Artificial Intelligence-based Tools Research Articles

A unique artificial intelligence-based tool for automated CBCT segmentation of mandibular incisive canal.

To develop and validate a novel artificial intelligence (AI) tool for automated segmentation of mandibular incisive canal on cone beam computed tomography (CBCT) scans. After ethical approval, a data set of 200 CBCT scans were selected and categorized into training (160), validation (20), and test (20) sets. CBCT scans were imported into Virtual Patient Creator and ground truth for training and validation were manually segmented by three oral radiologists in multiplanar reconstructions. Intra- and interobserver analysis for human segmentation variability was performed on 20% of the data set. Segmentations were imported into Mimics for standardization. Resulting files were imported to 3-Matic for analysis using surface- and voxel-based methods. Evaluation metrics involved time efficiency, analysis metrics including Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Root mean square error (RMSE), precision, recall, accuracy, and consistency. These values were calculated considering AI-based segmentation and refined-AI segmentation compared to manual segmentation. Average time for AI-based segmentation, refined-AI segmentation and manual segmentation was 00:10, 08:09, and 47:18 (284-fold time reduction). AI-based segmentation showed mean values of DSC 0.873, IoU 0.775, RMSE 0.256 mm, precision 0.837 and recall 0.890 while refined-AI segmentation provided DSC 0.876, IoU 0.781, RMSE 0.267 mm, precision 0. 852 and recall 0.902 with the accuracy of 0.998 for both methods. The consistency was one for AI-based segmentation and 0.910 for manual segmentation. An innovative AI-tool for automated segmentation of mandibular incisive canal on CBCT scans was proofed to be accurate, time efficient, and highly consistent, serving pre-surgical planning.

Urologist validation of an artificial intelligence-based tool for automated estimation of penile curvature

Severity of penile curvature (PC) is commonly used to select the optimal surgical intervention for hypospadias, either alone or in conjunction with other phenotypic characteristics. Despite this, current literature on the accuracy and precision of different PC measurement techniques in hypospadias patients remains limited. Assess the feasibility and validity of an artificial intelligence (AI)-based model for automatic measurement of PC. Seven 3D-printed penile models with variable degrees of ventral PC were used to evaluate and compare interobserver agreement in estimation of penile curvatures using various measurement techniques (including visual inspection, goniometer, manual estimation via a mobile application, and an AI-based angle estimation app. In addition, each participant was required to complete a questionnaire about their background and experience. Thirty-five clinical practitioners participated in the study, including pediatric urologists, pediatric surgeons, and urologists. For each PC assessment method, time required, mean absolute error (MAE), and inter-rater agreement were assessed. For goniometer-based measurement, the lowest MAE achieved was derived from a model featuring 86° PC. When using either UVI (unaid visual inspection), mobile apps, or AI-based measurement, MAE was lowest when assessing a model with 88° PC, indicating that high-grade cases can be quantified more reliably. Indeed, MAE was highest when PC angle ranged between 40° and 58° for all the investigated measurement tools. In fact, among these methodologies, AI-based assessment achieved the lowest MAE and highest level of inter-class correlation, with an average measurement time of only 22s. AI-based PC measurement models are more practical and consistent than the alternative curvature assessment tools already available. The AI method described in this study could help surgeons and hypospadiology researchers to measure PC more accurately.

The Role of Patients’ Advocacy Groups to Reduce Headache Pain in the European Union: from the Italian Case History to the European Migraine Action Plan

