Development Of Model Research Articles

Analytical model for material removal rate in rotary tool micro-ultrasonic machining of hard and brittle materials

The rotary tool micro-ultrasonic machining (RT-MUSM) process is a newly developed variant of conventional micro-USM. It is preferred over conventional micro-USM due to its ability to remove material at a faster rate and with better accuracy than the machined microfeatures. Some experimental investigations have been conducted on the RT-MUSM process for the machining of hard and brittle materials. However, the theoretical model of the material removal rate (MRR) for the RT-MUSM process has not been discussed in the literature. Thus, the present research work reports on developing a predictive model of the MRR for the RT-MUSM process during glass machining. Pure brittle fracture mode was considered to be the material removal mechanism during the model's development. The developed model was verified through experiments. The estimated results were compared with the experimental results and found to be in good agreement with each other. Additionally, statistical analysis was carried out for the prediction accuracy of the developed model. The results revealed that the model is adequate, with a correlation coefficient of 0.9976 and a mean absolute percentage error of 2.45%. Hence, the developed model can be used to estimate the MRR for the RT-MUSM process of hard and brittle materials such as ceramics.

A lifelong meta-learning approach for learning deep grey-box representative thermal dynamics models for residential buildings

Thermal dynamics models of residential buildings are crucial for managing energy use and maintaining desirable indoor environment quality, in the context of decarbonizing the electric grid. Despite recent developments in data-driven models, the implementation with a massive number of buildings in the real world remains infeasible because the computation is expensive and the thermostat data of buildings can be online and sparse. A representative model for aggregated study and acceleration of personalizing models is needed for many downstream tasks. In addition, most existing data-driven models lack physical interpretability questioning the representativeness of any aggregated model. In this paper, we present an online framework for learning meta-models of thermal dynamics across various residential buildings. The meta-models are physically explainable according to the building types and can accelerate fine-tuning personalized models for any building saving data and computation. Experiments suggest that our method performs well with a real-world online dataset that produces representative models of different climate zones and effective initialization for training individual models.

A systematic review of models for the prediction of corporate insolvency

A thorough evaluation of recent developments in bankruptcy prediction models developed specifically for listed firms in India is presented in this research. Beginning with influential contributions from the evolution of bankruptcy prediction methodologies is traced through various statistical techniques, including logistic regression, neural networks and discriminant analysis. Recent innovations, such as duration models, partial least squares with support vector machines, and efficiency-driven distress prediction, are discussed in the context of their applicability to the Indian market. The paper highlights the significance of early warning systems in the wake of bankruptcy reforms in India and examines the regulatory framework's impact on bankruptcy prediction modeling. And it goes further into how macroeconomic variables and industry-specific variables might make bankruptcy models better predictors. Limitations such as small sample sizes, short time periods for samples, and the incorporation of qualitative data into predictive models are highlighted in the study as areas that require further investigation in future studies. Overall, this paper provides valuable insights for academics, practitioners, and policymakers involved in bankruptcy prediction and risk management within the Indian corporate sector.

Evolutionary Game Dynamics with Environmental Feedback in a Network with Two Communities

Recent developments of eco-evolutionary models have shown that evolving feedbacks between behavioral strategies and the environment of game interactions, leading to changes in the underlying payoff matrix, can impact the underlying population dynamics in various manners. We propose and analyze an eco-evolutionary game dynamics model on a network with two communities such that players interact with other players in the same community and those in the opposite community at different rates. In our model, we consider two-person matrix games with pairwise interactions occurring on individual edges and assume that the environmental state depends on edges rather than on nodes or being globally shared in the population. We analytically determine the equilibria and their stability under a symmetric population structure assumption, and we also numerically study the replicator dynamics of the general model. The model shows rich dynamical behavior, such as multiple transcritical bifurcations, multistability, and anti-synchronous oscillations. Our work offers insights into understanding how the presence of community structure impacts the eco-evolutionary dynamics within and between niches.

