Search Results (824)

Objective: We evaluate the performance and relevance of MRI to detect csPC in men with normal PSA. Methods: Out of our database of patients referred for prostate MRI, we selected men with PSA < 4 ng/mL for whom histopathology or at least 2 years of clinical follow-up data were available as standard of reference. Subgroup analyses were performed for the patients with PSA < 3 ng/mL, <2 ng/mL, and 2–3.9 ng/mL. The reasons for prostate MRI referral despite their normal PSA level were retrieved by exploring the patients’ files. The prostate MRIs were reported according to the Prostate Imaging and Reporting Data System (PI-RADS), and the overall assessment score was registered. For evaluation of the performance, PI-RADS ≥ 3 was set as a threshold for a positive exam. The patients without PC or only International Society of Urological Pathology (ISUP) grade group 1 PC (Gleason 3+3) were considered as one category having no csPC. The performance of prostate MRI was separately evaluated for detection of ISUP ≥ 2 and for ISUP ≥ 3 csPC. Results: A total of 148 men were included, with PSA ranging from 0.42 to 3.99 ng/mL (median 2.95, IQR 1.68–3.50) and age ranging from 36 to 84 years (median 58, IQR 52–66). A total of 74 men (50.0%) had a PSA level < 3 ng/mL, 42 (28.4%) had a PSA level < 2 ng/mL, and 106 (71.6%) had a PSA level of 2–3.9 ng/mL. They were referred for prostate MRI for a wide variety, and usually a combination of, reasons, such as younger age (<60 years in 55.4%, N = 82; <50 years in 17.6%, N = 26), abnormal digital rectal examination in 31.8% of cases (N = 47), suspicious PSA dynamics in 29.7% (N = 44), positive familial history in 27.0% (N = 40), clinical signs of prostatitis in 18.2% (N = 27), suspicious findings on Transrectal Ultrasound (TRUS) in 16.9% (N = 25), hematospermia in 7.4% (N = 11), hematuria in 4.1% (N = 6), incidental hot spot in the prostate on Fluoro-Deoxy-Glucose (FDG) Positron Emission Tomography (PET)–Computed Tomography (CT) in 4.1% (N = 6), lymphadenopathies on CT in 2.7% (N = 4), or severe patient anxiety in 3.4% (N = 5). Overall, ISUP ≥ 2 PC was present in 18.9% (N = 28) of cases, and MRI detected this with a sensitivity of 92.9%, a specificity of 66.7%, and a positive predictive value of 39.4%. ISUP ≥ 3 PC was present in 9.5% (N = 14) of cases, and prostate MRI detected this with a sensitivity of 100%, a specificity of 61.2%, and a positive predictive value of 21.2%. In patients with PSA < 2 ng/mL (N = 42), no csPC was found, but MRI generated false positives in 33.3%. Conclusions: Performing prostate MRI in men with normal PSA (<4 ng/mL) seems useful if there are other reasons that increase the clinical suspicion of csPC. In about one-fifth of these patients, csPC is present and MRI has high sensitivity for its detection. Prostate MRI has, however, low positive predictive value in this patient group, and clinicians should be aware of the risk of false-positive MRI. Below a PSA level of 2 ng/mL, no csPC was found and prostate MRI generated only false positives, suggesting limited value in this subgroup. Full article

Background and Objectives: Posthepatectomy liver failure (PHLF) remains a major cause of morbidity and mortality for patients undergoing major liver resections. Recent research highlights the expanding role of machine learning (ML), a crucial subfield of artificial intelligence (AI), in optimizing risk stratification. The aim of the current study was to review, elaborate on and critically analyze the available literature regarding the use of ML-driven risk prediction models for posthepatectomy liver failure. Materials and Methods: A systematic search was conducted in the PubMed/MEDLINE, Scopus and Web of Science databases. Fifteen studies that trained and validated ML models for prediction of PHLF were further included and analyzed. Results: The available literature supports the value of ML-derived models for PHLF prediction. Perioperative clinical, laboratory and imaging features have been combined in a variety of different algorithms to provided interpretable and accurate models for identifying patients at risk of PHLF. The ML-based algorithms have consistently demonstrated high area under the curve and sensitivity values, surpassing traditionally used risk scores in predictive performance. Limitations include the small sample sizes, heterogeneity in populations included, lack of external validation and a reported poor ability to distinguish between true positive and false positive cases in several studies. Conclusions: Despite the constraints, ML-driven tools, in combination with traditional scoring systems and clinical insight, may enable early and accurate PHLF risk detection, personalized surgical planning and optimization of postoperative outcomes in liver surgery. Full article

