Artificial Intelligence Algorithms Research Articles

Data-Driven Decisions: A Systematic Review of Artificial Intelligence and Machine Learning in Cleft Orthognathic Surgery

Introduction: In recent times, there has been a growing interest in the integration of artificial intelligence (AI) and machine learning (ML) into the realm of cleft orthognathic surgery, presenting an exciting avenue for transformative innovations. These technologies offer the promise of optimizing treatment plans, facilitating surgical decision-making, and contributing to a more patient-centric approach. However, a systematic and in-depth exploration of the existing literature is essential to discern the true impact, challenges, and potential future directions of AI and ML in this specialized field. The present systematic review aimed to provide an overview of AI and ML algorithms and their applications in cleft orthognathic surgery. Methodology: A comprehensive search was conducted in databases using MeSH terms and other relevant terms including PubMed, Embase, and Scopus until January 2024. This systematic review was conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Results: The search strategy resulted in a total of 124 articles. After applying the inclusion and exclusion criteria, a total of 5 studies were included for final review. AI has profoundly impacted the prediction of the need for orthognathic surgeries in cleft patients using cephalometric variables with a clinically acceptable accuracy range. Also, provide guidelines to determine the amount and direction of movements of the maxilla and mandible. Conclusions: Understanding the role of AI and ML in cleft orthognathic surgery is paramount for clinicians, researchers, and policymakers alike.AI reduces the work burden of the clinician by eliminating the tedious registration procedures, thereby helping in efficient and automated planning.

Exploring the synergy between generative AI and software engineering: Automating code optimization and bug fixing

As applied to software engineering, generative AI is quickly transitioning from a zero-sum industry game changer into the primary automation tool for code optimization, bug identification, and problem-solving. This technology takes advantage of artificial intelligence algorithms within machine learning models to analyze and write code, resulting in improved quality and speed of an application development process. The generative AI replenishes productivity in development work and enhances centralization between development work teams through code handling and intelligent suggestions for essential codes. However, the integration of AI in software engineering poses the following problems and ethical questions: the question of accuracy, bias, and data. This paper will review the existing knowledge on generative AI in software engineering regarding its current use, future evolutions and advancements, issues and limitations, and ethical factors in using this technology. This paper considers these aspects to give a global outlook on how generative AI will transform software development in the future and how responsible AI should be employed.

Radiomics in precision medicine for colorectal cancer: a bibliometric analysis (2013–2023)

BackgroundThe incidence and mortality of colorectal cancer (CRC) have been rising steadily. Early diagnosis and precise treatment are essential for improving patient survival outcomes. Over the past decade, the integration of artificial intelligence (AI) and medical imaging technologies has positioned radiomics as a critical area of research in the diagnosis, treatment, and prognosis of CRC.MethodsWe conducted a comprehensive review of CRC-related radiomics literature published between 1 January 2013 and 31 December 2023 using the Web of Science Core Collection database. Bibliometric tools such as Bibliometrix, VOSviewer, and CiteSpace were employed to perform an in-depth bibliometric analysis.ResultsOur search yielded 1,226 publications, revealing a consistent annual growth in CRC radiomics research, with a significant rise after 2019. China led in publication volume (406 papers), followed by the United States (263 papers), whereas the United States dominated in citation numbers. Notable institutions included General Electric, Harvard University, University of London, Maastricht University, and the Chinese Academy of Sciences. Prominent researchers in this field are Tian J from the Chinese Academy of Sciences, with the highest publication count, and Ganeshan B from the University of London, with the most citations. Journals leading in publication and citation counts are Frontiers in Oncology and Radiology. Keyword and citation analysis identified deep learning, texture analysis, rectal cancer, image analysis, and management as prevailing research themes. Additionally, recent trends indicate the growing importance of AI and multi-omics integration, with a focus on improving precision medicine applications in CRC. Emerging keywords such as deep learning and AI have shown rapid growth in citation bursts over the past 3 years, reflecting a shift toward more advanced technological applications.ConclusionsRadiomics plays a crucial role in the clinical management of CRC, providing valuable insights for precision medicine. It significantly contributes to predicting molecular biomarkers, assessing tumor aggressiveness, and monitoring treatment efficacy. Future research should prioritize advancing AI algorithms, enhancing multi-omics data integration, and further expanding radiomics applications in CRC precision medicine.