Combined with the semantic features of CT and selected clinical variables, a machine learning model for accurately predicting the prognosis of omicron was established

Abstract Objective To efficiently use medical resources and offer optimal personalized treatment for individuals with Omicron infection, it's vital to predict the disease's outcome early on. This research developed three machine learning models to foresee the results for Omicron-infected patients. Methods Data from 253 Omicron-infected patients, including their CT scans, clinical details, and relevant laboratory values, were studied. The patients were categorized into two groups based on their disease progression: favorable prognosis and unfavorable prognosis. Patients manifesting respiratory failure, acute liver or kidney impairment, or fatalities were placed in the “poor” group. Those lacking such symptoms were allocated to the “good” group. The participants were randomly split into training set (202) and validation set (51) with an 8:2 ratio. Radiomics features were produced using image processing, focused segmentation, feature extraction, and selection, leading to the establishment of a radiomics model. A univariate logistic regression method identified potential clinical factors contributing to a clinical model's development. Eventually, the fused feature set, integrating radiomics features and clinical indicators, was used for the combined model. The model's prediction performance was assessed using the area under the receiver operating characteristic curve (AUC). The model's clinical usefulness was evaluated by generating calibration and decision curves. Results Compared to other classification models, the combined model showcased the best classification performance. It achieved an AUC of 0.848 and accuracy of 0.763 in the training set, and 0.797 and 0.750 in the validation set, respectively. Conclusion This study employed machine learning model to accurately predict the prognosis of Omicron-infected patients. ADVANCES IN KNOWLEDGE： (1) Topic innovation: At present, there is a lack of research on the use of CT images to construct machine learning models to predict the prognosis of patients with Omicjon infection. This study intends to establish clinical, radiomics and combined models to provide more possibilities for the identification of the two. (2) Platform innovation: The feature extraction and screening and the establishment of omics model in this study will be completed in the intelligent scientific research platform, which can reduce the error caused by human error, simplify the operation steps and save the time of data processing time.

PENTA as a model for advanced integrated cancer management in low-resource settings.

e13783 Background: Cancer models are reactive, fragmented, and volume-based care, in which the lack of coordination between different layers of care, along with clinical and quality variability, technological pressure, and dominant positions, result in high levels of inefficiency and waste, affecting economic sustainability and hence effective access. This is prohibitive for low-resource settings. Modern healthcare system models in this scenarios should focus on solve this problems. Comprehensive cancer management needs a reorientation towards value- and efficiency-generating architectures.Usual redesigns have focused on direct cost containment through intensified audits or new contracting and payment modalities between stakeholders. However, the real impact of these strategies is marginal, increasing sectoral mistrust and failing to meet the quintuple aim. PENTA is a comprehensive cancer management framework to optimize cancer patient care, with emphasis on clinical governance, data guidance, and clinical-administrative alignment. This is an operational model for stakeholders in low-resource settings. PENTA model has been successfully implemented in health insurance companies in Colombia and in private insurance branch in Peru. Methods: PENTA has been applied since 2020. We employed a work methodology based on the Agile-2 matrix, which allows for fluidity and lower resource consumption. This methodology is hybrid and its execution begins with an initial diagnosis of the process within the partner company. This diagnosis includes prioritizing interventions from each of the axes, but understanding that this is a systemic project that includes 5 fundamental axes. This axes must be worked on simultaneously and interdependently to achieve the greatest value. Axes are: 1. Smart Risk Management, 2. Advanced Clinical Gobernance, 3. Value Audit, 4. Technology Management and 5. Value-based Contracting Focus on managing inefficiencies and waste (optimization model), PENTA can generate early results across various dimensions such as clinical variability and technology costs. Structuring and using a cross-cutting data architecture is fundamental to the model's development into a data control tower. Results: Implementation of a data control tower for cancer cohorts , development of joint clinical pathways (insurer-payer) for prioritized diseases, clinical practice agreements with hierarchical positions on high-cost medications, implementation of mini health technology assessments (HTAs), and generation of new prospective payment modalities based on performance adjustment. The estimated savings generated are $1.5 million for the phase 2 in Colombia and $800,000 for the phase 1 in Peru. Conclusions: PENTA is an agile framework for cancer management that can be sucessfully adapted to low-resource settings.