From the “patients’ right to avoid unnecessary suffering and pain” to the “Charter of Rights of the Cephalalgic Patient”, over the years the role of the civic & patients’ organisations to reduce headache pain in the European Union is increased: this research article explores the experiences of individuals affected by migraines and headaches, highlighting challenges, rights, and the need for improved healthcare support. Based on an extensive civic survey conducted at the national level by the Italian NGO Cittadinanzattiva, the study reveals the profound impact of headaches on people's lives, emphasising the lack of disability recognition and difficulties in accessing quality care. Through compelling testimonies, the article underscores the emotional, psychological, and physical burden of these conditions, and describes an innovative artificial intelligence-based tool called “EMI”. Together with many actors, since decades Cittadinanzattiva has been engaged in the fight against chronic pain for decades, both at the national and European levels. In this regard, what attention will the new MEPs (Members of the European Parliament) and the new leadership of the EU Commission DG Santé (Directorate-General for Health and Food Safety) dedicate to this issue in the wake of the upcoming European elections in 2024? From various quarters, there is a call for a European Migraine Action Plan aimed at finally placing this issue on the agenda of European political institutions, promoting a more inclusive and patient-centered vision within the European healthcare landscape, where the pain of those suffering from migraines and headaches is no longer invisible or unheard.

Chest X-ray Foreign Objects Detection Using Artificial Intelligence.

Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis.

Development of a Radiomics-Based Model to Predict Graft Fibrosis in Liver Transplant Recipients: A Pilot Study.

Liver Transplantation is complicated by recurrent fibrosis in 40% of recipients. We evaluated the ability of clinical and radiomic features to flag patients at risk of developing future graft fibrosis. CT scans of 254 patients at 3-6months post-liver transplant were retrospectively analyzed. Volumetric radiomic features were extracted from the portal phase using an Artificial Intelligence-based tool (PyRadiomics). The primary endpoint was clinically significant (≥F2) graft fibrosis. A 10-fold cross-validated LASSO model using clinical and radiomic features was developed. In total, 75 patients (29.5%) developed ≥F2 fibrosis by a median of 19 (4.3-121.8)months. The maximum liver attenuation at the venous phase (a radiomic feature reflecting venous perfusion), primary etiology, donor/recipient age, recurrence of disease, brain-dead donor, tacrolimus use at 3months, and APRI score at 3months were predictive of ≥F2 fibrosis. The combination of radiomics and the clinical features increased the AUC to 0.811 from 0.793 for the clinical-only model (p = 0.008) and from 0.664 for the radiomics-only model (p < 0.001) to predict future ≥F2 fibrosis. This pilot study exploring the role of radiomics demonstrates that the addition of radiomic features in a clinical model increased the model's performance. Further studies are required to investigate the generalizability of this experimental tool.

Artificial Intelligence-Based Tools in Software Development Processes: Application of ChatGPT

Software development processes are continuously evolving and rapidly transforming alongside the rapid changes in technology. Recently, innovations in the field of Artificial Intelligence (AI) have led to significant changes in software development practices. AI tools can greatly enhance traditional software development processes by offering developers the ability to create projects more intelligently, swiftly, and effectively. These tools can be employed in various tasks, such as code generation, test automation, error analysis, and performance improvements. Particularly, ChatGPT, an AI-based language model that has had a profound impact on almost every domain, can assist software developers in writing code faster and in a more natural language manner. In this research article, essential information about the usage of ChatGPT in the software development process is presented. To evaluate some capabilities of ChatGPT in the software development context, applications were performed on a software project. For this purpose, a software development process was constructed based on the responses provided by ChatGPT. Various questions related to software development processes were formulated, and the responses generated by GPT were evaluated. The obtained results indicated that ChatGPT exhibited excellent performance in the software development process. Based on these findings, it was observed that AI-based models like ChatGPT could be effectively utilized as assisting tools in software development processes, accelerating traditional workflows. Furthermore, AI-based tools can automate testing processes, enhancing software quality while saving time and effort.

Analysis of Factors Affecting Common Use of Generative Artificial Intelligence-Based Tools by Machine Learning Methods

Artificial Intelligence is a sub-branch of artificial intelligence used to produce new data or content. These methods can create recent examples in different categorical fields such as natural language processing, image processing, music, and video creation by using models from learning clusters with artificial intelligence (AI) tools in this field. AI tools that can solve real-world problems are also created using different methods apart from generative AI methods. With generative-based artificial intelligence tools, it can facilitate people's work in jobs that require creativity. However, they can offer the opportunity to build advanced models that learn from data with other artificial intelligence methods. In the study, the public dataset has been used. This dataset includes trending artificial intelligence tools, AI methods, and user scores. In this study the working area and user trend of the ai tools in the dataset and the effect of generative AI methods on the development of the tool are discussed. Random Forest and Naive Bayes algorithms from classification methods have been used to measure the impact and estimation. Several AI tools help solve real-life problems. Identifying what type of category is needed for AI tools and method selection are interlinked, and the research provides an overview of this connection.

