Search Results (3,084)

Background. Radiographic bone loss is a primary determinant of periodontitis stage. Intraoral radiographs (periapical and bitewing) are the standard projections for assessing interproximal bone levels, yet their interpretation is subjective and poorly reproducible, and previous syntheses have pooled intraoral with panoramic imaging. Objectives. To appraise and synthesise studies developing or validating deep learning (DL) models for detecting, quantifying, staging or classifying alveolar bone loss on intraoral radiographs. Methods. Seven databases and six supplementary sources were searched from 1 January 2015 to 12 June 2026 (PROSPERO CRD420261455818, registered retrospectively). Two reviewers screened, extracted and appraised in duplicate using QUADAS-2 with AI-specific signalling questions, CLAIM and APPRAISE-AI; certainty was rated by GRADE. Heterogeneity precluded pooling; synthesis was narrative. Results. Sixteen publications reporting 15 independent datasets (2018–2026) were included (11 periapical, two bitewing, three mixed; 39–21,819 radiographs). The architectures employed comprised classification, segmentation, object-detection, keypoint-localisation and transformer-based models. Accuracy for binary detection ranged from 0.73 to 0.97, Dice coefficients reached ≥0.91, and intraclass correlation with expert measurement was 0.75–0.85. Performance fell for multiclass staging, posterior sites and furcations. Only two studies used an external test set; none was prospective; risk of bias was mostly high or unclear. Conclusions. Performance lies within the range observed for calibrated readers, but the evidence is dominated by small, single-centre, retrospective datasets with annotation-based reference standards and almost no external validation; certainty is very low. Deep learning is best regarded as a clinician-supervised adjunct for screening, triage and quality assurance rather than an autonomous diagnostic device. Full article

Sports cardiologists frequently face the challenge of distinguishing physiological cardiac adaptation to intensive training (“athlete’s heart”) from early or mild cardiac pathology, a dilemma in which both false-positive and false-negative assessments carry meaningful consequences for the athlete. Cardiopulmonary exercise testing (CPET) provides a dynamic, functional complement to structural and electrical assessment, but its value in this specific differential-diagnosis context has not been comprehensively synthesized. This narrative review examines current evidence on CPET in distinguishing athlete’s heart from hypertrophic cardiomyopathy, dilated cardiomyopathy, arrhythmogenic cardiomyopathy, and thoracic wall deformities such as pectus excavatum, alongside the physiological basis of high exercise capacity in athletes and the athlete-specific reference values now becoming available. Across conditions, peak oxygen uptake alone proved an unreliable discriminator. Ventilatory efficiency, oxygen-pulse kinetics, and the exercise arrhythmic and blood-pressure response appear to carry additional information when interpreted within a multiparametric framework, although the supporting studies are observational, mostly single-centre, and were not designed to compare these variables against one another. Special populations, including veteran athletes, women, and those with congenital heart disease, require dedicated reference data that remain incomplete, and emerging artificial-intelligence-based interpretive tools require athlete-specific validation before clinical adoption. A practical, stepwise diagnostic framework integrating CPET with imaging and shared decision-making is proposed. It reflects the authors’ synthesis of the reviewed evidence rather than a prospectively validated or society-endorsed pathway. Closing the evidence gaps identified here, particularly for underrepresented athlete populations and prospective outcomes data, should be a priority for future research in sports cardiology. Full article

