Artificial Intelligence Models Research Articles

New empirical equations to assess energy efficiency of flow-dissipating vortex dropshaft

Vortex structures are generally considered the common energy-dissipating infrastructures in order to convey urban surface waters. During transferring wastewater in such urban networks, the major proportion of water energy is dissipated and additionally, the hydraulic performance of these infrastructures is evaluated on the basis of the dissipation of energy efficiency in the vortex dropshaft. Over the last decade, the literature review of vortex structures demonstrated that there is no reliable mathematical expression to predict energy efficiency although various experimental investigations were done to evaluate the hydraulics of vortex structures. In this research, robust numerical models based on conceptions of Artificial Intelligence (AI) methodologies (i.e., classification, non-parametric analysis, and evolutionary computing) were developed by using 144 experimental data extracted from a physical model of vortex dropshaft. Through analysis of experiments, three main factors, called flow Froude number (Fr), the ratio of sump height (Hs) to shaft diameter (D), and the ratio of drop total height (L) to shaft diameter (D) were determined to estimate the efficiency of flow energy dissipation. The numerical models were calibrated/trained by different algorithms such as multi/single-objective optimization strategies (e.g., Particle Swarm Optimization [PSO] and Multi-Objective Genetic Algorithm [MOGA]). Various statistical tests, known as quantitative assessments for training and testing stages of AI models, demonstrated that Evolutionary Polynomial Regression (EPR) had the best performance (Coefficient of Correlation [R] = 0.9736, Root Mean Square Error [RMSE] = 0.5923, Mean Absolute Percentage Error [MAPE] = 0.5035) and followed by MT (R = 0.9665, RMSE = 0.7039, MAPE = 0.6225), GEP (R = 0.9113, RMSE = 1.0803, MAPE = 0.9535), and MARS (R = 0.8337, RMSE = 1.6473, MAPE = 1.1010). Additionally, expressions given by AI models provided more accurate predictions of energy dissipation efficiency when compared with a regression-based equation (R = 0.9113, RMSE = 0.7039, MAPE = 0.6225) from the literature. Additionally, variations of three effective factors versus the predicted responses (values of energy dissipation efficiency given by AI models) were in coincidence with experimental observations. Overall, this study proved that the usability of mathematical expressions could provide new insights for experts to assess the hydraulic evaluation of vortex structures before their physical and prototype models are constructed.

Physics-informed neural networks in groundwater flow modeling: Advantages and future directions

In recent years, there has been enormous development in soft computing, especially artificial intelligence (AI), which has developed robust methods for solving complex engineering problems. Researchers in the field of water resources engineering have applied these AI methods to solve a variety of hydrological problems. Despite their widespread use in the surface and atmospheric hydrology fields, groundwater hydrologists have not widely used AI methods in their routine field-scale modeling efforts. This is because AI models have been primarily considered black box models that lack physical meaning. Furthermore, using AI models to generate the space-time distribution of transient groundwater level variations is challenging and requires further flux balance and mass transport analyses. More recently, a new type of physics-informed neural network (PINN) model has been developed to address several limitations by integrating governing physics (groundwater flow equations) into the AI tools. This study presents the systematic advantages of the PINN algorithm for solving groundwater problems using a set of classic test problems. As discussed in detail in the article, these advantages and potentials are associated with the meshless nature of PINN, its continuous time and space dimensions, its independence from time-stepping and incremental marching in space, and its efficiency in running time. However, despite PINN's promising attributes, it is important to acknowledge its nascent stage of development and the inherent limitations of all neural network models, such as training challenges and hyperparameter selection. Thus, collaborative efforts between groundwater modelers and computer scientists are imperative to explore and exploit the full potential of PINN in tackling increasingly complex groundwater problems and nurturing PINN into a dependable modeling tool in industry and academia.

