Search Results (149)

Background/Objectives: Telemedicine has emerged as a pivotal approach to improving access to diabetic retinopathy (DR) screening, diagnosis, management, and monitoring. Over the past two decades, rapid advancements in digital imaging, mobile health technologies, and artificial intelligence have substantially expanded the role of teleophthalmology in DR, resulting in a large volume of pertinent publications. This study aimed to provide a scientific overview of telemedicine applied to DR through bibliometric analysis. Methods: A search of the Web of Science Core Collection was conducted on 15 November 2025 to identify English-language original research and review articles regarding telemedicine for DR. Bibliographic data from relevant publications were extracted and underwent quantitative analysis and visualization using the tools Bibliometrix and VOSviewer. Results: A total of 515 articles published between 1998 and 2025 were included in our analysis. During this period, the research field of telemedicine for DR exhibited an annual growth rate of 13.14%, with publication activity markedly increasing after 2010 and peaking in 2020–2021. Based on the number of publications, United States, China, and Australia were the most productive countries, while Telemedicine and e-Health, Journal of Telemedicine and Telecare, and British Journal of Ophthalmology were the most relevant journals in the field. Keyword co-occurrence analysis revealed three major thematic clusters within the broader topic of telemedicine and DR, namely, public health-oriented work, telehealth service models, and applications of artificial intelligence technologies. Conclusions: The role of telemedicine in DR detection and care represents an expanding multidisciplinary field of research supported by contributions from multiple authors and institutions worldwide. As technological capabilities continue to evolve, ongoing innovation and cross-domain collaboration could further advance the applications of teleophthalmology for DR, promoting more accessible, efficient, and equitable identification and management of this condition. Full article

Background/Objectives: Mild cognitive impairment (MCI) affects multiple functional and cognitive domains, rendering it challenging to diagnose. Brief mental status exams are insensitive while detailed neuropsychological testing is time-consuming and presents accessibility issues. By contrast, the Oculo-Cognitive Addition Test (OCAT) is a rapid, objective tool that measures oculometric features during mental addition tasks under one minute. This study aims to develop artificial intelligence (AI)-derived predictive models using OCAT eye movement and time-based features for the early detection of those at risk for MCI, requiring more thorough assessment. Methods: The OCAT with integrated eye tracking was completed by 250 patients at the Mayo Clinic Arizona Department of Neurology. Raw gaze data analysis yielded time-related and eye movement features. Random Forest and univariate decision trees were the feature selection methods used to identify predictors of Dementia Rating Scale (DRS) outcomes. Logistic regression (LR) and K-nearest neighbors (KNN) supervised models were trained to classify PMCI using three feature sets: time-only, eye-only, and combined. Results: LR models achieved the highest performance using the combined time and eye movement features, with an accuracy of 0.97, recall of 0.91, and an AUPRC of 0.95. The eye-only and time-only LR models also performed well (accuracy = 0.93), though with slightly lower F1-scores (0.87 and 0.86, respectively). Overall, models leveraging both time and eye movement features consistently outperformed those using individual feature sets. Conclusions: Machine learning models trained on OCAT-derived features can reliably predict DRS outcomes (PASS/FAIL), offering a promising approach for early MCI identification. With further refinement, OCAT has the potential to serve as a practical and scalable cognitive screening tool, suitable for use in clinics, at the bedside, or in remote and resource-limited settings. Full article

Background: Drug-resistant tuberculosis (DR-TB) constitutes a serious threat to global public health due to the increase in strains resistant to multiple drugs, especially isoniazid and rifampicin. This resistance increases mortality, estimated at 25.6% globally, and complicates treatments due to its high toxicity and cost. Materials and Methods: A quantitative ecological study was carried out with data on drug-resistant tuberculosis reported in Sivigila in the years (2020–2023) SIVIGILA database. 1694 cases were analyzed, considering sociodemographic variables such as age, sex, nationality and prioritized population groups. Departmental rates per 100,000 inhabitants were calculated with DANE projection, from these choropleth maps were developed. Applying a Kulldorff spatial scan under a Poisson model using the SMERC package of R (version 4.5.1), with windows centered on each department and Monte Carlo simulation contrast to identify high-risk clusters (RR > 1). Results: (DR-TB) Predominantly in men aged 30–44 years, with a progressive increase until 2023 (IRR = 2.11). Three high-risk clusters were detected in the southwest and center of the country. Discussion: Drug-resistant tuberculosis in Colombia showed a sustained increase in the years of study, with a cumulative increase of 110% compared to 2020, associated with economically active people more exposed due to occupational and social factors. The greatest burden was observed in the general population. Cases also increased in groups with social and health vulnerability conditions. Conclusions: The departments of Risaralda, Meta, and Valle del Cauca presented the highest drug resistance rates in Colombia. Full article