Background: Dental caries detection using intraoral radiographs is essential for early diagnosis but may be affected by observer variability. Artificial intelligence (AI)-based systems have emerged as potential tools to improve diagnostic consistency. This study evaluated the diagnostic accuracy of a deep learning-based AI system (Better Diagnostics Caries Assist (BDCA) Version 1.0) for detecting dental caries on bitewing (BW) and intraoral periapical (IOPA) radiographs, and examined its performance across demographic, technical, and lesion-based subgroups. Methods: A retrospective validation study was conducted using anonymized digital BW and IOPA radiographs with expert-defined ground truth at the tooth-surface level. The AI software independently analyzed each image to identify carious lesions. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated with 95% confidence intervals. Generalized estimating equations were applied to adjust for the clustering of multiple surfaces per image. Subgroup analyses were performed by age, sex, digital sensor type, and lesion category. Results: The AI system demonstrated high diagnostic accuracy for both modalities. For BW radiographs, sensitivity was 0.892 and specificity was 0.995, while for IOPA radiographs sensitivity was 0.882 and specificity was 0.991. NPVs exceeded 0.99 for both modalities. Across age groups, BW sensitivity ranged from 0.881 to 0.901 and IOPA sensitivity from 0.854 to 0.906, with consistently high specificity (>0.989). Sex-based differences were minimal. Sensor-wise analysis showed sensitivity ranging from 0.833 to 0.933 for BW and 0.828 to 0.933 for IOPA, while specificity remained above 0.984 for all sensors. Detection performance was comparable for primary (sensitivity 0.883) and secondary caries (0.879), although PPV was slightly lower for secondary lesions. The lower AUC indicated reduced accuracy in lesion identification in the absence of BDCA v 1.0, the difference between BDCA v1.0 and Ground truth was 0.042 at 95% CI 0.030 to 0.055, and the difference was also statistically significant with (p ≤ 0.001). Conclusions: The evaluated AI system demonstrated excellent and consistent performance for detecting dental caries on both BW and IOPA radiographs across demographic groups, sensor technologies, and lesion types, supporting its potential role as a reliable decision support tool in dental radiographic interpretation. Full article

Fault diagnosis in industrial equipment plays a crucial role in ensuring reliable system functionality and minimizing the costs associated with repair and maintenance. Traditionally, fault diagnosis has relied on human intelligence (HI), with skilled personnel applying knowledge and expertise based on reasoning and contextual understanding. Nevertheless, as modern industrial systems have grown more complex, operated at higher speeds, and generated massive amounts of data, relying solely on HI has become increasingly challenging and less scalable. Consequently, modern fault diagnosis has turned toward artificial intelligence (AI). This paper presents a comparative study of HI and AI in industrial fault diagnosis, based on a literature-driven analysis. The results confirm that while AI-based fault diagnosis systems perform well in processing large datasets and achieve improved diagnostic accuracy, these practices also face limitations related to data dependency, explainability, and deployment cost. By contrast, human intelligence remains indispensable in handling uncertain, rare, or new fault conditions that require contextual judgment and flexibility. The review further indicates that augmented intelligence (AuI) provides a collaborative framework that combines the complementary strengths of HI and AI for industrial fault diagnosis. Furthermore, emerging research directions, such as explainable and trustworthy AI, foundation models, large language models, physics-informed AI, digital twins, and human-centered AI, are identified as promising developments for next-generation intelligent diagnostic systems. The findings suggest that augmented intelligence is the most promising approach for advancing the performance and reliability of diagnostic systems in industrial machines. Full article

Contemporary intensive care operates in an environment with high-complexity cases, large volumes of information, and vast physiological, biological, and therapeutic data, collected from laboratory results, investigations, and therapies for organ support, as well as from systems that operate in parallel. The lack of interoperability contributes to information overload, alarm fatigue, and delayed decision-making. The Smart ICU concept has been developed to address these limitations by integrating medical devices, information systems, and artificial intelligence into a unified system that allows interoperable data integration and predictive analytics. Aim: The purpose of this article is to provide a narrative review of the Smart ICU concept, with a specific focus on the cardiac intensive care unit. It summarizes Smart ICU architecture, data integration, clinical support, and applicability in monitoring perioperative inflammation in cardiac surgery. We describe the Smart ICU architecture, from data acquisition to storage and analytics, highlighting the differences between Smart ICU, artificial intelligence, and Tele-ICU, and we underline predictive analytics as a supportive tool, as well as its influence on clinical outcomes. Cardiac ICU application: Cardiac ICUs offer a data-dense, temporally well-defined model following cardiac surgery with cardiopulmonary bypass, where data concerning patients’ hemodynamics, perfusion data, and biological and inflammatory markers intertwine. Cardiac Smart ICU models could recognize early signs of hemodynamic compromise and low cardiac output states and identify early indicators of post-cardiac surgery complications. Neutrophil activation and complete blood count-derived indices may be used as dynamic biological data for Smart Cardiac ICU models. Conclusion: The Smart Cardiac ICU may support earlier risk stratification, and therefore earlier diagnostic and therapeutic interventions, but its clinical value requires prospective, multicenter validation. Cardiopulmonary bypass-induced inflammation may offer an ideal setting to integrate physiological, procedural, and immunological data into bedside predictive models. Full article

