The Management of Cardiogenic Shock From Diagnosis to Devices: A Narrative Review.

Cardiogenic shock (CS) is a heterogenous syndrome broadly characterized by inadequate cardiac output leading to tissue hypoperfusion and multisystem organ dysfunction that carries an ongoing high mortality burden. The management of CS has advanced rapidly, especially with the incorporation of temporary mechanical circulatory support (tMCS) devices. A thorough understanding of how to approach a patient with CS and to select appropriate monitoring and treatment paradigms is essential in modern ICUs. Timely characterization of CS severity and hemodynamics is necessary to optimize outcomes, and this may be performed best by multidisciplinary shock-focused teams. In this article, we provide a review of CS aimed to inform both the cardiology-trained and non-cardiology-trained intensivist provider. We briefly describe the causes, pathophysiologic features, diagnosis, and severity staging of CS, focusing on gathering key information that is necessary for making management decisions. We go on to provide a more detailed review of CS management principles and practical applications, with a focus on tMCS. Medical management focuses on appropriate medication therapy to optimize perfusion-by enhancing contractility and minimizing afterload-and to facilitate decongestion. For more severe CS, or for patients with decompensating hemodynamic status despite medical therapy, initiation of the appropriate tMCS increasingly is common. We discuss the most common devices currently used for patients with CS-phenotyping patients as having left ventricular failure, right ventricular failure, or biventricular failure-and highlight key available data and particular points of consideration that inform tMCS device selection. Finally, we highlight core components of sedation and respiratory failure management for patients with CS.

AI in evaluating ambulation of stroke patients: severity classification with video and functional ambulation category scale.

BACKGROUND: The evaluation of gait function and severity classification of stroke patients are important to determine the rehabilitation goal and the level of exercise. Physicians often qualitatively evaluate patients' walking ability through visual gait analysis using naked eye, video images, or standardized assessment tools. Gait evaluation through observation relies on the doctor's empirical judgment, potentially introducing subjective opinions. Therefore, conducting research to establish a basis for more objective judgment is crucial. OBJECTIVE: To verify a deep learning model that classifies gait image data of stroke patients according to Functional Ambulation Category (FAC) scale. METHODS: Gait vision data from 203 stroke patients and 182 healthy individuals recruited from six medical institutions were collected to train a deep learning model for classifying gait severity in stroke patients. The recorded videos were processed using OpenPose. The dataset was randomly split into 80% for training and 20% for testing. RESULTS: The deep learning model attained a training accuracy of 0.981 and test accuracy of 0.903. Area Under the Curve(AUC) values of 0.93, 0.95, and 0.96 for discriminating among the mild, moderate, and severe stroke groups, respectively. CONCLUSION: This confirms the potential of utilizing human posture estimation based on vision data not only to develop gait parameter models but also to develop models to classify severity according to the FAC criteria used by physicians. To develop an AI-based severity classification model, a large amount and variety of data is necessary and data collected in non-standardized real environments, not in laboratories, can also be used meaningfully.

Parameter-based transfer learning for severity classification of atopic dermatitis using hyperspectral imaging.

BACKGROUND/PURPOSE: Because atopic dermatitis (AD) is a chronic inflammatory skin condition that causes structural changes, there is a growing need for noninvasive research methods to evaluate this condition. Hyperspectral imaging (HSI) captures skin structure features by exploiting light wavelength variations in penetration depth. In this study, parameter-based transfer learning was deployed to classify the severity of AD using HSI. Therefore, we aimed to obtain an optimal combination of classification results from the four models after constructing different source- and target-domain datasets. METHODS: We designated psoriasis, skin cancer, eczema, and AD datasets as the source datasets, and the set of images acquired via hyperspectral camera as the target dataset for wavelength-specific AD classification. We compared the severity classification performances of 96 combinations of sources, models, and targets. RESULTS: The highest classification performance of 83% was achieved when ResNet50 was trained on the augmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target Near-infrared radiation (NIR) dataset. The second highest classification accuracy of 81% was achieved when ResNet50 was trained on the unaugmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target R dataset. ResNet50 demonstrated potential as a generalized model for both the source and target data, also confirming that the psoriasis dataset is an effective training resource. CONCLUSION: The present study not only demonstrates the feasibility of the severity classification of AD based on hyperspectral images, but also showcases combinations and research scalability for domain exploration.

