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Pivotal crash factors are investigated, and crash-severity model for the safety assessment at roundabouts and its vicinity in non-lane based heterogenous traffic is developed. An ordered-probit model was developed using crash-data collected between 2015–2019 for 20 roundabouts in India. The analysis revealed critical influencing parameters for determining the severity-level of crash outcomes at roundabouts, namely, inscribed-circle diameter, height of central island, number of circulatory lanes, presence of splitter island and median, posted-speed limit, type of collision, type of violation behaviour, collision partner, the pattern of collision, presence of road lane-marking, presence of street-light and age of victims. To precisely quantify the impact of each significant factor, marginal effects analysis was also carried out. The results show that the probability of fatal-injuries increased by 14.28% due to angle-collision, 15% for hit-pedestrians, 20.6% due to the pattern of collision and 15.60% due to the collision-partner, Whereas the probability of occurrence of grievous injury was the highest for rear-end with 17%, followed by sideswipe collision with 16% respectively. This study's findings can aid in developing effective remedies to reduce the crash severity for roundabouts road-users and updating the roundabout design standards, considering the safety perceptive. [ABSTRACT FROM AUTHOR]

Introduction: Indonesia is significantly affected by the emergence of the Omicron variant during the coronavirus disease 2019 COVID-19 pandemic, with one of the most prominent case increases on February 2022 (third wave of the pandemic). Several factors, ranging from demographics to laboratory tests, have been linked to COVID-19 patients clinical manifestations and outcomes. One of the possible factors that can affect COVID-19 patients' status is the neutrophil extracellular traps (NETs). We aimed to determine NETs level along with demographic, clinical, and laboratory characteristics in COVID-19 patients and analyze its relationship with disease severity and survival. Methods: One hundred confirmed COVID-19 patients at our hospital were recruited during February to September 2022. They were then divided into groups based on severity (mild-moderate and severe-critical) and outcome (survive and non-survive). The analysis included univariate and bivariate testing, including hazard ratio examination. Results: Clinical symptoms (headache, shortness of breath), pneumonia on chest X-ray, comorbidities (immune disorders, cerebrovascular disease), physical examination (unconsciousness, increased respiratory rate), and laboratory test (elevated C-reactive protein/CRP) all had an impact on patient survival. Meanwhile, similar patterns also observed on the severity-affecting factors, in addition to some variables such as abdominal pain, onset, systolic blood pressure, urea, creatinine, and procalcitonin. The NETs itself did not have any statistically significant role in the severity and survival of COVID-19 patients. Conclusions: Clinical and laboratory variables played a crucial role in the prognostic and severity determination of COVID-19 patients, while NETs did not contribute to it.

The Internet of Things (IoT) refers to a network of interconnected devices as well as technology that enables objects to communicate with one another and the cloud for modern medical treatment. To analyze and handle remotely collected electronic clinical records, it is important to create a disease prediction model with increased accuracy. An RHMIoT framework is proposed in a secure cloud context using lightweight block encryption and decryption approaches. The accuracy levels of cardiac disease are calculated using machine learning and deep learning methods. The ensemble voting classifier provided the greatest accuracy of 95%. [ABSTRACT FROM AUTHOR]

Road traffic accidents are considered a significant problem which ruins the life of many people and also causes major economic losses. So, this issue is considered a hot research topic, and many researchers all over the world are focusing on developing a solution to this most challenging problem. Traditionally the accident spots are detected by means of transportation experts, and following that, some of the statistical models such as linear and nonlinear regression were used for accident severity prediction. However, these traditional approaches do not have the capability to analyze the relationship between the influential factor and accident severity. To address this issue, an Artificial Neural Network (ANN) classifier based vulnerable accident prediction model is proposed in this current research. Initially, the past accident data over the past period of years is collected from a specified area. The acquired data consists of a variable factor related to road infrastructure, weather condition, area of the accident, type of injury and driving characteristics. Then, to standardize the raw input data, min-max normalization is used as a pre-processing technique. The pre-processed is sent for the feature selection process in which essential features are selected by correlating the variable factor with accident severity prediction. Following that, the dimension of the features is reduced using Latent Sematic Index (LSI). Finally, the reduced features are fetched into the ANN classifier for predicting the severity of accidents such as low, medium and high. Simulation analysis of the proposed accident prediction model is carried out by evaluating some of the performance metrics for three datasets. Accuracy, error, specificity, recall and precision attained for the proposed model using dataset 1 is 96.3, 0.03, 98 and 98%. Through this proposed vulnerable accident prediction model, the severity of accidents can be analyzed effectively, and road safety levels can be improved. [ABSTRACT FROM AUTHOR]