Environmental public opinion: methodological principles of research

Based on the structural analysis of the toolkit of the foreign monitoring studies, the methodological principles of measuring environmental public opinion are analyzed. Modern developments of socio-psychological models for predicting environmental concern and patterns of environmental behavior, the influence of the social context on environmental activism, the influence of methods of measuring environmental concern on conclusions regarding the relationship between material and post-material values are considered in detail. It is noted that traditional indicators of environmental concern are replaced by tools of sociological measurement of environmental awareness, primarily of anthropocentrism and its relationship with ecocentrism. The need to transition from the (post-Soviet) ecological paradigm to the modern European one, the synchronization of advancement to full membership in the EU with the transit of Ukrainian society from the patronage to the liberal model of democracy determine the features of the social context of measuring environmental public opinion in Ukraine. The specified features of the social context require a radical revision of the methodological principles of measuring environmental public opinion with relevant sociological indicators. First, we are talking about the development of indicators of environmental democracy, such as the level of access to environmental information, participation in the adoption of environmentally important decisions, and access to justice on environmental issues. It is argued that the main task of monitoring environmental public opinion is to identify the dynamics of social changes as a derivative of anthropogenic impact on the environment, to adjust environmental policy considering the existing ecological orientations of the population.

Development of a Novel Nonlinear Dynamic Cavitation Model and Its Numerical Validations

Development of a Novel Nonlinear Dynamic Cavitation Model and Its Numerical Validations

Unsupervised Domain Adaptation of Object Detectors: A Survey.

Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications such as classification, segmentation, and detection. However, learning highly accurate models relies on the availability of large-scale annotated datasets. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images, termed as domain adaptation problem. There are a plethora of works to adapt classification and segmentation models to label-scarce target dataset through unsupervised domain adaptation. Considering that detection is a fundamental task in computer vision, many recent works have focused on developing novel domain adaptive detection techniques. Here, we describe in detail the domain adaptation problem for detection and present an extensive survey of the various methods. Furthermore, we highlight strategies proposed and the associated shortcomings. Subsequently, we identify multiple aspects of the problem that are most promising for future research. We believe that this survey shall be valuable to the pattern recognition experts working in the fields of computer vision, biometrics, medical imaging, and autonomous navigation by introducing them to the problem, and familiarizing them with the current status of the progress while providing promising directions for future research.

Face mask classification using convolutional neural networks with facial image regions and super resolution

<p>Face mask classification is relevant to public health and safety, so an approach for face mask classification using Multi-Task Cascaded Convolutional Networks (MTCNN) for face detection on image data, ResNet152 architecture for feature extraction, and super-resolution method, BSRGAN, for enhanced image quality was proposed. The classification model was trained by a fully connected layer of neural networks. The goal is to classify each facial image into three classes: the image with a mask, without a mask, or with an incorrectly worn mask. The performance of each classification model on two real-world datasets was evaluated by Accuracy, Precision, Recall, and F1 score for different sets of input patterns which were features extracted from the facial image regions including their combinations. Using multiple image regions, i.e. face, nose, and mouth, as resources for preparing input features showed the improved classification performance compared to using single image regions. In addition, the super-resolution technique applied to medium or large-sized images can improve the performance of the face mask classification model. Our findings may further guide the development for greater effective models and techniques on face mask classification contributing to practical scenarios.</p>

The machine learning-based model for lateral lymph node metastasis of thyroid medullary carcinoma improved the prediction ability of occult metastasis.

For medullary thyroid carcinoma (MTC) with no positive findings in the lateral neck before surgery, whether prophylactic lateral neck dissection (LND) is needed remains controversial. A better way to predict occult metastasis in the lateral neck is needed. From January 2010 to January 2022, patients who were diagnosed with MTC and underwent primary surgery at our hospital were retrospectively reviewed. We collected the patients' baseline characteristics, surgical procedure, and rescored the ultrasound images of the primary lesions using American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Regularized logistic regression, 5-fold cross-validation and decision curve analysis was applied for lateral lymph node metastasis (LLNM) model's development and validation. Then, we tested the predictive ability of the LLNM model for occult LLNM in cN0-1a patients. A total of 218 patients were enrolled. Five baseline characteristics and two TI-RADS features were identified as high-risk factors for LLNM: gender, baseline calcitonin (Ctn), tumor size, multifocality, and central lymph node (CLN) status, as well as TI-RADS margin and level. A LLNM model was developed and showed a good discrimination with 5-fold cross-validation mean area under curve (AUC) = 0.92 ± 0.03 in the test dataset. Among cN0-1a patients, our LLNM model achieved an AUC of 0.91 (95% CI, 0.88-0.94) for predicting occult LLNM, which was significantly higher than the AUCs of baseline Ctn (0.83) and CLN status (0.64). We developed a LLNM prediction model for MTC using machine learning based on clinical baseline characteristics and TI-RADS. Our model can predict occult LLNM for cN0-1a patients more accurately, then benefit the decision of prophylactic LND.