Use of Nasopharyngoscopy Severity Classification of Chronic Epipharyngitis and Its Application for Evaluating the Treatment Outcomes of Epipharyngeal Abrasive Therapy.

Background Chronic epipharyngitis causes postnasal drip, pharyngeal pain, pharyngeal discomfort, headache, and shoulder stiffness. Additionally, autonomic nervous system symptoms such as dizziness, general fatigue, and sleeplessness may occur. It can also contribute to the development of focal diseases. Although epipharyngeal abrasive therapy (EAT) is effective for chronic epipharyngitis involving the abrasion of the epipharynx with a zinc chloride solution, there is a lack of clear diagnostic criteria, and treatment outcomes are rarely reported. Methodology A classification of the severity of chronic epipharyngitis was attempted in 154 cases based on nasopharyngeal endoscopic findings, with a subsequent examination of treatment outcomes using EAT. Diagnosis of chronic epipharyngitis involved identifying redness, swelling, postnasal drip, and crusting of the epipharyngeal mucosa. Severity classification relied on a four-point scale measuring the degree of redness and swelling, with additional points assigned for the presence of postnasal drip and crusting. This classification also served as a criterion for judging treatment effectiveness. The prevalence and improvement rate of black spots and granular changes were assessed through nasopharyngeal endoscopy with narrow-band imaging. Subjective symptoms were evaluated using before and after treatment questionnaires, employing a four-point scale for symptoms commonly associated with chronic epipharyngitis (headache, postnasal drip, nasal obstruction, pharyngeal discomfort, pharyngeal pain, shoulder stiffness, tinnitus, ear fullness, dizziness, cough, and sputum). A 10-point numerical rating scale (NRS) was used to assess the physical condition. Results Following EAT, the severity of nasopharyngeal endoscopic findings notably improved, with a 76.0% (117/154) improvement rate (remarkable improvement: 19.5% (30), improvement: 56.5% (87)). The improvement rate for the chief complaint reached 85.7% (132/154), demonstrating significant enhancement in the score for each symptom. NRS scores also improved at a rate of 76.0% (117/154). A significant correlation was observed between the improvement in local findings and chief complaints. The prevalence of black spots and granular changes before EAT was 83.8% (129/154) and 64.3% (99/154), exhibiting improvement rates of 65.9% (87/132) and 54.8% (57/104), respectively. Conclusions Nasopharyngeal endoscopy proves valuable for diagnosing and assessing the severity of chronic epipharyngitis, as well as evaluating treatment effectiveness. The findings indicate that EAT is an effective treatment for chronic epipharyngitis, with improvements in local findings correlating with enhancements in the chief complaint. This underscores the importance of employing aggressive EAT in managing patients with chronic epipharyngitis.

Validation of the Rome Severity Classification of Chronic Obstructive Pulmonary Disease Exacerbation: A Multicenter Cohort Study.

Background: The Rome severity classification is an objective assessment tool for the severity of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) based on readily measurable variables but has not been widely validated. The aim of this study is to evaluate the validity of the Rome classification in distinguishing the severity of AECOPD based on short-term mortality and other adverse outcomes. Methods: The Rome severity classification was applied to a large multicenter cohort of inpatients with AECOPD. Differences in clinical features, in-hospital and 60-day mortality, intensive care unit (ICU) admission, mechanical ventilation (MV) and invasive mechanical ventilation (IMV) usage were compared among the mild, moderate and severe AECOPD according to the Rome proposal. Moreover, univariate logistic analysis and Kaplan Meier survival analysis were also performed to find the association between the Rome severity classification and those adverse outcomes. Results: A total of 7712 patients hospitalized for AECOPD were included and classified into mild (41.88%), moderate (40.33%), or severe (17.79%) group according to the Rome proposal. The rate of ICU admission (6.4% vs 12.0% vs 14.9%, P <0.001), MV (11.7% vs 33.7% vs 45.3%, P <0.001) and IMV (1.4% vs 6.8% vs 8.9%, P <0.001) increased significantly with the increase of severity classification from mild to moderate to severe AECOPD. The 60-day mortality was higher in the moderate or severe group than in the mild group (3.5% vs 1.9%, 4.3% vs 1.9%, respectively, P <0.05) but showed no difference between the moderate and severe groups (2.6% vs 2.5%, P >0.05), results for in-hospital mortality showed the same trends. Similar findings were observed by univariate logistic analysis and survival analysis. Conclusion: Rome severity classification demonstrated excellent performance in predicting ICU admission and the need for MV or IMV, but how it performs in differentiating short-term mortality still needs to be confirmed.