In diagnosing and treating Diabetic Retinopathy, segmenting and classifying retinal images is a difficult job. Fundus Retinal Imaging is used to diagnose diabetics and it provides additional detail for obtaining Retinal Image sequences. The proposed study uses a Fractional Jaya Optimizer-based Deep Convolutional Neural Network (FJO-DCNN) to classify blood vessels in retinal images segmented by the optic disc. The segments are created using a clustering mechanism known as Particle Swarm Optimization (PSO), which ensures the efficacy of optimum segment placement, allowing for more precise detection of the optic disc. Finally, using this hybrid algorithm, the intensity degree is determined, and a better output score is obtained, despite the Fuzzy judgement mapping. The proposed study computed the best values for accuracy, sensitivity, and specificity in FJO-DCNN for blood vessel classification. [ABSTRACT FROM AUTHOR]

The wheat canopy reflectance spectrum is affected by many internal and external factors such as diseases and growth stage. Separating the effects of disease stress on the crop from the observed mixed signals is crucial for increasing the precision of remote sensing monitoring of wheat stripe rust. The canopy spectrum of winter wheat infected by stripe rust was processed with the difference-in-differences (DID) algorithm used in econometrics. The monitoring accuracies of wheat stripe rust before and after processing with the DID algorithm were compared in the presence of various external factors, disease severity, and several simulated satellite sensors. The correlation between the normalized difference vegetation index processed by the DID algorithm (NDVI-DID) and the disease severity level (SL) increased in comparison with the NDVI before processing. The increase in precision in the natural disease area in the field in the presence of large differences in growth stage, growth, planting, and management of the crop was greater than that in the controlled experiment. For low disease levels (SL 40%). According to the measured hyperspectral data, the spectral reflectance of three satellite sensor levels was simulated. The wide-band NDVI was calculated. Compared with the wide-band NDVI and vegetation indexes (VIS) before DID processing, there were increases in the correlation between SL and the various types of VIS-DID, as well as in the correlation between SL and NDVI-DID. It is feasible to apply the DID algorithm to multispectral satellite data and diverse types of VIS for monitoring wheat stripe rust. Our results improve the quantification of independent effects of stripe rust infection on canopy reflectance spectrum, increase the precision of remote sensing monitoring of wheat stripe rust, and provide a reference for remote sensing monitoring of other crop diseases.

The evaluation of earthquake damage is central to assessing its severity and damage characteristics. However, the methods of assessment encounter difficulties concerning the subjective judgments and interpretation of the evaluators. Thus, it is mainly geologists, seismologists, and engineers who perform this exhausting task. Here, we explore whether an evaluation made by semiskilled people and by the crowd is equivalent to the experts' opinions and, thus, can be harnessed as part of the process. Therefore, we conducted surveys in which a cohort of graduate students studying natural hazards (n = 44) and an online crowd (n = 610) were asked to evaluate the level of severity of earthquake damage. The two outcome datasets were then compared with the evaluation made by two of the present authors, who are considered experts in the field. Interestingly, the evaluations of both the semiskilled cohort and the crowd were found to be fairly similar to those of the experts, thus suggesting that they can provide an interpretation close enough to an expert's opinion on the severity level of earthquake damage. Such an understanding may indicate that although our analysis is preliminary and requires more case studies for this to be verified, there is vast potential encapsulated in crowd-sourced opinion on simple earthquake-related damage, especially if a large amount of data is to be handled. [ABSTRACT FROM AUTHOR]

