Kappa Values Research Articles

Update on the reliability of gait analysis interpretation in cerebral palsy: Inter-institution agreement

BackgroundStudies have shown good reliability for gait analysis interpretation among surgeons from the same institution. However, reliability among surgeons from different institutions remains to be determined. Research questionIs gait analysis interpretation by surgeons from different institutions as reliable as it is for surgeons from the same institution? MethodsGait analysis data for 67 patients with cerebral palsy (CP) were reviewed prospectively by two orthopedic surgeons from different institutions in the same state, each with > 10 years’ experience interpreting gait analysis data. The surgeons identified gait problems and made treatment recommendations for each patient using a rating form. Percent agreement between raters was calculated for each problem and treatment, and compared to expected agreement based on chance using Cohen’s kappa. ResultsFor problem identification, the greatest agreement was seen for equinus (85% agreement), calcaneus (88%), in-toeing (89%), and out-toeing (90%). Agreement for the remaining problems ranged between 66–78%. Percent agreement was significantly higher than expected due to chance for all issues (p ≤ 0.01) with modest kappa values ranging from 0.12 to 0.51. Agreement between surgeons for treatment recommendations was highest for triceps surae lengthening (89% agreement), tibial derotation osteotomy (90%), and foot osteotomy (87%). Agreement for the remaining treatments ranged between 72–78%. Percent agreement for all treatments was significantly higher than the expected values (p ≤ 0.002) with modest kappa values ranging from 0.22 to 0.52. SignificancePrevious research established that computerized gait analysis data interpretation is reliable for surgeons within a single institution. The current study demonstrates that gait analysis interpretation can also be reliable among surgeons from different institutions. Future research should examine reliability among physicians from more institutions to confirm these results.

Feasibility, validity and reliability of the Dutch translation of INCRESE (INCRESE-NL) inventory to characterize mental health recovery narratives

Objective: Methods for characterizing mental health recovery narratives are needed to enable the selection of helpful narratives which can support the recovery processes of other people. The Inventory of Characteristics of Recovery Stories (INCRESE) is an existing English-language tool to characterize recovery narratives, which is not available in Dutch. The aims of this study were to create a Dutch translation of INCRESE and to evaluate the feasibility of sustained and routine use, as well as its validity and reliability.
 Research design and methods: INCRESE was translated into Dutch (INCRESE-NL) using an established translation methodology. Six coders with different professional backgrounds rated 30 purposively selected Dutch narratives using INCRESE-NL and then completed an evaluation survey. Feasibility was assessed qualitatively and content validity both qualitatively and quantitatively. Gwet’s AC1 agreement coefficient was used to calculate intercoder reliability. 
 Results: Coders evaluated INCRESE-NL as a suitable instrument to capture important characteristics of recovery narratives with an acceptable administrative burden. The content validity index was sufficient for 5/7 sections of INCRESE-NL. Most items were found to be clear and suitable for capturing the corresponding characteristic, although some missed strict coding rules or clear descriptions, especially in section 4 on narrative characteristics. Gwet’s AC1 could be calculated for 67/77 items, of which 62 scored sufficient and five scored below threshold. Gwet’s AC1 coefficients corresponded well with the original INCRESE Fleiss Kappa values. 
 Conclusions: INCRESE-NL has sufficient feasibility, validity and reliability and can be used to characterize Dutch recovery narratives.

A new classification of lumbar Basstrup's disease based on radiographic and clinical symptoms.

