Performance Of Prediction Models Research Articles

Predictors of Symptom Reduction and Remission Among People with Anxiety: Secondary Analyses from a Randomized Controlled Trial.

Despite the substantial disease burden of anxiety disorders, only limited or conflicting data on prognostic factors is available. Most studies include patients in the secondary healthcare sector thus, the generalizability of findings is limited. The present study examines predictors of symptom reduction and remission in patients with anxiety disorders in a primary care setting. 214 patients with anxiety disorders, recruited as part of the Collabri Flex trial, were included in secondary analyses. Data on potential predictors of anxiety symptoms at 6-month follow-up was collected at baseline, including patient characteristics related to demography, illness, comorbidity, functional level, life quality, and self-efficacy. The outcomes were symptom reduction and remission. Univariate and multivariate linear and logistic regression analyses were conducted to assess the associations between predictor variables and the outcome, and machine-learning methods were also applied. In multiple linear regression analysis, anxiety severity at baseline (β = -6.05, 95% CI = -7.54,-4.56, p < 0.001) and general psychological problems and symptoms of psychopathology (SCL-90-R score) (β = 2.19, 95% CI = 0.24,4.14, p = 0.028) were significantly associated with symptom change at 6months. Moreover, self-efficacy was associated with the outcome, however no longer significant in the multiple regression model. In multiple logistic regression analysis, anxiety severity at baseline (OR = 0.54, 95% CI = -1.13,-0.12, p = 0.018) was significantly associated with remission at 6months. There was no predictive performance of the machine-learning models. Our study contributes with information that could be valuable knowledge for managing anxiety disorders in primary care.

Performance of risk prediction models for diabetic foot ulcer: a meta-analysis

Performance of risk prediction models for diabetic foot ulcer: a meta-analysis

Systematic Evaluation of Hematoma Expansion Models in Spontaneous Intracerebral Hemorrhage: A Meta-Analysis and Meta-Regression Approach.

Accurate prediction of hematoma expansion (HE) in spontaneous intracerebral hemorrhage (sICH) is crucial for tailoring patient-specific treatments and improving outcomes. Recent advancements have yielded numerous HE risk factors and predictive models. This study aims to evaluate the characteristics and efficacy of existing HE prediction models, offering insights for performance enhancement. A comprehensive search was conducted in PubMed for observational studies and randomized controlled trials focusing on HE prediction, written in English. The prediction models were categorized based on their incorporated features and modeling methodology. Rigorous quality and bias assessments were performed. A meta-analysis of studies reporting C-statistics was executed to assess and compare the performance of current HE prediction models. Meta-regression was utilized to explore heterogeneity sources. From 358 initial records, 22 studies were deemed eligible, encompassing traditional models, hematoma imaging feature models, and models based on artificial intelligence (AI) or radiomics. Meta-analysis of 11 studies, involving 12,087 sICH patients, revealed an aggregated C-statistic of 0.74 (95% CI: 0.69 - 0.78) across seven HE prediction models. Eight characteristics related to development cohorts were identified as key factors contributing to performance variability among these models. The findings indicate that the current predictive capacity for HE risk remains suboptimal. Enhanced accuracy in HE prediction is vital for effectively targeting patient populations most likely to benefit from tailored treatment strategies.

Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations.

Prior studies have shown that computational fluid dynamics (CFD) simulations help assess patient-specific hemodynamics in abdominal aortic aneurysms (AAAs); patient-specific hemodynamic stressors are frequently used to predict an AAA's growth. Previous studies have utilized both laminar and turbulent simulation models to simulate hemodynamics. However, the impact of different CFD simulation models on the predictive modeling of AAA growth remains unknown and is thus the knowledge gap that motivates this study. Specifically, CFD simulations were performed for 70 AAA models derived from 70 patients' computed tomography angiography (CTA) data with known growth status (i.e., fast-growing [> 5mm/yr] or slowly growing [< 5mm/yr]). We used laminar and large eddy simulation (LES) models to obtain hemodynamic parameters to predict AAAs' growth status. Predicting the growth status of AAAs was based on morphological, hemodynamic, and patient health parameters in conjunction with three classical machine learning (ML) classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and generalized linear model (GLM). Our preliminary results estimated aneurysmal flow stability and wall shear stress (WSS) were comparable in both laminar and LES flow simulations. Moreover, computed WSS and velocity-related hemodynamic variables obtained from the laminar and LES simulations showed comparable abilities in differentiating the growth status of AAAs. More importantly, the predictive modeling performance of the three ML classifiers mentioned above was similar, with less than a 2% difference observed (p-value>0.05). In closing, our findings suggest that two different flow simulations investigated did not significantly affect outcomes of computational hemodynamics and predictive modeling of AAAs' growth status, given the data investigated.