How Explainable Artificial Intelligence Can Increase or Decrease Clinicians' Trust in AI Applications in Health Care: Systematic Review.

Artificial intelligence (AI) has significant potential in clinical practice. However, its "black box" nature can lead clinicians to question its value. The challenge is to create sufficient trust for clinicians to feel comfortable using AI, but not so much that they defer to it even when it produces results that conflict with their clinical judgment in ways that lead to incorrect decisions. Explainable AI (XAI) aims to address this by providing explanations of how AI algorithms reach their conclusions. However, it remains unclear whether such explanations foster an appropriate degree of trust to ensure the optimal use of AI in clinical practice. This study aims to systematically review and synthesize empirical evidence on the impact of XAI on clinicians' trust in AI-driven clinical decision-making. A systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, searching PubMed and Web of Science databases. Studies were included if they empirically measured the impact of XAI on clinicians' trust using cognition- or affect-based measures. Out of 778 articles screened, 10 met the inclusion criteria. We assessed the risk of bias using standard tools appropriate to the methodology of each paper. The risk of bias in all papers was moderate or moderate to high. All included studies operationalized trust primarily through cognitive-based definitions, with 2 also incorporating affect-based measures. Out of these, 5 studies reported that XAI increased clinicians' trust compared with standard AI, particularly when the explanations were clear, concise, and relevant to clinical practice. In addition, 3 studies found no significant effect of XAI on trust, and the presence of explanations does not automatically improve trust. Notably, 2 studies highlighted that XAI could either enhance or diminish trust, depending on the complexity and coherence of the provided explanations. The majority of studies suggest that XAI has the potential to enhance clinicians' trust in recommendations generated by AI. However, complex or contradictory explanations can undermine this trust. More critically, trust in AI is not inherently beneficial, as AI recommendations are not infallible. These findings underscore the nuanced role of explanation quality and suggest that trust can be modulated through the careful design of XAI systems. Excessive trust in incorrect advice generated by AI can adversely impact clinical accuracy, just as can happen when correct advice is distrusted. Future research should focus on refining both cognitive and affect-based measures of trust and on developing strategies to achieve an appropriate balance in terms of trust, preventing both blind trust and undue skepticism. Optimizing trust in AI systems is essential for their effective integration into clinical practice.

LSTM‐based real‐time stress detection using PPG signals on raspberry Pi

AbstractStress, widely recognised for its profound adverse effects on both physical and mental health, necessitates the development of innovative real‐time detection methods. In this context, the escalating prevalence of wearable embedded systems, integrated with artificial intelligence (AI) for the continuous monitoring of critical physiological indicators like heart rate and blood pressure, accentuates their growing relevance in the efficient detection of stress. This article presents an innovative methodology employing deep learning algorithms on the Raspberry Pi 3, a platform distinguished by its cost‐effectiveness and limited resources. The authors have developed an advanced AI algorithm that achieves high accuracy in real‐time stress detection using photoplethysmography (PPG) sensors while significantly reducing computational demands. The authors’ method utilises an artificial neural network with long short‐term memory (LSTM) layers, proving highly effective in time‐series data analysis. In this study, the authors implement key TensorFlow toolkit optimisation techniques including quantisation aware training (QAT), Pruning and prune‐preserving quantisation aware training. These techniques are applied to refine the authors’ model, decreasing size and latency without sacrificing accuracy. The results highlight the LSTM‐based model's proficiency in accurately detecting stress using raw PPG sensor data on the Raspberry Pi 3, a comparatively affordable platform. The model attains an accuracy of 89.32% and an F1 score of 89.55% on the diverse wearable stress and affect detection stress‐level dataset. Additionally, the authors’ optimised model exhibits substantial reductions in both size and latency while maintaining high accuracy. This approach shows great potential for various applications, such as stress monitoring in healthcare, sports, and workplace settings. The use of the Raspberry Pi 3 makes the system portable, cost‐effective, and energy‐efficient, enhancing its potential impact and accessibility.

Artificial Intelligence Algorithms in Cardiovascular Medicine: An Attainable Promise to Improve Patient Outcomes or an Inaccessible Investment?

