Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19) patients.

Background: The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists' assessments. Methods: A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using CT scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analysed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts. Results: The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (P<0.05). No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (P<0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and the rating by radiological experts. Conclusions: The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.

Comparison of fetal heart rate baseline estimation by the cardiotocograph network and clinicians: a multidatabase retrospective assessment study.

Background: This study aims to compare the fetal heart rate (FHR) baseline predicted by the cardiotocograph network (CTGNet) with that estimated by clinicians. Material and methods: A total of 1,267 FHR recordings acquired with different electrical fetal monitors (EFM) were collected from five datasets: 84 FHR recordings acquired with F15 EFM (Edan, Shenzhen, China) from the Guangzhou Women and Children's Medical Center, 331 FHR recordings acquired with SRF618B5 EFM (Sanrui, Guangzhou, China), 234 FHR recordings acquired with F3 EFM (Lian-Med, Guangzhou, China) from the NanFang Hospital of Southen Medical University, 552 cardiotocographys (CTG) recorded using STAN S21 and S31 (Neoventa Medical, Mölndal, Sweden) and Avalon FM40 and FM50 (Philips Healthcare, Amsterdam, The Netherlands) from the University Hospital in Brno, Czech Republic, and 66 FHR recordings acquired using Avalon FM50 fetal monitor (Philips Healthcare, Amsterdam, The Netherlands) at St Vincent de Paul Hospital (Lille, France). Each FHR baseline was estimated by clinicians and CTGNet, respectively. And agreement between CTGNet and clinicians was evaluated using the kappa statistics, intra-class correlation coefficient, and the limits of agreement. Results: The number of differences <3 beats per minute (bpm), 3-5 bpm, 5-10 bpm and ≥10 bpm, is 64.88%, 15.94%, 14.44% and 4.74%, respectively. Kappa statistics and intra-class correlation coefficient are 0.873 and 0.969, respectively. Limits of agreement are -6.81 and 7.48 (mean difference: 0.36 and standard deviation: 3.64). Conclusion: An excellent agreement was found between CTGNet and clinicians in the baseline estimation from FHR recordings with different signal loss rates.

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases.

Background: The aim of the current study was to investigate the distribution and extent of lung involvement in patients with COVID-19 with AI-supported, automated computer analysis and to assess the relationship between lung involvement and the need for intensive care unit (ICU) admission. A secondary aim was to compare the performance of computer analysis with the judgment of radiological experts. Methods: A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using computed tomography (CT) scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analyzed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts. Results: The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (p < 0.05) No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (p < 0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves of the lungs and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and expert rating by radiological experts. Conclusion: The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.

Long-Term Continuous Computer Registration and Analysis of Motor Activity of a Group of Zebrafish Danio rerio.

A new algorithm for long-term continuous computer recording and analysis of motor activity of a group of zebrafish in the home tank has been developed. The movements of a group of Danio rerio during the entire light period and for several days are recorded at a frequency of 1 frame/sec in the form of short (15 min) files. Then these files are analyzed by the unique DanioStudo software, which, using a threshold algorithm and appropriate masks, calculates for each frame the sum of pixels associated with fish (the sum of fish silhouettes), and for two consecutive frames, the sum of altered pixels (the sum of altered fish silhouettes). The following indexes are calculated: the rate of sum of silhouettes alteration as the ratio of the sum of altered silhouettes to the sum of silhouettes (1) and the time spent in the selected area of the home tank as the ratio of the sum of silhouettes in this area to the sum of silhouettes in the entire tank (2). The mean rate of silhouette alteration correlates to the length of the path travelled by the fish and, therefore, serves as a correct measure of the motor activity of a group of fish. Using these algorithms, completely new data were obtained: it was shown that the motor activity of fish remains constant throughout the entire light period, but depends on the size of the home tank. The proposed approach, together with the DanioStudio software, can be effective in studying the dynamics of changes in the behavior of fish under long-term exposure to short daylight, drugs and toxic substances.