Interrater Reliability for Classifying Craniofacial Microsomia Severity: A Call for Objective Evaluation.

OBJECTIVE: The severity of craniofacial microsomia (CFM) is generally classified using the Orbit, Mandible, Ear, Soft tissue, Nerve (OMENS) classification score. The global assessment of the Phenotypic Assessment Tool for Craniofacial Microsomia (PAT-CFM), is a pictorial modification of the OMENS classification. The aim of this study was to assess the interrater reliability of the PAT-CFM global assessment score. DESIGN: In this prospective cohort study, three clinicians completed the global assessment form of the PAT-CFM. The mandible was classified based on orthopantomogram- and/or computed tomography images. PARTICIPANTS: Consecutive patients with CFM or microtia.Interrater agreement was calculated using the weighted Krippendorff alpha (α), with 95% confidence intervals (CI). RESULTS: In total, 53 patients were included (106 hemifaces). The reliabilities of the main classification components ranged from high for the mandible (α = 0.904 [95% CI 0.860-0.948]) and ear (α = 0.958 [95% CI 0.934-0.983]) subscales, to tentative for the orbital summary score (α = 0.682 [0.542-0.821]), and nerve summary score (α = 0.782 [0.666-0.900]) subscales. CONCLUSIONS: The reliability of the ear and radiographic mandible scales of the PAT-CFM global classification were high, while the orbit, facial nerve and soft tissue subscales may have limited reliability. Research focusing on radiographic severity scores for hypoplasia of the orbits and soft tissues, as well as objective measures for overall facial hypoplasia using non-ionizing forms of imaging for early classification, are warranted.

Severity Classification Using Dynamic Time Warping-Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study.

Background: In Japan, individuals with mild COVID-19 illness previously required to be monitored in designated areas and were hospitalized only if their condition worsened to moderate illness or worse. Daily monitoring using a pulse oximeter was a crucial indicator for hospitalization. However, a drastic increase in the number of patients resulted in a shortage of pulse oximeters for monitoring. Therefore, an alternative and cost-effective method for monitoring patients with mild illness was required. Previous studies have shown that voice biomarkers for Parkinson disease or Alzheimer disease are useful for classifying or monitoring symptoms; thus, we tried to adapt voice biomarkers for classifying the severity of COVID-19 using a dynamic time warping (DTW) algorithm where voice wavelets can be treated as 2D features; the differences between wavelet features are calculated as scores. Objective: This feasibility study aimed to test whether DTW-based indices can generate voice biomarkers for a binary classification model using COVID-19 patients' voices to distinguish moderate illness from mild illness at a significant level. Methods: We conducted a cross-sectional study using voice samples of COVID-19 patients. Three kinds of long vowels were processed into 10-cycle waveforms with standardized power and time axes. The DTW-based indices were generated by all pairs of waveforms and tested with the Mann-Whitney U test (α<.01) and verified with a linear discrimination analysis and confusion matrix to determine which indices were better for binary classification of disease severity. A binary classification model was generated based on a generalized linear model (GLM) using the most promising indices as predictors. The receiver operating characteristic curve/area under the curve (ROC/AUC) validated the model performance, and the confusion matrix calculated the model accuracy. Results: Participants in this study (n=295) were infected with COVID-19 between June 2021 and March 2022, were aged 20 years or older, and recuperated in Kanagawa prefecture. Voice samples (n=110) were selected from the participants' attribution matrix based on age group, sex, time of infection, and whether they had mild illness (n=61) or moderate illness (n=49). The DTW-based variance indices were found to be significant (P<.001, except for 1 of 6 indices), with a balanced accuracy in the range between 79% and 88.6% for the /a/, /e/, and /u/ vowel sounds. The GLM achieved a high balance accuracy of 86.3% (for /a/), 80.2% (for /e/), and 88% (for /u/) and ROC/AUC of 94.8% (95% CI 90.6%-94.8%) for /a/, 86.5% (95% CI 79.8%-86.5%) for /e/, and 95.6% (95% CI 92.1%-95.6%) for /u/. Conclusions: The proposed model can be a voice biomarker for an alternative and cost-effective method of monitoring the progress of COVID-19 patients in care.