To propose a new classification method for lumbar Basstrup's disease based on radiographic and clinical symptoms, and to test its credibility and reproducibility. A retrospective analysis of 116 patients with complete data of lumbar Basstrup's disease in our hospital from January 2019 to January 2022, radiological imaging including X-ray, CT and MRI, were divided into two main types according to the number of segments: type I (single segment) and type II (multiple segments), each type was divided into a total of eight subtypes according to the degree of degeneration: Ia, Ib, Ic, Id, IIa, IIb, IIc, IId; it was further divided into 16 subtypes according to the presence or absence of clinical symptoms (i.e. low back pain or/and lower limb neurological symptoms: no 0, yes 1): Ia0, Ia1, Ib0, Ib1, Ic0, Ic1, Id0, Id1; IIa0, IIa1, IIb0, IIb1, IIc0, IIc1, IId0, IId1.The 116 patients with Basstrup's disease were successively typed twice (1month interval) by five physicians according to the new classification method, and the reliability and reproducibility of the new classification were assessed using the Kappa consistency test. Among 116 patients with lumbar Basstrup's disease: there were 60 cases of type I (single segment), accounting for 51.72%, including Ia 19 cases, Ib 16 cases, Ic 10 cases, and Id 15 cases; there were 56 cases of type II (multiple segments), accounting for 48.28%, including IIa 10 cases, IIb 20 cases, IIc 14 cases, and IId 12 cases. Of the 116 patients, 47 cases had no clinical symptoms (Ia0 16 cases, Ib0 3 cases, Ic0 6 cases, Id0 1 case, IIa0 8 cases, IIb0 2 cases, IIc0 10 cases, IId0 1 case), accounting for 40.52%; 69 cases were accompanied by low back pain or/and lower extremity nerve symptoms (Ia1 3 cases, Ib1 13 cases, Ic1 4 cases, Id1 14 cases, IIa1 2 cases, IIb1 18 cases, IIc1 4 cases, IId1 11 cases), accounting for 59.48%.Five physicians completed two rounds of a total of 1160 typings, with inter-assessor typing agreement of 82.47% (80.17-85.34%) and a mean Kappa value of 0.723 (0.771-0.831); intra-assessor typing agreement of 82.76% (77.59-86.21%) and a mean Kappa value of 0.801 (0.742-0.841). The new classification method for lumbar Basstrup's disease based on radiographic and clinical symptoms is simple, practical, with good credibility and reproducibility, and provides some guidance for clinical treatment.

Optimal Image Characterization for In-Bed Posture Classification by Using SVM Algorithm

Identifying patient posture while they are lying in bed is an important task in medical applications such as monitoring a patient after a surgical intervention, sleep supervision to identify behavioral and physiological markers, or for bedsore prevention. An acceptable strategy to identify the patient’s position is the classification of images created from a grid of pressure sensors located in the bed. These samples can be arranged based on supervised learning methods. Usually, image conditioning is required before images are loaded into a learning method to increase classification accuracy. However, continuous monitoring of a person requires large amounts of time and computational resources if complex pre-processing algorithms are used. So, the problem is to classify the image posture of patients with different weights, heights, and positions by using minimal sample conditioning for a specific supervised learning method. In this work, it is proposed to identify the patient posture from pressure sensor images by using well-known and simple conditioning techniques and selecting the optimal texture descriptors for the Support Vector Machine (SVM) method. This is in order to obtain the best classification and to avoid image over-processing in the conditioning stage for the SVM. The experimental stages are performed with the color models Red, Green, and Blue (RGB) and Hue, Saturation, and Value (HSV). The results show an increase in accuracy from 86.9% to 92.9% and in kappa value from 0.825 to 0.904 using image conditioning with histogram equalization and a median filter, respectively.

Machine learning algorithms predicting bladder cancer associated with diabetes and hypertension: NHANES 2009 to 2018.

