Search Results (6,472)

Neuromuscular hip dysplasia (NHD) is a common deformity in children with cerebral palsy (CP). Although some predictive factors of NHD are known, the prediction of NHD is in its infancy. We present a Clinical Decision Support System (CDSS) designed to calculate the probability of developing NHD in children with CP. The system utilizes an ensemble of three machine learning (ML) algorithms: Neural Network (NN), Support Vector Machine (SVM), and Logistic Regression (LR). The development and evaluation of the CDSS followed the DECIDE-AI guidelines for AI-driven clinical decision support tools. The ensemble was trained on a data series from 182 subjects. Inclusion criteria were age between 12 and 18 years and diagnosis of CP from two specialized units. Clinical and functional data were collected prospectively between 2005 and 2023, and then analyzed in a cross-sectional study. Accuracy and area under the receiver operating characteristic (AUROC) were calculated for each method. Best logistic regression scores highlighted history of previous orthopedic surgery (p = 0.001), poor motor function (p = 0.004), truncal tone disorder (p = 0.008), scoliosis (p = 0.031), number of affected limbs (p = 0.05), and epilepsy (p = 0.05) as predictors of NHD. Both accuracy and AUROC were highest for NN, 83.7% and 0.92, respectively. The novelty of this study lies in the development of an efficient Clinical Decision Support System (CDSS) prototype, specifically designed to predict future outcomes of neuromuscular hip dysplasia (NHD) in patients with cerebral palsy (CP) using clinical data. The proposed system, PredictMed-CDSS, demonstrated strong predictive performance for estimating the probability of NHD development in children with CP, with the highest accuracy achieved using neural networks (NN). PredictMed-CDSS has the potential to assist clinicians in anticipating the need for early interventions and preventive strategies in the management of NHD among CP patients. Full article

If we do not urgently educate current and future medical professionals to critically evaluate and distinguish credible AI-assisted diagnostic tools from those whose performance is artificially inflated by data leakage or improper validation, we risk undermining clinician trust in all AI diagnostics and jeopardizing future advances in patient care. For instance, machine learning models have shown high accuracy in diagnosing Parkinson’s Disease when trained on clinical features that are themselves diagnostic, such as tremor and rigidity. This study systematically investigates the impact of data leakage and feature selection on the true clinical utility of machine learning models for early Parkinson’s Disease detection. We constructed two experimental pipelines: one excluding all overt motor symptoms to simulate a subclinical scenario and a control including these features. Nine machine learning algorithms were evaluated using a robust three-way data split and comprehensive metric analysis. Results reveal that, without overt features, all models exhibited superficially acceptable F1 scores but failed catastrophically in specificity, misclassifying most healthy controls as Parkinson’s Disease. The inclusion of overt features dramatically improved performance, confirming that high accuracy was due to data leakage rather than genuine predictive power. These findings underscore the necessity of rigorous experimental design, transparent reporting, and critical evaluation of machine learning models in clinically realistic settings. Our work highlights the risks of overestimating model utility due to data leakage and provides guidance for developing robust, clinically meaningful machine learning tools for early disease detection. Full article