Proteomics has emerged as a powerful tool for advancing our understanding of brain disorders by enabling large-scale characterization of protein expression, post-translational modifications, and interaction networks. Neurological conditions are often characterized by complex and dynamic molecular changes that are not fully captured by traditional diagnostic approaches. Proteomic technologies, particularly mass spectrometry-based and affinity-based methods, offer the ability to identify disease-specific protein signatures and elucidate underlying pathophysiological mechanisms, including neurodegeneration, neurodevelopment and neuroinflammation and alterations happening to the extracellular matrix and body fluid homeostasis. In recent years, artificial intelligence has emerged as a powerful tool to proteomics, enabling improved analysis of complex biological datasets. This integration has significantly enhanced the discovery of biomarkers for early diagnosis, disease stratification, and monitoring of therapeutic responses. Thus, cerebrospinal fluid and blood-based proteomic analyses have revealed promising candidates for neurological diseases. This review summarizes current advances in proteomics across a range of brain disorders, highlighting key molecular pathways, biomarker discovery efforts, and evolving clinical applications. Furthermore, it outlines future directions, including the application of machine learning for improved biomarker identification and precision medicine. Full article

The integration of artificial intelligence (AI) and data science into organizational evaluation practices creates socio-technical systems in which human rating behavior, organizational templates, regulatory pressure, and analytical algorithms jointly determine what can be measured and learned. This single-case study examines the measurement of Environmental, Social, and Governance (ESG) compliance in financial software engineering projects, treating one firm’s project evaluation practice as a socio-technical system and applying an open-source data science and machine learning workflow to 207 anonymized archival project records. Correlation analysis revealed near-unity associations between the stakeholder-rated social and governance factors and the overall project score (r = +0.995 and +0.966, p < 0.001), while the environmental factor was unrelated to the score; post-hoc diagnostics (a seven-component principal-component structure, Harman’s screen, selective near-zero same-source correlations, and marker-variable partial correlations) bind, but cannot eliminate, method-based explanations, so the coefficients are interpreted as a descriptive property of the firm’s evaluation system rather than as estimates of relationships between validated, distinct constructs. Exploratory machine learning classifiers performed weakly—kNN at chance (AUC = 0.497) and SVM only modestly above the no-information baseline (accuracy 61.8%)—a result consistent with the constraints that the social subsystem imposes on the learnability of the records it generates, although technical factors, including the dichotomization of the target variable, the modest sample size, and model configuration, cannot be ruled out as contributing explanations; descriptive statistics are reported for all variables, and a diagnostic analysis reconciles the apparent divergence between the near-unity correlations and the weak classification performance by showing that the two rest on different feature sets, the near-redundant social and governance ratings having been withheld from the classifiers. The findings offer a proof of concept and a structured agenda for AI-enabled, project-level ESG measurement in socio-technical systems. Full article