Diagnosis, Classification, and Management Strategies for Mitral Annular Calcification: A Heart Valve Collaboratory Position Statement.

Mitral annular calcium (MAC) with severe mitral valvular dysfunction presents a complex problem, as valve replacement, either surgical or transcatheter, is challenging because of anatomy, technical considerations, concomitant comorbidities, and advanced age. The authors review the clinical and anatomical features of MAC that are favorable (green light), challenging (yellow light), or prohibitive (red light) for surgical or transcatheter mitral valve interventions. Under the auspices of the Heart Valve Collaboratory, an expert working group of cardiac surgeons, interventional cardiologists, and interventional imaging cardiologists was formed to develop recommendations regarding treatment options for patients with MAC as well as a proposed grading and staging system using both anatomical and clinical features.

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases.

Introduction: Paddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production. Methods: In this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered. Results: Three infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%. Discussion: The findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models.

Severity Classification of Laboratory Animal Procedures in Two Belgian Academic Institutions.

According to the EU Directive 2010/63, all animal procedures must be classified as non-recovery, mild, moderate or severe. Several examples are included in the Directive to help in severity classification. Since the implementation of the Directive, different publications and guidelines have been disseminated on the topic. However, due to the large variety of disease models and animal procedures carried out in many different animal species, guidance on the severity classification of specific procedures or models is often lacking or not specific enough. The latter is especially the case in disease models where the level of pain, suffering, distress and lasting harm depends on the duration of the study (for progressive disease models) or the dosage given (for infectious or chemically induced disease models). This, in turn, may lead to inconsistencies in severity classification between countries, within countries and even within institutions. To overcome this, two Belgian academic institutions with a focus on biomedical research collaborated to develop a severity classification for all the procedures performed. This work started with listing all in-house procedures and assigning them to 16 (sub)categories. First, we determined which parameters, such as clinical signs, dosage or duration, were crucial for severity classification within a specific (sub)category. Next, a severity classification was assigned to the different procedures, which was based on professional judgment by the designated veterinarians, members of the animal welfare body (AWB) and institutional animal ethics committee (AEC), integrating the available literature and guidelines. During the classification process, the use of vague terminology, such as 'minor impact', was avoided as much as possible. Instead, well-defined cut-offs between severity levels were used. Furthermore, we sought to define common denominators to group procedures and to be able to classify new procedures more easily. Although the primary aim is to address prospective severity, this can also be used to assess actual severity. In summary, we developed a severity classification for all procedures performed in two academic, biomedical institutions. These include many procedures and disease models in a variety of animal species for which a severity classification was not reported so far, or the terms that assign them to a different severity were too vague.

Association Between Rome Classification Among Hospitalized Patients With COPD Exacerbations and Short-Term and Intermediate-Term Outcomes.

BACKGROUND: Recently, the Rome proposal updated the definition of exacerbation of COPD (ECOPD). However, such severity grade has not yet demonstrated intermediate-term clinical relevance. RESEARCH QUESTION: What is the association between the Rome severity classification and short-term and intermediate-term clinical outcomes? STUDY DESIGN AND METHODS: We retrospectively grouped hospitalized patients with ECOPD according to the Rome severity classification (ie, mild, moderate, severe). Baseline, clinical, microbiologic, gas analysis, and laboratory variables were collected. In addition, data about the length of hospital stay and mortality (in-hospital and a follow-up time line from 6 months until 3 years) were assessed. RESULTS: Of the 347 hospitalized patients, 39% were categorized as mild, 31% were categorized as moderate, and 30% were categorized as severe. Overall, patients with severe ECOPD had an extended length of hospital stay. Although in-hospital mortality was similar among groups, patients with severe ECOPD presented a worse prognosis in all follow-up time points. The Kaplan-Meier curves show the role of the severe classification in the cumulative survival at 1 and 3 years (Gehan-Breslow-Wilcoxon test, P = .032 and P = .004, respectively). The multivariable Cox regression analysis showed a higher risk of death at 1 year when patients presented a severe (hazard ratio, 1.99; 95% CI, 1.49-2.65) or moderate grade (hazard ratio, 1.47; 95% CI, 1.10-1.97) compared with a mild grade. Older patients (aged ≥ 80 years), patients requiring long-term oxygen therapy, or patients reporting previous ECOPD episodes had a higher mortality risk. A BMI between 25 and 29 kg/m2 was associated with a lower risk. INTERPRETATION: The Rome classification makes it possible to discriminate patients with a worse prognosis (severe or moderate) until a 3-year follow-up.