Artificial Intelligence in Education: Literature Review on The Role of Conversational Agents in Improving Learning Experience

A growing number of teachers and students are turning to artificial intelligence-powered conversational bots as a means of bolstering and facilitating learning and teaching. Conventional educational agents are intended to do the duties that would normally be carried out by human educators, specifically, providing students with instructions. The purpose of this research is to review the literature on the use of chatbots and conversational agents to promote learning and enhance student engagement. To better understand the complexities of deploying conversational agents for classroom use, it is helpful to first recognize the role that conversational technologies play in facilitating student engagement during learning. The study found credible evidence to support the need for conversational agents in education as teachers grapple with urgency to implement artificial intelligence-based tools and services in classroom and online learning.

Conversation between a clinical biologist and an artificial intelligence on prostate cancer biomarkers: a critical reading.

Artificial intelligence is increasingly used in the field of medicine as a diagnostic aid, particularly for image analysis and more generally for data processing. Many artificial intelligence-based tools have been specifically developed for clinical biology, but some more general ones can help to improve the dissemination of medical knowledge. To test whether and to what extent an automated conversation tool could answer questions on a clinical biology topic (i.e. prostate cancer biomarkers), we questioned ChatGPT, an artificial intelligence-powered model dedicated to optimize language models for dialogue. Then we analyzed its responses.

AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway

ABSTRACT A structural understanding of entire cellular processes has been an uncharted realm, until now. The artificial intelligence-based tool AlphaFold2 (AF2) has substantially changed the prediction accuracy, and predicted models of entire proteomes are now available. Here, we have examined AF2’s prediction of 38 core macroautophagic/autophagic proteins and 378 interacting partners representing the human autophagic interactome. Prior to AF2, ~50% of the proteins lacked atomistic level resolution and we found significant improvement in structural coverage by AF2, with an addition of ~ 47% of the residues modeled with reasonable confidence. We also augmented this structural information with μs timescale molecular dynamics simulations, in particular, ATG2, ATG10, and ATG14. ATG2A, a bipartite membrane protein with rodlike architecture was predicted with high accuracy and our simulations revealed dynamic transitions of cavity-lining residues that might play a critical role in regulating lipid transfer. In addition, a promising approach of multimeric prediction by AF2 revealed the architecture of ATG7-ATG10, a tetrameric complex that participates in conjugation machinery in autophagy. By combining computational and experimental approaches, we demonstrated that three salt bridges were crucial to ATG7-ATG10 complex formation and mutating these residues abrogated the binding. We have also generated a web resource with curated AF2 structural models, simulated conformational ensemble, and structural analysis that will be highly pertinent to the autophagy community. Altogether, our work presents a robust pipeline to utilize AF2 as a tool for a starting point to provide the dynamic behavior of molecules in a given biological pathway. Abbreviations AF2: AlphaFold2; AF2-Mult: AlphaFold2 multimer; ATG: autophagy-related; CTD: C-terminal domain; ECTD: extreme C-terminal domain; FR: flexible region; MD: molecular dynamics; NTD: N-terminal domain; pLDDT: predicted local distance difference test; UBL: ubiquitin-like

Development and Assessment of an Artificial Intelligence-Based Tool for Ptosis Measurement in Adult Myasthenia Gravis Patients Using Selfie Video Clips Recorded on Smartphones