Background and Objective: Adverse pathology to high-risk prostate cancer (PCa) after radical prostatectomy (upgrading) poses a threat to risk stratification and treatment planning. The impact on sexual function, urinary continence, and health-related quality of life (HRQOL) remains unclear. Methods: From 2004 to 2024, 4189 patients with preop low-/intermediate-risk PCa (Gleason score 6 or 7a, PSA ≤ 20 ng/mL) underwent radical prostatectomy at our department and were analyzed. Primary endpoint was HRQOL, erectile function, and urinary continence. Secondary endpoint was rate of salvage therapies and biochemical-free survival. Propensity score matching was performed using “operative time”, “robot-assisted surgery”, “blood loss”, “nerve-sparing surgery”, “age”, and “BMI” to represent comparable surgical approach. Median follow-up was 39 months (Interquartile-range (IQR) 15–60). Key Findings and Limitations: Patients who were upgraded to high-risk PCa showed a higher rate of postoperative radiotherapy and androgen-deprivation therapy compared to patients who were not upgraded (21% vs. 7%, p < 0.001; 9% vs. 3%, p = 0.002). Five-year biochemical recurrence-free survival was 68% in the upgrading group vs. 84% in the no-upgrading group (p < 0.001). We saw no difference in patient-reported HRQOL, urinary continence, or erectile function. Multivariable analysis showed that postoperative upgrading was a significant risk for not achieving good overall HRQOL (OR: 0.77, 95% CI: 0.61–0.97, p = 0.028) during the follow-up. Conclusions and Clinical Implications: Although postoperative upgrading to high-risk PCa leads to worse oncologic outcomes and higher salvage therapy rates, this study indicates that its impact on health-related quality of life is minimal and should not deter a cautious approach to radical prostatectomy. Full article

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a worldwide challenge. There are an estimated 17–24 million patients worldwide, with an estimated 60 percent or more who have not been diagnosed. Without a known cure, no specific curative medication, disability lasting years to being life-long, and disagreement among healthcare providers as to how to most appropriately treat these patients, ME/CFS patients are in need of assistance. Appropriate healthcare provider education would increase the percentage of patients diagnosed and treated; however, in-school healthcare provider education is limited. To address the latter issue, the New Jersey Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Association (NJME/CFSA) has developed an independent, incentive-driven, learning program for students of the health professions. NJME/CFSA offers a yearly scholarship program in which applicants write a scholarly paper on an ME/CFS-related topic. The efficacy of the program is demonstrated by the 2024–2025 first place scholarship winner’s essay, which addresses the biological basis of ME/CFS and how the healthcare provider can improve the quality of life of ME/CFS patients. For the reader, the essay provides an update on what is known regarding the biological underpinnings of ME/CFS, as well as a medical student’s perspective as to how the clinician can provide care and support for ME/CFS patients. The original essay has been slightly modified to demonstrate that ME/CFS is a worldwide problem and for publication. Full article

Background and Objectives: Hysterectomy is a common non-obstetric procedure. Minimally invasive techniques, such as laparoscopy and robot-assisted surgery, have replaced open surgery for benign gynecologic conditions. Robotic surgery offers reduced blood loss and shorter hospital stays but is limited by high costs. Articulating laparoscopic instruments aim to replicate robotic dexterity cost-effectively. However, comparative data on these two approaches in hysterectomy are limited. Materials and Methods: This multicenter study analyzed the outcomes of hysterectomies for benign gynecological diseases using articulating laparoscopic instruments (prospectively recruited) and robot-assisted surgery (retrospectively reviewed). The surgeries were performed by minimally invasive gynecological surgeons in South Korea, Japan, and Taiwan. The baseline characteristics, operative details, and outcomes, including operative time, blood loss, complications, and hospital stay, were compared. Statistical significance was set at p < 0.05. Results: A total of 151 patients were analyzed, including 67 in the articulating laparoscopy group and 84 in the robot-assisted group. The operating times were comparable (114.9 vs. 119.9 min, p = 0.22). The articulating group primarily underwent dual-port surgery (79.1%), whereas the robot-assisted group required four or more ports in 71.4% of the cases (p < 0.001). Postoperative complications occurred in both groups, without a significant difference (9.0% vs. 3.6%, p = 0.17). No severe complications or significant differences in the 30-day readmission rates were observed. Conclusions: Articulating laparoscopic instruments provide outcomes comparable to robot-assisted surgery in hysterectomy while reducing the number of ports required. Further studies are needed to explore the learning curve and long-term impact on surgical outcomes. Full article