Diagnostic Prediction Model for Tuberculous Meningitis: An Individual Participant Data Meta-Analysis.

No accurate and rapid diagnostic test exists for tuberculous meningitis (TBM), leading to delayed diagnosis. We leveraged data from multiple studies to improve the predictive performance of diagnostic models across different populations, settings, and subgroups to develop a new predictive tool for TBM diagnosis. We conducted a systematic review to analyze eligible datasets with individual-level participant data (IPD). We imputed missing data and explored three approaches: stepwise logistic regression, classification and regression tree (CART), and random forest regression. We evaluated performance using calibration plots and C-statistics via internal-external cross-validation. We included 3,761 individual participants from 14 studies and nine countries. A total of 1,240 (33%) participants had "definite" (30%) or "probable" (3%) TBM by case definition. Important predictive variables included cerebrospinal fluid (CSF) glucose, blood glucose, CSF white cell count, CSF differential, cryptococcal antigen, HIV status, and fever presence. Internal validation showed that performance varied considerably between IPD datasets with C-statistic values between 0.60 and 0.89. In external validation, CART performed the worst (C = 0.82), and logistic regression and random forest had the same accuracy (C = 0.91). We developed a mobile app for TBM clinical prediction that accounted for heterogeneity and improved diagnostic performance (https://tbmcalc.github.io/tbmcalc). Further external validation is needed.

Impact of Incorporating Community Smells with Object-Oriented Metrics to Predict Change-Prone Classes

Change-proneness is an important quality attribute that can help developers to maintain source code more effectively. Change-prone classes refer to those classes which are more likely to change across releases. Existing approaches have used object-oriented metrics to predict change-prone classes. However, community smells related information, which represents the communication issues among developers’ community, has not yet been considered to predict change-prone classes. This study investigates the impact of including community smells-related information with existing Object Oriented (OO) metrics to predict a class to be change-prone in future software releases. It aims to understand the extent to which the addition of community smell-related information can contribute to the performance of change prediction models. To perform analysis, 317 releases of 14 open-source Java projects were selected from GitHub. The resulting dataset is used to predict change-prone classes with five different popular and widely used machine learning algorithms, namely K-nearest neighbor (KNN), Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR) and Multilayer Perceptron (MLP). To evaluate the performance of the classifiers, accuracy, and F1-measure are used. The experimental results suggest that including community smell metrics along with the existing OO metrics gives better performance in predicting change-prone classes. Moreover, MLP and LR algorithms, which are both parametric models, are found to predict change-prone classes better when Community Smell(CMS) related features are incorporated, in comparison with other non-parametric models. By including CMS along with OO metrics, 1.51% increase in average accuracy and 3.52% increase in average F1 score for Logistic Regression, and 1.69% increase in average accuracy and 3.26% increase in average F1 score for Multi-layer Perceptron have been found. DUJASE Vol. 8 (1) 32-41, 2023 (January)

Exploring capelin (Mallotus villosus) population dynamics using Empirical Dynamic Modelling (EDM)

Capelin (Mallotus villosus) populations on the Newfoundland shelf collapsed in the early 1990s, coinciding with a regime shift and greatly reduced capelin and groundfish biomasses which both persist to this day. The biphasic nature of this stock’s history suggest it may experience nonlinear dynamics, which are difficult to predict using linear models. This study explores the application of Empirical Dynamic Modelling (EDM) nonlinear, nonparametric time series forecasting tools to capelin biomass data, seeking to detect nonlinear dynamics, compare performance of linear and nonlinear multivariate predictive models, identify drivers of capelin biomass using convergent cross-mapping, and measure the sign and strength of capelin species interactions. We found capelin dynamics were nonlinear, and multivariate EDM predictive models returned equal or improved model diagnostics to linear models in most situations. We identified long-term climate dynamics and timing of sea ice retreat as the primary drivers of capelin dynamics, but some indications of potential top-down effects from Greenland halibut were also detected. Atlantic cod biomass, and capelin catch were investigated as potential drivers of capelin dynamics, but both were found more likely to be driven by capelin dynamics. Overall, our results support the idea that capelin dynamics are mostly bottom-up driven, and that capelin itself is a driver of its predators, suggesting that the overall ecosystem may be largely bottom-up driven. This study also clearly identifies the utilities of EDM as a complementary tool for stock assessments by detecting and forecasting nonlinear stock dynamics, and identifying and characterizing relationships between stock biomass and the factors which drive it.