Bladder cancer is 1 of the 10 most common cancers in the world. However, the relationship between diabetes, hypertension and bladder cancer are still controversial, limited study used machine learning models to predict the development of bladder cancer. This study aimed to explore the association between diabetes, hypertension and bladder cancer, and build predictive models of bladder cancer. A total of 1789 patients from the National Health and Nutrition Examination Survey were enrolled in this study. We examined the association between diabetes, hypertension and bladder cancer using multivariate logistic regression model, after adjusting for confounding factors. Four machine learning models, including extreme gradient boosting (XGBoost), Artificial Neural Networks, Random Forest and Support Vector Machine were compared to predict for bladder cancer. Model performance was assessed by examining the area under the subject operating characteristic curve, accuracy, recall, specificity, precision, and F1 score. The mean age of bladder cancer group was older than that of the non-bladder cancer (74.4 years vs 65.6 years, P < .001), and men were more likely to have bladder cancer. Diabetes was associated with increased risk of bladder cancer (odds ratio = 1.24, 95%confidence interval [95%CI]: 1.17-3.02). The XGBoost model was the best algorithm for predicting bladder cancer; an accuracy and kappa value was 0.978 with 95%CI:0.976 to 0.986 and 0.01 with 95%CI:0.01 to 0.52, respectively. The sensitivity was 0.90 (95%CI:0.74-0.97) and the area under the curve was 0.78. These results suggested that diabetes is associated with risk of bladder cancer, and XGBoost model was the best algorithm to predict bladder cancer.

Age and flexors as risk factors for cervical radiculopathy: A new machine learning method.

This study aimed to investigate the risk factors for cervical radiculopathy (CR) along with identifying the relationships between age, cervical flexors, and CR. This was a retrospective cohort study, including 60 patients with CR enrolled between December 2018 and June 2020. In this study, we measured C2 to C7 Cobb angle, disc degeneration, endplate degeneration, and morphology of paraspinal muscles and evaluated the value of predictive methods using receiver operating characteristic curves. Next, we established a diagnostic model for CR using Fisher discriminant model and compared different models by calculating the kappa value. Age and cervical flexor factors were used to construct clinical predictive models, which were further evaluated by C-index, receiver operating characteristic curve, calibration curve, and decision curve analysis. Multivariate analysis showed that age and cervical flexors were potential risk factors for CR, while the diagnostic model indicated that both exerted the best diagnostic effect. The obtained diagnostic equation was as follows: y1 = 0.33 × 1 + 10.302 × 2-24.139; y2 = 0.259 × 1 + 13.605 × 2-32.579. Both the C-index and AUC in the training set reached 0.939. Moreover, the C-index and AUC values in the external validation set reached 0.961. We developed 2 models for predicting CR and also confirmed their validity. Age and cervical flexors were considered potential risk factors for CR. Our noninvasive inspection method could provide clinicians with a more potential diagnostic value to detect CR accurately.

Diagnostic Performance of a Novel Ultra-Thin Endoscopy under Narrow-Band Imaging for Superficial Squamous Cell Carcinoma of the Pharynx and Esophagus

This study aimed to evaluate the diagnostic utility of the ultra-thin endoscope (UTE) for superficial squamous cell carcinoma (SSCC) compared to magnifying endoscopy (ME) under narrow-band imaging. Participants underwent endoscopic examination, and images of pharyngeal and esophageal SCCs, as along with suspicious SSCC lesions, were collected using UTE and ME on the same day. Three image catalogs (UTE, ME-1, and ME-2) were created and reviewed by three expert endoscopists. ME-1 and ME-2 contained the same endoscopic images. The primary endpoint was the intra-observer agreement for diagnosing SCC. Eighty-six lesions (SCC = thirty-nine, non-SCC = forty-seven) in 43 participants were identified. The kappa values for the intra-observer agreement between UTE and ME-1 vs. the control (ME-1 vs. ME-2) were 0.74 vs. 0.84, 0.63 vs. 0.76, and 0.79 vs. 0.88, respectively. The accuracies for diagnosing SCC by UTE and ME-1 were 87.2% vs. 86.0%, 78.0% vs. 73,2%, and 75.6 vs. 82.6%, respectively, with no significant differences (p &gt; 0.05). The rates of lesions that were diagnosed with confidence by UTE and ME-1 were 30.2% vs. 27.9%, 55.8% vs. 62.8%, and 58.1% vs. 55.8%, respectively. UTE demonstrates substantial diagnostic performance for SSCC in the pharynx and esophagus.

Holistic quality evaluation method of Epimedii Folium based on NIR spectroscopy and chemometrics.