Farmers may decide to select an appropriate insecticide for rice-leaf disease treatment in a paddy rice field based on disease class and severity level. To classify the class of rice leaf disease and estimate the severity level in a paddy rice field, several parts of the rice leaf are included in a captured image, and sometimes there exists more than one disease boundary in a part of rice leaf. This article proposes a method of rice-leaf disease classification and severity level estimation for multiple diseases on a multiple rice-leaf image. This method first finds rice-leaf candidate boundaries and identifies the rice leaf based on its feature of color, shape, and area ratio. To enlarge classification tolerance based on the coarse-to-fine concept, disease candidate boundaries are categorized into two major groups in the coarse level, and then both groups are classified into rice leaf classes in the fine level. To evaluate the performance of the proposed method, experiments were performed with 8,303 images of three rice leaf diseases including brown spot, rice blast, rice hispa and healthy rice leaf, and our proposed method achieved 99.27% which outperformed the deep learning approach by 0.43%. [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIFyield (normalized by absorbed photosynthetically active radiation), fluorescence yield (ΦF) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (ΦF-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL < 20%). The strongest correlation between NDVI and SL (R = 0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), ΦF-r (R = 0.53) and SIF (R = 0.51), and the response of ΦF (R = 0.45) and SIFyield (R = 0.34) to SL was weak. Under lightly diseased conditions, ΦF-r (R = 0.62) showed the strongest response to disease, followed by SIFyield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between ΦF and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, ΦF-r estimated by the newly proposed 'NIRvR approach' (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for ΦF) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of ΦF-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress. [ABSTRACT FROM AUTHOR]

Road safety is of major interest for highway and traffic engineers worldwide. In Jordan, road networks have recently displayed relatively high traffic volumes, specifically in urban centers and in the Central Business District (CBD) areas of major cities. Irbid is one of the major cities suffering from serious traffic accidents problems that should have received more attention from decision makers. Based on data of 94,356 accidents occurred over a five-year period (from 2013 to 2017), Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques have been employed to predict and classify traffic accidents in this city. ANN has been used to model the relationship between driver injury severity and traffic accident factors, such as age and gender of drivers, type and faults of vehicles, weather conditions and reasons of accidents. The structure of the used ANN model has been one input layer of 6 neurons, with one hidden layer of 15 neurons and one output layer of 3 neurons representing severity level. The ANN model has showed high correlation based on the high value of correlation coefficient R=0.87. The used ANN model has exhibited better results in predicting new accidents with MSE equal to 0.05, compared with SVM model with MSE of 0.09. Sensitivity analysis has been carried out on the trained neural network to identify the importance of crash-related factors. The traffic accident data have been used to build the classifier, using SVM. The overall model classification performance has been 90.4%, which accounts for the circumstances under which drivers are more likely to be killed or injured in a vehicle accident. It has been concluded that the comprehensive performance of the SVM model is better than the ANN model for traffic accidents classification. Copyright © 2023 Praise Worthy Prize S.r.l. - All rights reserved. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) refers to a network of interconnected devices as well as technology that enables objects to communicate with one another and the cloud for modern medical treatment. To analyze and handle remotely collected electronic clinical records, it is important to create a disease prediction model with increased accuracy. An RHMIoT framework is proposed in a secure cloud context using lightweight block encryption and decryption approaches. The accuracy levels of cardiac disease are calculated using machine learning and deep learning methods. The ensemble voting classifier provided the greatest accuracy of 95%.

Aphasia is a type of speech disorder that can cause speech defects in a person. Identifying the severity level of the aphasia patient is critical for the rehabilitation process. In this research, we identify ten aphasia severity levels motivated by specific speech therapies based on the presence or absence of identified characteristics in aphasic speech in order to give more specific treatment to the patient. In the aphasia severity level classification process, we experiment on different speech feature extraction techniques, lengths of input audio samples, and machine learning classifiers toward classification performance. Aphasic speech is required to be sensed by an audio sensor and then recorded and divided into audio frames and passed through an audio feature extractor before feeding into the machine learning classifier. According to the results, the mel frequency cepstral coefficient (MFCC) is the most suitable audio feature extraction method for the aphasic speech level classification process, as it outperformed the classification performance of all mel-spectrogram, chroma, and zero crossing rates by a large margin. Furthermore, the classification performance is higher when 20 s audio samples are used compared with 10 s chunks, even though the performance gap is narrow. Finally, the deep neural network approach resulted in the best classification performance, which was slightly better than both K-nearest neighbor (KNN) and random forest classifiers, and it was significantly better than decision tree algorithms. Therefore, the study shows that aphasia level classification can be completed with accuracy, precision, recall, and F1-score values of 0.99 using MFCC for 20 s audio samples using the deep neural network approach in order to recommend corresponding speech therapy for the identified level. A web application was developed for English-speaking aphasia patients to self-diagnose the severity level and engage in speech therapies. [ABSTRACT FROM AUTHOR]