The temperature at the base of the ear is highly correlated with the core body temperature of sheep and responds sensitively to febrile conditions, making it a valuable indicator of sheep health. In northern China, the closed housing environment during winter increases the incidence of seasonal diseases such as upper respiratory infections and pneumonia, which severely affect the economic efficiency of sheep farming. To address this issue, this study proposes an early-warning method for winter diseases in sheep based on ear-base temperature. Ear temperature, body weight, and environmental data were collected, and Random Forest was employed for feature selection. Bayesian optimization was used to fine-tune the hyperparameters of a one-dimensional convolutional neural network to construct a predictive model of ear-base temperature using data from healthy sheep. Based on the predicted normal range, an early-warning strategy was established to detect abnormal temperature patterns associated with disease onset. Experimental results demonstrated that the proposed method achieved a high detection rate for common winter diseases while maintaining a low false positive rate, and validation experiments confirmed its effectiveness under practical farming conditions. Combined with low-cost temperature-sensing ear tags, the proposed approach enables real-time health monitoring and provides timely early warnings for winter diseases in large-scale sheep farming, thereby improving management efficiency and economic performance. Full article

Prostate cancer (PCa) is the most common malignancy in men worldwide. Multiparametric MRI (mpMRI) improves the detection of clinically significant PCa (csPCa); however, it remains limited by false-positive findings and inter-observer variability. Time-dependent diffusion (TDD) MRI provides microstructural information that may enhance csPCa characterization beyond standard mpMRI. This prospective observational diagnostic accuracy study protocol describes the evaluation of PROS-TD-AI, an in-house developed AI workflow integrating TDD-derived metrics for zone-aware csPCa risk prediction. PROS-TD-AI will be compared with PI-RADS v2.1 in routine clinical imaging using MRI-targeted prostate biopsy as the reference standard. Full article

Between 2015 and 2022, we evaluated a novel broad-range (BR) 16S PCR rDNA PCR/Sanger sequencing assay to improve diagnosis of invasive infections in culture-negative specimens. Using dual-priming oligonucleotides (DPO), this assay analyzed ribosomal DNA from sterile fluids or tissues. A total of 762 specimens were analyzed from 661 patients: 61% had negative cultures and BR 16S PCR tests; 35% had negative cultures but positive BR 16S PCR tests; and only 4% had negative cultures with indeterminate BR 16S PCR results. After resolution of indeterminate BR 16S PCR results (i.e., 29 negative, 1 false-positive, and 1 positive) the assay showed a sensitivity of 98.26% (95% CI = 96.00–99.43%), specificity of 99.79% (95% CI: 99.82–99.99%), positive predictive value of 99.65% (95% CI: 97.56–99.95%), negative predictive value of 98.94% (95% CI: 97.51–99.55%), and accuracy of 99.21% (95% CI: 98.28–99.71%) for a disease prevalence of 38.10% (95% CI: 34.62–41.66%). Gram stain purulence predicted the BR 16S PCR result better (69.4%) than organisms (24.6%), but the latter had a higher PPV (78.5%). Increased peripheral WBC (86.1%) or CRP (71.8%) predicted positive BR 16S PCR results. Our DPO BR 16S PCR assay improved pathogen detection over culture and minimized contamination. Broad range 16S rDNA PCR/sequencing (BR 16S PCR) is an important diagnostic technique in cases with invasive infection due to fastidious or uncultivatable pathogens. However, appropriate case selection, the quality of clinical specimen, and the specific assay primers affect its performance. Our novel BR 16S PCR assay uses unique dual-priming oligonucleotides (DPO) primers and fast protocols for rapid, optimal detection of bacterial pathogens, while minimizing contamination. Fast BR 16S PCR assay reports occurred within 24–48 h. BR 16S PCR and culture analyzed a diverse range of clinical specimens from patients with invasive infections. BR 16S PCR demonstrated a high performance for accurately detecting pathogens, ruling out infections, and minimizing contamination. BR 16S PCR detection of a pathogen allowed the appropriate clinical management of one-third of patients in this cohort. BR 16S PCR is an essential tool for the clinical management of patients with invasive infection when primary cultures are negative or contaminated. Full article