Advanced method for estimating the volumetric intensity along tunnels using ANN

ABSTRACT Simulating realistic scenarios with numerical models often demands substantial computational resources, which can be excessively time-consuming. In complex Discrete Fracture Network (DFN) simulations where mutual influence among fracture parameters is crucial, efficient Artificial Intelligence (AI) algorithms offer a promising solution. This study focuses on the Monte Seco tunnel in Brazil, employing Artificial Neural Networks (ANN) with the Levenberg-Marquardt Algorithm (ANN-LM) to estimate Volumetric discontinuity intensity (P32). Comparative analysis with traditional DFN-based methods reveals superior predictive performance of the ANN model over Multiple Linear Regression (MLR). MATLAB was utilized for implementation, considering the interdependence of geometric parameters across fracture sets to estimate P32 values. Sensitivity analysis identified correlations between F1 parameters (density and trace length) and P32 estimates for F2, aiding in predicting potential tunnel instability. A Graphical User Interface (GUI) was developed to streamline calculations, replacing cumbersome spreadsheet methods.

Evaluating and Enhancing Artificial Intelligence Models for Predicting Student Learning Outcomes

Predicting student outcomes is an essential task and a central challenge among artificial intelligence-based personalised learning applications. Despite several studies exploring student performance prediction, there is a notable lack of comprehensive and comparative research that methodically evaluates and compares multiple machine learning models alongside deep learning architectures. In response, our research provides a comprehensive comparison to evaluate and improve ten different machine learning and deep learning models, either well-established or cutting-edge techniques, namely, random forest, decision tree, support vector machine, K-nearest neighbours classifier, logistic regression, linear regression, and state-of-the-art extreme gradient boosting (XGBoost), as well as a fully connected feed-forward neural network, a convolutional neural network, and a gradient-boosted neural network. We implemented and fine-tuned these models using Python 3.9.5. With a keen emphasis on prediction accuracy and model performance optimisation, we evaluate these methodologies across two benchmark public student datasets. We employ a dual evaluation approach, utilising both k-fold cross-validation and holdout methods, to comprehensively assess the models’ performance. Our research focuses primarily on predicting student outcomes in final examinations by determining their success or failure. Moreover, we explore the importance of feature selection using the ubiquitous Lasso for dimensionality reduction to improve model efficiency, prevent overfitting, and examine its impact on prediction accuracy for each model, both with and without Lasso. This study provides valuable guidance for selecting and deploying predictive models for tabular data classification like student outcome prediction, which seeks to utilise data-driven insights for personalised education.

Predicting the 28-day prognosis of acute-on-chronic liver failure patients based on machine learning

BackgroundWe aimed to establish a prognostic predictive model based on machine learning (ML) methods to predict the 28-day mortality of acute-on-chronic liver failure (ACLF) patients, and to evaluate treatment effectiveness. MethodsACLF patients from six tertiary hospitals were included for analysis. Features for ML models’ development were selected by LASSO regression. Models’ performance was evaluated by area under the curve (AUC) and accuracy. Shapley additive explanation was used to explain the ML model. ResultsOf the 736 included patients, 587 were assigned to a training set and 149 to an external validation set. Features selected included age, hepatic encephalopathy, total bilirubin, PTA, and creatinine. The eXtreme Gradient Boosting (XGB) model outperformed other ML models in the prognostic prediction of ACLF patients, with the highest AUC and accuracy. Delong's test demonstrated that the XGB model outperformed Child-Pugh score, MELD score, CLIF-SOFA, CLIF-C OF, and CLIF-C ACLF. Sequential assessments at baseline, day 3, day 7, and day 14 improved the predictive performance of the XGB-ML model and can help clinicians evaluate the effectiveness of medical treatment. ConclusionsWe established an XGB-ML model to predict the 28-day mortality of ACLF patients as well as to evaluate the treatment effectiveness.

