Artificial Intelligence-based Tools Research Articles

Artificial intelligence-based tools with automated segmentation and measurement on CT images to assist accurate and fast diagnosis in acute pancreatitis.

To develop an artificial intelligence (AI) tool with automated pancreas segmentation and measurement of pancreatic morphological information on CT images to assist improved and faster diagnosis in acute pancreatitis. This study retrospectively contained 1124 patients suspected for AP and received non-contrast and enhanced abdominal CT examination between September 2013 to September 2022. Patients were divided into training (N = 688), validation (N = 145), testing dataset (N = 291, N = 104 for normal pancreas, N = 98 for AP, N = 89 for AP complicated with PDAC (AP&PDAC)). A model based on convolutional neural network (MSAnet) was developed. The pancreas segmentation and measurement were performed via eight open-source models and MSAnet based tools, and the efficacy was evaluated using Dice similarity coefficient (DSC) and Intersection over union (IoU). The DSC and IoU for patients with different ages were also compared. The outline of tumor and edema in the AP and were segmented by clustering. The diagnostic efficacy for radiologists with or without the assistance of MSAnet tool in AP and AP&PDAC was evaluated using receiver operation curve and confusion matrix. Among all models, MSAnet based tool showed best performance on the training and validation dataset, and had high efficacy on testing dataset. The performance was age-affected. With assistance of the AI tool, the diagnosis time was significantly shortened by 26.8% and 32.7% for junior and senior radiologists, respectively. The area under curve in diagnosis of AP was improved from 0.91 to 0.96 for junior radiologist and 0.98 to 0.99 for senior radiologist. In AP&PDAC diagnosis, AUC was increased from 0.85 to 0.92 for junior and 0.97 to 0.99 for senior. MSAnet based tools showed good pancreas segmentation and measurement performance, which help radiologists improve diagnosis efficacy and workflow in both AP and AP with PDAC conditions. This study developed an AI tool with automated pancreas segmentation and measurement and provided evidence for AI tool assistance in improving the workflow and accuracy of AP diagnosis.

Beyond the AJR: Clinical Application of Artificial Intelligence-Based Tools for ARIA Detection and Severity Scoring.

Beyond the AJR: Clinical Application of Artificial Intelligence-Based Tools for ARIA Detection and Severity Scoring.

Investigating the rheological characteristics of alkali-activated concrete using contemporary artificial intelligence approaches

Abstract Using artificial intelligence-based tools, this research aims to establish a direct correlation between the alkali-activated concrete (AAC) mix design factors and their performances. More specifically, the machine learning system was fed new property data obtained from AAC mixes used in laboratory experiments. The rheological parameters (yield stress [static/dynamic] and plastic viscosity) of AAC were predicted using the multilayer perceptron neural network (MLPNN) and bagging ensemble (BE) models. In addition, the R 2 values, k-fold analyses, statistical checks, and the dissimilarity between the experimental and predicted compressive strength were employed to assess the performance of the created models. Also, the SHapley additive exPlanation (SHAP) approach was used for examining the relevance of influencing parameters. The BE approach was found to be significantly accurate in all prediction models, with R 2 greater than 0.90, and MLPNN models were found to be moderately precise, with R 2 slightly below 0.90. However, the error assessment through statistical checks and k-fold analysis also validated the higher precision of BE models over the MLPNN models. Building models that can calculate rheological properties of AAC for different values of input parameters could save a lot of time and money compared to doing the tests in a laboratory. In order to ascertain the required amounts of raw materials of AAC, investigators, as well as businesses, may find the SHAP study helpful.

Evaluating the effectiveness of artificial intelligence-based tools in detecting and understanding sleep health misinformation: Comparative analysis using Google Bard and OpenAI ChatGPT-4.