Background/Objectives: Whole exome sequencing (WES) is an effective method for detecting disease-causing variants. However, copy number variation (CNV) detection using WES data often has limited sensitivity and high false-positive rates. Methods: In this study, we constructed a reference CNV set using chromosomal microarray analysis (CMA) data from 44 of 180 individuals who underwent WES and CMA and evaluated four WES-based CNV callers (CNVkit, CoNIFER, ExomeDepth, and cn.MOPS) against this benchmark. For each tool, we first defined software-specific problematic genomic regions across the full WES cohort and filtered out the CNVs that overlapped these regions. Results: The four algorithms showed low mutual concordance and distinct distributions in the problematic regions. On average, 2210 sequencing target baits (1.23%) were classified as problematic; these baits had lower mappability scores and higher coefficients of variation in RPKM than the remaining probes. After the supplementary filtration step, all tools demonstrated improved performance. Notably, ExomeDepth achieved gains of 14.4% in sensitivity and 7.9% in positive predictive value. Conclusions: We delineated software-specific problematic regions and demonstrated that targeted filtration markedly reduced false positives in WES-based CNV detection. Full article

Aiming to address the safety and stability issues caused by typical faults of Unmanned Aerial Vehicle (UAV) navigation sensors, a novel fault-tolerant method is proposed, which can capture the temporal dependencies of fault feature evolution, and complete the classification, prediction, and data reconstruction of fault data. In this fault-tolerant method, the feature extraction module adopts the FNKPCA method—integrating the Frobenius Norm (F-norm) with Kernel Principal Component Analysis (KPCA)—to optimize the kernel function’s ability to capture signal features, and enhance the system reliability. By combining FNKPCA with Support Vector Machine (SVM) and Bidirectional Long Short-Term Memory (BiLSTM), an active fault-tolerant processing method, namely FNKPCA–SVM–BiLSTM, is obtained. This study conducts comparative experiments on public datasets, and verifies the effectiveness of the proposed method under different fault states. The proposed approach has the following advantages: (1) It achieves a detection accuracy of 98.64% for sensor faults, with an average false alarm rate of only 0.15% and an average missed detection rate of 1.16%, demonstrating excellent detection performance. (2) Compared with the Long Short-Term Memory (LSTM)-based method, the proposed fault-tolerant method can reduce the RMSE metrics of Global Positioning System (GPS), Inertial Measurement Unit (IMU), and Ultra-Wide-Band (UWB) sensors by 77.80%, 14.30%, and 75.00%, respectively, exhibiting a significant fault-tolerant effect. Full article