Surface displacement measurement and modeling of the Shah-Gheyb salt dome in southern Iran using InSAR and machine learning techniques

Salt domes play a crucial role in hydrocarbon storage, underground construction, solution mining, and mineralization. Therefore, deformation monitoring is essential for analyzing the kinematics and impact of salt domes. This study aims to measure the temporal displacements of the Shah-Gheyb salt dome from 2016 to 2019 and from 2020 to 2022 using the New Small Baseline Subset (NSBAS) Interferometric Synthetic Aperture Radar (InSAR) technique and to predict future displacements through machine learning models. A total of 14 data layers, including topography, remote sensing, hydrology, and geology group were used in Machine Learning (ML). Random Forest Regression (RFR) and Support Vector Regression (SVR) models were employed to project displacements in both the East-West (E-W) and Up-Down (U-D) components through 29 scenarios.In the E-W direction, the salt dome exhibits a displacement rate of 39 mm/year, while in the U-D direction, it varies between −18 and +6 mm/year. ML predictions and SAR interferometry data processing results for the period 2020–2022 were validated using Root Mean Square Error (RMSE) and the correlation coefficient (R). The RFR model demonstrated the lowest RMSE of 1.9 mm for the E-W component, achieving a maximum R-value of 97.3 %. For the U-D component, the RMSE was 2.8 mm, with an R-value of 55.8 %. Evaluation of the predictive performance of the ML models and a comparison of InSAR and ML outcomes indicated that the RFR model predicted displacement along the E-W and U-D directionsbetween 2020 and 2022 with greater accuracy than the SVR. Furthermore, comparing the displacement predictedby the RFR model using SAR interferometry along two perpendicular profiles confirmedthe model's precision.

Physics-based natural gradient boosting probabilistic prediction model for seismic failure modes of reinforced concrete columns

A novel physics-based natural gradient boosting (PNGB) probabilistic prediction model for flexure failure (FF), flexure-shear failure (FSF) and shear failure (SF) of reinforced concrete (RC) columns under strong earthquakes has been developed to address the limitations of empirical and machine learning (ML) models, which often fail to learn physical laws, insufficiently consider uncertainties, and exhibit poor generalization performance. Firstly, an improved natural gradient boosting (NGB) probabilistic model of seismic failure modes was created by using natural gradient boosting and gradient boosting regressor algorithms. Then a new hyperparameter optimization method for the improved NGB probabilistic model was proposed based on the ML algorithms and the physical laws. Finally, the predictive performance and engineering practicability of the PNGB probabilistic model were verified compared to empirical and ML models. According to the analysis results, the PNGB model can effectively improve the predictive recall of FF, FSF and SF by about 19–65 %, 20–67 % and 20–56 % respectively compared with empirical models, and by about 1–7 %, 7–43 % and 6–32 % respectively compared with ML models. Moreover, the PNGB model exhibits satisfactory generalization performance and minimal dispersion, which can be used to calibrate the ML models based on the confidence intervals. Furthermore, the result predicted by the PNGB model satisfies the physical laws relating to the seismic failure modes and design parameters, which can further represent the competitive relationships of various seismic failure modes.

Monitoring the invasion of Campulocliniummacrocephalum (less) DC plants using a novel MaxEnt and machine learning ensemble in the Cradle Nature Reserve, South Africa

The proliferation of non-native plant species has caused significant changes in global ecosystems, leading to a surge in international interest in the use of remote sensing technologies for both local and global detection applications. The Greater Cradle Nature Reserve, a UNESCO World Heritage Site, is facing a decline in its global status due to the spread of pompom weeds, affecting its biodiversity. A significant reduction in grazing capacity leads to the displacement of game animals and the replacement of native vegetation. We used Sentinel-2A multispectral images to map the distribution of pompom weeds. At the nature reserve from 2019 to 2024, which allowed us to distinguish it from other land cover types and determine the appropriateness of the habitat. The SVM model provided 44% and 50.7% spatial coverage of pompom weed at the nature reserve in 2019 and 2024, respectively, whereas the RF model yielded 31.1% and 39.3%, respectively. The MaxEnt model identified both soil and rainfall as the most important environmental factors in fostering the aggressive proliferation of pompom weeds at the nature reserves. The MaxEnt predictive model obtained an area under curve score of 0.94, indicating outstanding prediction model performance. Classification of above 75%, indicating that they could distinguish pompom weeds from existing land cover types. For sustainable environmental management, this study suggests using predictive models to effectively eradicate the spatial distribution of invasive weeds in the present and future.