Quantitative diagnostic algorithm using endocytoscopy for superficial nonampullary duodenal epithelial tumors.

BACKGROUND AND AIM: Optical biopsy using endocytoscopy for superficial nonampullary duodenal epithelial tumors (SNADETs) is practical; however, a diagnostic algorithm has not been established. The aim of this study was to determine correlations of endocytoscopic findings of SNADETs with histology using computer analysis and to establish an algorithm. METHODS: Endocytoscopic images and histological images of duodenal lesions from 70 patients were retrospectively collected. The numbers of glands and densely stained areas with methylene blue (DSMs) per 1 mm2 and the percentage of DSMs per screen in endocytoscopy were determined. Moreover, correlations in DSMs and glands between endocytoscopy and histological images were analyzed. Histopathological diagnoses were assessed according to the revised Vienna classification. The primary outcome was correlation between the number of glands in endocytoscopy and that in histology. Finally, a diagnostic algorithm for endoscopic intervention of SNADETs with a statistical program command was established. RESULTS: The number of glands in endocytoscopic images was correlated with that in histopathological images (ρ 0.64, P < 0.001). There were significant differences in the mean number of glands between category 4/5 and category 3 (P = 0.03) and the mean percentage of DSMs between category 4/5 and category 1 (P < 0.001). When the cutoffs for the number of glands and percentage of DSMs were set at 47 per 1 mm2 and 20.8% in one screen, respectively, the area under the ROC curve was 0.89. CONCLUSIONS: Endocytoscopic images of SNADETs reflect histopathological atypia, and computer analysis provides a practical diagnostic algorithm for endoscopic intervention.

Computer analysis of abnormal proliferation and transformation cells in gastric mucosa and its clinical significance.

To investigate the image computer analysis of abnormally proliferating transformed cells of gastric mucosa and its clinical significance. The pathological pictures of gastric adenomatous polyp cells, abnormally proliferating altered cells, and tubular adenocarcinoma cells in the stomach mucosa were assessed by image computer method on a total of 96 gastroscopic biopsy and ESD resection specimens. The data of cytoplasmic area, nuclear area, nuclear-cytoplasmic ratio, nuclear factor and N-heterotypic index of gastric adenomatous polyps, abnormal proliferative transformation and gastric intramucosal tubular adenocarcinoma were collected, and the mean, standard deviation and variance were calculated respectively. Standard Error, Maximum, Minimum Parameters and Statistical Structure. There were substantial discrepancies between gastric mucosal gastric adenomatous polyp cells and gastric mucosal abnormally proliferating transformed cells, according to the five data in the abnormal cells in the stomach mucosal proliferation area (p < 0.01); There was no significant difference between cells (p > 0.05). Computer analysis of cell images can provide quantitative values for the pathological diagnosis of gastric adenomatous polyp cells, abnormally proliferating transformed cells and tubular adenocarcinoma cells in the gastric mucosa, especially the degree of atypical proliferation. The monitoring of abnormally proliferated and transformed cells in gastric mucosa is of great significance for clinicians to accurately treat and track cell transformation, and to control the occurrence and development of gastric adenocarcinoma.

Traditional vs. Automated Computer Image Analysis-A Comparative Assessment of Use for Analysis of Digital SEM Images of High-Temperature Ceramic Material.

Image analysis is a powerful tool that can be applied in scientific research, industry, and everyday life, but still, there is more room to use it in materials science. The interdisciplinary cooperation between materials scientists and computer scientists can unlock the potential of digital image analysis. Traditional image analysis used in materials science, manual or computer-aided, permits for the quantitative assessment of the coexisting components at the cross-sections, based on stereological law. However, currently used cutting-edge tools for computer image analysis can greatly speed up the process of microstructure analysis, e.g., via simultaneous extraction of quantitative data of all phases in an SEM image. The dedicated digital image processing software Aphelion was applied to develop an algorithm for the automated image analysis of multi-phase high-temperature ceramic material. The algorithm recognizes each phase and simultaneously calculates its quantity. In this work, we compare the traditional stereology-based methods of image analysis (linear and planimetry) to the automated method using a developed algorithm. The analysis was performed on a digital SEM microstructural image of high-temperature ceramic material from the Cu-Al-Fe-O system, containing four different phase components. The results show the good agreement of data obtained by classical stereology-based methods and the developed automated method. This presents an opportunity for the fast extraction of both qualitative and quantitative from the SEM images.