Emerging evidence indicates a high rate (>10%) of drug resistance (DR) associated with integrase strand transfer inhibitors (INSTIs) in developed countries, although there is limited information on DR during INSTI treatment in Uganda. With the increased use of INSTIs as standard first-line treatment, monitoring for DR using next-generation sequencing (NGS) has become essential. NGS can detect the lower-frequency variants that may be missed by traditional Sanger sequencing (SS). This study evaluates the diagnostic accuracy of next-generation sequencing (NGS) compared to Sanger sequencing for detecting HIV-1 INSTI resistance mutations and estimates the prevalence and factors associated with drug resistance among adults with virologic failure on INSTI-based regimens in Uganda. Utilizing the Illumina MiSeq platform for NGS, data was analyzed using STATA V.18 and a logistic regression model at 5% level of significance. This study demonstrates that NGS achieved 100% sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy in detecting major mutations. NGS identified INSTI DRMs in 4% of adults at a ≥20% threshold and was able to detect both high- and low-abundance variants, which could have important implications for clinical outcomes. This study emphasizes the need for HIVDR testing before antiretroviral therapy (ART) initiation, given the increasing use of INSTIs. We recommend that healthcare providers adopt more sensitive diagnostics such as NGS and use detailed resistance profiles to tailor antiretroviral therapies. This approach is critical for effectively managing and preventing drug-resistant HIV strains. Full article

Background and Objectives: Helicobacter pylori (H. pylori) infection remains one of the most common chronic bacterial infections worldwide and is associated with a wide range of gastrointestinal disorders, including gastritis, peptic ulcer disease, and gastric cancer. Increasing rates of antibiotic resistance, particularly to clarithromycin and fluoroquinolones, represent a major therapeutic challenge. The objective of this study was to determine the prevalence of resistance-associated mutations in H. pylori-positive gastric biopsy samples from western Romania. Materials and Methods: We conducted a prospective study from January to December 2024, enrolling 138 patients undergoing gastroscopy. Biopsies were collected from the gastric antrum, and H. pylori infection was confirmed using the rapid urease test (RUT). Positive samples were further analyzed with the GenoType HelicoDR assay to detect mutations in the 23S rRNA gene (clarithromycin resistance) and gyrA gene (fluoroquinolone resistance). Clinical, demographic, and endoscopic data were also collected. Results:H. pylori infection was confirmed in 41.3% of the patients (57), of whom 63.2% (36) were treatment-naïve and 36.8% (21) had prior eradication therapy. Among treatment-naïve patients, clarithromycin resistance was identified in 19.4%, whereas previously treated patients showed a markedly higher resistance rate of 47.6% (p = 0.018). All clarithromycin-resistant cases carried the A2147G (23S MUT3) mutation. Fluoroquinolone resistance was present in 13.9% of naïve patients and increased to 23.8% in those with prior eradication therapy, with resistance linked to gyrA mutations at codons 87 (N87K) and 91 (D91 variants). Combined resistance to both antibiotics was observed only in a subset of previously treated patients. Conclusions: Primary resistance to clarithromycin in western Romania exceeds the 15% threshold defined by international guidelines, making clarithromycin-based triple therapy unsuitable as an empirical first-line option. The findings support the use of bismuth quadruple therapy as the preferred empirical regimen in this region. Also, molecular testing proved effective for rapid identification of resistance-associated mutations. Full article