Background: Pediatric head trauma is a common reason for emergency department presentation. Although most children have minor injuries, a small proportion harbor clinically important traumatic brain injuries requiring urgent intervention. Artificial intelligence (AI) may offer structured support in computed tomography (CT) decision making, but evidence regarding the performance of general-purpose large language models in pediatric head trauma remains limited. Objective: To evaluate the association between AI-based cranial CT recommendations and clinically meaningful outcomes in pediatric patients with blunt head trauma and to assess the diagnostic performance and clinical utility of the model. Methods: This retrospective single-center observational study included pediatric patients younger than 18 years with blunt head trauma who underwent cranial CT imaging and had complete outcome data. A general-purpose large language model generated binary CT recommendations (“CT recommended” or “CT not recommended”) using structured clinical information available at the time of emergency department presentation. The primary outcome was a composite adverse clinical outcome defined as the occurrence of at least one of the following: emergency surgical intervention, intensive care unit admission, intubation, neurological sequelae or mortality. Diagnostic performance metrics, calibration analysis and decision curve analysis were performed. Results: A total of 819 pediatric patients were included, and the AI model recommended CT in 530 patients (64.7%). The primary outcome occurred in 143 patients (17.5%) and was significantly more frequent in the CT-recommended group than in the CT-not recommended group (24.5% vs. 4.5%; OR 6.90, 95% CI 3.82–12.45; p < 0.001). Abnormal CT findings, emergency surgery, intubation and neurological sequelae were also significantly more common in patients for whom CT was recommended by the AI system. For the primary outcome, the AI recommendation demonstrated a sensitivity of 90.9%, specificity of 40.8%, positive predictive value of 24.5% and negative predictive value of 95.5%. Calibration analysis showed acceptable agreement between predicted probabilities and observed event rates. Decision curve analysis demonstrated greater net benefit than both the “treat-all” and “treat-none” strategies across a range of threshold probabilities. Conclusions: In this clinically selected cohort of pediatric patients with blunt head trauma who underwent cranial CT imaging, AI-based CT recommendations were strongly associated with adverse clinical outcomes and demonstrated high sensitivity and negative predictive value for identifying children at risk of clinically important events. These findings suggest that, within a clinically selected cohort of children who underwent cranial CT imaging, AI-generated CT recommendations were associated with clinically meaningful outcomes. However, these results should not be interpreted as validation of CT decision making in the broader pediatric head trauma population and require prospective validation in unselected cohorts. Full article

Background/Objectives: This study aimed to evaluate whether artificial intelligence-derived vertebral volumetric bone mineral density (AI-vBMD) and paraspinal intermuscular adipose tissue (IMAT) ratio from routine computed tomography (CT) could identify moderate-to-severe vertebral compression fractures (VCFs) in breast cancer survivors, and whether paraspinal IMAT ratio and routinely available clinical variables improved diagnostic performance. Methods: This retrospective study included 275 women with breast cancer who underwent routine non-contrast CT and lumbar quantitative computed tomography (QCT). Hounsfield unit-derived volumetric bone mineral density (HU-vBMD) was derived using a QCT-referenced HU-to-vBMD conversion equation, whereas AI-vBMD and paraspinal IMAT ratio were extracted using automated software. Moderate-to-severe VCF was defined as Genant grade ≥ 2. Agreement with QCT-vBMD was assessed using correlation, intraclass correlation coefficient (ICC), and Bland–Altman analysis. Model discrimination was evaluated using receiver operating characteristic analysis and DeLong tests. Results: Moderate-to-severe VCF was present in 75 patients (27.3%). HU-vBMD and AI-vBMD showed excellent agreement with QCT-vBMD (ICC, 0.978 and 0.987, respectively). AI-vBMD outperformed HU-vBMD for identifying VCFs (AUC, 0.738 vs. 0.714; p < 0.001). IMAT ratio showed comparable standalone discrimination to AI-vBMD (AUC, 0.760 vs. 0.738; p = 0.604). Adding IMAT ratio to AI-vBMD improved discrimination (AUC, 0.786 vs. 0.738; p = 0.038). The full model incorporating clinical covariates achieved the highest AUC (0.828; 95% CI, 0.778–0.878). Conclusions: AI-vBMD and paraspinal IMAT ratio automatically extracted from routine CT improved the diagnostic assessment of prevalent moderate-to-severe VCFs in breast cancer survivors. This study supports an automated CT-based approach that integrates vertebral bone density and paraspinal muscle–fat information for opportunistic identification of clinically relevant VCFs. Full article

Coronary artery disease (CAD) is the leading cause of death worldwide, highlighting the need for more reliable and efficient diagnostic tools beyond conventional methods. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown strong potential for detecting obstructive CAD by learning complex patterns from electrocardiogram (ECG) and coronary computed tomography angiography (CCTA) data. This rapid systematic review assesses and compares the diagnostic performance and methodological quality of AI models built for CAD prediction using ECG and CCTA data. A systematic search following PRISMA-ScR guidelines was conducted for primary studies published between 2021 and 2025. Eleven studies were included, six using ECG data and five using CCTA data. Methodological quality was evaluated using the PROBAST+AI tool. ECG-based models achieved AUCs of 0.72–0.961 and CCTA-based models showed slightly stronger top-end performance, with AUCs of 0.77–0.97. External validation was uncommon in both groups, applied in only 40% of CCTA studies and 33% of ECG studies, so neither modality demonstrated clearly greater validation maturity. Despite these strong results, PROBAST+AI assessment revealed a high risk of bias in 90.9% of the included studies, largely due to weaknesses in the analysis domain, including poor handling of missing data and the absence of model calibration reporting. AI models show strong diagnostic accuracy for CAD across both modalities, although external validation was limited and applied in a minority of studies. However, the widespread methodological bias means these tools should currently support clinical decision-making rather than replace standard diagnostic methods. Future studies should focus on prospective multicentre validation and the use of multimodal data. Full article