Variation of Chemical Composition and Antioxidant Properties of Kiwiberry (Actinidia arguta) in a Three-Year Study.

The quality of fruit as a source of bioactive ingredients is related to the genetic characteristics of plants, but it can also be modified by growing conditions. Therefore, long-term research can be extremely valuable in evaluating various crop plants, especially novel ones. The aim of the research was to test four popular European kiwiberry (Actinidia arguta) cultivars ('Geneva', 'Bingo', 'Weiki', 'Anna') in terms of selected morphological features, yield, and chemical composition as well as their variability over 3 years. It can be concluded that the studied genotypes were very diverse in terms of the biochemical compounds' concentration in individual seasons. The cultivars 'Anna' and 'Weiki' were the most similar ones with respect to each other in terms of morphology and chemical composition. The cultivars 'Bingo' and 'Geneva' were definitely different. 'Bingo' was characterized by the largest and most uniform fruits in each season and had the highest concentration of vitamin C but the lowest carotenoid concentration. In turn, 'Geneva' produced the smallest fruit in each season with the highest concentration of polyphenols and a high concentration of carotenoids and displayed the highest antioxidant capacity regardless of the determination method. The research was performed with the application of computer-supported statistical analysis.

A Review of Network and Computer Analysis of Epileptiform Discharge Free EEG to Characterize and Detect Epilepsy.

Objectives. There is emerging evidence that network/computer analysis of epileptiform discharge free electroencephalograms (EEGs) can be used to detect epilepsy, improve diagnosis and resource use. Such methods are automated and can be performed on shorter recordings of EEG. We assess the evidence and its strength in the area of seizure detection from network/computer analysis of epileptiform discharge free EEG. Methods. A scoping review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance was conducted with a literature search of Embase, Medline and PsychINFO. Predesigned inclusion/exclusion criteria were applied to selected articles. Results. The initial search found 3398 articles. After duplicate removal and screening, 591 abstracts were reviewed, 64 articles were selected and read leading to 20 articles meeting the requisite inclusion/exclusion criteria. These were 9 reports and 2 cross-sectional studies using network analysis to compare and/or classify EEG. One review of 17 reports and 10 cross-sectional studies only aimed to classify the EEGs. One cross-sectional study discussed EEG abnormalities associated with autism. Conclusions. Epileptiform discharge free EEG features derived from network/computer analysis differ significantly between people with and without epilepsy. Diagnostic algorithms report high accuracies and could be clinically useful. There is a lack of such research within the intellectual disability (ID) and/or autism populations, where epilepsy is more prevalent and there are additional diagnostic challenges.

Construction and significance of prediction model for chronic obstructive pulmonary disease assessment test based on fusion deep network fused with air data / 中华健康管理学杂志

Objective:To construct a chronic obstructive pulmonary disease (COPD) assessment test (CAT) score prediction model based on a deep network fused with air data, and to explore its significance.Methods:From February 2015 to December 2017, the outdoor environmental monitoring air data near the residential area of the patients with COPD from the Respiratory Outpatient Clinics of Peking University Third Hospital, Peking University People′s Hospital and Beijing Jishuitan Hospital were collected and the daily air pollution exposure of patients was calculated. The daily CAT scores were recorded continuously. The CAT score of the patients in the next week was predicted by fusing the time series algorithm and neural network to establish a model, and the prediction accuracy of the model was compared with that of the long short-term memory model (LSTM), the LSTM-attention model and the autoregressive integrated moving average model (ARIMA).Results:A total of 47 patients with COPD were enrolled and followed up for an average of 381.60 days. The LSTM-convolutional neural networks (CNN)-autoregression (AR) model was constructed by using the collected air data and CAT score, and the root mean square error of the model was 0.85, and the mean absolute error was 0.71. Compared with LSTM, LSTM-attention and ARIMA, the average prediction accuracy was improved by 21.69%.Conclusion:Based on the air data in the environment of COPD patients, the fusion deep network model can predict the CAT score of COPD patients more accurately.