Machine learning techniques for estimation of Pc5 geomagnetic pulsations observed at geostationary orbits during solar cycle 23

Pc5 geomagnetic pulsations can accelerate electrons in the radiation belts, which can pose adverse threats to both astronauts and satellites in space. The estimation of Pc5 waves in space is crucial to radiation belt dynamics studies and will help mitigate these challenges. Here, we explore the advantages of the Feed-forward Neural Network (FFNN) and Random Forest (RF) algorithm for effective estimation of Pc5 geomagnetic pulsations observed in space at geostationary orbit during solar cycle 23. The dataset used in this study is the vector magnetic field measurements retrieved from the Geostationary Operational Environmental Satellite-10 (GOES-10) and the solar wind parameters: Bz and Vx component of the solar wind in the Geocentric Solar Ecliptic (GSE) coordinate system, proton density, flow pressure, and plasma beta obtained from the OMNI Web database during part of solar cycle 23. Pc5 geomagnetic pulsations were extracted from the toroidal component of the magnetic field time series using a bandpass Butterworth filter. The continuous wavelet transform (CWT) was utilized to study the characteristics of the extracted wave in the time-frequency domain for its validation. The validated Pc5 events were used as the target in the model's development, with the solar wind parameters as the inputs. In addition to the solar wind parameters, we included an attribute of the magnetic field time series as an input variable in the model. The dataset is carefully divided to ensure effective training and testing of the models. Finally, we trained both models using the same inputs and targets and explored their estimation abilities. The model was tested during the maximum, descending, and minimum phases of solar cycle 23. Both the FFNN and RF models have a similar estimation, with average cross-correlation score (R) values of 0.74 and 0.73 and corresponding average root mean squared error (RMSEs) of 0.16 nT and 0.67 nT, respectively. The model was deployed to investigate the response of Pc5 waves during three storm days in each testing year. The machine learning (ML) model outputs showed good coherence with the observed Pc5 waves. To validate the models, we studied the correlation between the estimated Pc5 events with the Kp index, and a good correlation was seen to exist between both events. This validates the good performance of the developed models. This work will aid in the study of radiation belt dynamics and the construction of electron depletion regions in the radiation belt.

Can we accurately predict the distribution of soil microorganism presence and relative abundance?

Soil microbes play a key role in shaping terrestrial ecosystems. It is therefore essential to understand what drives their distribution. While multivariate analyses have been used to characterise microbial communities and drivers of their spatial patterns, few studies have focused on predicting the distribution of amplicon sequence variants (ASVs). Here, we evaluate the potential of species distribution models (SDMs) to predict the presence–absence and relative abundance distribution of bacteria, archaea, fungi, and protist ASVs in the western Swiss Alps. Advanced automated selection of abiotic covariates was used to circumvent the lack of knowledge on the ecology of each ASV. Presence–absence SDMs could be fitted for most ASVs, yielding better predictions than null models. Relative abundance SDMs performed less well, with low fit and predictive power overall, but displayed a good capacity to differentiate between sites with high and low relative abundance of the modelled ASV. SDMs for bacteria and archaea displayed better predictive power than for fungi and protists, suggesting a closer link of the former with the abiotic covariates used. Microorganism distributions were mostly related to edaphic covariates. In particular, pH was the most selected covariate across models. The study shows the potential of using SDM frameworks to predict the distribution of ASVs obtained from topsoil DNA. It also highlights the need for further development of precise edaphic mapping and scenario modelling to enhances prediction of microorganism distributions in the future.