This study evaluates the performance of two major artificial intelligence-based tools (ChatGPT-4 and Google Bard) in debunking sleep-related myths. More in detail, the present research assessed 20 sleep misconceptions using a 5-point Likert scale for falseness and public health significance, comparing responses of artificial intelligence tools with expert opinions. The results indicated that Google Bard correctly identified 19 out of 20 statements as false (95.0% accuracy), not differing from ChatGPT-4 (85.0% accuracy, Fisher's exact test p = 0.615). Google Bard's ratings of the falseness of the sleep misconceptions averaged 4.25 ± 0.70, showing a moderately negative skewness (-0.42) and kurtosis (-0.83), and suggesting a distribution with fewer extreme values compared with ChatGPT-4. In assessing public health significance, Google Bard's mean score was 2.4 ± 0.80, with skewness and kurtosis of 0.36 and -0.07, respectively, indicating a more normal distribution compared with ChatGPT-4. The inter-rater agreement between Google Bard and sleep experts had an intra-class correlation coefficient of 0.58 for falseness and 0.69 for public health significance, showing moderate alignment (p = 0.065 and p = 0.014, respectively). Text-mining analysis revealed Google Bard's focus on practical advice, while ChatGPT-4 concentrated on theoretical aspects of sleep. The readability analysis suggested Google Bard's responses were more accessible, aligning with 8th-grade level material, versus ChatGPT-4's 12th-grade level complexity. The study demonstrates the potential of artificial intelligence in public health education, especially in sleep health, and underscores the importance of accurate, reliable artificial intelligence-generated information, calling for further collaboration between artificial intelligence developers, sleep health professionals and educators to enhance the effectiveness of sleep health promotion.

Artificial Intelligence for the Interventional Cardiologist: Powering and Enabling OCT Image Interpretation.

Intravascular optical coherence tomography (IVOCT) is a form of intra-coronary imaging that uses near-infrared light to generate high-resolution, cross-sectional, and 3D volumetric images of the vessel. Given its high spatial resolution, IVOCT is well-placed to characterise coronary plaques and aid with decision-making during percutaneous coronary intervention. IVOCT requires significant interpretation skills, which themselves require extensive education and training for effective utilisation, and this would appear to be the biggest barrier to its widespread adoption. Various artificial intelligence-based tools have been utilised in the most contemporary clinical IVOCT systems to facilitate better human interaction, interpretation and decision-making. The purpose of this article is to review the existing and future technological developments in IVOCT and demonstrate how they could aid the operator.

Discovery of Potential Neonicotinoid Insecticides by an Artificial Intelligence Generative Model and Structure-Based Virtual Screening.

The identification of neonicotinoid insecticides bearing novel scaffolds is of great importance for pesticide discovery. Here, artificial intelligence-based tools and virtual screening strategy were integrated to discover potential leads of neonicotinoid insecticides. A deep generative model was successfully constructed using a recurrent neural network combined with transfer learning. The model evaluation showed that the pretrained model could accurately grasp the SMILES grammar of drug-like molecules and generate potential neonicotinoid compounds after transfer learning. The generated molecules were evaluated by hierarchical virtual screening, hits were subjected to a similarity search, and the most similar structures were purchased for the bioassay. Compounds A2 and A5 displayed 52.5 and 50.3% mortality rates against Aphis craccivora at 100 mg/L, respectively. The docking study indicated that these two compounds have similar binding modes to neonicotinoids, which were verified by further molecular dynamics simulations.

Performance evaluation of three versions of a convolutional neural network for object detection and segmentation using a multiclass and reduced panoramic radiograph dataset

ObjectivesTo evaluate the diagnostic performance of three versions of a deep-learning convolutional neural network in terms of object detection and segmentation using a multiclass panoramic radiograph dataset. MethodsA total of 600 orthopantomographies were randomly selected for this study and manually annotated by a single operator using an image annotation tool (COCO Annotator v.11.0.1) to establish ground truth. The annotation classes included teeth, maxilla, mandible, inferior alveolar nerve, dento- and implant-supported crowns/pontics, endodontic treatment, resin-based restorations, metallic restorations, and implants. The dataset was then divided into training, validation, and testing subsets, which were used to train versions 5, 7, and 8 of You Only Look Once (YOLO) Neural Network. Results were stored, and a posterior performance analysis was carried out by calculating the precision (P), recall (R), F1 Score, Intersection over Union (IoU), and mean average precision (mAP) at 0.5 and 0.5–0.95 thresholds. The confusion matrix and recall precision graphs were also sketched. ResultsYOLOv5s showed an improvement in object detection results with an average R = 0.634, P = 0.781, mAP0.5 = 0.631, and mAP0.5–0.95 = 0.392. YOLOv7m achieved the best object detection results with average R = 0.793, P = 0.779, mAP0.5 = 0.740, and mAP0.5–0.95 = 0,481. For object segmentation, YOLOv8m obtained the best average results (R = 0.589, P = 0.755, mAP0.5 = 0.591, and mAP0.5–0.95 = 0.272). ConclusionsYOLOv7m was better suited for object detection, while YOLOv8m demonstrated superior performance in object segmentation. The most frequent error in object detection was related to background classification. Conversely, in object segmentation, there is a tendency to misclassify True Positives across different dental treatment categories. Clinical SignificanceGeneral diagnostic and treatment decisions based on panoramic radiographs can be enhanced using new artificial intelligence-based tools. Nevertheless, the reliability of these neural networks should be subjected to training and validation to ensure their generalizability.