Background and Objectives: Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. An early and accurate diagnosis is essential for effective clinical management and risk stratification. Recent advances in machine learning (ML) have provided opportunities to enhance the diagnostic performance by integrating multidimensional patient data. This study aimed to develop and compare several supervised ML algorithms for early CAD diagnosis using demographic, anthropometric, biochemical, and psychosocial parameters. Materials and Methods: A total of 300 adult patients (165 CAD-positive and 135 controls) were retrospectively analyzed using a dataset comprising 21 biochemical markers, body composition metrics, and self-reported eating behavior scores. Six ML algorithms, k-nearest neighbors (k-NNs), support vector machines (SVMs), artificial neural networks (ANNs), logistic regression (LR), naïve Bayes (NB), and decision trees (DTs), were trained and evaluated using 10-fold cross-validation. Model performance was assessed based on accuracy, sensitivity, false-negative rate, and area under the Receiver Operating Characteristic (ROC) curve (AUC). Results: The k-NN model achieved the highest performance, with 98.33% accuracy and an AUC of 0.99, followed by SVM (96.67%, AUC = 0.95) and ANN (95.33%, AUC = 0.98). Patients with CAD exhibited significantly higher levels of glucose, triglycerides (TGs), LDL cholesterol (LDL-C), and abdominal obesity, while vitamin B12 levels were lower (p < 0.001). Although emotional and mindful eating scores differed significantly between the groups, their contribution to model performance was limited. Conclusions: Machine learning models, particularly k-NN, SVM, and ANN, have demonstrated high accuracy in distinguishing CAD patients from healthy controls when applied to a diverse set of clinical and behavioral variables. This study highlights the potential of integrating psychosocial and clinical data to enhance CAD prediction models beyond traditional biomarkers. Full article

Background: Head and neck (H&N) cutaneous melanomas have poorer outcomes than melanomas at other sites, yet sentinel lymph node biopsy (SLNB)—a key prognostic tool in clinically node-negative disease—is less frequently performed, particularly outside tertiary centers. We evaluated the feasibility and prognostic relevance of SLNB in a medium-volume regional institution. Methods: We retrospectively reviewed patients with primary H&N cutaneous melanoma who underwent SLNB at the Department of Oral and Maxillofacial Surgery, University of Szeged, between 2010 and 2022. Clinicopathological features, nodal outcomes, recurrence patterns, recurrence-free survival (RFS), and overall survival (OS) were analyzed using Kaplan–Meier methods and univariate Cox regression. Results: Thirty-eight patients underwent SLNB, with a 100% sentinel lymph node identification rate and no major complications. Positive sentinel lymph nodes were identified in 8 patients (21.1%). Two false-negative events occurred, resulting in a false-omission rate of 6.7% and a negative predictive value of 93.3%. SLN-negative patients demonstrated longer RFS and OS, although differences were not statistically significant. Among patients with intermediate-risk melanoma (pT1b–pT3a), 18.5% had a positive SLN. Conclusions: SLNB is a safe and clinically meaningful staging procedure for H&N melanoma in a medium-volume regional center. Sentinel node status provides important prognostic information and supports appropriate patient selection for contemporary adjuvant therapy. Full article

Financial crises increasingly exhibit complex, interconnected patterns that traditional risk models fail to capture. The 2008 global financial crisis, 2020 pandemic shock, and recent banking sector stress events demonstrate how systemic risks propagate through multiple channels simultaneously—e.g., network contagion, extreme co-movements, and information cascades—creating a multidimensional phenomenon that exceeds the capabilities of conventional actuarial or econometric approaches alone. This paper addresses the fundamental challenge of modeling this multidimensional systemic risk phenomenon by proposing a mathematically formalized three-tier integration framework that achieves 19.2% accuracy improvement over traditional models through the following: (1) dynamic network-copula coupling that captures 35% more tail dependencies than static approaches, (2) semantic-temporal alignment of textual signals with network evolution, and (3) economically optimized threshold calibration reducing false positives by 35% while maintaining 85% crisis detection sensitivity. Empirical validation on historical data (2000–2023) demonstrates significant improvements over traditional models: 19.2% increase in predictive accuracy (R² from 0.68 to 0.87), 2.7 months earlier crisis detection compared to Basel III credit-to-GDP indicators, and 35% reduction in false positive rates while maintaining 85% crisis detection sensitivity. Case studies of the 2008 crisis and 2020 market turbulence illustrate the model’s ability to identify subtle precursor signals through integrated analysis of network structure evolution and semantic changes in regulatory communications. These advances provide financial regulators and institutions with enhanced tools for macroprudential supervision and countercyclical capital buffer calibration, strengthening financial system resilience against multifaceted systemic risks. Full article