Monitoring the invasion of Campulocliniummacrocephalum (less) DC plants using a novel MaxEnt and machine learning ensemble in the Cradle Nature Reserve, South Africa

The proliferation of non-native plant species has caused significant changes in global ecosystems, leading to a surge in international interest in the use of remote sensing technologies for both local and global detection applications. The Greater Cradle Nature Reserve, a UNESCO World Heritage Site, is facing a decline in its global status due to the spread of pompom weeds, affecting its biodiversity. A significant reduction in grazing capacity leads to the displacement of game animals and the replacement of native vegetation. We used Sentinel-2A multispectral images to map the distribution of pompom weeds. At the nature reserve from 2019 to 2024, which allowed us to distinguish it from other land cover types and determine the appropriateness of the habitat. The SVM model provided 44% and 50.7% spatial coverage of pompom weed at the nature reserve in 2019 and 2024, respectively, whereas the RF model yielded 31.1% and 39.3%, respectively. The MaxEnt model identified both soil and rainfall as the most important environmental factors in fostering the aggressive proliferation of pompom weeds at the nature reserves. The MaxEnt predictive model obtained an area under curve score of 0.94, indicating outstanding prediction model performance. Classification of above 75%, indicating that they could distinguish pompom weeds from existing land cover types. For sustainable environmental management, this study suggests using predictive models to effectively eradicate the spatial distribution of invasive weeds in the present and future.

Radiomics incorporating deep features for predicting Parkinson’s disease in 123I-Ioflupane SPECT

Purpose123I-Ioflupane SPECT is an effective tool for the diagnosis and progression assessment of Parkinson’s disease (PD). Radiomics and deep learning (DL) can be used to track and analyze the underlying image texture and features to predict the Hoehn-Yahr stages (HYS) of PD. In this study, we aim to predict HYS at year 0 and year 4 after the first diagnosis with combined imaging, radiomics and DL-based features using 123I-Ioflupane SPECT images at year 0.MethodsIn this study, 161 subjects from the Parkinson’s Progressive Marker Initiative database underwent baseline 3T MRI and 123I-Ioflupane SPECT, with HYS assessment at years 0 and 4 after first diagnosis. Conventional imaging features (IF) and radiomic features (RaF) for striatum uptakes were extracted from SPECT images using MRI- and SPECT-based (SPECT-V and SPECT-T) segmentations respectively. A 2D DenseNet was used to predict HYS of PD, and simultaneously generate deep features (DF). The random forest algorithm was applied to develop models based on DF, RaF, IF and combined features to predict HYS (stage 0, 1 and 2) at year 0 and (stage 0, 1 and ≥ 2) at year 4, respectively. Model predictive accuracy and receiver operating characteristic (ROC) analysis were assessed for various prediction models.ResultsFor the diagnostic accuracy at year 0, DL (0.696) outperformed most models, except DF + IF in SPECT-V (0.704), significantly superior based on paired t-test. For year 4, accuracy of DF + RaF model in MRI-based method is the highest (0.835), significantly better than DF + IF, IF + RaF, RaF and IF models. And DL (0.820) surpassed models in both SPECT-based methods. The area under the ROC curve (AUC) highlighted DF + RaF model (0.854) in MRI-based method at year 0 and DF + RaF model (0.869) in SPECT-T method at year 4, outperforming DL models, respectively. And then, there was no significant differences between SPECT-based and MRI-based segmentation methods except for the imaging feature models.ConclusionThe combination of radiomic and deep features enhances the prediction accuracy of PD HYS compared to only radiomics or DL. This suggests the potential for further advancements in predictive model performance for PD HYS at year 0 and year 4 after first diagnosis using 123I-Ioflupane SPECT images at year 0, thereby facilitating early diagnosis and treatment for PD patients. No significant difference was observed in radiomics results obtained between MRI- and SPECT-based striatum segmentations for radiomic and deep features.