Introduction: Myasthenia gravis (MG) is a rare autoimmune disease characterized by muscle weakness and fatigue. Ptosis (eyelid drooping) occurs due to fatigue of the muscles for eyelid elevation and is one symptom widely used by patients and healthcare providers to track progression of the disease. Margin reflex distance 1 (MRD1) is an accepted clinical measure of ptosis and is typically assessed using a hand-held ruler. In this work, we develop an AI model that enables automated measurement of MRD1 in self-recorded video clips collected using patient smartphones. Methods: A 3-month prospective observational study collected a dataset of video clips from patients with MG. Study participants were asked to perform an eyelid fatigability exercise to elicit ptosis while filming “selfie” videos on their smartphones. These images were collected in nonclinical settings, with no in-person training. The dataset was annotated by non-clinicians for (1) eye landmarks to establish ground truth MRD1 and (2) the quality of the video frames. The ground truth MRD1 (in millimeters, mm) was calculated from eye landmark annotations in the video frames using a standard conversion factor, the horizontal visible iris diameter of the human eye. To develop the model, we trained a neural network for eye landmark detection consisting of a ResNet50 backbone plus two dense layers of 78 dimensions on publicly available datasets. Only the ResNet50 backbone was used, discarding the last two layers. The embeddings from the ResNet50 were used as features for a support vector regressor (SVR) using a linear kernel, for regression to MRD1, in mm. The SVR was trained on data collected remotely from MG patients in the prospective study, split into training and development folds. The model’s performance for MRD1 estimation was evaluated on a separate test fold from the study dataset. Results: On the full test fold (N = 664 images), the correlation between the ground truth and predicted MRD1 values was strong (r = 0.732). The mean absolute error was 0.822 mm; the mean of differences was −0.256 mm; and 95% limits of agreement (LOA) were −0.214–1.768 mm. Model performance showed no improvement when test data were gated to exclude “poor” quality images. Conclusions: On data generated under highly challenging real-world conditions from a variety of different smartphone devices, the model predicts MRD1 with a strong correlation (r = 0.732) between ground truth and predicted MRD1.

Application of an artificial intelligence-based tool in [18F]FDG PET/CT for the assessment of bone marrow involvement in multiple myeloma

Purpose[18F]FDG PET/CT is an imaging modality of high performance in multiple myeloma (MM). Nevertheless, the inter-observer reproducibility in PET/CT scan interpretation may be hampered by the different patterns of bone marrow (BM) infiltration in the disease. Although many approaches have been recently developed to address the issue of standardization, none can yet be considered a standard method in the interpretation of PET/CT. We herein aim to validate a novel three-dimensional deep learning-based tool on PET/CT images for automated assessment of the intensity of BM metabolism in MM patients.Materials and methodsWhole-body [18F]FDG PET/CT scans of 35 consecutive, previously untreated MM patients were studied. All patients were investigated in the context of an open-label, multicenter, randomized, active-controlled, phase 3 trial (GMMG-HD7). Qualitative (visual) analysis classified the PET/CT scans into three groups based on the presence and number of focal [18F]FDG-avid lesions as well as the degree of diffuse [18F]FDG uptake in the BM. The proposed automated method for BM metabolism assessment is based on an initial CT-based segmentation of the skeleton, its transfer to the SUV PET images, the subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, six different SUV thresholds (Approaches 1–6) were applied for the definition of pathological tracer uptake in the skeleton [Approach 1: liver SUVmedian × 1.1 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 2: liver SUVmedian × 1.5 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 3: liver SUVmedian × 2 (axial skeleton), gluteal muscles SUVmedian × 4 (extremities). Approach 4: ≥ 2.5. Approach 5: ≥ 2.5 (axial skeleton), ≥ 2.0 (extremities). Approach 6: SUVmax liver]. Using the resulting masks, subsequent calculations of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in each patient were performed. A correlation analysis was performed between the automated PET values and the results of the visual PET/CT analysis as well as the histopathological, cytogenetical, and clinical data of the patients.ResultsBM segmentation and calculation of MTV and TLG after the application of the deep learning tool were feasible in all patients. A significant positive correlation (p < 0.05) was observed between the results of the visual analysis of the PET/CT scans for the three patient groups and the MTV and TLG values after the employment of all six [18F]FDG uptake thresholds. In addition, there were significant differences between the three patient groups with regard to their MTV and TLG values for all applied thresholds of pathological tracer uptake. Furthermore, we could demonstrate a significant, moderate, positive correlation of BM plasma cell infiltration and plasma levels of β2-microglobulin with the automated quantitative PET/CT parameters MTV and TLG after utilization of Approaches 1, 2, 4, and 5.ConclusionsThe automated, volumetric, whole-body PET/CT assessment of the BM metabolic activity in MM is feasible with the herein applied method and correlates with clinically relevant parameters in the disease. This methodology offers a potentially reliable tool in the direction of optimization and standardization of PET/CT interpretation in MM. Based on the present promising findings, the deep learning-based approach will be further evaluated in future prospective studies with larger patient cohorts.