The evaluation of earthquake damage is central to assessing its severity and damage characteristics. However, the methods of assessment encounter difficulties concerning the subjective judgments and interpretation of the evaluators. Thus, it is mainly geologists, seismologists, and engineers who perform this exhausting task. Here, we explore whether an evaluation made by semiskilled people and by the crowd is equivalent to the experts’ opinions and, thus, can be harnessed as part of the process. Therefore, we conducted surveys in which a cohort of graduate students studying natural hazards (n = 44) and an online crowd (n = 610) were asked to evaluate the level of severity of earthquake damage. The two outcome datasets were then compared with the evaluation made by two of the present authors, who are considered experts in the field. Interestingly, the evaluations of both the semiskilled cohort and the crowd were found to be fairly similar to those of the experts, thus suggesting that they can provide an interpretation close enough to an expert’s opinion on the severity level of earthquake damage. Such an understanding may indicate that although our analysis is preliminary and requires more case studies for this to be verified, there is vast potential encapsulated in crowd-sourced opinion on simple earthquake-related damage, especially if a large amount of data is to be handled.

Hyperlipidemia is an abnormal lipid metabolism disorder that is distinguished by an escalation of lipid levels in the blood. Hyperlipidemia is often accompanied by other diseases. Therefore, treatment of hyperlipidemia is often used in unification with other antidotes, which can increase the occurrence of antidote relationships. A bidder relationship is a state in which a substance or bidder's activity is capable of manifesting a peaking or diminishing effect or a new effect. The purpose of this research is to understand whether there is an interaction of hyperlipidemic drugs in Pertamina patients at one of Kimia Farma Pharmacies in Bandung City based on the number of patients, number of cases, interaction mechanism and severity. This research uses a nonexperimental descriptive research type approach by collecting data retrospectively based on prescription data during March 2021, with a sample of 87 prescriptions that meet the inclusion criteria. Data were analyzed descriptively using Stockley's Handbook of Drug Interactions literature. Of patients with hyperlipidemia, who have the potential to experience drug interactions as many as 28 Prescriptions. Based on the results of the research, it was found that the severity level of drug interaction cases which stated the severity level of major interactions was 1%, Moderate was 30% and minor was 1% while those that did not interact were 68%. Based on the research results above, the researchers summarized that there were 28 cases of prescription sheets containing hyperlipidemia drug interactions in March 2021 at Kimia Farma Pharmacy. The level of drug interactions consisted of major, moderate and minor categories with a severity level of 1% for the major category, 30% for the moderate category, 1% for the minor category, while 68% had no interactions. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify factors such as: roadway operational characteristic, crash characteristic, surrounded environment, vehicle type, driver information, severity level of driver, and temporal information, affecting driver injury severity involving in single-vehicle run off road accident occurred on 2-lanes highways and 4-lanes highway using Multinomial Logit model. The analyses used secondary data obtained from police accident record (extracted from Highway Accident Information Management System (HAIMS)).The variables were found to increase chance of fatality are driver older than 55-year-old, driver under influence of alcohol, drowsiness driver, run off road on straight and curve, accident on highways with depressed median and accident on concrete pavement. The variables were found to mitigate severity are adult driver 25-35-year-old, using seat belt, accident on highway with raised median and hit fixed object accident. The contributions of this study were drawn: Thailand related authorities such as Department of Highway or Royal Thai Police should emphasize their effort on education campaigns on road safety for all road users, especially old drivers, enforce the law on drunk driving and seatbelt; and for road design perspective: monitor and build roadside safety features such as safety barrier alongside the highway particularly run off road black spot and curve roads. The study also mentions the safety benefit of asphalt pavement over concrete pavement and safety planner should consider implementing raised median in urban area for safety purpose.

Detecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics. Knowing the degree of driver distraction can aid in accident prevention techniques, including transitioning of control to a level 4 semi-autonomous vehicle, when a high distraction severity level is reached. Thus, enhancement of Advanced Driving Assistance Systems (ADAS) is a critical component in the safety of vehicle drivers and other road users. In this paper, a new methodology is introduced, using an expert knowledge rule system to predict the severity of distraction in a contiguous set of video frames using the Naturalistic Driving American University of Cairo (AUC) Distraction Dataset. A multi-class distraction system comprises the face orientation, drivers' activities, hands and previous driver distraction, a severity classification model is developed as a discrete dynamic Bayesian (DDB). Furthermore, a Mamdani-based fuzzy system was implemented to detect multi-class of distractions into a severity level of safe, careless or dangerous driving. Thus, if a high level of severity is reached the semi-autonomous vehicle will take control. The result further shows that some instances of driver's distraction may quickly transition from a careless to dangerous driving in a multi-class distraction context.

Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIFyield (normalized by absorbed photosynthetically active radiation), fluorescence yield (ΦF) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (ΦF-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL < 20%). The strongest correlation between NDVI and SL (R = 0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), ΦF-r (R = 0.53) and SIF (R = 0.51), and the response of ΦF (R = 0.45) and SIFyield (R = 0.34) to SL was weak. Under lightly diseased conditions, ΦF-r (R = 0.62) showed the strongest response to disease, followed by SIFyield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between ΦF and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, ΦF-r estimated by the newly proposed ‘NIRvR approach’ (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for ΦF) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of ΦF-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress.

Maize lethal necrosis (MLN) is a severe disease in maize that significantly reduces yields by up to 90% in maize-growing regions such as Kenya and other countries in Africa. The disease causes chlorotic mottling of leaves and severe stunting which leads to plant death. The spread of MLN in the maize-growing regions of Kenya has intensified since the first outbreak was reported in September 2011. In this study, the RapidEye (5 m) imagery was combined with field-based data of MLN severity to map three MLN severity levels in Bomet County, Kenya. Two RapidEye images were acquired during maize stem elongation and inflorescence stages, respectively, and thirty spectral indices for each RapidEye time step were computed. A two-step random forest (RF) classification algorithm was used to firstly create a maize field mask and to predict the MLN severity levels (mild, moderate, and high). The RF algorithm yielded an overall accuracy of 91.0%, representing high model performance in predicting the MLN severity levels in a complex cropping system. The normalized difference red edge index (NDRE) was highly sensitive to MLN detection and demonstrated the ability to detect MLN-caused crop stress earlier than the normalized difference vegetation index (NDVI) and the green normalized difference vegetation index (GNDVI). These results confirm the potential of the RapidEye sensor and machine learning to detect crop disease infestation rates and for use in MLN monitoring in fragmented agro-ecological landscapes. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy is a vascular disease caused by uncontrolled diabetes. Its early detection can save diabetic patients from blindness. However, the detection of its severity level is a challenge for ophthalmologists since last few decades. Several efforts have been made for the identification of its limited stages by using pre‐ and post‐processing methods, which require extensive domain knowledge. This study proposes an improved automated system for severity detection of diabetic retinopathy which is a dictionary‐based approach and does not include pre‐ and post‐processing steps. This approach integrates pathological explicit image representation into a learning outline. To create the dictionary of visual features, points of interest are detected to compute the descriptive features from retinal images through speed up robust features algorithm and histogram of oriented gradients. These features are clustered to generate a dictionary, then coding and pooling are applied for compact representation of features. Radial basis kernel support vector machine and neural network are used to classify the images into five classes namely normal, mild, moderate, severe non‐proliferative diabetic retinopathy, and proliferative diabetic retinopathy. The proposed system exhibits improved results of 95.92% sensitivity and 98.90% specificity in relation to the reported state of the art methods.

The aim of this study was to identify factors such as: roadway operational characteristic, crash characteristic, surrounded environment, vehicle type, driver information, severity level of driver, and temporal information, affecting driver injury severity involving in single-vehicle run off road accident occurred on 2-lanes highways and 4-lanes highway using Multinomial Logit model. The analyses used secondary data obtained from police accident record (extracted from Highway Accident Information Management System (HAIMS)).The variables were found to increase chance of fatality are driver older than 55-year-old, driver under influence of alcohol, drowsiness driver, run off road on straight and curve, accident on highways with depressed median and accident on concrete pavement. The variables were found to mitigate severity are adult driver 25- 35-year-old, using seat belt, accident on highway with raised median and hit fixed object accident. The contributions of this study were drawn: Thailand related authorities such as Department of Highway or Royal Thai Police should emphasize their effort on education campaigns on road safety for all road users, especially old drivers, enforce the law on drunk driving and seatbelt; and for road design perspective: monitor and build roadside safety features such as safety barrier alongside the highway particularly run off road black spot and curve roads. The study also mentions the safety benefit of asphalt pavement over concrete pavement and safety planner should consider implementing raised median in urban area for safety purpose. [ABSTRACT FROM AUTHOR]