There are some problems in the quality control of Epimedii Folium (leaves of Epimedium brevicornum Maxim.), such as the mixed use of Epimedii Folium from different harvesting periods and regions, incomplete quality evaluation, and time-consuming analysis methods. Near-infrared (NIR) spectroscopy was conducted to establish a rapid overall quality evaluation method for Epimedii Folium. Quantitative models of the total solid, moisture, total flavonoid, and flavonol glycoside (Epimedin A, Epimedin B, Epimedin C, Icariin) contents of Epimedii Folium were established by partial least squares regression (PLSR). The root mean square error (RMSE) and correlation coefficient (R) were used to evaluate the performance of models. The qualitative models of Epimedii Folium from different geographic origins and harvest periods were established based on K-nearest neighbor (KNN), back-propagation neural network (BPNN), and random forest (RF). Accuracy and Kappa values were used to evaluate the performance of models. A new multivariable signal conversion strategy was proposed, which combines NIR spectroscopy with the PLSR model to predict the absorbance values of retention time points in the high-performance liquid chromatography (HPLC) fingerprint to obtain the predicted HPLC fingerprint. The Pearson correlation coefficient and cosine coefficient were used to evaluate the similarity between real and predicted HPLC fingerprints. Qualitative models, quantitative models, and the similarity between real and predicted HPLC fingerprints are satisfactory. The method serves as a fast and green analytical quality evaluation method of Epimedii Folium and can replace traditional methods to achieve the overall quality evaluation of Epimedii Folium.

Interpretable artificial intelligence-based app assists inexperienced radiologists in diagnosing biliary atresia from sonographic gallbladder images

BackgroundA previously trained deep learning-based smartphone app provides an artificial intelligence solution to help diagnose biliary atresia from sonographic gallbladder images, but it might be impractical to launch it in real clinical settings. This study aimed to redevelop a new model using original sonographic images and their derived smartphone photos and then test the new model’s performance in assisting radiologists with different experiences to detect biliary atresia in real-world mimic settings.MethodsA new model was first trained retrospectively using 3659 original sonographic gallbladder images and their derived 51,226 smartphone photos and tested on 11,410 external validation smartphone photos. Afterward, the new model was tested in 333 prospectively collected sonographic gallbladder videos from 207 infants by 14 inexperienced radiologists (9 juniors and 5 seniors) and 4 experienced pediatric radiologists in real-world mimic settings. Diagnostic performance was expressed as the area under the receiver operating characteristic curve (AUC).ResultsThe new model outperformed the previously published model in diagnosing BA on the external validation set (AUC 0.924 vs 0.908, P = 0.004) with higher consistency (kappa value 0.708 vs 0.609). When tested in real-world mimic settings using 333 sonographic gallbladder videos, the new model performed comparable to experienced pediatric radiologists (average AUC 0.860 vs 0.876) and outperformed junior radiologists (average AUC 0.838 vs 0.773) and senior radiologists (average AUC 0.829 vs 0.749). Furthermore, the new model could aid both junior and senior radiologists to improve their diagnostic performances, with the average AUC increasing from 0.773 to 0.835 for junior radiologists and from 0.749 to 0.805 for senior radiologists.ConclusionsThe interpretable app-based model showed robust and satisfactory performance in diagnosing biliary atresia, and it could aid radiologists with limited experiences to improve their diagnostic performances in real-world mimic settings.

Determination of viable myocardium through delayed enhancement cardiac magnetic resonance imaging combined with 18F-FDG PET myocardial perfusion/metabolic imaging before CABG.

Study aims to investigate the consistency of delayed enhancement cardiac magnetic resonance imaging (DE-CMR) and 18F-FDG PET myocardial imaging in evaluating myocardial viability before CABG. The study analyzed data from 100 patients who were examined with DE-CMR, PET imaging, and echocardiography before and after CABG. All subjects were followed up for 6-12 month post- CABG. DE-CMR and PET imaging have high consistency (90.1%; Kappa value = 0.71, p < 0.01) in determining myocardial viability. The degree of delayed enhancement was negatively correlated with the improvement in myocardial contractile function in this segment after revascularization (P < 0.001). The ratio of scarred myocardial segments and total DE score was significantly lower in the improvement group than non-improvement group. Multivariate regression identified that hibernating myocardium (OR = 1.229, 95%CI: 1.053-1.433, p = 0.009) was influencing factor of LVEF improvement after CABG. Both imaging techniques are consistent in evaluating myocardial viability. Detecting the number of hibernating myocardium by PET is also important to predict the left heart function improvement after CABG.