Computational Legal Studies Comes of Age

Computational analysis techniques are transforming empirical legal scholarship. Two paradigms have emerged: law-as-code, which seeks to represent legal rules in a logical, executable format; and law-as-data, which leverages quantitative analysis of legal texts to reveal patterns and insights. This article surveys these approaches, emphasizing recent developments in large language models and generative artificial intelligence (AI). Law-as-code systems have enabled applications from tax preparation software to smart contracts, but realizing the vision of fully computational law has proven challenging. Law-as-data techniques like natural language processing and machine learning have charted the semantic relationship between courts and illuminated changes in judicial culture. Generative models showcase AI's explosive progress, with impressive feats like passing the U.S. bar example, but they also highlight limitations like factual inaccuracy and interpretability issues. Hybrid approaches integrating computational law, data science, and AI offer a promising research direction. As these tools spread, legal scholars can analyze more legal data than ever before, but they must remain cognizant of challenges like biased or low-quality data and linguistic/cultural limitations. Used judiciously alongside traditional methods, computational analysis has the potential to revolutionize empirical legal studies.

Development and validation of a clinical prediction model for glioma grade using machine learning.

Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive. This study aimed to develop and validate a diagnostic prediction model for glioma by employing multiple machine learning algorithms to identify risk factors associated with high-grade glioma, facilitating the prediction of glioma grading. Data from 1114 eligible glioma patients were obtained from The Cancer Genome Atlas (TCGA) database, which was divided into a training set (n= 781) and a test set (n= 333). Fifty machine learning algorithms were employed, and the optimal algorithm was selected to construct a prediction model. The performance of the machine learning prediction model was compared to the clinical prediction model in terms of discrimination, calibration, and clinical validity to assess the performance of the prediction model. The area under the curve (AUC) values of the machine learning prediction models (training set: 0.870 vs. 0.740, test set: 0.863 vs. 0.718) were significantly improved from the clinical prediction models. Furthermore, significant improvement in discrimination was observed for the Integrated Discrimination Improvement (IDI) (training set: 0.230, test set: 0.270) and Net Reclassification Index (NRI) (training set: 0.170, test set: 0.170) from the clinical prognostic model. Both models showed a high goodness of fit and an increased net benefit. A strong prediction accuracy model can be developed using machine learning algorithms to screen for high-grade glioma risk predictors, which can serve as a non-invasive prediction tool for preoperative diagnostic grading of glioma.

Application of artificial intelligence for the classification of the clinical outcome and therapy in patients with viral infections: The case of COVID-19.

With the end of the coronavirus disease 2019 (COVID-19) pandemic, it becomes intriguing to observe the impact of innovative digital technologies on the diagnosis and management of diseases, in order to improve clinical outcomes for patients. The research aims to enhance diagnostics, prediction, and personalized treatment for patients across three classes of clinical severity (mild, moderate, and severe). What sets this study apart is its innovative approach, wherein classification extends beyond mere disease presence, encompassing the classification of disease severity. This novel perspective lays the foundation for a crucial decision support system during patient triage. An artificial neural network, as a deep learning technique, enabled the development of a complex model based on the analysis of data collected during the process of diagnosing and treating 1000 patients at the Tešanj General Hospital, Bosnia and Herzegovina. The final model achieved a classification accuracy of 82.4% on the validation data set, which testifies to the successful application of the artificial neural network in the classification of clinical outcomes and therapy in patients infected with viral infections. The results obtained show that expert systems are valuable tools for decision support in healthcare in communities with limited resources and increased demands. The research has the potential to improve patient care for future epidemics and pandemics.

Development and Validation of a Resistance Management Model for the Soybean Cyst Nematode, Heterodera glycines.