Radiomics incorporating deep features for predicting Parkinson’s disease in 123I-Ioflupane SPECT

Purpose123I-Ioflupane SPECT is an effective tool for the diagnosis and progression assessment of Parkinson’s disease (PD). Radiomics and deep learning (DL) can be used to track and analyze the underlying image texture and features to predict the Hoehn-Yahr stages (HYS) of PD. In this study, we aim to predict HYS at year 0 and year 4 after the first diagnosis with combined imaging, radiomics and DL-based features using 123I-Ioflupane SPECT images at year 0.MethodsIn this study, 161 subjects from the Parkinson’s Progressive Marker Initiative database underwent baseline 3T MRI and 123I-Ioflupane SPECT, with HYS assessment at years 0 and 4 after first diagnosis. Conventional imaging features (IF) and radiomic features (RaF) for striatum uptakes were extracted from SPECT images using MRI- and SPECT-based (SPECT-V and SPECT-T) segmentations respectively. A 2D DenseNet was used to predict HYS of PD, and simultaneously generate deep features (DF). The random forest algorithm was applied to develop models based on DF, RaF, IF and combined features to predict HYS (stage 0, 1 and 2) at year 0 and (stage 0, 1 and ≥ 2) at year 4, respectively. Model predictive accuracy and receiver operating characteristic (ROC) analysis were assessed for various prediction models.ResultsFor the diagnostic accuracy at year 0, DL (0.696) outperformed most models, except DF + IF in SPECT-V (0.704), significantly superior based on paired t-test. For year 4, accuracy of DF + RaF model in MRI-based method is the highest (0.835), significantly better than DF + IF, IF + RaF, RaF and IF models. And DL (0.820) surpassed models in both SPECT-based methods. The area under the ROC curve (AUC) highlighted DF + RaF model (0.854) in MRI-based method at year 0 and DF + RaF model (0.869) in SPECT-T method at year 4, outperforming DL models, respectively. And then, there was no significant differences between SPECT-based and MRI-based segmentation methods except for the imaging feature models.ConclusionThe combination of radiomic and deep features enhances the prediction accuracy of PD HYS compared to only radiomics or DL. This suggests the potential for further advancements in predictive model performance for PD HYS at year 0 and year 4 after first diagnosis using 123I-Ioflupane SPECT images at year 0, thereby facilitating early diagnosis and treatment for PD patients. No significant difference was observed in radiomics results obtained between MRI- and SPECT-based striatum segmentations for radiomic and deep features.

Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy

ObjectiveThe objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL).MethodsWe retrospectively reviewed the clinical data of 337 patients who received PCNL between May 2020 and June 2022. In our study, 80% of the data were used as the training set, and the remaining data were used as the testing set. Separate prediction models based on the six machine learning algorithms were created using the training set. The predictive performance of each machine learning model was determined by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity using the testing set. We used coefficients to interpret the contribution of each variable to the predictive performance.ResultsAmong the six machine learning algorithms, the support vector machine (SVM) delivered the best performance with accuracy of 0.868, AUC of 0.942 (95% CI 0.890–0.994) in the testing set. Further analysis using the SVM model showed that prealbumin contributed the most to the prediction of the outcome, followed by preoperative urine culture, systemic immune-inflammation (SII), neutrophil to lymphocyte ratio (NLR), staghorn stones, fibrinogen, operation time, preoperative urine white blood cell (WBC), preoperative urea nitrogen, hydronephrosis, stone burden, sex and preoperative lymphocyte count.ConclusionMachine learning-based prediction models can accurately predict the possibility of SIRS after PCNL in advance by learning patient clinical data, and should be used to guide surgeons in clinical decision-making.

Interfacial area concentration correlation for boiling steam-water two-phase flows in annulus flow channels

The investigation of boiling two-phase flows in annulus flow channels has garnered increasing significance given its extensive utilization in various heating devices and its crucial contribution as a fundamental basis for comprehending boiling two-phase flow behavior in intricate flow channels, such as rod bundle flow channels. Accurate modeling of Interfacial Area Concentration (IAC) in boiling steam-water two-phase flows is essential for enhancing the predictive performance of the two-fluid model in simulating complex two-phase behaviors in a diverse range of practical boiling systems. The present study evaluates the predictive performance of seven representative IAC correlations using available typical experimental IAC data from boiling steam-water two-phase flows in annulus flow channels. Although some representative IAC correlations exhibit favorable predictive performance for specific experimental databases, none of the reviewed existing correlations consistently maintain acceptable predictive performance across all collected experimental data. In order to enhance the accuracy and reliability of IAC predictions under boiling flow conditions, a novel IAC correlation has been proposed grounded in physical-based considerations to effectively model the IAC contributions of small (group-1) and large (group-2) bubbles in boiling steam-water two-phase flows in annulus flow channels. The newly proposed two-group bubble IAC correlation has been validated through the examination of 303 boiling steam-water data obtained from annulus flow channels, demonstrating satisfactory predictive performance with the mean relative error of 0.216. Moreover, the applicability of the newly proposed IAC correlation to rod bundle flow channels has been evaluated using 53 boiling steam-water data taken from a 3 × 3 rod bundle flow channel, yielding a favorable prediction result with a mean relative error of 0.173. The anticipated improvement in the accuracy of predicting IAC for steam-water two-phase flows through the newly proposed correlation is expected to provide a scientific foundation for enhancing the performance and safety of thermal systems.

