characteristic curve analysis was ≥1.9% (area under the curve, 0.70; sensitivity, 87%; specificity, 46%; accuracy, 60%). Using a sentinel segment approach (apex, midposterior, and midinferior for the left anterior descending, left circumflex, and right coronary artery territories, respectively), larger segmental ΔPLSS was also independently associated with significant CAD (odds ratio, 1.1; 95% CI, 1.1-1.2). Two-dimensional speckle-tracking echocardiographic strain analysis is feasible on DSE after STEMI and represents a promising new technique to detect significant angiographic CAD at follow-up.

Post-processing of structural MRI for individualized diagnostics.

Currently, a relevant proportion of all histopathologically proven focal cortical dysplasia (FCD) escape visual detection; this shows the need for additional improvements in analyzing MRI data. A positive MRI is still the strongest prognostic factor for postoperative freedom of seizures. Among several post-processing methods voxel-based morphometry (VBM) of T1- and T2-weighted sequences and T2 relaxometry are routinely applied in pre-surgical diagnostics of cryptogenic epilepsy in epilepsy centers. VBM is superior to conventional visual analysis with 9-15% more identified epileptogenic foci, while T2 relaxometry has its main application in (mesial) temporal lobe epilepsy. Further methods such as surface-based morphometry (SBM) or diffusion tensor imaging are promising but there is a lack of current studies comparing their individual diagnostic value. Post-processing methods represent an important addition to conventional visual analysis but need to be interpreted with expertise and experience so that they should be apprehended as a complementary tool within the context of the multi-modal evaluation of epilepsy patients. This review will give an overview of existing post-processing methods of structural MRI and outline their clinical relevance in detection of epileptogenic structural changes.

Utility of Ganglion Cell Complex Analysis in Early Diagnosis and Monitoring of Glaucoma using a Different Spectral Domain Optical Coherence Tomography.

ABSTRACTPurpose: To determine the importance of ganglion cell complex (GCC) analysis as a parameter for early diagnosis of glaucoma and for following glaucoma progression and to compare glaucoma progression with conventional visual field analysis using a different type of spectral-domain optical coherence tomography (SD-OCT).Materials and methods: Two hundred eyes including 68 normal eyes, 70 eyes with pre-perimetric glaucoma and 62 eyes with perimetric glaucoma were analyzed in this prospective study undertaken during Jan 2013 to Dec 2013 in a tertiary ophthalmology institute. Automated visual field examination was done to group the subjects in above three categories. The thicknesses of the GCC and retinal nerve fiber layer (pRNFL) were measured using Topcon model 2000 version 7.1 SD-OCT images and compared. The statistical analysis was carried out by z-test.Results: The average GCC was thickest in the normal group and the thickness decreased as the severity of glaucoma increased. The mean macular GCC at the start and end of the study in pre-perimetric (94.86 ± 8.31, 90.74 ± 8.46) and perimetric (82.48 ± 13.21, 79.80 ± 12.88) eyes was lower than those in normals (102.70 ± 7.19, 101.82 ± 7.42).Conclusion: Majority of the studies done on GCC analysis have used the Cirrus OCT (Zeiss). Our study has used the Topcon model 2000 version 7.1 to show that irrespective of the machine used, GCC analysis definitely plays an important role. To detect pre-perimetric glaucoma and may show progression earlier than pRNFL in pre-perimetric glaucoma.How to cite this article: Bhagat PR, Deshpande KV, Natu B. Utility of Ganglion Cell Complex Analysis in Early Diagnosis and Monitoring of Glaucoma using a Different Spectral Domain Optical Coherence Tomography. J Curr Glaucoma Pract 2014;8(3):101-106.

Optical Classification of Quartz Lascas by Artificial Neural Networks

A gradation method based on quartz lascas (lumps) transparency level is proposed. The samples were irradiated by transmitting light, and the images histograms were processed by artificial neural networks. Additionally, the results were compared to conventional classification methods, including density and visual analysis. The network designed with backpropagation architecture using 4 hidden layers of 10 neurons yielded to a relative error <24% in relation to manual classification, indicating a good agreement to the miners criteria. Furthermore, the implementation of competitive learning with 5 neurons resulted in correct discrimination of samples regarding their optical characteristics with a completely non-subjective approach.