Search Results (281)

Background/Objectives: The extensive use of computed tomography (CT) has led to a significant increase in the detection of small and non-palpable pulmonary nodules, necessitating the use of invasive methods for definitive diagnosis. Video-assisted thoracoscopic surgery (VATS) has become the preferred procedure for nodule resections; however, intraoperative localization remains challenging, especially for deep or subsolid lesions. This study explores whether SPECT/CT improves the technical and clinical outcomes of radio-guided occult lesion localization (ROLL) before uniportal video-assisted thoracoscopic surgery (u-VATS). Methods: This is a retrospective study involving consecutive patients referred for the resection of pulmonary nodules who underwent CT-guided ROLL followed by u-VATS between September 2017 and December 2024. From January 2023, SPECT/CT was systematically added after planar imaging. The cohort was divided into a planar group and a planar + SPECT/CT group. The inclusion criteria involved nodules sized ≤ 2 cm, with ground glass or solid characteristics, located at a depth of <6 cm from the pleural surface. ^99mTc-MAA injected activity, timing, the classification of planar and SPECT/CT image findings (focal uptake, multisite with focal uptake, multisite without focal uptake), spillage, and post-procedure complications were evaluated. Statistical analysis was performed, with continuous data expressed as the median and categorical data as the number. Comparisons were made using chi-square tests for categorical variables and the Mann–Whitney U test for procedural duration. Cohen’s kappa coefficient was calculated to assess agreement between imaging modalities. Results: In total, 125 patients were selected for CT-guided radiotracer injection followed by uniportal-VATS. The planar group and planar + SPECT/CT group comprised 60 and 65 patients, respectively. Focal uptake was detected in 68 (54%), multisite with focal uptake in 46 (36.8%), and multisite without focal uptake in 11 patients (8.8%). In comparative analyses between planar and SPECT/CT imaging in 65 patients, 91% exhibited focal uptake, revealing significant differences in classification for 40% of the patients. SPECT/CT corrected the classification of 23 patients initially categorized as multisite with focal uptake to focal uptake, improving localization accuracy. The mean procedure duration was 39 min with SPECT/CT. Pneumothorax was more frequently detected with SPECT/CT (43% vs. 1.6%). The intraoperative localization success rate was 96%. Conclusions: SPECT/CT imaging in the ROLL procedure for detecting pulmonary nodules before u-VATs demonstrates a significant advantage in reclassifying radiotracer positioning compared to planar imaging. Considering its limited impact on surgical success rates and additional procedural time, SPECT/CT should be reserved for technically challenging cases. Larger sample sizes, multicentric and prospective randomized studies, and formal cost–utility analyses are warranted. Full article

Background/Objectives: Since the World Health Organization (WHO) recommended HIV self-testing as an alternative to traditional facility-based testing in 2016, it has been increasingly adopted worldwide. This study aimed to evaluate the performance of the ConfiSign HIV Self-Test (GenBody Inc., Republic of Korea), a newly developed blood-based immunochromatographic assay for the qualitative detection of total antibodies (IgG and IgM) against HIV-1/HIV-2. Methods: The evaluation included four components: (1) retrospective analysis of 1400 archived serum samples (400 HIV-positive and 1000 HIV-negative samples), (2) prospective self-testing by 335 participants (112 HIV-positive participants and 223 individuals with an unknown HIV status, including healthy volunteers), (3) assessment using seroconversion panels and diverse HIV subtypes, and (4) analytical specificity testing for cross-reactivity and interference. The Elecsys HIV combi PT and Alinity I HIV Ag/Ab Combo assays were used as reference assays. Results: In retrospective testing, the ConfiSign HIV Self-Test achieved a positive percent agreement (PPA) of 100%, a negative percent agreement (NPA) of 99.2%, and a Cohen’s kappa value of 0.986, showing excellent agreement with the reference assays. In the prospective study, the test showed 100% sensitivity and specificity, with a low invalid result rate of 1.8%. All HIV-positive samples, including those with low signal-to-cutoff (S/Co) values in the Alinity I assay, were correctly identified. The test also reliably detected early seroconversion samples and accurately identified a broad range of HIV-1 subtypes (A, B, C, D, F, G, CRF01_AE, CRF02_AG, and group O) as well as HIV-2. No cross-reactivity or interference was observed with samples that were positive for hepatitis viruses, cytomegalovirus, Epstein–Barr virus, varicella zoster virus, influenza, HTLV-1, HTLV-2, or malaria. Conclusions: The ConfiSign HIV Self-Test demonstrated excellent sensitivity, specificity, and robustness across diverse clinical samples, supporting its reliability and practicality as a self-testing option for HIV-1/2 antibody detection. Full article