This opinion paper highlights the advancements in artificial intelligence (AI) technology for cardiovascular disease (CVD), presents best practices and transformative impacts, and addresses current concerns that must be resolved for broader adoption. With the evolution of digitization in data collection, large amounts of data have become available, surpassing the human capacity for processing and analysis, thus enabling the application of AI. These models can learn complex spatial and temporal patterns from large amounts of data, providing patient-specific outputs. These advantages have resulted, at the moment, in more than 900 AI-based devices being approved, today, by regulatory entities, for clinical use, with similar to improved performance and efficiency compared to traditional technologies. However, issues such as model generalization, bias, transparency, interpretability, accountability, and data privacy remain significant barriers for broad adoption of these technologies. AI shows great promise in enhancing CVD care through more accurate and efficient approaches. Yet, widespread adoption is hindered by unresolved concerns of interested stakeholders. Addressing these challenges is crucial for fully integrating AI into clinical practice and shaping the future of CVD prevention, diagnosis and treatment.

Morphometric analysis and tortuosity typing of the large intestine segments on computed tomography colonography with artificial intelligence

Background:Morphological properties such as length and tortuosity of the large intestine segments play important roles, especially in interventional procedures like colonoscopy. Objective:Using computed tomography (CT) colonoscopy images, this study aimed to examine the morphological features of the colon's anatomical sections and investigate the relationship of these sections with each other or with age groups. The shapes of the transverse colon were analyzed using artificial intelligence. Materials and Methods:The study was conducted as a two- and three-dimensional examination of CT colonography images of people between 40 and 80 years old, which were obtained retrospectively. An artificial intelligence algorithm (YOLOv8) was used for shape detection on 3D colon images. Results:160 people with a mean age of 89 men and 71 women included in the study was 57.79±8.55 and 56.55±6.60, respectively, and there was no statistically significant difference (p=0.24). The total colon length was 166.11±25.07 cm for men and 158.73±21.92 cm for women, with no significant difference between groups (p=0.12). As a result of the training of the model Precision, Recall, and mAP were found to be 0.8578, 0.7940, and 0.9142, respectively. Conclusion:The study highlights the importance of understanding the type and morphology of the large intestine for accurate interpretation of CT colonography results and effective clinical management of patients with suspected large intestine abnormalities. Furthermore, this study showed that 88.57% of the images in the test data set were detected correctly and that AI can play an important role in colon typing.

Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction.

The first line of treatment for urinary incontinence is pelvic floor muscle (PFM) training, aimed at reducing leakage episodes by strengthening these muscles. However, many women struggle with performing correct PFM contractions or have misconceptions about their contractions. To address this issue, we present a novel PFM contraction quality assessment system. This system combines a PFM contraction detector with a maximal PFM contraction performance classifier. The contraction detector first identifies whether or not a PFM contraction was performed. Then, the contraction classifier autonomously quantifies the quality of maximal PFM contractions across different features, which are also combined into an overall rating. Both algorithms are based on artificial intelligence (AI) methods. The detector relies on a convolutional neural network, while the contraction classifier uses a custom feature extractor followed by a random forest classifier to predict the strength rating based on the modified Oxford scale. The AI algorithms were trained and tested using datasets measured by vaginal dynamometry, combined in some cases with digital assessment results from expert physiotherapists. The contraction detector was trained on one dataset and then tested on two datasets measured with different dynamometers, achieving 97% accuracy on the first dataset and 100% accuracy on the second. For the contraction performance classifier, the results demonstrate that important clinical features can be extracted automatically with an acceptable error. Furthermore, the contraction classifier is able to predict the strength rating within a ±1 scale point with 97% accuracy. These results demonstrate the system's potential to enhance PFM training and rehabilitation by enabling women to monitor and improve their PFM contractions autonomously.

Social sustainability: Viability analysis of social firms

AbstractSocial enterprises are firms that actively create jobs for people with disabilities and reduce social and labour inequalities, thereby participating in the social economy. Due to their importance to society and the work they do towards diversity integration and social sustainability, they have recently attracted much attention in academic literature. For this reason, the main objective of this study was to analyse the survival of social enterprises, identifying key variables that condition their continuity in the market or their failure. The initial sample consisted of 999 social enterprises for the year 2022. The Altman Z‐score and artificial intelligence (AI) algorithms were used to obtain the basic survival patterns. The main findings were that, on average, social enterprises are highly experienced companies and only one‐third of them are at risk of bankruptcy. This means that most of these enterprises can continue their social function. Moreover, the results of return on assets (ROA), equity and debt turnover can predict being at risk of bankruptcy of social enterprises. This study contributes to the scarce academic literature on social enterprises and promotes the existence of such enterprises for social and economic sustainability.