Background/Objectives: Diabetes is Mexico’s second-leading cause of death, primary cause of disability, and diabetic retinopathy (DR) associated with this disease is the leading cause of vision loss among the working population. Limited healthcare funding and inequitable access hinder diagnosis and treatment, leaving 32% undiagnosed and at risk of developing serious complications such as DR. With screening rates declining, nonprofits like the Association to Prevent Blindness in Mexico (APEC) play a crucial role in detecting diabetes and DR, reducing healthcare costs, and improving patient outcomes. Methods: This study analyzes data from over 25,000 first-time patients screened at APEC in 2023, providing a unique empirical resource on diabetes and DR in Mexico. Using the Social Return on Investment (SROI) approach, it evaluates program costs (medical resources, equipment, and personnel) against patient benefits. These benefits are quantified as the probability that newly diagnosed or uncontrolled diabetes patients begin treatment, thus preventing DR, weighted by the Value of Statistical Life (VSL). Results: Of the total screened patients, 17.2% had diabetes. Among them, 20.0% were unaware of their condition, while the remaining 80.0% knew their diagnosis. Notably, 25.8% of those who were aware of their diagnosis did not have diabetes under control. Considering all costs associated with the first-time ophthalmology patients screening program and assuming only a portion of patients would seek treatment, every peso invested by APEC has the potential to generate the equivalent to 542 pesos in patient well-being. When factoring in the subsequent costs of diabetes control treatment borne by the patients, the potential Benefit–Cost Ratio is estimated at 9:1. These results proved consistent to sensitivity analysis for key assumptions affecting the estimated benefits and costs. Conclusions: The study demonstrates that integrating routine diabetes screening into specialized ophthalmologic care can generate substantial social value through timely intervention, as early detection promotes better diabetes management and helps prevent complications beyond diabetic retinopathy. Full article

A tracking method based on adaptive morphological correlation and neural predictive models is presented. The morphological correlation filters are optimized according to the aggregated binary dissimilarity-to-matching ratio criterion and are adapted online to appearance variations of the target across frames. Morphological correlation filtering enables reliable detection and accurate localization of the target in the scene. Furthermore, trained neural models predict the target’s expected location in subsequent frames and estimate its bounding box from the correlation response. Effective stages for drift correction and tracker reinitialization are also proposed. Performance evaluation results for the proposed tracking method on four image datasets are presented and discussed using objective measures of detection rate (DR), location accuracy in terms of normalized location error (NLE), and region-of-support estimation in terms of intersection over union (IoU). The results indicate a maximum average performance of 90.1% in DR, 0.754 in IoU, and 0.004 in NLE on a single dataset, and 83.9%, 0.694, and 0.015, respectively, across all four datasets. In addition, the results obtained with the proposed tracking method are compared with those of five widely used correlation filter-based trackers. The results show that the suggested morphological-correlation filtering, combined with trained neural models, generalizes well across diverse tracking conditions. Full article

Background/Objectives: Sentinel lymph node biopsy (SLNB) is currently the standard approach for axillary staging in breast cancer. Conventional techniques are radioisotope-based (Technetium-99m, Tc99m) and remain widely used, but novel tracers like Indocyanine Green (ICG) fluorescence provide potential advantages regarding feasibility and logistics. Methods: We conducted a prospective, observational study including 476 female patients diagnosed with primary invasive breast cancer who underwent SLNB at the Institute of Oncology “Prof. Dr. I. Chiricuță”, Cluj-Napoca, Romania, between January 2022 and May 2025. Clinical, surgical, and pathological variables were systematically extracted. SLNB was performed using either Tc99m or ICG, according to institutional protocols. Comparative analyses were performed to evaluate sentinel node characteristics, histopathological parameters, and positive surgical margins predictors. Results: The median age was 60 years (IQR: 52–69). Breast-conserving surgery (BCS) was performed in 77.9% of cases, while mastectomy was performed in 22.1%. Sentinel lymph node positivity was reported in 25.6% of cases, with no significant differences in the number of excised or metastatic nodes between Tc99m and ICG (mean nodes: 3.23 vs. 3.20, p = 0.860; mean positive nodes: 0.35 vs. 0.36, p = 0.897). Histologically, invasive carcinoma NST was predominant (90.1%), and surgical margins were negative in 96.8% of patients, with all margin-positive cases occurring following BCS. No pathological markers (grade, Ki67, TILs, DCIS extent) predicted margin status or nodal involvement. Notably, younger age correlated inversely with the extent of ductal carcinoma in situ (r = −0.21, p < 0.00001). Conclusions: Tc99m and ICG provided comparable diagnostic performance in performing SLNB, with equivalent rates of nodal detection and pathological yield. These findings support that ICG is a safe and effective alternative for routine axillary staging in breast cancer. Full article