An integrative data-centric approach to derivation and characterization of an adverse outcome pathway network for cadmium-induced toxicity

Cadmium is a prominent toxic heavy metal that contaminates both terrestrial and aquatic environments. Owing to its high biological half-life and low excretion rates, cadmium causes a variety of adverse biological outcomes. Adverse outcome pathway (AOP) networks were envisioned to systematically capture toxicological information to enable risk assessment and chemical regulation. Here, we leveraged AOP-Wiki and integrated heterogeneous data from four other exposome-relevant resources to build the first AOP network relevant for inorganic cadmium-induced toxicity. From AOP-Wiki, we filtered 309 high confidence AOPs, identified 312 key events (KEs) associated with inorganic cadmium from five exposome-relevant databases using a data-centric approach, and thereafter, curated 30 cadmium relevant AOPs (cadmium-AOPs). By constructing the undirected AOP network, we identified a large connected component of 18 cadmium-AOPs. Further, we analyzed the directed network of 59 KEs and 82 key event relationships (KERs) in the largest component using graph-theoretic approaches. Subsequently, we mined published literature using artificial intelligence-based tools to provide auxiliary evidence of cadmium association for all KEs in the largest component. Finally, we performed case studies to verify the rationality of cadmium-induced toxicity in humans and aquatic species. Overall, cadmium-AOP network constructed in this study will aid ongoing research in systems toxicology and chemical exposome.

University students’ perceptions of artificial intelligence-based tools for English writing courses

This research explores the perceptions of Korean university students regarding artificial intelligence (AI)-based writing tools that include tools guided by machine learning, such as Google Translate and Naver Papago, and generative AI tools, such as Grammarly. A mixed methodology was used, including both quantitative and qualitative data. Among students who have taken English writing courses, 80 Korean university students volunteered for the online survey. After the survey, the research team recruited interview participants, and five volunteered participants joined the focus group interview. The study results indicate that these AI-based writing tools could improve English language learners (ELLs) writing skills. ELLs also noted the strengths and weaknesses of each AI-based tool, including the accessibility of translation machine learning and the error-checking capabilities of generative AI. However, interview data analysis indicates that the excessive use of AI-based writing tools could interfere with ELLs’ English writing process. This study highlights the need to effectively integrate AI-based tools in English language teaching for adult ELLs worldwide.

ChatGPT as an educational support tool: an analysis of its potential in the teaching and learning process

The integration of new technologies in education has been strengthening in recent years, and Artificial Intelligence-based tools are becoming increasingly popular for enhancing the learning experience. This paper discusses the potential of ChatGPT, an Artificial Intelligence-based language model trained with millions of text examples from the internet, to improve learning and enhance teaching efficiency. This paper explores four main areas where we can use ChatGPT: computer-mediated teaching and content creation; online and distance learning environments; personalized feedback; and self-directed learning. The results suggest that ChatGPT has the potential to be a valuable tool for students and teachers, although further optimization and examination of its usage in diverse contexts is required.