An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images

BackgroundThis study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs.MethodsNon-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong’s test was used to compare the CPIs group with the VMIs group.ResultsWhen distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong’s test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05).ConclusionThe AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.

A Survey of Artificial Intelligence Applications in Nuclear Power Plants

Nuclear power plants (NPPs) rely on critical, complex systems that require continuous monitoring to ensure safe operation under both normal and abnormal conditions. Despite the potential of artificial intelligence (AI) to enhance predictive capabilities in these systems, limited research has been conducted on the application of AI algorithms within NPPs. This presents a knowledge gap in the integration of AI for improving safety, reliability, and decision making in NPP. In this study, we explore the use of AI methods, including machine learning and real-time data analytics, applied to NPP components to address the nonlinearity and dynamic behavior inherent in reactor operations. Through the implementation of AI and Internet of Things (IoT) devices, we propose a system that enables early warning and real-time data transmission to regulatory authorities and decision-makers, ensuring better coordination during incidents. Lessons from past nuclear accidents, such as Chernobyl, emphasize the importance of timely information dissemination to mitigate risks. However, this integration also presents challenges, including cybersecurity risks and the need for updated regulations to address AI use in safety-critical environments. The results of this study highlight the urgent need for further research on the application of AI in NPPs, with a particular focus on addressing these challenges to ensure safe implementation.

Unveiling the Power of AI-Driven Personalization: Transforming Consumer Behavior in the Age of Digital Marketing

Artificial intelligence (AI) is revolutionizing digital marketing, with AI-driven personalization emerging as a powerful tool for shaping consumer experiences and driving engagement. This study explores how AI-powered technologies, such as recommendation engines, dynamic content delivery, and targeted advertisements, are transforming consumer behavior in the digital landscape. By leveraging big data, machine learning, and predictive analytics, marketers are now able to deliver hyper-personalized experiences that cater to individual preferences, leading to enhanced customer satisfaction and increased conversion rates. This research examines the impact of AI-driven personalization on key aspects of consumer behavior, including decision-making processes, brand loyalty, and purchase intentions. Additionally, it addresses the ethical challenges that arise from the use of AI in marketing, particularly around data privacy, algorithmic bias, and consumer trust. Through case studies from various industries, this study provides a comprehensive analysis of how personalization influences consumer psychology and purchasing patterns. Ultimately, the research offers valuable insights into the benefits and risks of integrating AI into marketing strategies, helping businesses understand how to harness the power of AI while maintaining ethical standards and safeguarding customer relationships in an increasingly digital world. Moreover, the study delves into the nuances of AI-driven personalization by exploring how consumers respond differently based on demographic factors such as age, gender, and cultural background. By analyzing customer data in real-time, AI allows for more accurate segmentation, ensuring that marketing messages are highly relevant and resonate with individual consumers. This level of precision not only increases customer engagement but also fosters a sense of exclusivity, making consumers feel more valued and understood by the brand. The research also investigates the role of AI in predicting future consumer behavior, using historical data to forecast trends and preferences, thereby enabling brands to stay ahead of the competition. As AI algorithms continuously learn and adapt, they can refine their recommendations, becoming more effective over time. However, there are concerns about over-reliance on AI, as excessive personalization may lead to "filter bubbles," where consumers are only exposed to a limited range of products or content, potentially stifling creativity and innovation.

Revolutionizing Radiology With Artificial Intelligence.