Introduction: Antimicrobial resistance is a significant public health issue worldwide, associated with limited treatment options and with major consequences for healthcare systems. Our study aims to assess rates and patterns of resistance to five last-resort antimicrobials in a cohort of carbapenemase-producing Klebsiella pneumoniae strains, isolated over a one-year interval. Additionally, we have tested two potentially synergistic combinations for in vitro efficacy. Methods: This prospective observational study evaluated Klebsiella pneumoniae strains with diminished carbapenem susceptibility from patients admitted to the National Institute for Infectious Diseases “Prof. Dr. Matei Balș” in Bucharest between August 2023 and July 2024. Strains presenting a minimum inhibitory concentration to meropenem of >0.125 μg/mL underwent phenotypic enzyme production testing, followed by synergistic testing to identify antimicrobial salvage therapy options. A subset of these strains was analysed for the detection of plasmid-mediated resistance genes, using a custom workflow for DNA extraction and amplification/detection. Results: A total of 139 non-duplicate strains were isolated, with 129 (92.8%) being carbapenemase producers. These 129 strains were phenotypically diverse: 29 (22.5%) were NDM, 12 (9.3%) OXA-48 type, 8 (6.2%) KPC, while most of them (62.0%) were double carbapenemase producers: 79 (61.2%) NDM and OXA-48-type, and one strain was NDM and KPC. Forty-six strains were resistant to cefiderocol (35.7%), 108 (83.7%) to ceftazidime/avibactam, 127 (98.4%) to ceftolozane/tazobactam, 116 (90.0%) to imipenem/relebactam and 127 (98.4%) to aztreonam. The association of aztreonam with ceftazidime/avibactam demonstrated a synergistic effect in 127 (98.5%) strains, while aztreonam with imipenem/relebactam was efficient in vitro against 103 (79.8%) strains. Conclusions: Antimicrobial resistance remains a concerning phenomenon among Enterobacterales, especially when considering the increasing resistance rates even against salvage therapy antimicrobials. Full article

Background/Objectives: Cytomegalovirus (CMV) infection is a frequent complication after lung transplantation, especially in high-risk donor-positive/recipient-negative (D+/R−) patients. CMV-specific hyperimmunoglobulin (CMV-HIG), administered either with antivirals or as monotherapy, may be beneficial for preventing or treating CMV infection in selected clinical scenarios. This study evaluated CMV-HIG indications and their impact on clinical outcomes in our lung transplant unit. Methods: We retrospectively analyzed adult lung transplant recipients (2010–2023) who received ≥2 doses of CMV-HIG for universal prophylaxis, monotherapy prophylaxis, preemptive therapy, or treatment of invasive disease. Results: CMV-HIG was administered to 204 out of 336 recipients (61%). CMV-HIG was well tolerated, with no treatment-related adverse events. Indications were preemptive therapy (63%), universal prophylaxis (24%), monotherapy prophylaxis (7%), and treatment of invasive disease (6%). CMV-HIG was well tolerated, with no treatment-related adverse events. No patients developed invasive disease during combination prophylaxis or preemptive treatment. The combination treatment of patients with invasive disease was also effective, and no cases of VGC resistance were detected. CMV-HIG monoprophylaxis has allowed us to delay or prevent viral replication in recipients who developed VGC side effects. Rates of acute rejection, Chronic Lung Allograft Dysfunction (CLAD), and overall survival were similar across CMV risk groups. Conclusions: Our results showed that the combined use of CMV-HIG and antiviral agents is effective in preventing CMV infection and disease in high-risk lung transplant recipients. This combination is also useful in treating invasive disease and preventing VGC resistance. Additionally, CMV-HIG monoprohylaxis can delay or prevent viral replication in recipients experiencing VGC-related side effects. These findings support the use of CMV-HIG in selected clinical settings, although prospective studies are needed to define its potential benefits within the current therapeutic armamentarium. Full article