Aggregation operators are central to multi-criteria decision-making under complex fuzzy information, where the additional phase dimension of complex fuzzy sets captures periodic or cyclical uncertainty beyond the reach of classical fuzzy sets. Existing Archimedean families used in the complex-fuzzy aggregation-operator literature, namely the algebraic, Einstein, Hamacher, and Aczél–Alsina families, are all generated from integer-order kernels; complete monotonicity of a generator’s pseudo-inverse, the condition known to be necessary and sufficient for a bivariate Archimedean construction to extend consistently to an arbitrary number of arguments, has not, to our knowledge, been established or invoked as a design criterion within that literature. To address this gap, this paper introduces a new family of Complex Fuzzy Mittag-Leffler (CFML) operators generated by a two-parameter additive generator that is built from the one-parameter Mittag-Leffler function $E_{\alpha }$ and a positive exponent $\lambda$. The completely monotone character of this generator guarantees the above consistency across dimensions for every aggregation exponent no smaller than one, and the fractional order $\alpha$ supplies a tunable additional degree of freedom that reweights how criteria are compensated during aggregation. The associated operational laws and the corresponding weighted averaging and weighted geometric operators are defined and proved to be idempotent, bounded, and monotone. Notably, the classical Aczél–Alsina and algebraic product operators emerge as exact limiting cases as the fractional order tends to one. A complete multi-criteria decision-making algorithm is proposed and illustrated, for demonstration purposes only, through a hypothetical case study evaluating generative artificial-intelligence diagnostic systems, with a sensitivity analysis showing that varying the fractional order and the aggregation exponent can alter alternative rankings relative to the classical limiting operators, illustrating the added flexibility that the fractional order provides. Full article

Background: Vesicoureteral reflux (VUR) is a common pediatric urological disorder traditionally diagnosed by voiding cystourethrography (VCUG). Although ureteric orifice (UO) morphology during cystoscopy correlates with VUR severity, its assessment remains subjective. To our knowledge, artificial intelligence (AI) has not previously been applied to cystoscopic images for VUR prediction. We aimed to develop and evaluate the first AI model for predicting VUR from pediatric cystoscopic images and videos. Methods: In this retrospective single-center study, cystoscopic videos from children with and without VUR diagnosed by VCUG were analyzed. Individual frames were extracted, anonymized and annotated according to VUR grade. Four YOLOv12 object detection models were trained to classify UOs as no/grade I, low-grade (grades II–III), or high-grade (grades IV–V) VUR. To better understand sources of model failure, additional experiments evaluated binary image classification and class-agnostic UO localization. Finally, frame-level predictions were aggregated across complete cystoscopic sequences using majority voting to assess UO-level performance. Results: Three-class object detection demonstrated limited frame-level performance, with a maximum mAP@50 of 0.37 and mAP@50–95 of 0.17. Binary classification achieved a macro precision of 0.62, recall of 0.58, and F1 score of 0.49. Class-agnostic object detection improved localization performance (mAP@50 0.63). Aggregating predictions across complete video sequences substantially improved diagnostic performance, achieving UO-level accuracies of up to 77%, with leave-one-out cross-validation accuracies ranging from 0.69 to 0.77. Conclusions: This proof-of-concept study demonstrates the feasibility of AI-assisted VUR prediction from pediatric cystoscopic videos. While frame-level performance was limited, sequence-level aggregation markedly improved diagnostic accuracy, highlighting the importance of temporal information for future video-based AI models in pediatric endourology. Full article