An artificial intelligence-based tool for student-generated question

The benefits of student question generation have been extensively established, yet most students do not engage in question-generation activities throughout their formal education and are not used to writing questions. The goal of this project was to create a flexible online learning environment that highlights several types of scaffolding in order to better assist student question production activities in a timely, adaptable, and practically possible manner. The system’s underlying architecture and design principles are outlined. The objective of this work is so utilise Chat-GPT that guides student generated questions. The potential of the student-generated question method of instruction is explored, as is an initial examination of students’ impressions of the helpfulness of the different built-in support systems. Suggestions on how the study’s findings could be used in the classroom and in future research are offered. It has been established that perceived utility plays a significant influence in the dissemination and acceptance of new technology for a wide variety of innovations. Finally, the link between the students’ impressions of the utility of the framework in the established system and their cognitive skills regarding the educational potential of student-generated questions was investigated.

Gender Differences in Perception of Artificial Intelligence-Based Tools

Participation of girls in Science, Technology, Engineering, and Mathematics (STEM) subjects at the higher education (HE) level continues to lag behind that of boys and consequently may affect artificial intelligence proliferation in Ghana. Numerous factors affect girls' engagement in science, one of which is the mistaken belief among girls that STEM subjects are better suited for boys. This study investigates the gender differences in AI-based tools which have become integral for teaching and learning in HE schools. Based on results from 128 students in higher education in Ghana, this article argues that gender is a significant determinant of students’ use of AI-based tools in education. The results further revealed a significant disparity in the overall levels of perceived innovation characteristics based on gender. This study urges managers of Higher Education Institutions (HEIs) to implement policies and measures aimed at facilitating women's engagement and greater familiarity with AI technologies.

Advanced Digital Tools for Data-Informed and Performance-Driven Design: A Review of Building Energy Consumption Forecasting Models Based on Machine Learning

Cities and buildings represent the core of human life, the nexus of economic activity, culture, and growth. Although cities cover less than 10% of the global land area, they are notorious for their substantial energy consumption and consequential carbon dioxide (CO2) emissions. These emissions significantly contribute to reducing the carbon budget available to mitigate the adverse impacts of climate change. In this context, the designers’ role is crucial to the technical and social response to climate change, and providing a new generation of tools and instruments is paramount to guide their decisions towards sustainable buildings and cities. In this regard, data-informed digital tools are a viable solution. These tools efficiently utilise available resources to estimate the energy consumption in buildings, thereby facilitating the formulation of effective urban policies and design optimisation. Furthermore, these data-driven digital tools enhance the application of algorithms across the building industry, empowering designers to make informed decisions, particularly in the early stages of design. This paper presents a comprehensive literature review on artificial intelligence-based tools that support performance-driven design. An exhaustive keyword-driven exploration across diverse bibliographic databases yielded a consolidated dataset used for automated analysis for discerning the prevalent themes, correlations, and structural nuances within the body of literature. The primary findings indicate an increasing emphasis on master plans and neighbourhood-scale simulations. However, it is observed that there is a lack of a streamlined framework integrating these data-driven tools into the design process.

The future of personalized cardiovascular medicine demands 3D and 4D printing, stem cells, and artificial intelligence

Cardiovascular diseases remain the most lethal disorders worldwide. Employing preeminent techniques is paramount for addressing this global challenge. Recent advances in lab-on-a-chip technology have the potential to transform cardiovascular medicine by providing new tools for understanding the biological variability that underlies disease and drug response. Coupling improved fabrication techniques and cardiovascular cellular models with artificial intelligence-based tools for design and analysis primes the field to model and explore cardiovascular biology more accurately than ever before. The fabrication of lab-on-a-chip systems has dramatically evolved with the development of additive manufacturing technologies. Not only can 3D printing be used to fabricate intricate microfluidic networks, but now through the incorporation of smart materials, 4D printing can change the shape and properties of devices based on external stimuli. By leveraging advances in induced pluripotent stem cells, lab-on-a-chip devices open new opportunities within personalized cardiovascular medicine. As customizable systems, devices can be fabricated and populated with donor stem cell-derived cardiovascular cells to mimic the cardiovascular system. These capabilities are only going to improve with the increasingly important role of artificial intelligence in lab-on-a-chip centric design, real-time analysis, data gathering, processing and analysis. Here, we address the potential to personalize cardiovascular medicine through the lens of advances in the fabrication of lab-on-a-chip devices, development of induced pluripotent stem cells derived engineered vasculature and heart tissues, and explore how artificial intelligence continues to shape this field.