Japanese encephalitis virus (JEV), a mosquito-borne flavivirus, poses a significant public health threat in Asia. Although Culex species are primary vectors, the role of Aedes albopictus in JEV transmission has gained attention under changing ecological conditions. This study evaluated the vector competence of Ae. albopictus for three JEV genotypes: I (GI), III (GIII), and V (GV). Laboratory-reared Ae. albopictus were orally challenged with each genotype, and infection rate (IR), dissemination rate (DR), head–thorax positivity rate (HTR, proxy for potential transmission), and transmission rate (defined as saliva positivity) were assessed at 7 and 14 days post-infection (dpi). Ae. albopictus showed marked genotype-specific differences. By 14 dpi, GV had the highest DR (100.0%) and HTR (71.7%), with viral RNA detected in 36.7% of TR. GIII showed moderate competence (76.9% DR, 39.3% HTR), but low TR (6.6%). In contrast, GI-infected mosquitoes exhibited minimal infection and negligible transmission, with viral RNA rarely detected beyond the midgut. These findings indicate that Ae. albopictus is highly competent for transmitting JEV genotype V and moderately for genotype III, but not genotype I, under laboratory conditions. This highlights its potential role in the transmission dynamics of emerging JEV genotypes and underscores the need for continued surveillance. Full article

Early detection of breast cancer via mammography enhances patient survival rates, prompting this study to assess object detection models—Cascade R-CNN, YOLOv12 (S, L, and X variants), RTMDet-X, and RT-DETR-X—for detecting masses and calcifications across four public datasets (INbreast, CBIS-DDSM, VinDr-Mammo, and EMBED). The evaluation employs a standardized preprocessing approach (CLAHE, cropping) and augmentation (rotations, scaling), with transfer learning tested by training on combined datasets (e.g., INbreast + CBIS-DDSM) and validating on held-out sets (e.g., VinDr-Mammo). Performance is measured using precision, recall, mean Average Precision at IoU 0.5 (${\mathrm{mAP}}_{50}$), and F1-score. YOLOv12-L excels in mass detection with an ${\mathrm{mAP}}_{50}$ of 0.963 and F1-score up to 0.917 on INbreast, while RTMDet-X achieves an ${\mathrm{mAP}}_{50}$ of 0.697 on combined datasets with transfer learning. Preprocessing improves ${\mathrm{mAP}}_{50}$ by up to 0.209, and transfer learning elevates INbreast performance to an ${\mathrm{mAP}}_{50}$ of 0.995, though it incurs 5–11% drops on CBIS-DDSM (0.566 to 0.447) and VinDr-Mammo (0.59 to 0.5) due to domain shifts. EMBED yields a low ${\mathrm{mAP}}_{50}$ of 0.306 due to label inconsistencies, and calcification detection remains weak (${\mathrm{mAP}}_{50}$ < 0.116), highlighting the value of high-capacity models, preprocessing, and augmentation for mass detection while identifying calcification detection and domain adaptation as key areas for future investigation. Full article