Background/Objectives: Historically, echocardiographic imaging of the right heart has been challenging because its abnormal geometry is not conducive to reproducible anatomical and functional assessment. With the development of advanced echocardiographic techniques, it is now possible to complete an integrated assessment of the right heart that has fewer assumptions, resulting in increased accuracy and precision. Echocardiography continues to be the first-line imaging modality for diagnostic analysis and the management of acute and chronic right heart failure because of its portability, versatility, and affordability compared to cardiac computed tomography, magnetic resonance imaging, nuclear scintigraphy, and positron emission tomography. Virtually all echocardiographic parameters have been well-validated and have demonstrated prognostic significance. The goal of this narrative review of the echocardiographic parameters of the right heart chambers and hemodynamic alterations associated with right ventricular dysfunction is to present information that must be acquired during each examination to deliver a comprehensive assessment of the right heart and to discuss their clinical significance in right heart failure. Methods: Using a literature search in the PubMed database from 1985 to 2025 and the Cochrane database, which included but was not limited to terminology that are descriptive of right heart anatomy and function, disease states involving acute and chronic right heart failure and pulmonary hypertension, and the application of conventional and advanced echocardiographic modalities that strive to elucidate the pathophysiology of right heart failure, we reviewed randomized control trials, observational retrospective and prospective cohort studies, societal guidelines, and systematic review articles. Conclusions: In addition to the conventional 2-dimensional echocardiography and color, spectral, and tissue Doppler measurements, a contemporary echocardiographic assessment of a patient with suspected or proven right heart failure must include 3-dimensional echocardiographic-derived measurements, speckle-tracking echocardiography strain analysis, and hemodynamics parameters to not only characterize the right heart anatomy but to also determine the underlying pathophysiology of right heart failure. Complete and point-of-care echocardiography is available in virtually all clinical settings for routine care, but this imaging tool is particularly indispensable in the emergency department, intensive care units, and operating room, where it can provide an immediate assessment of right ventricular function and associated hemodynamic changes to assist with real-time management decisions. Full article

Background/Objectives: The diagnosis of pediatric dental conditions from panoramic radiographs is uniquely challenging due to the dynamic nature of the mixed dentition phase, which can lead to subjective and inconsistent interpretations. This study aims to develop and rigorously validate an advanced deep learning model to enhance diagnostic accuracy and efficiency in pediatric dentistry, providing an objective tool to support clinical decision-making. Methods: An initial comparative study of four state-of-the-art YOLO variants (YOLOv8, v9, v10, and v11) was conducted to identify the optimal architecture for detecting four common findings: Dental Caries, Deciduous Tooth, Root Canal Treatment, and Pulpotomy. A stringent two-tiered validation strategy was employed: a primary public dataset (n = 644 images) was used for training and model selection, while a completely independent external dataset (n = 150 images) was used for final testing. All annotations were validated by a dual-expert team comprising a board-certified pediatric dentist and an experienced oral and maxillofacial radiologist. Results: Based on its leading performance on the internal validation set, YOLOv11x was selected as the optimal model, achieving a mean Average Precision (mAP50) of 0.91. When evaluated on the independent external test set, the model demonstrated robust generalization, achieving an overall F1-Score of 0.81 and a mAP50 of 0.82. It yielded clinically valuable recall rates for therapeutic interventions (Root Canal Treatment: 88%; Pulpotomy: 86%) and other conditions (Deciduous Tooth: 84%; Dental Caries: 79%). Conclusions: Validated through a rigorous dual-dataset and dual-expert process, the YOLOv11x model demonstrates its potential as an accurate and reliable tool for automated detection in pediatric panoramic radiographs. This work suggests that such AI-driven systems can serve as valuable assistive tools for clinicians by supporting diagnostic workflows and contributing to the consistent detection of common dental findings in pediatric patients. Full article