Artificial intelligence (AI) is rapidly transforming the field of radiology, offering significant advancements in diagnostic accuracy, workflow efficiency, and patient care. This article explores AI's impact on various subfields of radiology, emphasizing its potential to improve clinical practices and enhance patient outcomes. AI-driven technologies such as machine learning, deep learning, and natural language processing (NLP) are playing a pivotal role in automating routine tasks, aiding in early disease detection, and supporting clinical decision-making, allowing radiologists to focus on more complex diagnostic challenges. Key applications of AI in radiology include improving image analysis through computer-aided diagnosis (CAD) systems, which enhance the detection of abnormalities in imaging, such as tumors. AI tools have demonstrated high accuracy in analyzing medical images, integrating data from multiple imaging modalities such as CT, MRI, and PET to provide comprehensive diagnostic insights. These advancements facilitate personalized treatment planning and complement radiologists' workflows. However, for AI to be fully integrated into radiology workflows, several challenges must be addressed, including ensuring transparency in how AI algorithms work, protecting patient data, and avoiding biases that could affect diverse populations. Developing explainable AI systems that can clearly show how decisions are made is crucial, as is ensuring AI tools can seamlessly fit into existing radiology systems. Collaboration between radiologists, AI developers, and policymakers, alongside strong ethical guidelines and regulatory oversight, will be key to ensuring AI is implemented safely and effectively in clinical practice. Overall, AI holds tremendous promise in revolutionizing radiology. Through its ability to automate complex tasks, enhance diagnostic capabilities, and streamline workflows, AI has the potential to significantly improve the quality and efficiency of radiology practices. Continued research, development, and collaboration will be crucial in unlocking AI's full potential and addressing the challenges that accompany its adoption.

Harnessing AI and Predictive Analytics to Revolutionize Customer Retention Strategies

Artificial Intelligence (AI) and predictive analytics are rapidly transforming how businesses approach customer retention, enabling more proactive and personalized strategies. This research investigates the role of AI-driven predictive analytics in identifying at-risk customers, forecasting churn, and optimizing retention efforts across various industries. By analyzing historical data, machine learning models can accurately predict future customer behavior, enabling businesses to implement targeted retention strategies such as personalized offers, timely engagement, and customized support. This study explores how AI-powered tools enhance customer segmentation, allowing marketers to identify the factors that contribute to customer attrition and loyalty. Moreover, the research delves into the use of predictive analytics in monitoring customer interactions, identifying warning signs of dissatisfaction, and responding with interventions before churn occurs. Ethical concerns, such as the balance between personalization and privacy, will also be addressed, particularly regarding how businesses can maintain consumer trust while leveraging personal data for predictive purposes. Through case studies and industry analysis, this research aims to demonstrate the significant advantages of incorporating AI and predictive analytics into customer retention strategies. It will provide insights into best practices for utilizing these technologies to enhance customer loyalty, improve satisfaction, and ultimately drive sustainable business growth. this research examines the practical applications of AI and predictive analytics across different business sectors, such as e-commerce, telecommunications, and financial services. By integrating AI-powered tools into customer relationship management (CRM) systems, companies can develop more effective retention strategies that are both data-driven and customer-centric. This includes deploying AI algorithms to monitor customer lifetime value (CLV) and predict the likelihood of repeat purchases, helping businesses prioritize high-value customers while minimizing churn rates among at-risk segments.

Artificial intelligence for automated left ventricular systolic dysfunction detection using a wearable cardiac patch incorporated with synchronized phonocardiogram and electrocardiogram