Language barriers hinder healthcare, particularly during case history-taking, a key part of diagnosis. While multilingual artificial intelligence (AI) chatbots offer solutions, there is fragmented evidence of their effectiveness and impact. This systematic review followed PRISMA 2020 guidelines, examining studies published between 2015 and 2025 on multilingual AI chatbots in healthcare across four databases (Google Scholar, Scopus, Web of Science, and PubMed), using a two-stage screening process. Data extraction focused on applications, supported languages, underlying technologies, target populations, and clinical outcomes. From 503 records, 49 studies, covering primary care, telemedicine, oncology, mental health, and other areas, met the criteria. Supported languages included English, Spanish, Arabic, Chinese, Hindi, and other underrepresented languages. In individual system evaluations using heterogeneous methodologies and evaluation settings, AI chatbots achieved a diagnostic accuracy ranging from 72–92%. Core technologies included large language models (LLMs), bidirectional encoder representations from transformers (BERT), a generative pre-trained transformer (GPT), retrieval-augmented generation (RAG), speech recognition, and distillation. The findings show that these improve clinical workflow (30–70% time savings) and patient engagement, reduce language barriers, and promote health equity. However, the overall evidence certainty was low to moderate, reflecting the predominance of prototype and proof-of-concept studies. Multilingual AI chatbots demonstrate a boost in healthcare efficiency, a reduction in language barriers, and the promotion of health equity, but exhibit challenges regarding validation, workflow integration, and evaluation standards, along with ethical issues such as privacy and bias. Future research should include real-world studies, diverse populations, standardized outcome measures, and long-term equity assessments. Full article

Hematologic malignancies, including acute myeloid leukemia, myelodysplastic syndromes, lymphoma, and multiple myeloma, are characterized by profound biological heterogeneity and highly dynamic treatment trajectories that conventional, static prognostic systems incompletely capture. Cellular therapies, such as chimeric antigen receptor T-cell therapy and hematopoietic stem cell transplantation, offer potentially curative options for relapsed or refractory disease, yet outcomes remain highly variable, and management decisions regarding conditioning intensity, lymphodepletion, immunosuppression, and toxicity surveillance continue to be largely protocol-driven rather than individually adapted. Artificial intelligence (AI) and machine learning (ML) have demonstrated substantial promise in diagnostic support, prognostic stratification, and multimodal data integration across hematologic malignancies, but existing models remain predominantly static and related to pre-treatment in orientation, limiting their utility for real-time clinical guidance. This review summarizes current AI applications in hematologic oncology; critically compares the strengths, limitations, and clinical applicability of major AI model classes, including traditional machine learning, deep learning, multimodal integrative frameworks, reinforcement learning, digital twins, and emerging foundation models and large language models; and proposes an adaptive, multimodal paradigm. We examine key enabling technologies and address the clinical, regulatory, ethical, and implementation challenges that must be resolved before these systems can be deployed at the bedside. We argue that the central challenge facing the field is no longer whether AI can predict outcomes, but whether it can actively guide real-time therapeutic decisions, and that achieving this transition will require interdisciplinary collaboration, prospective validation, and governance frameworks capable of ensuring interpretability, equity, and clinical trustworthiness. Full article

Breast cancer is the most common cancer among women, and early detection through mammography is essential for reducing mortality. Artificial intelligence can support radiologists by improving diagnostic accuracy. To develop and evaluate a two-stage ensemble machine learning pipeline for breast cancer diagnosis from digital mammograms. The proposed framework combines image preprocessing, multiple convolutional neural networks trained under different conditions, and a second-stage classifier that integrates the CNN outputs. Several machine learning models and feature selection techniques were evaluated using publicly available mammography datasets. Results: The ensemble approach consistently outperformed the individual CNN models. The MLP classifier achieved the best overall balance between precision and recall, while the heuristic fusion method provided the highest sensitivity. Feature selection reduced model complexity while maintaining comparable performance, and cross-validation confirmed the robustness of the proposed methodology. Combining complementary information from multiple CNNs with classical machine learning improves diagnostic performance and provides a robust framework for computer-aided breast cancer diagnosis. The proposed two-stage ensemble offers an effective and interpretable approach for mammographic breast cancer classification. A demonstration application incorporating Grad-CAM explainability further supports its potential use as a clinical decision-support tool. Full article