Background: Veterinary antibiotics are widely used in food-producing animals, raising public health concerns due to drug residues and the risk of antimicrobial resistance. Rapid and reliable detection systems are critical to ensure food safety and regulatory compliance. Colloidal gold immunoassay (CGIA)-based antigen–antibody test cards are widely used in food safety for the rapid screening of veterinary antibiotic residues. However, manual interpretation of test cards remains inefficient and inconsistent. Methods: To address this, we propose a complete AI-based detection system for veterinary antibiotic residues. The system is built on the Rockchip RK3568 platform and integrates a five-megapixel OV5640 autofocus USB camera (60° field of view) with a COB LED strip (6000 K, rated 5 W/m). It enables high-throughput, automated interpretation of colloidal gold test cards and can generate structured detection reports for regulatory documentation and quality control. The core challenge lies in achieving accurate and fast inference on resource-constrained embedded devices, where traditional detection networks often struggle to balance model size and performance. To this end, we propose VetStar, a lightweight detection algorithm specifically optimized for this task. VetStar integrates StarBlock, a shallow feature extractor, and Depthwise Separable-Reparameterization Detection Head (DR-head), a compact, partially decoupled detection head that accelerates inference while preserving accuracy. Results: Despite its compact size, with only 0.04 M parameters and 0.3 GFLOPs, VetStar maintains strong performance after distillation with the Bridging Cross-task Protocol Inconsistency Knowledge Distillation (BCKD) method. For our custom Veterinary Drug Residue Rapid Test Card (VDR-RTC) dataset, it achieves an mAP50 of 97.4 and anmAP50-95of 89.5. When deployed on the RK3568 device, it delivers results in just 5.4 s—substantially faster than comparable models. Conclusions: These results highlight the system’s strong potential for high-throughput, cost-effective, and rapid veterinary antibiotic residue screening, supporting food safety surveillance efforts. Full article

Background/Objectives: Lymph node (LN) assessment for cases of endometrial intraepithelial neoplasia (EIN), a known precursor to endometrial cancer (EC), is a topic of debate. Some experts believe this practice could avoid re-staging of disease and influence the decision to administer adjuvant treatment. However, it is known that obtaining sentinel lymph node (SLN) biopsies in patients with an elevated body mass index (BMI) can be more challenging. We thus sought to evaluate the effect of BMI on the SLN detection rate (DR) during robotic hysterectomy in EIN cases. Methods: We conducted a retrospective chart review for patients with a pre-operative diagnosis of EIN who underwent robotic hysterectomy with SLN sampling. Five BMI categories were determined according to the literature. Distribution normality was assessed with the Kolmogorov–Smirnov test. Continuous variables, non-parametric continuous variables and categorical variables were assessed with the appropriate statistical tests (two-tailed Student’s t-tests, Mann–Whitney U-tests, and chi-squared tests, respectively). Results: 115 patients were included (average BMI of 34.75 ± 9.38 SD). The bilateral SLN DR was not significantly different between BMI groups (p = 0.606). The difference in unilateral SLN DR between BMI groups was also non-significant (p = 0.269). When examining high BMI subgroups (BMI > 30 and BMI > 40), no significant difference was found in bilateral nor unilateral SLN DR. A logistic regression model showed that for every unit of BMI, the likelihood of SLN DR did not change significantly. Conclusions: We found no connection between obesity (BMI > 30) or morbid obesity (BMI > 40) and reduced SLN DR in EIN cases, nor a significant variation in the DR when comparing all the different BMI subgroups. Full article

Road traffic accidents remain a major global public health concern, where complex urban driving environments significantly elevate drivers’ visual load and accident risks. Unlike existing research that adopts a macro perspective by considering multiple factors such as the driver, vehicle, and road, this study focuses on the driver’s visual load, a key safety factor, and its direct source—the driver’s visual environment. We have developed an interpretable framework combining computer vision and machine learning to quantify how road scene features influence oculomotor behavior and scene-induced visual load, establishing a complete and interpretable link between scene features, eye movement behavior, and visual load. Using the DR(eye)VE dataset, visual attention demand is established through occlusion experiments and confirmed to correlate with eye-tracking metrics. K-means clustering is applied to classify visual load levels based on discriminative oculomotor features, while semantic segmentation extracts quantifiable road scene features such as the Green Visibility Index, Sky Visibility Index and Street Canyon Enclosure. Among multiple machine learning models (Random Forest, Ada-Boost, XGBoost, and SVM), XGBoost demonstrates optimal performance in visual load detection. SHAP analysis reveals critical thresholds: the probability of high visual load increases when pole density exceeds 0.08%, signage surpasses 0.55%, or buildings account for more than 14%; while blink duration/rate decrease when street enclosure exceeds 38% or road congestion goes beyond 25%, indicating elevated visual load. The proposed framework provides actionable insights for urban design and driver assistance systems, advancing traffic safety through data-driven optimization of road environments. Full article