A Hybrid Deep Neural Approach for Segmenting the COVID Affection Area from the Lungs X-Ray Images.

Nowadays, COVID severity prediction has attracted widely in medical research because of the disease severity. Hence, the image processing application is also utilized to analyze COVID severity identification using lungs X-ray images. Thus, several intelligent schemes were employed to detect the COVID-affected part of the lungs X-ray images. However, the traditional neural approaches reported less severity classification accuracy due to the image complexity score. So, the present study has presented a novel chimp-based Adaboost Severity Analysis (CbASA) implemented in the MATLAB environment. Hence, the lung's X-ray images are utilized to test the working performance of the designed model. All public imaging data sources contain more noisy features, so the noise features are removed in the initial hidden layer of the novel CbASA then the noise-free data is imported into the classification phase. Feature extraction, segmentation, and severity specification have been performed in the classification layer. Finally, the performance of the classification score has been measured and compared with other models. Subsequently, the presented novel CbASA has earned the finest classification outcome.

Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images.

One of the most common and challenging medical conditions to deal with in old-aged people is the occurrence of knee osteoarthritis (KOA). Manual diagnosis of this disease involves observing X-ray images of the knee area and classifying it under five grades using the Kellgren-Lawrence (KL) system. This requires the physician's expertise, suitable experience, and a lot of time, and even after that the diagnosis can be prone to errors. Therefore, researchers in the ML/DL domain have employed the capabilities of deep neural network (DNN) models to identify and classify KOA images in an automated, faster, and accurate manner. To this end, we propose the application of six pretrained DNN models, namely, VGG16, VGG19, ResNet101, MobileNetV2, InceptionResNetV2, and DenseNet121 for KOA diagnosis using images obtained from the Osteoarthritis Initiative (OAI) dataset. More specifically, we perform two types of classification, namely, a binary classification, which detects the presence or absence of KOA and secondly, classifying the severity of KOA in a three-class classification. For a comparative analysis, we experiment on three datasets (Dataset I, Dataset II, and Dataset III) with five, two, and three classes of KOA images, respectively. We achieved maximum classification accuracies of 69%, 83%, and 89%, respectively, with the ResNet101 DNN model. Our results show an improved performance from the existing work in the literature.

Effectiveness of the Patient's Severity Classification Competency Promotion Virtual Reality Program of Nursing Students during the COVID-19 Pandemic Period.

The purpose of this study was to develop a virtual reality-based nursing education program aimed at improving nursing students' severity classification competency. Severity classification in the emergency room is key to improving the efficiency of emergency room services worldwide. Prioritizing treatment based on correctly identifying the severity of a disease or an injury also ensures patients' safety. The five actual clinical scenarios in the program helped to promptly classify patients into five clinical situations based on the 2021 Korean Emergency Patient Classification Tool. Seventeen nursing students were in an experimental group that had access to a virtual reality-based simulation combined with clinical practice. Seventeen nursing students were in a control group that only participated in routine clinical practice. The virtual reality-based nursing education program effectively improved students' severity classification competency, performance confidence, and clinical decision-making ability. Although the pandemic continues, the virtual reality-based nursing education program provides realistic indirect experiences to nursing students in situations where clinical nursing practice is not possible. In particular, it will serve as basic data for the expansion and utilization strategy of virtual reality-based nursing education programs to improve nursing capabilities.

[Imaging of muscle injuries in sports medicine]. / Bildgebung von Muskelverletzungen im Sport.