An artificial intelligence model for detecting pathological lymph node metastasis in prostate cancer using whole slide images: a retrospective, multicentre, diagnostic study

The pathological examination of lymph node metastasis (LNM) is crucial for treating prostate cancer (PCa). However, the limitations with naked-eye detection and pathologist workload contribute to a high missed-diagnosis rate for nodal micrometastasis. We aimed to develop an artificial intelligence (AI)-based, time-efficient, and high-precision PCa LNM detector (ProCaLNMD) and evaluate its clinical application value. In this multicentre, retrospective, diagnostic study, consecutive patients with PCa who underwent radical prostatectomy and pelvic lymph node dissection at five centres between Sep 2, 2013 and Apr 28, 2023 were included, and histopathological slides of resected lymph nodes were collected and digitised as whole-slide images for model development and validation. ProCaLNMD was trained at a dataset from a single centre (the Sun Yat-sen Memorial Hospital of Sun Yat-sen University [SYSMH]), and externally validated in the other four centres. A bladder cancer dataset from SYSMH was used to further validate ProCaLNMD, and an additional validation (human-AI comparison and collaboration study) containing consecutive patients with PCa from SYSMH was implemented to evaluate the application value of integrating ProCaLNMD into the clinical workflow. The primary endpoint was the area under the receiver operating characteristic curve (AUROC) of ProCaLNMD. In addition, the performance measures for pathologists with ProCaLNMD assistance was also assessed. In total, 8225 slides from 1297 patients with PCa were collected and digitised. Overall, 8158 slides (18,761 lymph nodes) from 1297 patients with PCa (median age 68 years [interquartile range 64-73]; 331 [26%] with LNM) were used to train and validate ProCaLNMD. The AUROC of ProCaLNMD ranged from 0.975 (95% confidence interval 0.953-0.998) to 0.992 (0.982-1.000) in the training and validation datasets, with sensitivities > 0.955 and specificities > 0.921. ProCaLNMD also demonstrated an AUROC of 0.979 in the cross-cancer dataset. ProCaLNMD use triggered true reclassification in 43 (4.3%) slides in which micrometastatic tumour regions were initially missed by pathologists, thereby correcting 28 (8.5%) missed-diagnosed cases of previous routine pathological reports. In the human-AI comparison and collaboration study, the sensitivity of ProCaLNMD (0.983 [0.908-1.000]) surpassed that of two junior pathologists (0.862 [0.746-0.939], P=0.023; 0.879 [0.767-0.950], P=0.041) by 10-12% and showed no difference to that of two senior pathologists (both 0.983 [0.908-1.000], both P>0.99). Furthermore, ProCaLNMD significantly boosted the diagnostic sensitivity of two junior pathologists (both P=0.041) to the level of senior pathologists (both P>0.99), and substantially reduced the four pathologists' slide reviewing time (-31%, P<0.0001;-34%, P<0.0001;-29%, P<0.0001; and-27%, P=0.00031). ProCaLNMD demonstrated high diagnostic capabilities for identifying LNM in prostate cancer, reducing the likelihood of missed diagnoses by pathologists and decreasing the slide reviewing time, highlighting its potential for clinical application. National Natural Science Foundation of China, the Science and Technology Planning Project of Guangdong Province, the National Key Research and Development Programme of China, the Guangdong Provincial Clinical Research Centre for Urological Diseases, and the Science and Technology Projects in Guangzhou.

A Research Study on How AI Creates Fiction Stories

Abstract: This paper includes the study of complex process by which artificial intelligence systems create fictional stories. In this process we cover the five processes such as The pre -processing steps for using AI to create a fictional story, Plot structure and plot production process for creating fictional stories using artificial intelligence, Character growth in the process of creating a virtual story using artificial intelligence, Generating dialogue in the process of creating a virtual story using artificial intelligence, and Using AI to revisit and iterate the process of creating fictional stories. Using state-of-the-art artificial intelligence models, especially those based on deep learning architectures such as recurrent neural networks (RNNs) and transformer models such as generative pre- trained transformers (GPTs), we analyse how these systems understand and manipulate language to create consistent fictional pieces. This paper This Make up story through empirical experiments and qualitative analysis, we carefully study the impact of different parameters, including Algorithm, Education, Fine-tuning Hint, Generation, and Evaluation methods, on the quality and diversity of the generated stories. The study also explores the role of artificial intelligence as well as human collaboration in the fiction stories writing and creative process. It discuss the Future Directions of AI’S role in creating fiction stories.