Design and application of arterial blood gas analysis assisted by electronic computation: analysis of two cases attached / 中国小儿急救医学

Objective:To make Excel spreadsheet of arterial blood gas analysis to judge the types of acid-base imbalance quickly and accurately, and guide the clinical treatment of acid-base disorders.Methods:According to the Henderson-Hasselbalch equation, the compensation formula of acid-base imbalance prediction, the theory of acid-base balance and the related research progress, the analysis process of acid-base balance disorder was settled, and the IF function in Microsoft Office Excel 2003 was used to edit the formula to make Excel spreadsheet for arterial blood gas analysis.Once the pH value, artery blood carbon dioxide pressure (PaCO 2), actual bicarbonate (HCO 3-), sodiumion (Na + ), chlorineion (Cl -) and compensatory time-limited parameters were input, the types of acid-base imbalance can be shown.Arterial blood gas analysis of 185 cases from intensive care unit at Xuzhou Central Hospital was determined by Excel spreadsheet group and manual group respectively, the results and time of judging the type of acid-base imbalance were compared between two groups for statistical analysis. Results:The results of acid-base imbalance between two groups were compared, and 42 cases were normal and simple acid-base and the consistent rate was 100%, double acid-base imbalances of 107 cases with the consistent rate of 97.20%, triple acid-base imbalances of 36 cases with the consistent rate of 91.67%.After Kappa consistency test (Kappa value=0.944) and Pair chi-square Test (McNemar-Bowker Test)( P=0.223), the results of two groups were consistent.It took less time to judge the results of normal or simple acid-base imbalance[(32.32±4.26)s vs.(75.88±19.22)s], double acid-base imbalance[(31.28±5.31)s vs.(137.56±37.64)s] and triple acid-base imbalance[(32.98±4.23)s vs.(315.09±89.37)s] by the Excel spreadsheet group compared with the manual group, and the differences were statistically significant ( P<0.01). Conclusion:The automatic judging of Excel spreadsheet for arterial blood gas analysis can quickly and accurately determine the types of acid-base imbalance in arterial blood gas analysis and has more advantages for triple acid and base imbalance especially with simple interface and simple operation.And it can avoid the missing judgment of acid and base imbalance when pH is 7.35 to 7.45.

A Survey of Methods and Technologies Used for Diagnosis of Scoliosis.

The purpose of this article is to present diagnostic methods used in the diagnosis of scoliosis in the form of a brief review. This article aims to point out the advantages of select methods. This article focuses on general issues without elaborating on problems strictly related to physiotherapy and treatment methods, which may be the subject of further discussions. By outlining and categorizing each method, we summarize relevant publications that may not only help introduce other researchers to the field but also be a valuable source for studying existing methods, developing new ones or choosing evaluation strategies.

Neural Reduction of Image Data in Order to Determine the Quality of Malting Barley.

Image analysis using neural modeling is one of the most dynamically developing methods employing artificial intelligence. The feature that caused such widespread use of this technique is mostly the ability of automatic generalization of scientific knowledge as well as the possibility of parallel analysis of the empirical data. A properly conducted learning process of artificial neural network (ANN) allows the classification of new, unknown data, which helps to increase the efficiency of the generated models in practice. Neural image analysis is a method that allows extracting information carried in the form of digital images. The paper focuses on the determination of imperfections such as contaminations and damages in the malting barley grains on the basis of information encoded in the graphic form represented by the digital photographs of kernels. This choice was dictated by the current state of knowledge regarding the classification of contamination that uses undesirable features of kernels to exclude them from use in the malting industry. Currently, a qualitative assessment of kernels is carried by malthouse-certified employees acting as experts. Contaminants are separated from a sample of malting barley manually, and the percentages of previously defined groups of contaminations are calculated. The analysis of the problem indicates a lack of effective methods of identifying the quality of barley kernels, such as the use of information technology. There are new possibilities of using modern methods of artificial intelligence (such as neural image analysis) for the determination of impurities in malting barley. However, there is the problem of effective compression of graphic data to a form acceptable for ANN simulators. The aim of the work is to develop an effective procedure of graphical data compression supporting the qualitative assessment of malting barley with the use of modern information technologies. Image analysis can be implemented into dedicated software.