Abstract Background Left ventricular systolic dysfunction (LVSD) is characterized by a reduced left ventricular ejection fraction (LVEF) and is associated with three times the risk of developing Heart failure (HF). We aimed to test an artificial intelligence (AI) algorithm applied to synchronized phonocardiography (PCG) and electrocardiography (ECG) signals, recorded with a wearable device, to validate its potential as a screening tool for LVSD. Methods Using ECG and PCG data collected from 1960 admitted patients in a hospital, we trained an AI algorithm to detect the LVSD. There are two ways of defining LVSD, one group is defined by LVEF &amp;lt;50% and the other is defined by LVEF 40% as measured by echocardiography. The AI algorithm followed a two-step detection structure. In the first phase, ECG signals were processed by wavelet denoising and baseline wander correction, and PCG signals were converted to the frequency domain via wavelet transformation. We developed a 1D CNN-based U-Net variant model to pinpoint electromechanical activation time (EMAT). Addressing the challenge of processing multimodal signals, we devised a contrastive learning approach that mandates the encoders for different modalities to generate embeddings within the same feature space. This trained encoder, along with the decoder in U-Net, is subsequently fine-tuned for EMAT detection. The AI-EMAT% was obtained by adjusting detected EMAT based on RR interval. For the second phase, random forest regression is used to estimate LVEF, incorporating features such as AI-EMAT% and clinical data, including age and gender. For LVSD detection, we evaluated the performance of methods by bifurcating AI-estimated LVEF% (AI-predict LVEF%) based on thresholds of 40% and 50%, and by directly estimating AI-EMAT% cutoff. The evaluation of LVSD detection algorithm, conducted via 2-fold cross-validation, involves dividing the dataset into two subsets, with each alternately serving as training and testing sets to ensure a fair assessment. Results We recruited 1960 patients (1430 [72.96%] male). 357 (18.21%) had an ejection fraction of 50% or lower, and 157 (8.01%) had an ejection fraction of 40% or lower. In detecting LVSD of LVEF&amp;lt;50%, AI-EMAT% yielded an AUROC of 0.86, sensitivity of 73.7%, and specificity of 80.7%. While AI-predict LVEF% approach resulted in an AUROC of 0.89, sensitivity of 81.8%, and specificity of 81.5%. In detecting LVSD of LVEF&amp;lt;40%, AI-EMAT% yielded an AUROC of 0.89, sensitivity of 85.4%, and specificity of 76.0%. While AI-predict LVEF% outputs resulted in an AUROC of 0.91, sensitivity of 92.4%, and specificity of 76.8%. Conclusions We developed an automated algorithm to detect LVSD using a wearable device that incorporated synchronized PCG and ECG signals, demonstrating robust performance across different subgroups. This approach represents a strategy for automated screening of LV systolic dysfunction, particularly beneficial in resource-limited settings.Baseline and AUROCAI algorithm

Multi-modal artificial intelligence-based prediction for cardiovascular mortality after transcatheter aortic valve implantation: a time-to-event survival prediction

Abstract Background Predicting long-term outcomes following Transcatheter Aortic Valve Implantation (TAVI) presents a significant challenge. Objective We aimed to predict post-TAVI long-term cardiovascular mortality using multimodal data and employing time-to-event artificial intelligence (AI) algorithms. Methods This study enrolled 3,973 consecutive patients from the prospective TAVI registry (between August 2007 and June 2023). Multimodal information was collected and extracted as numerical data for each patient, encompassing clinical history, medication, laboratory, procedural characteristics, ECG, angiography, echocardiography (TTE and TEE), and CT. Clinical outcomes were adjudicated by an independent clinical event committee. From the multimodal information, 147 features were extracted at baseline before the TAVI procedure. The datasets were divided into a training/validation set (80%) and a hold-out test set (20%) for model development and evaluation, respectively. Cox-LASSO feature selection was employed to identify the most relevant features, followed by the implementation of Random Survival Forest (RSF), Gradient Boosting Survival Analysis (GBSA), and Survival Support Vector Machine (SSVM) to predict the timing and occurrence of cardiac death events in patients. Feature selection, model parameters, and hyperparameters selection were performed using 10-fold cross-validation on the training set. Model performance was evaluated using different metrics, such as the concordance index (c-index) and cumulative Area Under the Curve (C-AUC) on a 20% untouched test set. Results In the patient cohort, 28.4% experienced cardiovascular death. Features were selected from various modalities, including 22 from clinical history, two from medication, eight from laboratory tests, one from ECG, eight from CT scans, and five from echocardiography and angiography. In the univariate Cox-PH model, all features exhibited a C-index ranging from 0.50 to 0.57. In the test set, the conventional Multivariate Cox-PH model achieves a c-index of 0.59 (C-AUC: 0.56). RSF, GBSA, and SSVM achieve a C-index of 0.70 (C-AUC: 0.69), 0.66 (C-AUC: 0.64), and 0.69 (C-AUC: 0.68), respectively, in the holdout test set. Conclusion Integrating multimodal information, encompassing imaging, signal, laboratory data, and clinical history, with AI algorithms has markedly enhanced the prediction of long-term cardiac outcomes following the TAVI procedure. The initially developed model can predict the probability of cardiac death up to 10 years. Furthermore, the RFS model significantly outperformed the conventional Cox model in predicting time-to-event outcomes.