Background/Objectives: Intracranial hemorrhage (ICH) is a life-threatening condition that can be rapidly detected by non-contrast head computed tomography (NCCT). RAPID ICH is a deep learning (DL) tool for automatic ICH identification using NCCT. Our aim was to assess the real-world performance of RAPID ICH compared to that of a first-year radiology resident on consecutively acquired NCCTs from patients referred from the Emergency Department. Methods: This single-center retrospective cohort study included NCCTs acquired on the same CT scanner over three months. Exclusion criteria were motion or metallic artifacts that substantially degraded the NCCT quality and incomplete NCCTs. Two senior neuroradiologists conducted ground-truth labeling of the NCCTs regarding ICH presence in a binary manner. The first-year radiology resident assessed NCCTs for ICH presence and was blinded to the ground-truth labeling. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were computed for the RAPID ICH identifications and for the first-year radiology resident’s ICH identifications. Results: After applying exclusion criteria, 844 NCCTs remained. Ground-truth labeling found ICH in 63 NCCTs. RAPID ICH showed 87.3% sensitivity, 74% specificity, 21.3% PPV, and 98.6% NPV, while the first-year radiology resident achieved 95.2% sensitivity, 90.8% specificity, 45.5% PPV, and 99.6% NPV. There were 8 false-negative and 203 false-positive RAPID ICH identifications. Conclusions: RAPID ICH’s sensitivity and specificity were lower than in prior studies performed using RAPID ICH, and there was a high number of false-positive RAPID ICH identifications, limiting the generalizability of the assessed version of this DL tool. Testing DL tools by comparing them with radiologists of varying experience can provide valuable insights into their performance. Full article

Detecting fraudulent and anomalous transactions in large-scale digital payment systems is significantly challenging due to severe class imbalance and the fact that transactional risk is tightly coupled to the historical interactions and behaviors of transacting parties. In this study, a scalable Behavioral Interaction-Aware Gradient Boosting (BI-GBDT) framework is proposed for anomaly detection in tabular transaction data to overcome these challenges. The methodology models sending and receiving behaviors separately through direction-specific clustering based on transaction frequency and amount. Each transaction is characterized by cluster-pair prevalence ratios, which capture the population-level prevalence of sender–receiver interaction patterns. To handle extreme class imbalance, all transactions are clustered, and a cluster-level risk score is computed as the ratio of anomalous transactions to the total number of transactions within each cluster. This score is incorporated as a feature, serving as a behavioral risk prior highlighting concentrated anomaly. These interaction-aware features are integrated into a GBDT in a big data environment. Experiments were conducted on a large masked real-world payment dataset spanning six months and containing more than 456 million transactions, with the prediction task defined as binary classification between fraudulent and non-fraudulent transactions. Unlike standard GBDT models trained only on transactional attributes and graph-based approaches, BI-GBDT captures sender–receiver interaction patterns in a graph-free manner and outperforms a baseline GBDT, reducing the false positive rate from 37.0% to 4.3%, increasing recall from 52.3% to 72.0%, and improving accuracy from 63.0% to 95.7%. Full article

Background/Objective: The aim of the present study is to compare diagnostic accuracies of MRI, PET/CT and fused PET/MRI in the assessment of cervical lymph nodes in patients with head and neck cancer (HNC). Methods: Imaging data of 37 patients who underwent MRI, PET/CT, and surgery at our center were retrospectively merged into PET/MR images. Histopathological results of neck dissections and lymph node resections served as the gold standard. Results: MRI and PET/CT were performed on the same day. The mean interval between imaging and surgery was 20 (±19.5) days. All three imaging modalities identified the same number of true positive and false negative cases, resulting in identical sensitivity estimates of 66.7%. Specificities were 90.9% for MRI, 95.5% for PET/CT, and 100% for PET/MRI. The corresponding positive predictive values (PPVs) were 83.3%, 80.7%, and 81.5%, while the negative predictive values (NPVs) were 80.0%, 90.9%, and 100%, respectively. Ten false results are further analyzed regarding side and level of the affected lymph node, and intersections of the three modalities are displayed. In 12 (32.4%) cases, additional findings are depicted in PET/CT, 5 (13.5%) of which are histologically confirmed to be further malignancies. Conclusions: Software-based PET/MRI is an easy-to-perform procedure and provides valuable clinical information in select clinical questions. Furthermore, whole-body acquisition by PET/CT leads to a notable number of additional malignant diagnoses, which especially favors its use in high-risk patients. Full article