Plant-parasitic nematodes are a key yield-limiting pest of crops around the world. Deployment of plant resistance genes are an important management tactic for many economically important plant-parasitic nematodes. The selection for virulence in nematode populations is a major threat to the effectiveness of resistance gene-based management. Little research has gone into resistance management modelling despite the importance of both plant-parasitic nematodes and resistance genes for their management. In this paper, we report on a cyst nematode resistance management model created to explore the factors which are most important for determining the durability of resistance genes to this important family of plant-parasitic nematodes. The relative dominance of virulence expression, the level of inbreeding, and the number of generations per cropping season were the most important factors in predicting resistance gene durability. Aspects of cyst nematode biology that reduce the number of generations per season for a portion of the population had a much smaller effect on the durability of resistance genes. These factors included delayed hatching within a season and early dormancy. The accuracy and utility of the model was tested using the soybean cyst nematode (SCN) rhg1-mediated resistance system. The model accurately predicted the rate at which virulence to the rhg1b resistance gene developed in Iowa over a two-decade period. The model suggested resistance gene pyramids as the most durable management solution for SCN with multiple possible avenues to obtain acceptable efficacy and durability.

Pengembangan Modul Berbasis Elektronik Pada Keterampilan Membaca Permulaan Peserta Didik Kelas 1 SD/MI

Reading is one of the language skills and must be mastered by students. The ability to read is carried out through the initial reading stage which is taught at the 1st grade level of SD/MI. The development of the Modul elektronike for early reading is an innovation in early reading learning in the era of 21st century learning. This development research aims to develop an Modul elektronike for early reading learning for students in grade 1 SD/MI and determine the feasibility of the modul elektronike. The method used in this research is research and development method. The development model used in this study uses the RnD model ADDIE model (Analyze, Design, Develop, Implement and Evaluate). The research and development (RnD) procedure describes the steps that researchers will take in developing a product. The results of the study are (1) the product developed in the form of an Modul elektronike of early reading for grade 1 MI/SD students, the material contained in the module refers to the theory of the early reading stages (2) the results of the validity test by material experts obtained a percentage score of 93% with category is very valid and feasible to use. A media expert percentage score of 80% with a valid category based on conversion means that it is feasible to use. Meanwhile, the results of module trials conducted by students and teachers respectively obtained a score percentage of 95% and 88%, which based on the conversion of the module was feasible to use. With the results of the development of this modul elektronike, it is hoped that it will facilitate and be put to good use by teachers in the learning process for early reading in grade 1 MI/SD.

FPM-WSI: Fourier ptychographic whole slide imaging via feature-domain backdiffraction

Fourier ptychographic microscopy (FPM) theoretically provides a solution to the trade-off between spatial resolution and field of view (FOV), and has promising prospects in digital pathology. However, block reconstruction and then stitching has become an unavoidable procedure for reconstruction of large FOV due to vignetting artifacts. This introduces digital stitching artifacts, as the existing image-domain optimization algorithms are highly sensitive to systematic errors. Such obstacles significantly impede the advancement and practical implementation of FPM, explaining why, despite a decade of development, FPM has not gained widespread recognition in the field of biomedicine. We report a feature-domain FPM (FD-FPM) based on the structure-aware forward model to realize stitching-free, full-FOV reconstruction. The loss function is uniquely formulated in the feature domain of images, which bypasses the troublesome vignetting effect and algorithmic vulnerability via feature-domain backdiffraction. Through massive simulations and experiments, we show that FD-FPM effectively eliminates vignetting artifacts for full-FOV reconstruction, and still achieves impressive reconstructions despite the presence of various systematic errors. We also found it has great potential in recovering the data with a lower spectrum overlapping rate, and in realizing digital refocusing without a prior defocus distance. With FD-FPM, we achieved full-color and high-throughput imaging (4.7 mm diameter FOV, 336 nm resolution in the blue channel) free of blocking-and-stitching procedures on a self-developed Fourier ptychographic microscopy whole slide imaging platform. The reported FD-FPM shows the value of FPM for various experimental circumstances, and offers physical insights useful for the developments of models for other computational imaging techniques. The reported platform demonstrates high-quality, high-speed imaging and low cost, and could find applications in many fields of biomedical research, as well as in clinical applications.