AI-driven ethical issues in the development of personalised therapy for pediatric SIRS patients

Abstract Background Systemic Inflammatory Response Syndrome (SIRS) is a rare disease often presenting emergency. While the aetiology varies, current diagnostic criteria do not allow differentiation. Hopes are placed on OMICs research and development of Artificial intelligence (AI) algorithms for personalised disease management. Our report aims at identification and analysis of AI-driven ethical issues within the bigger ERA PerMed funded project of development of tailored immunotherapy for paediatric SIRS patients (TIPS) coordinated by Prof. Catharina Schütz from Technic University Dresden. Methods Systematic literature review followed by qualitative research methodology. Ten experts with different backgrounds were interviewed. Subsequent thematic analysis was performed by two independent researchers. Results Experts were on the opinion that AI can lead to a depart from individual approach in the medicine, as the physician may be induced to only act based on algorithms that are more or less representative (N = 2). Interviewees were aware that AI can lead to changes in the assessment of reality, as it is dependent on data quality (N = 4). AI-aided decision-making might be useful for special therapeutic situations, although it may not always capture the subtleties of individual situations (N = 2). Positive implications of AI were significantly more emphasised, though current frameworks were perceived as more limiting than enabling. Conclusions Personalised therapy is making its first steps while the ethical apprehensions has already been there with a strong emphasis on precautionary principle and resulting limitations on the application of AI-algorithms. The multifaceted nature of the AI-driven ethical issues in personalised medicine research requires an interdisciplinary discourse, strengthened ethics training of all professionals involved in the technology development and more active involvement of physicians in the shaping of AI. Key messages • AI algorithms promise therapeutic benefits, not achievable by other means, for specific categories of vulnerable pediatrics patients. • The multifaceted nature of the AI-driven ethical issues in personalised medicine research requires an interdisciplinary discourse and further ethics training of all stakeholders.

Attention as the dimension that deteriorates first in patients with cognitive impairment after myocardial infarction

Abstract Background Previous publications by our team indicated a high prevalence (approximately 35%) of cognitive impairment (CI) in patients after myocardial infarction (MI). Moreover, we observed that the presence of peri-infarction deficits was not always associated with their occurrence after 6 months of follow-up and vice versa. This was the basis for distinguishing 4 groups of patients: a) group 1 – CI present peri-infarction and after 6 months; b) group 2 – CI present only peri-infarction; c) group 3 – CI present only after 6 months; d) group 4 – no CI during the study. Purpose The aim of the current study is to analyse the type of CI in patients after MI with a focus on its changes within the 4 groups mentioned. Methods The study included 326 patients hospitalised for MI treated by percutaneous coronary intervention. Cognitive function was assessed twice using the Mini–Mental State Examination (MMSE) and the Clock Drawing Test (CDT)-Shulman version during the first hospitalisation (on day 2-3 after MI) and 6 months later. In addition, the occurrence of sleep, aggression and mood disorders was assessed. The constructed research model was analysed with artificial intelligence algorithms. Results Persistent CI (group1) was found in 8.9%, peri-infarct CI (group 2) in 16.3%, and only after 6 months (group 3) in 7.7% of study participants (as assessed by MMSE). Analysis of the cognitive domains showed that in those who improve (group 2) or worsen cognitive function (group 3), analogous changes in attention function occurred, respectively improving in 81% and worsening in 100% of subjects in a given group. It was observed that there is a group of patients with a CI on the MMSE who perform CDT correctly, 12% peri-infarct and 11% at 6-month follow-up, respectively. Patients with a normal CDT score but CIs found in the MMSE have impaired attentional function regardless of the time of assessment. Conclusion CIs can occur both immediately after MI and during subsequent follow-up. However, after 6 months of intensive anti-atherosclerotic treatment (post-MI), cognitive functions improve in some patients, and deteriorate in others. At the same time, the cognitive function that undergoes similar changes in these groups is attention. The uniform type of impaired cognitive function and the lack of specific differences between the 4 groups allow us to assume a uniform etiology of cognitive deficits. Patients with normal CDT and disorders present in MMSE show deficits in attention functions. Performing CDT and using the MMSE component assessing attention function could prove sufficient for the initial assessment of cognitive functions in the population of patients after MI. The results require confirmation in a larger patient population.