Background/Objectives: Strengthening antimicrobial stewardship (AMS) programs is an invaluable intervention in the ongoing efforts to contain the threat of antimicrobial resistance (AMR), particularly in low-resource settings. This study evaluates the impact of the Telementoring, Education, and Advocacy Collaboration initiative for Health through Antimicrobial Stewardship (TEACH AMS), which uses the virtual Extension for Community Healthcare Outcomes (ECHO) learning model to enhance AMS capacity in Kenya, Ghana, and Malawi. Methods: A mixed-methods approach was used, which included attendance data collection, facility-level assessments, post-session and follow-up surveys, as well as focus group discussions. Results: Between September 2023 and February 2025, 77 virtual learning sessions were conducted, engaging 2445 unique participants from hospital-based AMS committees and health professionals across the three countries. Participants reported significant knowledge gain, and data showed facility improvements in two core AMS areas, including the implementation of multidisciplinary ward-based interventions/communications and enhanced monitoring of antibiotic resistance patterns. Along those lines, participants reported that the program assisted them in improving prescribing and culture-based treatments, and also evidence-informed antibiotic selection. The evidence of implementing ward-based interventions was further stressed in focus group discussions, as well as other strengthened practices like point-prevalence surveys, and development or revision of stewardship policies. Substantial improvements in microbiology services were also shared by participants, particularly in Malawi. Other practices mentioned were strengthened multidisciplinary communication, infection prevention efforts, and education of patients and the community. Conclusion: Our findings suggest that a virtual case-based learning educational intervention, providing structured and tailored AMS capacity building, can drive behavior change and strengthen healthcare systems in low resource settings. Future efforts should aim to scale up the engagements and sustain improvements to further strengthen AMS capacity. Full article

Background/Objectives: Nowadays, intravascular lithotripsy (IVL) has emerged as a novel technique for treatment of vascular calcifications, first in coronary and then in peripheral arteries. In the current literature there is little evidence that describes IVL as an effective and safe solution in treating severe aortic and aorto-iliac calcifications. The aim of this study is to report current available data about the use of IVL in treating aortic and aorto-iliac calcified lesions and its application in facilitating other endovascular procedures. Methods: the present review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) Guidelines. Preliminary searches were conducted on MEDLINE and Pubmed from January 2015 to February 2025. Studies were divided into 3 main categories depending on the location of calcifications and the type of treatment: IVL in visceral and infrarenal obstructive disease (group 1), IVL in aorto-iliac obstructive disease (group 2), IVL used to facilitate other endovascular procedures. Main primary outcomes in the perioperative period were technical and clinical successes and perioperative complications. Primary outcomes at 30 days and mid-term (2 years) were overall survival, limb salvage rate, primary patency, primary assisted patency, secondary patency, and residual stenosis. Results: Sixteen studies were identified for a total of 1674 patients. Technical and clinical successes were 100%, with low rates of perioperative complications. Dissection rate reaches up to 16.1% in some studies, without any differences compared to plain old balloon angioplasty (POBA) alone (22.8%; p = 0.47). At 30 days, limb salvage and survival rates were 100%. At 2 years, primary patency, assisted primary patency, and secondary patency were 95%, 98%, and 100%, respectively, with no difference compared to IVL + stenting. Conclusions: IVL has emerged as a novel approach to treat severe calcified lesions in visceral and aorto-iliac atherosclerotic disease and to facilitate other endovascular procedures. This technique seems to offer satisfactory early and mid-term outcomes in terms of primary, primary assisted patency, and secondary patency with low complication rates. Full article

Adverse drug events (ADEs) significantly impact patient safety and health outcomes. Manual ADE detection from clinical narratives is time-consuming, labor-intensive, and costly. Recent advancements in large language models (LLMs), including transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformer (GPT) series, offer promising methods for automating ADE extraction from clinical data. These models have been applied to various aspects of pharmacovigilance and clinical decision support, demonstrating potential in extracting ADE-related information from real-world clinical data. Additionally, chatbot-assisted systems have been explored as tools in clinical management, aiding in medication adherence, patient engagement, and symptom monitoring. This narrative review synthesizes the current state of LLMs in ADE detection from a clinical perspective, organizing studies into categories such as human-facing decision support tools, immune-related ADE detection, cancer-related and non-cancer-related ADE surveillance, and personalized decision support systems. In total, 39 articles were included in this review. Across domains, LLM-driven methods have demonstrated promising performances, often outperforming traditional approaches. However, critical limitations persist, such as domain-specific variability in model performance, interpretability challenges, data quality and privacy concerns, and infrastructure requirements. By addressing these challenges, LLM-based ADE detection could enhance pharmacovigilance practices, improve patient safety outcomes, and optimize clinical workflows. Full article