Artificial intelligence vs. evolving super-complex tumor intelligence: critical viewpoints.

Recent developments in various domains have led to a growing interest in the potential of artificial intelligence to enhance our lives and environments. In particular, the application of artificial intelligence in the management of complex human diseases, such as cancer, has garnered significant attention. The evolution of artificial intelligence is thought to be influenced by multiple factors, including human intervention and environmental factors. Similarly, tumors, being heterogeneous and complex diseases, continue to evolve due to changes in the physical, chemical, and biological environment. Additionally, the concept of cellular intelligence within biological systems has been recognized as a potential attribute of biological entities. Therefore, it is plausible that the tumor intelligence present in cancer cells of affected individuals could undergo super-evolution due to changes in the pro-tumor environment. Thus, a comparative analysis of the evolution of artificial intelligence and super-complex tumor intelligence could yield valuable insights to develop better artificial intelligence-based tools for cancer management.

US Quantification of Liver Fat: Past, Present, and Future.

Fatty liver disease has a high and increasing prevalence worldwide, is associated with adverse cardiovascular events and higher long-term medical costs, and may lead to liver-related morbidity and mortality. There is an urgent need for accurate, reproducible, accessible, and noninvasive techniques appropriate for detecting and quantifying liver fat in the general population and for monitoring treatment response in at-risk patients. CT may play a potential role in opportunistic screening, and MRI proton-density fat fraction provides high accuracy for liver fat quantification; however, these imaging modalities may not be suited for widespread screening and surveillance, given the high global prevalence. US, a safe and widely available modality, is well positioned as a screening and surveillance tool. Although well-established qualitative signs of liver fat perform well in moderate and severe steatosis, these signs are less reliable for grading mild steatosis and are likely unreliable for detecting subtle changes over time. New and emerging quantitative biomarkers of liver fat, such as those based on standardized measurements of attenuation, backscatter, and speed of sound, hold promise. Evolving techniques such as multiparametric modeling, radiofrequency envelope analysis, and artificial intelligence-based tools are also on the horizon. The authors discuss the societal impact of fatty liver disease, summarize the current state of liver fat quantification with CT and MRI, and describe past, currently available, and potential future US-based techniques for evaluating liver fat. For each US-based technique, they describe the concept, measurement method, advantages, and limitations. © RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.

Non-traditional data sources in obesity research: a systematic review of their use in the study of obesogenic environments.

The complex nature of obesity increasingly requires a comprehensive approach that includes the role of environmental factors. For understanding contextual determinants, the resources provided by technological advances could become a key factor in obesogenic environment research. This study aims to identify different sources of non-traditional data and their applications, considering the domains of obesogenic environments: physical, sociocultural, political and economic. We conducted a systematic search in PubMed, Scopus and LILACS databases by two independent groups of reviewers, from September to December 2021. We included those studies oriented to adult obesity research using non-traditional data sources, published in the last 5 years in English, Spanish or Portuguese. The overall reporting followed the PRISMA guidelines. The initial search yielded 1583 articles, 94 articles were kept for full-text screening, and 53 studies met the eligibility criteria and were included. We extracted information about countries of origin, study design, observation units, obesity-related outcomes, environment variables, and non-traditional data sources used. Our results revealed that most of the studies originated from high-income countries (86.54%) and used geospatial data within a GIS (76.67%), social networks (16.67%), and digital devices (11.66%) as data sources. Geospatial data were the most utilised data source and mainly contributed to the study of the physical domains of obesogenic environments, followed by social networks providing data to the analysis of the sociocultural domain. A gap in the literature exploring the political domain of environments was also evident. The disparities between countries are noticeable. Geospatial and social network data sources contributed to studying the physical and sociocultural environments, which could be a valuable complement to those traditionally used in obesity research. We propose the use of information available on the Internet, addressed by artificial intelligence-based tools, to increase the knowledge on political and economic dimensions of the obesogenic environment.