Exploring the Impact of Chat-GPT on Teacher Professional Development: Opportunities, Challenges, and Implications

The study discusses the transformative impact of Chat-GPT (Generative Pre-trained Transformer), an artificial intelligence-based tool, in the field of education. Chat-GPT has garnered significant attention and praise for its capabilities in a rapidly evolving technological landscape. However, concerns about deception and misuse have led to bans on educational institutions. Educators' perspectives on Chat-GPT vary, with some viewing it as a threat to students' critical thinking and writing skills. The study emphasises the importance of educators adapting to technological advancements, including AI innovations like Chat-GPT. It posits that Chat-GPT has the potential to revolutionise professional development for educators and better prepare students for the challenges of the future. While initial reactions to new technologies often lean negative, history shows that innovations tend to yield optimistic outcomes. Psychological factors, such as the fear of the unknown and resistance to change, contribute to misaligned expectations. Acknowledging educators' valid concerns, the paper encourages a balanced approach. Educators are advised to integrate AI tools into professional development, leveraging them to enhance productivity and creativity. The literature review conducted in this study reveals valuable insights into the integration of Chat-GPT in the context of teacher professional development via the network of conceptual maps. In conclusion, this study highlights the need for educators to embrace AI as a tool for positive change in education.

A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps

Integrated Pest Management (IPM) is an essential approach used in smart agriculture to manage pest populations and sustainably optimize crop production. One of the cornerstones underlying IPM solutions is pest monitoring, a practice often performed by farm owners by using chromotropic sticky traps placed on insect hot spots to gauge pest population densities. In this paper, we propose a modular model-agnostic deep learning-based counting pipeline for estimating the number of insects present in pictures of chromotropic sticky traps, thus reducing the need for manual trap inspections and minimizing human effort. Additionally, our solution generates a set of raw positions of the counted insects and confidence scores expressing their reliability, allowing practitioners to filter out unreliable predictions. We train and assess our technique by exploiting PST - Pest Sticky Traps, a new collection of dot-annotated images we created on purpose and we publicly release, suitable for counting whiteflies. Experimental evaluation shows that our proposed counting strategy can be a valuable Artificial Intelligence-based tool to help farm owners to control pest outbreaks and prevent crop damages effectively. Specifically, our solution achieves an average counting error of approximately 9% compared to human capabilities requiring a matter of seconds, a large improvement respecting the time-intensive process of manual human inspections, which often take hours or even days.

Genetic newborn screening and digital technologies: A project protocol based on a dual approach to shorten the rare diseases diagnostic path in Europe.

Since 72% of rare diseases are genetic in origin and mostly paediatrics, genetic newborn screening represents a diagnostic "window of opportunity". Therefore, many gNBS initiatives started in different European countries. Screen4Care is a research project, which resulted of a joint effort between the European Union Commission and the European Federation of Pharmaceutical Industries and Associations. It focuses on genetic newborn screening and artificial intelligence-based tools which will be applied to a large European population of about 25.000 infants. The neonatal screening strategy will be based on targeted sequencing, while whole genome sequencing will be offered to all enrolled infants who may show early symptoms but have resulted negative at the targeted sequencing-based newborn screening. We will leverage artificial intelligence-based algorithms to identify patients using Electronic Health Records (EHR) and to build a repository "symptom checkers" for patients and healthcare providers. S4C will design an equitable, ethical, and sustainable framework for genetic newborn screening and new digital tools, corroborated by a large workout where legal, ethical, and social complexities will be addressed with the intent of making the framework highly and flexibly translatable into the diverse European health systems.

Prediction of therapeutic intensity level from automatic multiclass segmentation of traumatic brain injury lesions on CT-scans