Earthquake resistant design of framed reinforced concrete building using artificial intelligence model

Earthquake resistant design of framed reinforced concrete building using artificial intelligence model

Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an external validation study

Abstract Aim Recently, deep-learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and Results A convolutional neural network-based AI-ECG algorithm was developed previously in a single-center North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of HCM patients and non-HCM controls from 3 external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs non-HCM status from the ECG alone was examined. A total of 773 HCM patients and 3,867 non-HCM controls were included across 3 sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for HCM patients and 0.3% for controls (p &amp;lt; 0.001). Overall, the AI-ECG algorithm had an AUC of 0.922 (95% CI 0.910-0.934), with diagnostic accuracy 86.9%, sensitivity 82.8% and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case-control ratio 1:2), the AUC was 0.921 (95% CI 0.909-0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.

Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis.

Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.

Fetal Hypoxia Detection Using Machine Learning: A Narrative Review

Fetal hypoxia is a condition characterized by a lack of oxygen supply in a developing fetus in the womb. It can cause potential risks, leading to abnormalities, birth defects, and even mortality. Cardiotocograph (CTG) monitoring is among the techniques that can detect any signs of fetal distress, including hypoxia. Due to the critical importance of interpreting the results of this test, it is essential to accompany these tests with the evolving available technology to classify cases of hypoxia into three cases: normal, suspicious, or pathological. Furthermore, Machine Learning (ML) is a blossoming technique constantly developing and aiding in medical studies, particularly fetal health prediction. Notwithstanding the past endeavors of health providers to detect hypoxia in fetuses, implementing ML and Deep Learning (DL) techniques ensures more timely and precise detection of fetal hypoxia by efficiently and accurately processing complex patterns in large datasets. Correspondingly, this review paper aims to explore the application of artificial intelligence models using cardiotocographic test data. The anticipated outcome of this review is to introduce guidance for future studies to enhance accuracy in detecting cases categorized within the suspicious class, an aspect that has encountered challenges in previous studies that holds significant implications for obstetricians in effectively monitoring fetal health and making informed decisions.

Advancements in Text-to-Video Creation through AI Models: A Comprehensive Review

This review paper explores the dynamic landscape of text-to-video creation facilitated by Artificial Intelligence (AI) models. Examining the intersection of Natural Language Processing (NLP) and Computer Vision (CV), we delve into methodologies, challenges, and advancements shaping this evolving field. From traditional rule-based systems to advanced deep learning architectures like GPT and CLIP, the paper navigates through the diverse spectrum of AI models driving text-to-video synthesis. Challenges, such as context preservation and ethical considerations, are discussed, along with practical applications spanning entertainment, education, and communication. A comparative analysis evaluates the strengths and limitations of different models, offering insights for optimal usage. Looking forward, the paper explores future directions, emphasizing collaborative efforts and ethical considerations. This comprehensive review provides a valuable resource for those engaged in the intersection of NLP, CV, and AI-driven text-to-video technologies. This review delves into AI's role in text-to-video conversion, exploring methods like computer vision and natural language processing. It examines advancements, challenges, and implications in diverse sectors such as marketing, education, and entertainment.

Scalable Video Fidelity Enhancement: Leveraging the state-of-the-art AI Models

Improving visual quality is crucial as we navigate through the vast world of data. State-of-the-art (SOTA) artificial intelligence (AI) models provide highly effective solutions. Driven by the ever-growing demand for high-fidelity multimedia content, this research explores the groundbreaking capabilities of SOTA AI models to revolutionize video quality enhancement. Existing video capture methods often struggle with limitations in hardware, bandwidth, and compression, leading to subpar visual experiences. To address this challenge, we propose a novel Video Quality Enhancement Solution (VQES) that synergistically combines Google FILM for frame interpolation and Real-ESRGAN for image super-resolution. By applying these models to each video frame and integrating scalable post-processing techniques, a comprehensive VQES has been devised. Extensive experiments demonstrate that our VQES outperforms existing methods in terms of peak signal-to-noise ratio (PSNR) improvement and user-perceived visual quality. By advancing video fidelity, this research paves the way for consistently immersive, informative, and enjoyable visual experiences.

Prospective validation of a seizure diary forecasting falls short.

Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median = 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSSs) compared random forecasts and simple moving average forecasts to the AI. The AI had an area under the receiver operating characteristic curve of .82. At the group level, the AI outperformed random forecasting (BSS = .53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (nonverified) diaries (with presumed underreporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor-quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. The previously developed AI forecasting tool did not outperform a very simple moving average forecasting in this prospective cohort, suggesting that the AI model should be replaced.