Background: Osteoporosis and osteopenia are prevalent bone diseases characterized by reduced bone mineral density (BMD) and an increased risk of fractures, particularly in postmenopausal women. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, it has limitations regarding accessibility, cost, and predictive capacity for fracture risk. Machine learning (ML) approaches offer an opportunity to develop automated and more accurate diagnostic models by incorporating both BMD values and clinical variables. Method: This study retrospectively analyzed BMD data from 142 postmenopausal women, classified into 3 diagnostic groups: normal, osteopenia, and osteoporosis. Various supervised ML algorithms—including Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANN)—were applied. Feature selection techniques such as ANOVA, CHI2, MRMR, and Kruskal–Wallis were used to enhance model performance, reduce dimensionality, and improve interpretability. Model performance was evaluated using 10-fold cross-validation based on accuracy, true positive rate (TPR), false negative rate (FNR), and AUC values. Results: Among all models and feature selection combinations, SVM with ANOVA-selected features achieved the highest classification accuracy (94.30%) and 100% TPR for the normal class. Feature sets based on traditional diagnostic regions (L1–L4, femoral neck, total femur) also showed high accuracy (up to 90.70%) but were generally outperformed by statistically selected features. CHI2 and MRMR methods also yielded robust results, particularly when paired with SVM and k-NN classifiers. The results highlight the effectiveness of combining statistical feature selection with ML to enhance diagnostic precision for osteoporosis and osteopenia. Conclusions: Machine learning algorithms, when integrated with data-driven feature selection strategies, provide a promising framework for automated classification of osteoporosis and osteopenia based on BMD data. ANOVA emerged as the most effective feature selection method, yielding superior accuracy across all classifiers. These findings support the integration of ML-based decision support tools into clinical workflows to facilitate early diagnosis and personalized treatment planning. Future studies should explore more diverse and larger datasets, incorporating genetic, lifestyle, and hormonal factors for further model enhancement. Full article

Biomarker-based prevention is rapidly expanding, driven by advances in molecular diagnostics, genetic profiling, and commercial direct-to-consumer (DTC) testing. General practitioners (GPs) increasingly encounter biomarker results of uncertain relevance, often introduced outside the guideline frameworks. This creates new challenges in interpretation, communication, and equitable resource use in primary care. This narrative review synthesizes evidence from population-based studies, guideline frameworks, consensus statements, and communication research to evaluate the predictive value, limitations, and real-world implications of biomarkers in asymptomatic adults. Attention is given to polygenic risk scores, DTC genetic tests, neurodegenerative and cardiovascular biomarkers, and emerging multi-omics and aging markers. Several biomarkers, including high-sensitivity cardiac troponins, N-terminal pro–B-type natriuretic peptide, lipoprotein(a), coronary artery calcium scoring, and plasma p-tau species, showed robust predictive validity. However, many widely marketed biomarkers lack evidence of clinical utility, offer limited actionable benefits, or perform poorly in primary care populations. Unintended consequences, such as overdiagnosis, false positives, psychological distress, diagnostic cascades, and widening inequities, are well documented. Patients often misinterpret unvalidated biomarker results, whereas DTC testing amplifies demand without providing adequate counseling or follow-up. Only a minority of biomarkers currently meet the thresholds of analytical validity, clinical validity, and clinical utility required for preventive use in general practices. GPs play a critical role in contextualizing biomarker results, guiding shared decision-making, and mitigating potential harm. The responsible integration of biomarkers into preventive medicine requires clear communication, strong ethical safeguards, robust evidence, and system-level support for equitable, patient-centered care. Full article