Background: The implementation of the national health insurance program to fulfill the payment system related to a claim needs the documentation of services in the discharge summary. The objective of this study was to determine the influence of completeness supporting examination and discharge summary procedure to suitability of severity level determination in tertiary referral hospitals. Methods: This was an analytical study with cross-sectional design. The data were collected from observation toward inpatient's discharge summary. Ninety-nine samples were taken by simple random sampling technique. Results: The results of the statistical significance test showed that the congeniality between the supporting examination results and procedure (p<0.0001) and the completeness of secondary diagnosis in discharge summary (p<0.0001) had an influence on the suitability of severity levels. Conclusion: Completeness supporting examination and discharge summary procedure can influence severity level. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to statistically analyze the behavior of time-frequency features in digital recordings of wheeze sounds obtained from patients with various levels of asthma severity (mild, moderate, and severe), and this analysis was based on the auscultation location and/or breath phase. Method: Segmented and validated wheeze sounds were collected from the trachea and lower lung base (LLB) of 55 asthmatic patients during tidal breathing maneuvers and grouped into nine different datasets. The quartile frequencies F25, F50, F75, F90 and F99, mean frequency (MF) and average power (AP) were computed as features, and a univariate statistical analysis was then performed to analyze the behavior of the time-frequency features. Results: All features generally showed statistical significance in most of the datasets for all severity levels [χ2 = 6.021–71.65, p < 0.05, η2 = 0.01–0.52]. Of the seven investigated features, only AP showed statistical significance in all the datasets. F25, F75, F90 and F99 exhibited statistical significance in at least six datasets [χ2 = 4.852–65.63, p < 0.05, η2 = 0.01–0.52], and F25, F50 and MF showed statistical significance with a large η2 in all trachea-related datasets [χ2 = 13.54–55.32, p < 0.05, η2 = 0.13–0.33]. Conclusion: The results obtained for the time-frequency features revealed that (1) the asthma severity levels of patients can be identified through a set of selected features with tidal breathing, (2) tracheal wheeze sounds are more sensitive and specific predictors of severity levels and (3) inspiratory and expiratory wheeze sounds are almost equally informative. [ABSTRACT FROM AUTHOR]

• Severity levels of asthma patients can be identified using Integrated Power (IP) features with tidal breathing manoeuvre. • Analysis related to auscultation locations revealed that tracheal wheeze sounds are more specific and sensitive predictors. • Expiratory and inspiratory wheeze sounds are found to be equally informative for the classification of asthma severity. This study aimed to investigate and classify wheeze sound characteristics according to asthma severity levels (mild, moderate and severe) using integrated power (IP) features. Validated and segmented wheeze sounds were obtained from the lower lung base (LLB) and trachea recordings of 55 asthmatic patients with different severity levels during tidal breathing manoeuvres. From the segments, nine datasets were obtained based on the auscultation location, breath phases and their combination. In this study, IP features were extracted for assessing asthma severity. Subsequently, univariate and multivariate (MANOVA) statistical analyses were separately implemented to analyse behaviour of wheeze sounds according to severity levels. Furthermore, the ensemble (ENS), k-nearest- neighbour (KNN) and support vector machine (SVM) classifiers were applied to classify the asthma severity levels. The univariate results of this study indicated that the majority of features significantly discriminated (p < 0.05) the severity levels in all the datasets. The MANOVA results yielded significantly (p < 0.05) large effect size in all datasets (including LLB-related) and almost all post hoc results were significant (p < 0.05). A comparison of the performance of classifiers revealed that eight of the nine datasets showed improved performance with the ENS classifier. The Trachea inspiratory (T-Inspir) dataset produced the highest performance. The overall best positive predictive rate (PPR) for the mild, moderate and severe severity levels were 100% (KNN), 92% (SVM) and 94% (ENS) respectively. Analysis related to auscultation locations revealed that tracheal wheeze sounds are more specific and sensitive predictors of asthma severity. Additionally, phase related investigations indicated that expiratory and inspiratory wheeze sounds are equally informative for the classification of asthma severity. [ABSTRACT FROM AUTHOR]