The prediction of the therapeutic intensity level (TIL) for severe traumatic brain injury (TBI) patients at the early phase of intensive care unit (ICU) remains challenging. Computed tomography images are still manually quantified and then underexploited. In this study, we develop an artificial intelligence-based tool to segment brain lesions on admission CT-scan and predict TIL within the first week in the ICU. A cohort of 29 head injured patients (87 CT-scans; Dataset1) was used to localize (using a structural atlas), segment (manually or automatically with or without transfer learning) 4 or 7 types of lesions and use these metrics to train classifiers, evaluated with AUC on a nested cross-validation, to predict requirements for TIL sum of 11 points or more during the 8 first days in ICU. The validation of the performances of both segmentation and classification tasks was done with Dice and accuracy scores on a sub-dataset of Dataset1 (internal validation) and an external dataset of 12 TBI patients (12 CT-scans; Dataset2). Automatic 4-class segmentation (without transfer learning) was not able to correctly predict the apparition of a day of extreme TIL (AUC = 60 ± 23%). In contrast, manual quantification of volumes of 7 lesions and their spatial location provided a significantly better prediction power (AUC = 89 ± 17%). Transfer learning significantly improved the automatic 4-class segmentation (DICE scores 0.63 vs 0.34) and trained more efficiently a 7-class convolutional neural network (DICE = 0.64). Both validations showed that segmentations based on transfer learning were able to predict extreme TIL with better or equivalent accuracy (83%) as those made with manual segmentations. Our automatic characterization (volume, type and spatial location) of initial brain lesions observed on CT-scan, publicly available on a dedicated computing platform, could predict requirements for high TIL during the first 8 days after severe TBI. Transfer learning strategies may improve the accuracy of CNN-based segmentation models.Trial registrations Radiomic-TBI cohort; NCT04058379, first posted: 15 august 2019; Radioxy-TC cohort; Health Data Hub index F20220207212747, first posted: 7 February 2022.

Barriers to coach decision-making during Australian football matches and how it can be supported by artificial intelligence

Elite sport coaches are often expected to make high-stakes decisions in time-constrained environments, such as during matches. The quality or efficiency of these decisions is bound by context-specific constraints on their decision-making. Artificial intelligence-based tools could alleviate some of these constraints. In Australian football, it is not yet known what these context-specific constraints are, nor is there any understanding of the thoughts and perception of elite Australian football coaches towards the use of artificial intelligence to support their decision-making. This study is the first to explore these issues in the context of elite Australian football and aims to inform the effective design and integration of tools to support the in-match decision-making of coaches in a manner that is welcomed by coaches. Using semi-structured interviews with elite Australian football coaches ( n = 6) and reflexive thematic analysis, two domains were explored: ‘barriers to decision-making’ and ‘thoughts and perceptions of a decision support system (DSS)’. Coaches discussed seven ‘cognitive’ and ‘environmental’ barriers, some of which may be addressed using artificial intelligence. Meanwhile, four themes were generated for the second domain, namely ‘willingness to adopt’, ‘expectation’, ‘concern and criticism’, and ‘DSS role’. The findings enable practitioners to design tools that are intended to address some of these barriers, while also providing insights into important considerations for the implementation and design of these tools that may increase the likelihood of achieving the endorsement of elite coaches and their support staff.

Açık Uçlu Maddelerin Puanlanmasında ChatGPT ve Gerçek Puanlayıcıların Puanlayıcılar Arası Güvenirlik Bakımından İncelenmesi

The aim of this study is to examine the inter-rater reliability of the responses to open-ended items scored by ChatGPT, an artificial intelligence-based tool, and two real raters according to the scoring keys. The study group consists of 30 students, aged between 13 and 15, studying in Eskişehir province in the 2022-2023 academic year. The data of the study were collected face-to-face with the help of 16 open-ended items selected from the sample questions published in the International Student Assessment Program-PISA Reading Skills. Correlation, percentage of agreement and the Generalizability theory were used to determine inter-rater reliability. SPSS 25 was used for correlation analysis, Excel for percentage of agreement analysis, and EduG 6.1 for the Generalizability theory analysis. The results of the study showed that there was a positive and high level of correlation between the raters, the raters showed a high level of agreement, and the reliability (G) coefficients calculated using the Generalizability theory were lower than the correlation values and percentage of agreement. In addition, it was determined that all raters showed excellent positive correlation and full agreement with each other in the scoring of the answers given to the short-answer items whose answers were directly in the text. In addition, according to the results of the Generalizability theory, it was found out that the items (i) explained the total variance the most among the main effects and the student-item interaction (sxi) explained the most among the interaction effects. As a result, it can be suggested to educators to get support from artificial intelligence-based tools such as ChatGPT when scoring open-ended items that take a long time to score, especially in crowded classes or when time is limited.