Refraction artifact on abdominal sonogram.

Ultrasonography (US) is the first-line diagnostic tool for observing the whole abdomen. Unfortunately, a wide spectrum of refraction-related artefactual images is very frequently encountered in routine US examinations. In addition, most practitioners currently perform abdominal US examinations without sufficient knowledge of refraction artifacts (RAs). This review article was designed to present many representative RA images seen in the clinical setting, with a brief explanation of the mechanism of these images, in certain cases through an analyzed and reconstructed method using computer simulation that supports clinical observations. RAs are encountered not only with B-mode US but also with Doppler US, contrast-enhanced US, and shear wave elastography. RAs change their appearance according to the situation, but they always have a significant effect on detailed interpretation of abdominal US images. Correct diagnosis of abdominal US relies on a deep understanding of each characteristic artifactual finding, which necessitates knowledge of basic US physics. When analyzing mass lesions, computer simulation analysis helps to reveal the global images of RAs around a lesion.

[Influence of morphological parameters of natural teeth and fixed partial dentures supported by dental implants on the time of occlusion]. / Vliyanie morfologicheskikh parametrov estestvennykh zubov i nes"emnykh ortopedicheskikh konstruktsii s oporoi na dental'nye implantaty na vremya okklyuzii.

OBJECTIVE: To study the effect of morphological parameters of natural teeth and fixed partial dentures supported by dental implants on the time of occlusion. MATERIAL AND METHODS: The study of the morphological parameters of the first right upper and lower molars of natural teeth and fixed partial dentures supported by dental implants in the positions of the first molars of the upper and lower jaws, followed by determination of the relationship between the obtained data and the occlusion time using mathematical and statistical analysis. RESULTS: The study of the morphology of the occlusal surface of natural teeth and fixed partial dentures supported by dental implants did not reveal significant differences in the results of measuring the distances between the cusps of the upper and lower first molars, both natural teeth and fixed partial dentures supported by dental implants. However, the angles of the slopes of the cusps and the angles between cusps in natural teeth are sharper than those of artificial crowns. CONCLUSION: According to the correlation analysis in both groups, the relationship between the occlusion time and the morphological parameters of the lower molars was determined; the relationship between the morphological parameters of the upper molars and the occlusion time was not revealed.

Intrapartum cardiotocogram monitoring between obstetricians and computer analysis.

Purpose: To investigate the accuracy of computer analysis and its features to be used as a fu fetal heart rate (FHR) interpretation method in clinical settings.Methods: The Trium CTG Online® was used as the computer analysis software. Twenty-six cases of intrapartum FHR tracings (total time, 6900 min) were randomly selected from third-trimester pregnancies. Three obstetricians blinded to the patients' clinical information traced the decelerations, variability, and baseline cardiotocogram (CTG) data. Three obstetrician observer individually interpreted the data and only the waveforms they interpreted were adopted. The agreement between the deceleration and baseline, variability, and level of five-tier system was estimated. Weighted kappa (κ) statistics were used to assess reliability.Results: Based on the observers and Trium's classification, κ was 0.78 and the strength of agreement level was substantial. The obstetricians and Trium mostly agreed on the variability and baseline data. However, κ of each deceleration was approximately 0.65 (0.63-0.66), with substantial strength of agreement.Conclusion: Based on the obstetricians and Trium's interpretation, the latter was found to be excellent for FHR interpretation. However, it was difficult for Trium to interpret specific waveform patterns. Therefore, clinical staff should understand these characteristics to more sensitively evaluate the fetal well-being.