BACKGROUND: Early diagnosis of muscle injuries is indispensable in order to initiate appropriate treatment and to facilitate optimal healing. PURPOSE: The aim of this review is to provide an update on imaging of muscle injuries in sports medicine with a focus on ultrasound and magnetic resonance imaging (MRI) and to present experimental approaches in addition to routine diagnostic procedures. MATERIALS AND METHODS: A PubMed literature search for the years 2012-2022 using the following keywords was performed: muscle, muscle injury, muscle imaging, muscle injury classification, delayed onset muscle soreness, ultrasound, MRI, sodium MRI, potassium MRI, ultra-high-field MRI, injuries of athletes. RESULTS: Imaging is crucial to confirm and assess the extent of sports-related muscle injuries and may help establishing treatment decisions, which directly affect the prognosis. This is of importance when the diagnosis or grade of injury is unclear, when recovery is taking longer than expected, and when interventional or surgical management may be necessary. In addition to established methods such as B­mode ultrasound and 1H­MRI, individual studies show promising approaches to further improve the imaging of muscle injuries in the future. Prior to the integration of contrast-enhanced ultrasound and X­nuclei into clinical routine, additional studies are needed to validate these techniques further. CONCLUSION: B­mode ultrasound represents an easily available, cost-effective modality for the initial diagnosis of muscle injuries. MRI is still considered the reference standard and enables an accurate morphological assessment of the extent of the injury. There are still no imaging approaches available for the objective determination of the optimal point of return to play.

Privacy Preserving Image Encryption with Optimal Deep Transfer Learning Based Accident Severity Classification Model.

Effective accident management acts as a vital part of emergency and traffic control systems. In such systems, accident data can be collected from different sources (unmanned aerial vehicles, surveillance cameras, on-site people, etc.) and images are considered a major source. Accident site photos and measurements are the most important evidence. Attackers will steal data and breach personal privacy, causing untold costs. The massive number of images commonly employed poses a significant challenge to privacy preservation, and image encryption can be used to accomplish cloud storage and secure image transmission. Automated severity estimation using deep-learning (DL) models becomes essential for effective accident management. Therefore, this article presents a novel Privacy Preserving Image Encryption with Optimal Deep-Learning-based Accident Severity Classification (PPIE-ODLASC) method. The primary objective of the PPIE-ODLASC algorithm is to securely transmit the accident images and classify accident severity into different levels. In the presented PPIE-ODLASC technique, two major processes are involved, namely encryption and severity classification (i.e., high, medium, low, and normal). For accident image encryption, the multi-key homomorphic encryption (MKHE) technique with lion swarm optimization (LSO)-based optimal key generation procedure is involved. In addition, the PPIE-ODLASC approach involves YOLO-v5 object detector to identify the region of interest (ROI) in the accident images. Moreover, the accident severity classification module encompasses Xception feature extractor, bidirectional gated recurrent unit (BiGRU) classification, and Bayesian optimization (BO)-based hyperparameter tuning. The experimental validation of the proposed PPIE-ODLASC algorithm is tested utilizing accident images and the outcomes are examined in terms of many measures. The comparative examination revealed that the PPIE-ODLASC technique showed an enhanced performance of 57.68 dB over other existing models.

A scoping study of pulmonary paracoccidioidomycosis: severity classification based on radiographic and tomographic evaluation.

The lungs have great importance in patients with paracoccidioidomycosis since they are the portal of entry for the infecting fungi, the site of quiescent foci, and one of the most frequently affected organs. Although they have been the subject of many studies with different approaches, the severity classification of the pulmonary involvement, using imaging procedures, has not been carried out yet. This study aimed to classify the active and the residual pulmonary damage using radiographic and tomographic evaluations, according to the area involved and types of lesions.

Defect Severity Identification for a Catenary System Based on Deep Semantic Learning.