Size stable batch mode model updating method

In near-infrared spectroscopy analysis, the predictive performance of models may degrade due to variations in the measurement environment and sample properties. Maintenance or updates to the model are essential to uphold prediction accuracy. The state-of-the-art approach for model updates involves labeling new samples and incorporating them into the calibration set. However, this strategy leads to an escalating size of the calibration dataset, and the low ratio of new samples renders the model update process inefficient. To enhance update efficiency, a size-stable updating strategy is presented which involves the simultaneous addition of new samples and removal of old samples. This is achieved by utilizing the new set as a basis for identifying and deleting significantly different old labeled samples. To improve labeling efficiency, a batch mode update is employed. Initially, calibration samples are clustered to yield multiple clusters with comparable sizes to the new update set. Subsequently, differences between multiple old sample clusters and the new set are calculated. The old sample cluster that most different are removed, accompanied by the addition of the new sample set to maintain calibration set size stability. In calculating set similarity, a multi-indices fusion method is employed to ensure accurate judgments. The efficacy of this method is validated through simulated and real data.

P-261 A comparative analysis of artificial intelligence models for blastulation in In vitro fertilization: single-frame vs. full-frame approach

Abstract Study question How does the performance of an blastulation prediction model using full-frame data over 3 days compare to a single-frame model using day 3 data? Summary answer The single-frame model, based on a day 3 embryo image, demonstrated comparable predictive performance to the full-frame model What is known already Pregnancy rates following blastocyst transfer have been reported to be higher than those after cleavage transfer. However, not all cleavage embryos reach the blastocyst stage, leading to the exploration of predictive models using embryo images and artificial intelligence. Clinical practice often involves the decision to culture embryos to the blastocyst stage on day 3 based on embryo quality assessments. Recently, there has been interest in using morphokinetics data for predicting blastulation and pregnancy outcomes. While the importance of morphokinetics in IVF outcomes is acknowledged, the specific time frames and features contributing significantly to accurate predictions remain unclear. Study design, size, duration This retrospective study analyzed a dataset of 15,615 timelapse images from 906 patients at a single IVF center. The Single-Frame Model used a single image collected at 66.7 hours post-ICSI, and the Full-Frame Model used 145 frames collected from fertilization to 66.7 hours post-ICSI. Blastulation criteria included reaching a blastocyst on day 5, deemed suitable for transfer or freezing by an experienced embryologist. Participants/materials, setting, methods We trained the data using ResNet for the one-frame model and 3D CNN for the full-frame model. The ResNet model used pre-trained weights, while the 3D CNN model was built from scratch with no pre-trained weights. The models were evaluated using the AUROC, accuracy, and F1 score metrics. The statistical significance of the AUC performance differences was assessed using DeLong’s test. Main results and the role of chance Our study revealed similar AUC performance between the Single-Frame and Full-Frame Models for day 3 embryos. The Single-Frame Model exhibited an AUROC of 0.76, accuracy of 0.71, and an F1 score of 0.75, while the Full-Frame Model demonstrated comparable performance with an AUROC of 0.75, accuracy of 0.70, and an F1 score of 0.72. DeLong’s test was employed to assess the statistical significance of the difference in AUROC, yielding a non-significant p-value of 0.4884. This study underscores that the Single-Frame Model provides similar predictive performance for blastulation compared to the Full-Frame Model. Limitations, reasons for caution Our analysis was limited to images taken at 66.7 hours post-ICSI to mirror clinical practice. Further investigations at different time points are necessary to determine the optimal time for blastulation prediction. Additional analysis of the Day 1 and Day 2 single frames may offer further insights. Wider implications of the findings Accurate prediction of blastulation on day 3 is pivotal for embryo survival and IVF success. Our study challenges the assumption that more data improves AI models, highlighting the importance of day 3 observations. Further research is warranted to identify key time points and features for better predictive accuracy. Trial registration number not applicable