Development of a novel and rapid phenotype-based screening method to assess rice seedling growth.

BACKGROUND: Rice (Oryza sativa) is one of the most important model crops in plant research. Despite its considerable advantages, (phenotypic) bioassays for rice are not as well developed as for Arabidopsis thaliana. Here, we present a phenotype-based screening method to study shoot-related parameters of rice seedlings via an automated computer analysis. RESULTS: The phenotype-based screening method was validated by testing several compounds in pharmacological experiments that interfered with hormone homeostasis, confirming that the assay was consistent with regard to the anticipated plant growth regulation and revealing the robustness of the set-up in terms of reproducibility. Moreover, abiotic stress tests using NaCl and DCMU, an electron transport blocker during the light dependent reactions of photosynthesis, confirmed the validity of the new method for a wide range of applications. Next, this method was used to screen the impact of semi-purified fractions of marine invertebrates on the initial stages of rice seedling growth. Certain fractions clearly stimulated growth, whereas others inhibited it, especially in the root, illustrating the possible applications of this novel, robust, and fast phenotype-based screening method for rice. CONCLUSIONS: The validated phenotype-based and cost-efficient screening method allows a quick and proper analysis of shoot growth and requires only small volumes of compounds and media. As a result, this method could potentially be used for a whole range of applications, ranging from discovery of novel biostimulants, plant growth regulators, and plant growth-promoting bacteria to analysis of CRISPR knockouts, molecular plant breeding, genome-wide association, and phytotoxicity studies. The assay system described here can contribute to a better understanding of plant development in general.

[Pilot surveillance and evaluation of influenza-like illness based on automatic computer analysis of electronic medical record in sentinel hospital].

Objective: To compare the accuracy of influenza-like illness (ILI) surveillance by automatic computer analysis based on electronic medical records and by doctor's report. Methods: A total of 3 542 patients who presented to Yichang Central Hospital fever clinic, respiratory department or emergency department between April to October 2019 with an ICD-10 code for acute respiratory illness (J00-J22) and complete electronic medical information of ILI related syndromes were drawn as the study sample. Taking the classification of the study sample according to the ILI case definition by influenza surveillance professionals as the gold standard, draw the receiver operating characteristic (ROC) curve and calculate sensitivity, specificity, diagnostic consistency to compared the accuracy of ILI surveillance by automatic computer analysis and by doctor's report. Results: Median age of 3 542 cases was 30 (24, 38) years old; 1 179 cases (33.29%) compliance with the case definition, ILI reported by doctors was 1 306 cases (36.87%), and computer automatic identification ILI were 1 150 cases (32.47%); 1 391 (39.27%) cases were men. The results of automatic computer analysis and doctor report consistency of kappa values with gold standard judgment were 0.97 and 0.66 respectively; area under the ROC curve was 0.98 and 0.84, respectively. And the sensitivity and specificity of automatic computer analysis were higher than that of doctor's report (all P values were <0.001), the sensitivity was 96.95% and 82.27%, and the specificity was 99.70% and 85.78%, respectively. Conclusion: The automatic computer analysis based on electronic medical records can identified ILI cases with good sensitivity and specificity in ILI case surveillance.

Slow uniform electrical activation during sinus rhythm is an indicator of reentrant VT isthmus location and orientation in an experimental model of myocardial infarction.