Abstract PO3-07-02: Radiomics Models for B-mode Breast Ultrasound and Strain Elastography to improve Breast Cancer Diagnosis (INSPiRED 005): An International, Multicenter Analysis

Abstract Background: Breast Elastography, a technique that quantifies tissue stiffness, has been evaluated to objectify and improve the performance of B-mode breast ultrasound. However, large prospective trials showed benefits in BI-RADS 4a breast masses only and a high operator dependency. Modern Artificial Intelligence techniques for automated image processing like radiomics, a technique where quantified features are extracted from images, may overcome these limitations. We aimed to develop and validate radiomics models based on B-mode and Strain Elastography (SE) images for patients with BI-RADS 3 or 4 breast masses and compare their performance to the respective human experts. Methods: This is a secondary analysis of an international, multicenter trial (NCT02638935), evaluating the performance of SE in women with BI-RADS 3 or 4 breast masses. Women were recruited at 12 institutions in 7 countries and underwent B-mode breast ultrasound as well as SE. B-mode images were saved and re-assed by three ultrasound readers ( >10 years of experience), resulting in three independent assessments and a final consensus assessment. SE was interpreted using the E-/B ratio. B-mode and strain images were manually segmented and quantitative radiomics features were extracted using pyradiomics. We used 10-fold cross-validation to build machine learning models (XGBoostTree, MARS) based on data of 11 of 12 study sites. The data of the 12th (largest) study site was used as external validation set. Performance metrics included sensitivity, specificity and area under the receiver operator characteristic curve (AUROC). Results: The study included a total of 1288 patients, 1206 with evaluable B-mode images and 1190 with evaluable Strain images. Mean age was 46.6 years (SD 16.02) and a total number of 29.0% (350 of 1206) and 28.9% (344 of 1190) breast masses were malignant in the B-mode and Strain cohort, respectively. Distribution of BI-RADS categories was 33.0%, 34.5%, 14.5%, and 18.0% for BI-RADS 3, 4a, 4b, and 4c, respectively. In the external validation set (n = 342), the B-mode radiomics model (XGBoostTree) achieved an AUROC of 0.86 (95% CI 0.82 to 0.90), with a sensitivity of 97.4% (95% CI 0.93 to 1.00, 113 of 116) and a specificity of 27.0% (95% CI 0.21 to 0.33, 61 of 226). The model showed equivalent performance compared to the three ultrasound readers (P = 0.133); see also Table 1. In the external validation set (n = 333), the Strain radiomics model (MARS) achieved an AUROC of 0.84 (95% CI 0.79 to 0.88), with a sensitivity of 100% (95% CI 47.0 to 58.0, 115 of 115) and a specificity of 25.5% (95% CI 0.22 to 0.34, 60 of 218). The model showed equivalent performance compared to the three ultrasound readers (P = 0.696) and performed significantly better compared to SE (P = 0.002); see also Table 1. Sensitivity of the strain model was descriptively higher (100% vs. 97.4%, see Table 1). Both models were well-calibrated. Conclusion: This is the largest development and validation study for radiomics models based on B-mode breast ultrasound and SE, to date. The radiomics models performed on par with human readers, with the strain radiomics model showing potential to identify initially missed carcinomas in BI-RADS 3 breast masses. Future implementation studies may evaluate the performance of these image analysis algorithms in clinical routine and their integration into the multi-modal breast cancer diagnostics process, including mammography and MRI. Table 1. Diagnostic performance metrics Citation Format: André Pfob, Tanja He, Lie Cai, Richard G. Barr, Volker Duda, Zaher Alwafai, Corinne Balleyguier, Dirk-André Clevert, Sarah Fastner, Christina Gomez, Manuela Goncalo, Ines Gruber, Markus Hahn, André Hennigs, Panagiotis Kapetas, Sheng-Chieh Lu, Juliane Nees, Ralf Ohlinger, Fabian Riedel, Matthieu Rutten, Benedikt Schaefgen, Anne Stieber, Riku Togawa, Mitsuhiro Tozaki, Sebastian Wojcinski, Cai Xu, Geraldine Rauch, Jörg Heil, Chris Sidey-Gibbons, Michael Golatta. Radiomics Models for B-mode Breast Ultrasound and Strain Elastography to improve Breast Cancer Diagnosis (INSPiRED 005): An International, Multicenter Analysis [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-07-02.