Tool Condition Monitoring Using Machine Tool Spindle Current and Long Short-Term Memory Neural Network Model Analysis

In cutting processes, tool condition affects the quality of the manufactured parts. As such, an essential component to prevent unplanned downtime and to assure machining quality is having information about the state of the cutting tool. The primary function of it is to alert the operator that the tool has reached or is reaching a level of wear beyond which behaviour is unreliable. In this paper, the tool condition is being monitored by analysing the electric current on the main spindle via an artificial intelligence model utilising an LSTM neural network. In the current study, the tool is monitored while working on a cylindrical raw piece made of AA6013 aluminium alloy with a custom polycrystalline diamond tool for the purposes of monitoring the wear of these tools. Spindle current characteristics were obtained using external measuring equipment to not influence the operation of the machine included in a larger production line. As a novel approach, an artificial intelligence model based on an LSTM neural network is utilised for the analysis of the spindle current obtained during a manufacturing cycle and assessing the tool wear range in real time. The neural network was designed and trained to notice significant characteristics of the captured current signal. The conducted research serves as a proof of concept for the use of an LSTM neural network-based model as a method of monitoring the condition of cutting tools.

ChatGPT/GPT-4 (large language models): Opportunities and challenges of perspective in bariatric healthcare professionals.

ChatGPT/GPT-4 is a conversational large language model (LLM) based on artificial intelligence (AI). The potential application of LLM as a virtual assistant for bariatric healthcare professionals in education and practice may be promising if relevant and valid issues are actively examined and addressed. In general medical terms, it is possible that AI models like ChatGPT/GPT-4 will be deeply integrated into medical scenarios, improving medical efficiency and quality, and allowing doctors more time to communicate with patients and implement personalized health management. Chatbots based on AI have great potential in bariatric healthcare and may play an important role in predicting and intervening in weight loss and obesity-related complications. However, given its potential limitations, we should carefully consider the medical, legal, ethical, data security, privacy, and liability issues arising from medical errors caused by ChatGPT/GPT-4. This concern also extends to ChatGPT/GPT -4's ability to justify wrong decisions, and there is an urgent need for appropriate guidelines and regulations to ensure the safe and responsible use of ChatGPT/GPT-4.

Artificial Intelligence in Academic Translation: A Comparative Study of Large Language Models and Google Translate

Purpose. The advent of Large Language Model (LLM), a generative artificial intelligence (AI) model, in November 2022 has had a profound impact on various domains, including the field of translation studies. This motivated this study to conduct a rigorous evaluation of the effectiveness and precision of machine translation, represented by Google Translate (GT), in comparison to Large Language Models (LLMs), specifically ChatGPT 3.5 and 4, when translating academic abstracts bidirectionally between English and Arabic. &#x0D; Methods. Employing a mixed-design approach, this study utilizes a corpus comprising 20 abstracts sourced from peer-reviewed journals indexed in the Clarivate Web of Science, specifically the Journal of Arabic Literature and Al-Istihlal Journal. The abstracts are equally divided to represent both English-Arabic and Arabic-English translation directionality. The study’s design is rooted in a comprehensive evaluation rubric adapted from Hurtado Albir and Taylor (2015), focusing on semantic integrity, syntactic coherence, and technical adequacy. Three independent raters carried out assessments of the translation outputs generated by both GT and LLM models. &#x0D; Results. Results from quantitative and qualitative analyses indicated that LLM tools significantly outperformed MT outputs in both Arabic and English translation directions. Additionally, ChatGPT 4 demonstrated a significant advantage over ChatGPT 3.5 in Arabic-English translation, while no statistically significant difference was observed in the English-Arabic translation directionality. Qualitative analysis findings indicated that AI tools exhibited the capacity to comprehend contextual nuances, recognize city names, and adapt to the target language's style. Conversely, GT displayed limitations in handling specific contextual aspects and often provided literal translations for certain terms.