BACKGROUND: To validate the predictability of reentrant circuit isthmus locations without ventricular tachycardia (VT) induction during high-definition mapping, we used computer methods to analyse sinus rhythm activation in experiments where isthmus location was subsequently verified by mapping reentrant VT circuits. METHOD: In 21 experiments using a canine postinfarction model, bipolar electrograms were obtained from 196-312 recordings with 4mm spacing in the epicardial border zone during sinus rhythm and during VT. From computerized electrical activation maps of the reentrant circuit, areas of conduction block were determined and the isthmus was localized. A linear regression was computed at three different locations about the reentry isthmus using sinus rhythm electrogram activation data. From the regression analysis, the uniformity, a measure of the constancy at which the wavefront propagates, and the activation gradient, a measure that may approximate wavefront speed, were computed. The purpose was to test the hypothesis that the isthmus locates in a region of slow uniform activation bounded by areas of electrical discontinuity. RESULTS: Based on the regression parameters, sinus rhythm activation along the isthmus near its exit proceeded uniformly (mean r2= 0.95±0.05) and with a low magnitude gradient (mean 0.37±0.10mm/ms). Perpendicular to the isthmus long-axis across its boundaries, the activation wavefront propagated much less uniformly (mean r2= 0.76±0.24) although of similar gradient (mean 0.38±0.23mm/ms). In the opposite direction from the exit, at the isthmus entrance, there was also less uniformity (mean r2= 0.80±0.22) but a larger magnitude gradient (mean 0.50±0.25mm/ms). A theoretical ablation line drawn perpendicular to the last sinus rhythm activation site along the isthmus long-axis was predicted to prevent VT reinduction. Anatomical conduction block occurred in 7/21 experiments, but comprised only small portions of the isthmus lateral boundaries; thus detection of sinus rhythm conduction block alone was insufficient to entirely define the VT isthmus. CONCLUSIONS: Uniform activation with a low magnitude gradient during sinus rhythm is present at the VT isthmus exit location but there is less uniformity across the isthmus lateral boundaries and at isthmus entrance locations. These factors may be useful to verify any proposed VT isthmus location, reducing the need for VT induction to ablate the isthmus. Measured computerized values similar to those determined herein could therefore be assistive to sharpen specificity when applying sinus rhythm mapping to localize EP catheter ablation sites.

Phase Lag Analyses on Ictal Scalp Electroencephalography May Predict Outcomes of Corpus Callosotomy for Epileptic Spasms.

Objective: We aimed to clarify the patterns of ictal power and phase lag among bilateral hemispheres on scalp electroencephalography (EEG) recorded pre-operatively during epileptic spasms (ESs) and the correlation with the outcomes following corpus callosotomy. Methods: We enrolled 17 patients who underwent corpus callosotomy for ESs before 20 years of age. After corpus callosotomy, seven patients did not experience further ESs (favorable outcome group), and the remaining 10 patients had ongoing ESs (unfavorable outcome group). We used pre-operative scalp EEG data from monopolar montages using the average reference. The relative power spectrum (PS), ictal power laterality (IPL) among the hemispheres, and phase lag, calculated by the cross-power spectrum (CPS) among symmetrical electrodes (i.e., F3 and F4), were analyzed in the EEG data of ESs from 143 pre-operative scalp video-EEG records. Analyses were conducted separately in each frequency band from the delta, theta, alpha, beta, and gamma range. We compared the means of those data in each patient between favorable and unfavorable outcome groups. Results: Among all frequency bands, no significant differences were seen in the individual mean relative PSs in the favorable and unfavorable outcome group. Although the mean IPLs in each patient tended to be high in the unfavorable outcome group, no significant differences were found. The mean CPSs in the delta, theta, and gamma frequency bands were significantly higher in the unfavorable than in the favorable outcome group. Using the Youden index, the optimal cutoff points of those mean CPS values for unfavorable outcomes were 64.00 in the delta band (sensitivity: 100%, specificity: 80%), 74.20 in the theta band (100, 80%), and 82.05 in the gamma band (100, 80%). Subanalyses indicated that those CPS differences originated from pairs of symmetrical electrodes in the bilateral frontal and temporal areas. Significance: Ictal power and laterality of the ictal power in each frequency band were not associated with the outcomes of CC; however, the phase lags seen in the delta, theta, and gamma frequency bands were larger in the unfavorable than in the favorable outcome group. The phase lags may predict outcomes of CC for ESs on pre-surgical scalp-ictal EEGs.