The newest edition of the Diagnostic and Statistical Manual of Mental Disorders (5th ed., DSM-5) introduced substantial changes to the diagnostic criteria for autism spectrum disorder, including new severity level ratings for social communication and restricted and repetitive behavior domains. The purpose of this study was to evaluate the use of these new severity ratings and to examine their relation to other measures of severity and clinical features. Participants included 248 children with autism spectrum disorder who received diagnostic evaluations at one of six Autism Treatment Network sites. Higher severity ratings in both domains were associated with younger age, lower intelligence quotient, and greater Autism Diagnostic Observation Schedule-Second Edition domain-specific symptom severity. Greater restricted and repetitive behavior severity was associated with higher parent-reported stereotyped behaviors. Severity ratings were not associated with emotional or behavioral problems. The new DSM-5 severity ratings in both domains were significantly associated with behavioral observations of autism severity but not with measures of other behavioral or emotional symptoms. However, the strong associations between intelligence quotient and DSM-5 severity ratings in both domains suggest that clinicians may be including cognitive functioning in their overall determination of severity. Further research is needed to examine clinician decision-making and interpretation of these specifiers. [ABSTRACT FROM AUTHOR]

Annotations are critical for machine learning and developing computer aided diagnosis (CAD) algorithms. Good performance of CAD is critical to their adoption, which generally rely on training with a wide variety of annotated data. However, a vast amount of medical data is either unlabeled or annotated only at the image-level. This poses a problem for exploring data driven approaches like deep learning for CAD. In this paper, we propose a novel crowdsourcing and synthetic image generation for training deep neural net-based lesion detection. The noisy nature of crowdsourced annotations is overcome by assigning a reliability factor for crowd subjects based on their performance and requiring region of interest markings from the crowd. A generative adversarial network-based solution is proposed to generate synthetic images with lesions to control the overall severity level of the disease. We demonstrate the reliability of the crowdsourced annotations and synthetic images by presenting a solution for training the deep neural network (DNN) with data drawn from a heterogeneous mixture of annotations. Experimental results obtained for hard exudate detection from retinal images show that training with refined crowdsourced data/synthetic images is effective as detection performance in terms of sensitivity improves by 25%/27% over training with just expert-markings.

This study aims to automatically analyze and extract abnormalities in the lung field due to Coronavirus Disease 2019 (COVID-19). Types of abnormalities that can be detected are Ground Glass Opacity (GGO) and consolidation. The proposed method can also identify the location of the abnormality in the lung field, that is, the central and peripheral lung area. The location and type of these abnormalities affect the severity and confidence level of a patient suffering from COVID-19. The detection results using the proposed method are compared with the results of manual detection by radiologists. From the experimental results, the proposed system can provide an average error of 0.059 for the severity score and 0.069 for the confidence level. This method has been implemented in a web-based application for general users.•A method to detect the appearance of viral pneumonia imaging features, namely Ground Glass Opacity (GGO) and consolidation on the chest Computed Tomography (CT) scan images.•This method can separate the lung field to the right lung and the left lung, and it also can identify the detected imaging feature's location in the central or peripheral of the lung field.•Severity level and confidence level of the patient's suffering are measured., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Diabetic retinopathy, a symptomless complication of diabetes, is one of the significant causes of vision impairment in the world. The early detection and diagnosis can reduce the occurrence of severe vision loss due to diabetic retinopathy. The diagnosis of diabetic retinopathy depends on the reliable detection and classification of bright and dark lesions present in retinal fundus images. Therefore, in this work, reliable segmentation of lesions has been performed using iterative clustering irrespective of associated heterogeneity, bright and faint edges. Afterwards, a computer-aided severity level detection method is proposed to aid ophthalmologists for appropriate treatment and effective planning in the diagnosis of non-proliferative diabetic retinopathy. This work has been performed on a composite database of 5048 retinal fundus images having varying attributes such as position, dimensions, shapes and color to make a reasonable comparison with state-of-the-art methods and to establish generalization capability of the proposed method. Experimental results on per-lesion basis show that the proposed method outperforms state-of-the-methods with an average sensitivity/specificity/accuracy of 96.41/96.57/94.96 and 95.19/96.24/96.50 for bright and dark lesions respectively on composite database. Individual per-image based class accuracies delivered by the proposed method: No DR-95.9%, MA-98.3%, HEM-98.4%, EXU-97.4% and CWS-97.9% demonstrate the clinical competence of the method. Major contribution of the proposed method is that it efficiently grades the severity level of diabetic retinopathy in spite of huge variations in retinal images of different databases. Additionally, the substantial combined performance of these experiments on clinical and open source benchmark databases support a strong candidature of the proposed method in the diagnosis of non-proliferative diabetic retinopathy. [ABSTRACT FROM AUTHOR]