Background: Congenital diarrhea is persistent diarrhea that manifests during the neonatal period. Mutations in DGAT1, which is crucial for triglyceride synthesis and lipid absorption in the small intestine, are causal factors for congenital diarrhea. In this study, we aimed to determine the value of tissue RNA sequencing (RNA-seq) for assisting with the clinical diagnosis of some genetic variants of uncertain significance. Methods: We clinically evaluated a patient with watery diarrhea, vomiting, severe malnutrition, and total parenteral nutrition dependence. Possible pathogenic variants were detected using whole-exome sequencing (WES). RNA-seq was utilized to explore the transcriptional alterations in DGAT1 variants identified by WES with unknown clinical significance, according to the American College of Medical Genetics guidelines. Systemic examinations, including endoscopic and histopathological examinations of the intestinal mucosa, were conducted to rule out other potential diagnoses. Results: We successfully diagnosed a patient with congenital diarrhea and protein-losing enteropathy caused by a DGAT1 mutation and reviewed the literature of 19 cases of children with DGAT defects. The missense mutation c.620A>G, p.Lys207Arg located in exon 15, and the intronic mutation c.1249-6T>G in DGAT1 were identified by WES. RNA-seq revealed two aberrant splicing events in the DGAT1 gene of the patient’s small intestinal tissue. Both variants lead to loss-of-function consequences and are classified as pathogenic variants of congenital diarrhea. Conclusions: Rare DGAT1 variants were identified as pathogenic evidence of congenital diarrhea, and the detection of tissue-specific mRNA splicing and transcriptional effects can provide auxiliary evidence. Full article

Automated movement intention is crucial for brain–computer interface (BCI) applications. The automatic identification of movement intention can assist patients with movement problems in regaining their mobility. This study introduces a novel approach for the automatic identification of movement intention through finger tapping. This work has compiled a database of EEG signals derived from left finger taps, right finger taps, and a resting condition. Following the requisite pre-processing, the captured signals are input into the proposed model, which is constructed based on graph theory and deep convolutional networks. In this study, we introduce a novel architecture based on six deep convolutional graph layers, specifically designed to effectively capture and extract essential features from EEG signals. The proposed model demonstrates a remarkable performance, achieving an accuracy of 98% in a binary classification task when distinguishing between left and right finger tapping. Furthermore, in a more complex three-class classification scenario, which includes left finger tapping, right finger tapping, and an additional class, the model attains an accuracy of 92%. These results highlight the effectiveness of the architecture in decoding motor-related brain activity from EEG data. Furthermore, relative to recent studies, the suggested model exhibits significant resilience in noisy situations, making it suitable for online BCI applications. Full article

Background: Studies have demonstrated a positive correlation between high body mass index (BMI) and an increased risk of Clostridioides difficile infection (CDI), independent of antibiotic usage or healthcare exposures. Aim: To compare the outcomes of obese (BMI ≥ 30 kg/m²) and non-obese (BMI < 30 kg/m²) hospitalized patients with CDI. Methods: This retrospective cohort study included patients with CDI hospitalized in Beilinson hospital between January 2013 and January 2020. The primary outcome was 90-day all-cause mortality. Secondary outcomes included 30-day mortality, colectomy, intensive care unit (ICU) admission and length of hospital stay (LOS). Multivariate analysis was performed to identify the risk factors independently associated with 90-day mortality. Results: The study included 889 patients: 131 (15%) obese and 758 (85%) non-obese. The obese group was younger (median age 65 years vs. 73 years (p < 0.01)) and with a higher rate of diabetes mellitus (57/131 (44%) vs. 180/758 (24%) (p < 0.01)). The 90-day mortality was lower in the obese group: 19/131 (15%) vs. 170/752 (23%) (p = 0.04). The 30-day mortality was 8/131 (6%) vs. 96/757 (13%) (p = 0.03). ICU admission was 9/131 (7%) vs. 23/758 (3%) (p = 0.03), and median LOS was 19 vs. 12 days (p < 0.01) in obese and non-obese groups, respectively. In the multivariable analysis, after adjustment for age, Charlson’s comorbidity index ≥3, assistance in activities of daily living, treatment with proton pump inhibitors and severity of illness, obesity was not a significant risk factor for 90-day mortality (OR = 0.65, 95% CI: 0.38–1.01; p = 0.1). Conclusions: In this study, obesity was not significantly associated with 90-day mortality after adjustment for other risk factors; however, ICU admission was higher and LOS longer in this group. Full article