Single Room Fire Traceability and Prediction Models Based on Deep Learning Methods

ABSTRACT When a fire occurs, fast recognition on key information of the fire based on limited data available and predict fire smoke motion trends from limited fire information is desirable to develop effective emergency strategies. The proposed models include fire intensity traceability model and fire position traceability model based on Back Propagation Neural Network (BPNN). Smoke prediction model is proposed based on Transpose Convolutional Neural Network (TCNN). The numerical model is first validated by the single-room fire test, and then a numerical database of 165 transient room-fire scenarios is established under various fire locations, fire sizes, and vent sizes. The model test is conducted in new fire intensity and vent size scenarios, with R2 coefficients of 0.965 and 0.95, respectively. The experimental data validation shows that relative errors were less than 10%. The position traceability model has Kappa values of 0.758 and 0.75. The average visibility error values of the smoke prediction model’s output images in the validation of the test set generally fall within the range of ± 1.5 m. The results indicate that the models have good accuracy and strong adaptability to different compartment fire scenarios which can provides effective support for fire rescue and emergency strategy development in fire scenes.

Comparative Study of Potential Habitats for Simulium qinghaiense (Diptera: Simuliidae) in the Huangshui River Basin, Qinghai-Tibet Plateau: An Analysis Using Four Ecological Niche Models and Optimized Approaches.

The Huangshui River, a vital tributary in the upper reaches of the Yellow River within the eastern Qinghai-Tibet Plateau, is home to the endemic black fly species S. qinghaiense. In this study, we conducted a systematic survey of the distribution of the species in the Huangshui River basin, revealing its predominant presence along the river's main stem. Based on four ecological niche models-MaxEnt with parameter optimization; GARP; BIOCLIM; and DOMAIN-we conduct a comparative analysis; evaluating the accuracy of AUC and Kappa values. Our findings indicate that optimizing parameters significantly improves the MaxEnt model's predictive accuracy by reducing complexity and overfitting. Furthermore, all four models exhibit higher accuracy compared to a random model, with MaxEnt demonstrating the highest AUC and Kappa values (0.9756 and 0.8118, respectively), showcasing significant superiority over the other models (p < 0.05). Evaluation of predictions from the four models elucidates that potential areas of S. qinghaiense in the Huangshui River basin are primarily concentrated in the central and southern areas, with precipitation exerting a predominant influence. Building upon these results, we utilized the MaxEnt model to forecast changes in suitable areas and distribution centers during the Last Interglacial (LIG), Mid-Holocene (MH), and future periods under three climate scenarios. The results indicate significantly smaller suitable areas during LIG and MH compared to the present, with the center of distribution shifting southeastward from the Qilian Mountains to the central part of the basin. In the future, suitable areas under different climate scenarios are expected to contract, with the center of distribution shifting southeastward. These findings provide important theoretical references for monitoring, early warning, and control measures for S. qinghaiense in the region, contributing to ecological health assessment.

An Enhanced LightGBM-Based Breast Cancer Detection Technique Using Mammography Images.