A promising artificial intelligence-based tool to simulate the efficient and sustainable hydrogen sulfide elimination using deep eutectic solvents

This work applies artificial intelligence (AI) methodologies and relevance tests to investigate the hydrogen sulfide (H2S) elimination capacity of a wide range of DESs (deep eutectic solvents). A great deal of effort is made to deploy a practical model to predict the H2S removal ability of eighteen different DESs as a function of DES chemistry, equilibrium conditions, and H2S-DES interaction. Multiple linear regression and Pearson’s correlation methods are employed to measure the direction and relative influence of hydrogen bond acceptor (HBA), hydrogen-bond donor (HBD), the HBA and HBD concentration, water dose, pressure, temperature, and absorption enthalpy on the DES capacity to absorb H2S molecules. Both tests approve that absorption enthalpy (ΔH) of H2S by DESs, hydrogen bond acceptor (HBA), pressure, and temperature mainly control the considered separation process. The relevance tests also identify that the H2S solubility in DESs improves by either a decrease in ΔH, HBA molecular weight, and temperature or an increase in pressure. Then, five diverse AI types are applied to estimate H2S molality in DESs as a function of the involved explanatory variables, and their accuracy is compared. The sensitivity analysis clarifies that the cascade neural network (CNN) with only 20 hidden nodes is the most reliable AI tool to simulate the considered problem. Further investigations approve that the CNN tool provides more accurate predictions for H2S solubility in DESs when it is equipped with the logarithm and tangent sigmoid transfer functions in the hidden and output neuronic layers. Also, amongst eleven checked training algorithms, the Levenberg-Marquardt presented the best performance. This structure-regulated CNN tool can predict 512 actual H2S solubility records in DESs with the MSE = 0.0031, MAE = 0.02, RAE = 2.03%, AARE = 3.03%, and multiple-R = 0.99943.

Artificial intelligence to improve ischemia prediction in rubidium positron emission tomography

Abstract Funding Acknowledgements Type of funding sources: None. Background Patients are referred to coronary artery disease (CAD) testing based on their pre-test probability (PTP). The available, easy-to-use risk prediction tools use three variables only and their diagnostic accuracy is limited. Artificial intelligence-based tools incorporate multiple variables and could improve PTP assessment due to the resulting combinatorial power. The factors in the AI model are not seen as independent individual values, but are recognized as patterns derived by a combinatorial analysis of the individual metabolic profile of each patient. Purpose We aimed to compare the diagnostic accuracy of a novel, artificial intelligence-based tool with commonly used PTP tools to predict ischemia. Methods Consecutive patients (n = 2417) referred for Rubidium-Position Emission Tomography (PET) were evaluated. PTP was calculated using the ESC 2013/2019 and ACC 2012/2021 guidelines, and a memetic pattern-based algorithm (MPA) model incorporating symptoms, vitals, ECG and laboratory findings. Five risk categories (very low to very high risk) were defined (&amp;lt;5%, 5–15%, 15–50%, 50–85%, &amp;gt;85%). Ischemia was defined as summed difference score (SDS) ≥2 on PET. Receiver operator characteristics were calculated and compared using the DeLong method. Results Known CAD, ischemia and scar (detected on PET) were present in 46.3%, 37.1% and 24.6%, respectively. The MPA model was most accurate to assess PTP of ischemia (AUC: 0.758, ESC 2013: 0.661, ESC 2019: 0.673, ACC 2012: 0.585, ACC 2021: 0.667, p&amp;lt;0.0001 each) as depicted in figure 1. The MPA’s sensitivity and negative predictive value to rule-out ischemia were 99.1% and 96.4%, respectively. The model allocated patients more evenly across risk groups, reduced the proportion of patients in the 15–85% range by 29% (ACC 2012) to 51% (ESC 2013), and was the only risk tool to correctly estimate ischemia prevalence in the very low risk group (table 1). Conclusion The MPA model significantly improved PTP assessment of ischemia. The MPA model has originally been derived on the endpoint coronary stenosis &amp;gt;50% and validated in three clinical studies (1–3). This study now confirms that the MPA model applied to the endpoint ischemia outperforms the current risk assessment tools and is most accurate to assess PTP. It enables clinicians to safely exclude ischemia based on readily available variables without advanced testing. It could be used to improve risk stratification of patients and to significantly reduce unnecessary non-invasive functional tests.