A variety of Chinese textual operational text data has been recorded during the operation and maintenance of the high-speed railway catenary system. Such defect text records can facilitate defect detection and defect severity analysis if mined efficiently and accurately. Therefore, in this context, this paper focuses on a specific problem in defect text mining, which is to efficiently extract defect-relevant information from catenary defect text records and automatically identify catenary defect severity. The specific task is transformed into a machine learning problem for defect text classification. First, we summarize the characteristics of catenary defect texts and construct a text dataset. Second, we use BERT to learn defect texts and generate word embedding vectors with contextual features, fed into the classification model. Third, we developed a deep text categorization network (DTCN) to distinguish the catenary defect level, considering the contextualized semantic features. Finally, the effectiveness of our proposed method (BERT-DTCN) is validated using a catenary defect textual dataset collected from 2016 to 2018 in the China Railway Administration in Chengdu, Lanzhou, and Hengshui. Moreover, BERT-DTCN outperforms several competitive methods in terms of accuracy, precision, recall, and F1-score value.

Appearance quality classification method of Huangguan pear under complex background based on instance segmentation and semantic segmentation.

The 'Huangguan' pear disease spot detection and grading is the key to fruit processing automation. Due to the variety of individual shapes and disease spot types of 'Huangguan' pear. The traditional computer vision technology and pattern recognition methods have some limitations in the detection of 'Huangguan' pear diseases. In recent years, with the development of deep learning technology and convolutional neural network provides a new solution for the fast and accurate detection of 'Huangguan' pear diseases. To achieve automatic grading of 'Huangguan' pear appearance quality in a complex context, this study proposes an integrated framework combining instance segmentation, semantic segmentation and grading models. In the first stage, Mask R-CNN and Mask R-CNN with the introduction of the preprocessing module are used to segment 'Huangguan' pears from complex backgrounds. In the second stage, DeepLabV3+, UNet and PSPNet are used to segment the 'Huangguan' pear spots to get the spots, and the ratio of the spot pixel area to the 'Huangguan' pear pixel area is calculated and classified into three grades. In the third stage, the grades of 'Huangguan' pear are obtained using ResNet50, VGG16 and MobileNetV3. The experimental results show that the model proposed in this paper can segment the 'Huangguan' pear and disease spots in complex background in steps, and complete the grading of 'Huangguan' pear fruit disease severity. According to the experimental results. The Mask R-CNN that introduced the CLAHE preprocessing module in the first-stage instance segmentation model is the most accurate. The resulting pixel accuracy (PA) is 97.38% and the Dice coefficient is 68.08%. DeepLabV3+ is the most accurate in the second-stage semantic segmentation model. The pixel accuracy is 94.03% and the Dice coefficient is 67.25%. ResNet50 is the most accurate among the third-stage classification models. The average precision (AP) was 97.41% and the F1 (harmonic average assessment) was 95.43%.In short, it not only provides a new framework for the detection and identification of 'Huangguan' pear fruit diseases in complex backgrounds, but also lays a theoretical foundation for the assessment and grading of 'Huangguan' pear diseases.

Development and Validation of a Risk Score in Chinese Patients With Chronic Heart Failure.

Background: Acute exacerbation of chronic heart failure contributes to substantial increases in major adverse cardiovascular events (MACE). The study developed a risk score to evaluate the severity of heart failure which was related to the risk of MACE. Methods: This single-center retrospective observational study included 5,777 patients with heart failure. A credible random split-sample method was used to divide data into training and validation dataset (split ratio = 0.7:0.3). Least absolute shrinkage and selection operator (Lasso) logistic regression was applied to select predictors and develop the risk score to predict the severity category of heart failure. Receiver operating characteristic (ROC) curves, and calibration curves were used to assess the model's discrimination and accuracy. Results: Body-mass index (BMI), ejection fraction (EF), serum creatinine, hemoglobin, C-reactive protein (CRP), and neutrophil lymphocyte ratio (NLR) were identified as predictors and assembled into the risk score (P < 0.05), which showed good discrimination with AUC in the training dataset (0.770, 95% CI:0.746-0.794) and validation dataset (0.756, 95% CI:0.717-0.795) and was well calibrated in both datasets (all P > 0.05). As the severity of heart failure worsened according to risk score, the incidence of MACE, length of hospital stay, and treatment cost increased (P < 0.001). Conclusion: A risk score incorporating BMI, EF, serum creatinine, hemoglobin, CRP, and NLR, was developed and validated. It effectively evaluated individuals' severity classification of heart failure, closely related to MACE.