Breast cancer (BC) is the leading cause of mortality among women across the world. Earlier screening of BC can significantly reduce the mortality rate and assist the diagnostic process to increase the survival rate. Researchers employ deep learning (DL) techniques to detect BC using mammogram images. However, these techniques are resource-intensive, leading to implementation complexities in real-life environments. The performance of convolutional neural network (CNN) models depends on the quality of mammogram images. Thus, this study aimed to build a model to detect BC using a DL technique. Image preprocessing techniques were used to enhance image quality. The authors developed a CNN model using the EfficientNet B7 model's weights to extract the image features. Multi-class classification of BC images was performed using the LightGBM model. The Optuna algorithm was used to fine-tune LightGBM for image classification. In addition, a quantization-aware training (QAT) strategy was followed to implement the proposed model in a resource-constrained environment. The authors generalized the proposed model using the CBIS-DDSM and CMMD datasets. Additionally, they combined these two datasets to ensure the model's generalizability to diverse images. The experimental findings revealed that the suggested BC detection model produced a promising result. The proposed BC detection model obtained an accuracy of 99.4%, 99.9%, and 97.0%, and Kappa (K) values of 96.9%, 96.9%, and 94.1% in the CBIS-DDSM, CMMD, and combined datasets. The recommended model streamlined the BC detection process in order to achieve an exceptional outcome. It can be deployed in a real-life environment to support physicians in making effective decisions. Graph convolutional networks can be used to improve the performance of the proposed model.

Real-Time Tracheal Ultrasound vs. Capnography for Intubation Confirmation during CPR Wearing a Powered Air-Purifying Respirator in COVID-19 Era.

This study aimed to compare the accuracy of real-time trans-tracheal ultrasound (TTUS) with capnography to confirm intubation in cardiopulmonary resuscitation (CPR) while wearing a powered air-purifying respirator (PAPR). This setting reflects increased caution due to contagious diseases. This single-center, prospective, comparative study enrolled patients requiring CPR while wearing a PAPR who visited the emergency department of a tertiary medical center from December 2020 to August 2022. A physician performed the TTUS in real time and recorded the tube placement assessment. Another healthcare provider attached waveform capnography to the tube and recorded end-tidal carbon dioxide (EtCO2) after five ventilations. The accuracy and agreement of both methods compared with direct laryngoscopic visualization of tube placement, and the time taken by both methods was evaluated. Thirty-three patients with cardiac arrest were analyzed. TTUS confirmed tube placement with 100% accuracy, sensitivity, and specificity, whereas capnography demonstrated 97% accuracy, 96.8% sensitivity, and 100% specificity. The Kappa values for TTUS and capnography compared to direct visualization were 1.0 and 0.7843, respectively. EtCO2 was measured in 45 (37-59) seconds (median (interquartile range)), whereas TTUS required only 12 (8-23) seconds, indicating that TTUS was significantly faster (p < 0.001). No significant correlation was found between the physician's TTUS proficiency and image acquisition time. This study demonstrated that TTUS is more accurate and faster than EtCO2 measurement for confirming endotracheal tube placement during CPR, particularly in the context of PAPR usage in pandemic conditions.

Development and validation of a novel multiplex digital PCR assay for identification of pathogens in cerebrospinal fluid of children with bacterial meningitis

Background and aimsIdentifying the pathogens of bacterial meningitis (BM) is crucial for its diagnosis and treatment. The aim of this study is to develop and validate a novel method for detecting pathogens in cerebrospinal fluid (CSF) of children with BM using a digital polymerase chain reaction (dPCR) assay.Materials and methods:A novel multiplex dPCR assay method has been developed and validated. The diagnostic performance of the dPCR assay was compared with that of synchronous CSF culture, and the factors affecting its performance were analyzed.Results:A total of 69 children with BM were enrolled prospectively. The sensitivity of the dPCR assay was 94.44 %, specificity was 100 %, coincidence rate was 98.55 %, Kappa value was 0.959, and net reclassification improvement was 61.11 %. Compared with the CSF culture assay, the dPCR assay had higher sensitivity in different bacterial groups. Multiple factors affected its performance, including previous use of antibiotics, sampling time, BM complications, and levels of inflammatory biomarkers in CSF and blood (all P < 0.05). Patients who required intensive care and died had a higher bacterial DNA loads identified by dPCR assay (both P < 0.05).Conclusion:This novel assay has better pathogen detection ability than CSF culture. Its performance was influenced by sampling time, previous use of antibiotics, and disease severity.