Osteoporosis and Covid-19: Detected similarities in bone lacunar-level alterations via combined AI and advanced synchrotron testing

While advanced imaging strategies have improved the diagnosis of bone-related pathologies, early signs of bone alterations remain difficult to detect. The Covid-19 pandemic has brought attention to the need for a better understanding of bone micro-scale toughening and weakening phenomena. This study used an artificial intelligence-based tool to automatically investigate and validate four clinical hypotheses by examining osteocyte lacunae on a large scale with synchrotron image-guided failure assessment. The findings indicate that trabecular bone features exhibit intrinsic variability related to external loading, micro-scale bone characteristics affect fracture initiation and propagation, osteoporosis signs can be detected at the micro-scale through changes in osteocyte lacunar features, and Covid-19 worsens micro-scale porosities in a statistically significant manner similar to the osteoporotic condition. Incorporating these findings with existing clinical and diagnostic tools could prevent micro-scale damages from progressing into critical fractures.

Conversation-Based Assessments in Education: Design, Implementation, and Cognitive Walkthroughs for Usability Testing

Conversational agents have been widely used in education to support student learning. There have been recent attempts to design and use conversational agents to conduct assessments (i.e., conversation-based assessments: CBA). In this study, we developed CBA with constructed and selected-response tests using Rasa—an artificial intelligence-based tool. CBA was deployed via Google Chat to support formative assessment. We evaluated (1) its performance in answering students’ responses and (2) its usability with cognitive walkthroughs conducted by external evaluators. CBA with constructed-response tests consistently matched student responses to the appropriate conversation paths in most cases. In comparison, CBA with selected-response tests demonstrated perfect accuracy between system design and implementation. A cognitive walkthrough of CBA showed its usability as well as several potential issues that could be improved. Participating students did not experience these issues, however, we reported them to help researchers, designers, and practitioners improve the assessment experience for students using CBA.

Statistical optimization strategies on waste substrates for solving high-cost challenges in biosurfactants production: a review

Biosurfactants are bio-based amphiphilic molecules with extensive applications in various industries. These eco-friendly alternatives possess numerous advantages over chemical surfactants. However, high production costs hinder market competitiveness of biosurfactants. Production costs of synthetic surfactants range between $1-3/kg, while biosurfactants cost between $20-25/kg. Principal challenges hindering commercialization of biosurfactants are high costs of media constituents and downstream processing, accounting for 30% and 60-80% of production costs, respectively. Thus, cost-effective biosurfactant production would depend on the utilization of environment-friendly low-cost substrates and efficient product recovery. To this end, statistical tools such as Factorial Designs (FD) and Response Surface Methodology (RSM), are employed to optimize the production processes. FD as effective screening models comprise Plackett-Burman Design (PBD) and Taguchi design; and involves quantification of various significant factor effects including the main effect and level of dependency of one factor on the level of one or more factors. RSM predicts appropriate proportions of media constituents and optimal culture conditions; and is reportedly effective in reducing production cost and consequently, market price. Central Composite Design (CCD) and Box-Behnken Design (BBD) are common RSM for optimizing biosurfactants production. CCD assesses the relationship between one factor or more and a set of experimental variables. BBD is considered more proficient than CCD as it requires fewer experimental runs. Most recently, Artificial Neural Network which uses artificial intelligence-based tools to predict biosurfactant production using dependent variables of the process is gaining attention.