Objective: To develop and validate a ResNet-50-based deep learning model for automatic detection of osteomyelitis (DFO) in plain radiographs of patients with diabetic foot ulcers (DFUs). Research Design and Methods: This retrospective study included 168 patients with type one or type two diabetes and clinical suspicion of DFO confirmed via a surgical bone biopsy. An experienced clinician and a pretrained ResNet-50 model independently interpreted the radiographs. The model was developed using Python-based frameworks with ChatGPT assistance for coding. The diagnostic performance was assessed against the histopathological findings, calculating sensitivity, specificity, the positive predictive value (PPV), the negative predictive value (NPV), and the likelihood ratios. Agreement between the AI model and the clinician was evaluated using Cohen’s kappa coefficient. Results: The AI model demonstrated high sensitivity (92.8%) and PPV (0.97), but low-level specificity (4.4%). The clinician showed 90.2% sensitivity and 37.8% specificity. The Cohen’s kappa coefficient between the AI model and the clinician was −0.105 (p = 0.117), indicating weak agreement. Both the methods tended to classify many cases as DFO-positive, with 81.5% agreement in the positive cases. Conclusions: This study demonstrates the potential of IA to support the radiographic diagnosis of DFO using a ResNet-50-based deep learning model. AI-assisted radiographic interpretation could enhance early DFO detection, particularly in high-prevalence settings. However, further validation is necessary to improve its specificity and assess its utility in primary care. Full article

Background/Objectives: High-risk percutaneous coronary interventions (HR-PCIs) often require mechanical circulatory support (MCS) to maintain hemodynamic stability. Intra-aortic balloon pump (IABP) and percutaneous left ventricular assist device (PLVAD) are two commonly used MCS devices that differ in their mechanisms. We aimed to evaluate and compare the clinical outcomes associated with IABP and PLVAD use in HR-PCIs without cardiogenic shock. Methods: We conducted a search of PubMed, Scopus, Cochrane, Mendeley, Web of Science, and Embase to identify relevant randomized controlled trials and cohort studies, and we included 13 studies for the systematic review and meta-analysis. The primary goal was to define the difference in early mortality (in-hospital and 30-day mortality), major bleeding, and major adverse cardiovascular event (MACE) components (cardiogenic shock, acute kidney injury (AKI), and stroke/TIA) in IABP and PLVAD. We used a random-effects model with the Mantel–Haenszel statistical method to estimate odds ratios (ORs) and 95% confidence intervals. Results: Among 1 trial and 12 cohort studies (35,554 patients; 30,351 IABP and 5203 PLVAD), HR-PCI with IABP was associated with a higher risk of early mortality (OR = 1.53, 95% CI [1.21, 1.94]) and cardiogenic shock (OR = 2.56, 95% CI [1.98, 3.33]) when compared to PLVAD. No significant differences were found in the rates of arrhythmia, major bleeding, AKI, stroke/TIA, or hospital length of stay. Conclusions: In high-risk PCIs, PLVAD use is associated with lower early mortality and cardiogenic shock risk compared to IABP, with no significant differences in other major outcomes. Full article