Can Short Tau Inversion Recovery (STIR) Imaging Be Used as a Stand-Alone Sequence To Assess a Perianal Fistulous Tract on MRI? A Retrospective Cohort Study Comparing STIR and T1-Post Contrast Imaging.

Perianal fistulas demand precise preoperative assessment for optimal surgical outcomes. MRI, using Short Tau Inversion Recovery (STIR) and T1-post contrast sequences, plays a crucial role in this evaluation.This retrospective cohort study compared STIR imaging's diagnostic efficacy with T1-post contrast sequences in identifying perianal fistulous tracts. The study investigated whether STIR imaging could serve as the sole diagnostic sequence, simplifying clinical practice. In a tertiary care hospital, 100 patients underwent pelvic MRI for suspected perianal fistulas. Radiologists independently evaluated STIR and T1-post contrast sequences for internal openings, tract extent, distinction, abscess presence, and tract type. Sensitivity, specificity, area under the curve (AUC), and Cohen's kappa analysis were used for diagnostic assessment. STIR imaging showed notable sensitivity (79.8-97.9%) and specificity (100%) for identifying internal openings and tracts. Combined with T1-post contrast, diagnostic accuracy improved significantly, with near-perfect AUC values. Kappa values indicated moderate to substantial agreement between radiological assessments and clinical diagnosis. The combined sequences achieved 100% sensitivity and specificity for tract visualization. STIR imaging presents promise as a singular diagnostic tool for perianal fistulas, especially when combined with T1-post contrast sequences. While offering potential clinical diagnosis simplifications, further studies are warranted to validate its utility and ensure comprehensive diagnostic accuracy.

Automated detection of depression using wavelet scattering networks

Today, depression is a common problem that affects many people all over the world. It can impact a person's mood and quality of life unless identified and treated immediately. Due to the hectic and stressful modern life seems to be, depression has become a leading cause of mental health illnesses. Signals from electroencephalograms (EEG) are frequently used to detect depression. It is difficult, time-consuming, and highly skilled to manually detect depression using EEG data analysis. Hence, in the proposed study, an automated depression detection system using EEG signals is proposed.The proposed study uses a clinically available dataset and dataset provided by the Department of Psychiatry at the Government Medical College (GMC) in Kozhikode, Kerala, India which consisted of 15 depressed patients and 15 healthy subjects and a publically available Multi-modal Open Dataset (MODMA) for Mental-disorder Analysis available at UK Data service reshare that consisted of 24 depressed patients and 29 healthy subjects. In this study, we have developed a novel Deep Wavelet Scattering Network (DWSN) for the automated detection of depression EEG signals. The best-performing classifier is then chosen by feeding the features into several machine-learning algorithms. For the clinically available GMC dataset, Medium Neural Network (MNN) achieved the highest accuracy of 99.95% with a Kappa value of 0.999. Using the suggested methods, the precision, recall, and F1-score are all 1. For the MODMA dataset, Wide Neural Network (WNN) achieved the highest accuracy of 99.3% with a Kappa value of 0.987. Using the suggested methods, the precision, recall, and F1-score are all 0.99. In comparison to all current methodologies, the performance of the suggested research is superior. The proposed method can be used to automatically diagnose depression both at home and in clinical settings.

An Evaluation Analysis for Computed Tomography Image Quality of Primary Liver Cancer Lesions Based on Deep Learning Image Reconstruction.