Using ML to Predict User Satisfaction with ICT Technology for Educational Institution Administration

Effective and efficient use of information and communication technology (ICT) systems in the administration of educational organisations is crucial to optimise their performance. Earlier research on the identification and analysis of ICT users’ satisfaction with administration tasks in education is limited and inconclusive, as they focus on using ICT for nonadministrative tasks. To address this gap, this study employs Artificial Intelligence (AI) and machine learning (ML) in conjunction with a survey technique to predict the satisfaction of ICT users. In doing so, it provides an insight into the key factors that impact users’ satisfaction with the ICT administrative systems. The results reveal that AI and ML models predict ICT user satisfaction with an accuracy of 94%, and identify the specific ICT features, such as usability, privacy, security, and Information Technology (IT) support as key determinants of satisfaction. The ability to predict user satisfaction is important as it allows organisations to make data-driven decisions on improving their ICT systems to better meet the needs and expectations of users, maximising labour effort while minimising resources, and identifying potential issues earlier. The findings of this study have important implications for the use of ML in improving the administration of educational institutions and providing valuable insights for decision-makers and developers.

A digital twin of the infant microbiome to predict neurodevelopmental deficits.

Despite the recognized gut-brain axis link, natural variations in microbial profiles between patients hinder definition of normal abundance ranges, confounding the impact of dysbiosis on infant neurodevelopment. We infer a digital twin of the infant microbiome, forecasting ecosystem trajectories from a few initial observations. Using 16S ribosomal RNA profiles from 88 preterm infants (398 fecal samples and 32,942 abundance estimates for 91 microbial classes), the model (Q-net) predicts abundance dynamics with R2 = 0.69. Contrasting the fit to Q-nets of typical versus suboptimal development, we can reliably estimate individual deficit risk (Mδ) and identify infants achieving poor future head circumference growth with ≈76% area under the receiver operator characteristic curve, 95% ± 1.8% positive predictive value at 98% specificity at 30 weeks postmenstrual age. We find that early transplantation might mitigate risk for ≈45.2% of the cohort, with potentially negative effects from incorrect supplementation. Q-nets are generative artificial intelligence models for ecosystem dynamics, with broad potential applications.

AI guided workflows for efficiently screening the materials space

Artificial intelligence (AI) may capture the properties and functions of materials better than previous theoretical/computational methods because it targets correlations and does not assume a single, specific underlying physical model. Therefore, it addresses the full intricacy of the numerous processes that govern the function of materials. However, the statistical analysis and interpretation of AI models require careful attention. The review article started with a brief discussion of historical aspects of data-centric science. It then focused on the recently developed, explainable AI methods [8,10] and applications [2,11,12]. The identified "rules" determine the properties and functions of materials. The rules depend on descriptive parameters called "materials genes." As genes in biology, they are correlated with a certain material property or function. Thus, these materials genes help to identify materials that are, for example, better electrical conductors or better heat insulators or better catalysts.

Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles

Human factors are a primary cause of vehicle accidents. Driver monitoring systems, utilizing a range of sensors and techniques, offer an effective method to monitor and alert drivers to minimize driver error and reduce risky driving behaviors, thus helping to avoid Safety Critical Events (SCEs) and enhance overall driving safety. Artificial Intelligence (AI) tools, in particular, have been widely investigated to improve the efficiency and accuracy of driver monitoring or analysis of SCEs. To better understand the state-of-the-art practices and potential directions for AI tools in this domain, this work is an inaugural attempt to consolidate AI-related tools from academic and industry perspectives. We include an extensive review of AI models and sensors used in driver gaze analysis, driver state monitoring, and analyzing SCEs. Furthermore, researchers identified essential AI tools, both in academia and industry, utilized for camera-based driver monitoring and SCE analysis, in the market. Recommendations for future research directions are presented based on the identified tools and the discrepancies between academia and industry in previous studies. This effort provides a valuable resource for researchers and practitioners seeking a deeper understanding of leveraging AI tools to minimize driver errors, avoid SCEs, and increase driving safety.

Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review

Background and Objectives: The availability of multiple dental implant systems makes it difficult for the treating dentist to identify and classify the implant in case of inaccessibility or loss of previous records. Artificial intelligence (AI) is reported to have a high success rate in medical image classification and is effectively used in this area. Studies have reported improved implant classification and identification accuracy when AI is used with trained dental professionals. This systematic review aims to analyze various studies discussing the accuracy of AI tools in implant identification and classification. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study was registered with the International Prospective Register of Systematic Reviews (PROSPERO). The focused PICO question for the current study was “What is the accuracy (outcome) of artificial intelligence tools (Intervention) in detecting and/or classifying the type of dental implant (Participant/population) using X-ray images?” Web of Science, Scopus, MEDLINE-PubMed, and Cochrane were searched systematically to collect the relevant published literature. The search strings were based on the formulated PICO question. The article search was conducted in January 2024 using the Boolean operators and truncation. The search was limited to articles published in English in the last 15 years (January 2008 to December 2023). The quality of all the selected articles was critically analyzed using the Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2). Results: Twenty-one articles were selected for qualitative analysis based on predetermined selection criteria. Study characteristics were tabulated in a self-designed table. Out of the 21 studies evaluated, 14 were found to be at risk of bias, with high or unclear risk in one or more domains. The remaining seven studies, however, had a low risk of bias. The overall accuracy of AI models in implant detection and identification ranged from a low of 67% to as high as 98.5%. Most included studies reported mean accuracy levels above 90%. Conclusions: The articles in the present review provide considerable evidence to validate that AI tools have high accuracy in identifying and classifying dental implant systems using 2-dimensional X-ray images. These outcomes are vital for clinical diagnosis and treatment planning by trained dental professionals to enhance patient treatment outcomes.

Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images.

Can generative artificial intelligence (AI) models produce high-fidelity images of human blastocysts? Generative AI models exhibit the capability to generate high-fidelity human blastocyst images, thereby providing substantial training datasets crucial for the development of robust AI models. The integration of AI into IVF procedures holds the potential to enhance objectivity and automate embryo selection for transfer. However, the effectiveness of AI is limited by data scarcity and ethical concerns related to patient data privacy. Generative adversarial networks (GAN) have emerged as a promising approach to alleviate data limitations by generating synthetic data that closely approximate real images. Blastocyst images were included as training data from a public dataset of time-lapse microscopy (TLM) videos (n = 136). A style-based GAN was fine-tuned as the generative model. We curated a total of 972 blastocyst images as training data, where frames were captured within the time window of 110-120 h post-insemination at 1-h intervals from TLM videos. We configured the style-based GAN model with data augmentation (AUG) and pretrained weights (Pretrained-T: with translation equivariance; Pretrained-R: with translation and rotation equivariance) to compare their optimization on image synthesis. We then applied quantitative metrics including Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) to assess the quality and fidelity of the generated images. Subsequently, we evaluated qualitative performance by measuring the intelligence behavior of the model through the visual Turing test. To this end, 60 individuals with diverse backgrounds and expertise in clinical embryology and IVF evaluated the quality of synthetic embryo images. During the training process, we observed consistent improvement of image quality that was measured by FID and KID scores. Pretrained and AUG + Pretrained initiated with remarkably lower FID and KID values compared to both Baseline and AUG + Baseline models. Following 5000 training iterations, the AUG + Pretrained-R model showed the highest performance of the evaluated five configurations with FID and KID scores of 15.2 and 0.004, respectively. Subsequently, we carried out the visual Turing test, such that IVF embryologists, IVF laboratory technicians, and non-experts evaluated the synthetic blastocyst-stage embryo images and obtained similar performance in specificity with marginal differences in accuracy and sensitivity. In this study, we primarily focused the training data on blastocyst images as IVF embryos are primarily assessed in blastocyst stage. However, generation of an array of images in different preimplantation stages offers further insights into the development of preimplantation embryos and IVF success. In addition, we resized training images to a resolution of 256 × 256 pixels to moderate the computational costs of training the style-based GAN models. Further research is needed to involve a more extensive and diverse dataset from the formation of the zygote to the blastocyst stage, e.g. video generation, and the use of improved image resolution to facilitate the development of comprehensive AI algorithms and to produce higher-quality images. Generative AI models hold promising potential in generating high-fidelity human blastocyst images, which allows the development of robust AI models as it can provide sufficient training datasets while safeguarding patient data privacy. Additionally, this may help to produce sufficient embryo imaging training data with different (rare) abnormal features, such as embryonic arrest, tripolar cell division to avoid class imbalances and reach to even datasets. Thus, generative models may offer a compelling opportunity to transform embryo selection procedures and substantially enhance IVF outcomes. This study was supported by a Horizon 2020 innovation grant (ERIN, grant no. EU952516) and a Horizon Europe grant (NESTOR, grant no. 101120075) of the European Commission to A.S. and M.Z.E., the Estonian Research Council (grant no. PRG1076) to A.S., and the EVA (Erfelijkheid Voortplanting & Aanleg) specialty program (grant no. KP111513) of Maastricht University Medical Centre (MUMC+) to M.Z.E. Not applicable.