Deep learning improves detection of aortic stenosis in b-mode ultrasound: results of a pivotal multicenter validation study using artificial intelligence

Abstract Background Aortic stenosis (AS) is a highly prevalent and morbid condition, with moderate or severe AS present in 9% of patients over 75 years. Despite improved diagnostic and therapeutic methods, AS remains underdiagnosed, particularly in Black patients and underserved communities. Echocardiography (echo) is effective for detecting AS but requires expertise to quantify and interpret valve gradients and valve area. We hypothesized that a deep learning (DL) algorithm could be trained to detect AS severity using B-mode (non-Doppler) echo alone, the modality most likely to be available in point-of-care ultrasound (POCUS) studies. Here, we present the results of a multicenter pivotal trial to test whether artificial intelligence (AI) can help increase the detection of AS from B-mode echoes. Methods A convolutional neural network (CNN) was trained to classify the severity of aortic stenosis from 3 B-mode echo views of the aortic valve (parasternal long- and short-axis and apical 5-chamber). The training dataset had over 500,000 cine loops from 29,527 echoes in 26,981 patients. A fully crossed multireader multicase study was conducted using 220 echoes never seen during algorithm development. 5 level II-trained cardiologists (referred to as POCUS readers as they only reviewed B-mode clips) were presented with these echoes twice at least a month apart, once with and again without AI interpretation of AS severity. Readers identified 2 dichotomous severity levels (either "suggestive of moderate to severe AS" or not), with ground truth established by 3 level III-trained cardiology echo experts. While experts had access to full studies (including Doppler) to adjudicate severity, POCUS readers had no Doppler or measurements of any kind available. Sensitivity and specificity were compared for the 5 POCUS readers when aided by the AI algorithm vs unaided, with similar tests of inter-reader agreement. Results There was a statistically significant improvement in sensitivity (+5.5%, 95% CI [1.5%, 9.5%], p=0.007) with stable specificity (-0.3%, 95% CI [-3.6%, 3.0%], p=NS) when the 5 POCUS readers received the assistance of the AI software. Fig 1 shows 2,500 bootstrapping assessments to better show consistency of these findings. In addition, the POCUS inter-reader agreement (overall percent agreement) improved by approximately 7% (p&amp;lt;0.001), becoming comparable to that of expert readers (Fig 2). Conclusions In a comprehensive multireader pivotal trial, an AI algorithm improved the performance of POCUS cardiologist readers to identify significant AS from limited B-mode echoes as might be obtained in screening or outreach settings. Sensitivity for detection of moderate-severe AS increased significantly by 5.5% with stable specificity. Inter-reader agreement improved to resemble that of expert cardiologists given access to full study information, including Doppler. AI may have a role in assisting detection of significant AS from limited B-mode echoes.Fig 1Fig 2

Barriers and facilitators for implementation of AI algorithms for ECG analysis and triage in patients with chest pain

Abstract Introduction Numerous artificial intelligence (AI) algorithms have been developed to assist in clinical decision-making, with a focus on areas like electrocardiogram (ECG) analysis due to the signal complexity and the varying levels of expertise among healthcare professionals. However, ensuring acceptance of these algorithms by end-users is paramount for successful implementation. Purpose This study aimed to identify barriers and facilitators related to implementation of an AI algorithm for ECG analysis and triage in patients with chest pain. Methods A three-round modified electronic Delphi study was conducted among Dutch potential end-users of an ECG-AI algorithm, including physicians, nurses and ambulance professionals. During the first round, participants brainstormed on barriers and facilitators, which were subsequently mapped to the Consolidated Framework for Implementation Research (CFIR). In the following two rounds, participants reviewed the identified barriers and facilitators for relevance and ranked them according to their importance. Results Twenty-five out of the initial forty respondents completed all three rounds, resulting in a dropout rate of 38%. Four barriers and twelve facilitators were identified. The most important facilitator was "Faster recognition of subtle ECG abnormalities", while "Poor model performance" emerged as the most important barrier. The most frequently mentioned corresponding CFIR domains were the innovation and inner setting domains. Conclusion The identification of barriers and facilitators provides valuable insights into the acceptance and utilisation of ECG-AI algorithms by end-users. By considering the CFIR domains, including innovation and inner setting, this study offers a comprehensive understanding of the contextual factors that influence the successful implementation of AI algorithms in healthcare settings. Addressing these barriers and facilitators will be crucial to facilitating future implementation processes.Barriers and facilitators