Abdominal multi-slice helical computed tomography (CT) and contrast-enhanced scanning have been widely recognized clinically. The impact of the deep learning image reconstruction (DLIR) on the quality of dynamic contrast-enhanced CT imaging of primary liver cancer lesions was evaluated through comparison with the filtered back projection (FBP) and the new generation of adaptive statistical iterative reconstruction-V (ASIR-V). We evaluated the image noise of the lesion, fine structures inside the lesion, and diagnostic confidence in 48 liver cancer subjects. The CT values of the solid part of the lesion and the adjacent normal liver tissue and the systolic and diastolic blood pressure (SD) values of the right paravertebral muscle were measured. The muscle SD value was considered as the background noise of the image, and the signal noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion and normal liver parenchyma were calculated. High consistency in the evaluation of image noise (Kappa = 0.717). The Kappa values for margin/pseudocapsule, fine structure within the lesion, and diagnostic confidence were 0.463, 0.527, and 0.625, respectively. Besides, the differences in SD, SNR and CNR data of reconstructed lesion images among the six groups were statistically significant. The contrast-enhanced CT image noise of DLIR-H in the portal venous phase is much lower than that of ASIR-V and FBP in primary liver cancer patients. In terms of the lesion structure display, the new reconstruction algorithm DLIR is superior.

Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models

Wildfires represent a significant threat to both ecosystems and human assets in Mediterranean countries, where fire occurrence is frequent and often devastating. Accurate assessments of the initial fire severity are required for management and mitigation efforts of the negative impacts of fire. Evapotranspiration (ET) is a crucial hydrological process that links vegetation health and water availability, making it a valuable indicator for understanding fire dynamics and ecosystem recovery after wildfires. This study uses the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) and Operational Simplified Surface Energy Balance (SSEBop) ET models based on Landsat imagery to estimate fire severity in five large forest fires that occurred in Spain and Portugal in 2022 from two perspectives: uni- and bi-temporal (post/pre-fire ratio). Using-fine-spatial resolution ET is particularly relevant for heterogeneous Mediterranean landscapes with different vegetation types and water availability. ET was significantly affected by fire severity according to eeMETRIC (F &gt; 431.35; p-value &lt; 0.001) and SSEBop (F &gt; 373.83; p-value &lt; 0.001) metrics, with reductions of 61.46% and 63.92%, respectively, after the wildfire event. A Random Forest machine learning algorithm was used to predict fire severity. We achieved higher accuracy (0.60 &lt; Kappa &lt; 0.67) when employing both ET models (eeMETRIC and SSEBop) as predictors compared to utilizing the conventional differenced Normalized Burn Ratio (dNBR) index, which resulted in a Kappa value of 0.46. We conclude that both fine resolution ET models are valid to be used as indicators of fire severity in Mediterranean countries. This research highlights the importance of Landsat-based ET models as accurate tools to improve the initial analysis of fire severity in Mediterranean countries.

Clinical value of oral contrast-enhanced ultrasonography in diagnosis of gastric tumors.

The incidence of gastric cancer remains high, and it is the sixth most common cancer and the fourth leading cause of cancer deaths worldwide. Oral contrast-enhanced ultrasonography is a simple, non-invasive, and painless method for the diagnosis of gastric tumors. To explore the diagnostic value of oral contrast-enhanced ultrasonography for the detection of gastric tumors. The screening results based on oral contrast-enhanced ultrasonography and electronic gastroscopy were compared with those of the postoperative pathological examination. Among 42 patients with gastric tumors enrolled in the study, the diagnostic accordance rate was 95.2% for oral contrast-enhanced ultrasonography (n = 40) and 90.5% for electronic gastroscopy (n = 38) compared with postoperative pathological examination. The Kappa value of consistency test with pathological findings was 0.812 for oral contrast-enhanced ultrasonography and 0.718 for electronic gastroscopy, and there was no significant difference between them (P = 0.397). For the TNM staging of gastric tumors, the accuracy rate of oral contrast-enhanced ultrasonography was 81.9% for the overall T staging and 50%, 77.8%, 100%, and 100% for T1, T2, T3, and T4 staging, respectively. The sensitivity and specificity were both 100% for stages T3 and T4. The diagnostic accuracy rate of oral contrast-enhanced ultrasonography was 93.8%, 80%, 100%, and 100% for stages N0, N1-N3, M0, and M1, respectively. The accordance rate of qualitative diagnosis by oral contrast-enhanced ultrasonography is comparable to that of gastroscopy, and it could be used as the preferred method for the early screening of gastric tumors.