Objective: This proof-of-concept study aimed to evaluate and compare the clinical performance of two AI language models (ChatGPT-3.5 and OpenAI o3) in answering clinical pharmacy multiple-choice questions (MCQs), benchmarked against responses from specialist clinical pharmacists in Jordan, including academic preceptors and hospital-based clinicians. Methods: A total of 60 clinical pharmacy MCQs were developed based on current guidelines across four therapeutic areas: cardiovascular, endocrine, infectious, and respiratory diseases. Each item was reviewed by academic and clinical experts and then pilot-tested with five pharmacists to determine clarity and difficulty. Two ChatGPT models—GPT-3.5 and OpenAI o3—were tested using a standardized prompt for each MCQ, entered in separate sessions to avoid memory retention. Their answers were classified as true/false positives or negatives and retested after two weeks to assess reproducibility. Simultaneously, 25 licensed pharmacists (primarily from one academic institution and several hospitals in Amman) completed the same MCQs using validated references (excluding AI tools). Accuracy, sensitivity, specificity, and Cohen’s Kappa were used to compare AI and human performance, with statistical analysis conducted using appropriate tests at a significance level of p ≤ 0.05. Results: OpenAI o3 achieved the highest accuracy (83.3%), sensitivity (90.0%), and specificity (70.0%), outperforming GPT-3.5 (70.0%, 77.5%, 55.0%) and pharmacists (69.7%, 77.0%, 55.0%). AI performance declined significantly with increasing question difficulty. OpenAI o3 showed the highest accuracy in the cardiovascular domain (93.3%), while GPT-3.5 performed best in infectious diseases (80.0%). Reproducibility was higher for GPT-3.5 (81.6%, κ = 0.556) than OpenAI o3 (76.7%, κ = 0.364). Over two test rounds, GPT-3.5’s accuracy remained stable, whereas OpenAI o3’s accuracy decreased from 83.3% to 70.0%, indicating some variability. Conclusions: OpenAI o3 shows strong promise as a clinical decision-support tool in pharmacy, especially for low- to moderate-difficulty questions. However, inconsistencies in reproducibility and limitations in complex cases highlight the importance of cautious, supervised integration alongside human expertise. Full article

This retrospective study aimed to evaluate the prevalence, morphological types, and distribution patterns of palatoradicular grooves (PRGs) in maxillary anterior teeth using cone-beam computed tomography (CBCT) in a Turkish population. CBCT images of 1553 patients from the radiology archive of Ordu University Faculty of Dentistry (2021–2022) were reviewed. A total of 920 patients (4012 teeth) met the inclusion criteria. The presence, type, and localization of PRGs were assessed. Groove types were classified as Type 1, 2, 3A, or 3B; localization was recorded as mesial, distal, or midpalatal. Bilateral and unilateral occurrences were also analyzed. Statistical analysis involved chi-square tests, Tukey’s HSD, and Cohen’s kappa for intra-observer reliability. PRGs were detected in 23.6% of patients and 10.42% of teeth. Lateral incisors were most affected (87.56%). Type 1 grooves were most common (71.53%), with midpalatal localization being most frequent (54.07%). Bilateral grooves were significantly more prevalent than unilateral ones (p < 0.001). No significant association was found between groove type and tooth type or between gender and bilaterality. This study revealed a high prevalence of PRGs, especially in maxillary lateral incisors, with a significant tendency toward bilateral and midpalatal presentation. CBCT proved essential for detecting palatoradicular grooves, aiding diagnosis and treatment. Full article

Background: Artificial intelligence (AI) is increasingly integrated into orthodontic workflows, including digital model analysis modules embedded in orthodontic software. While these systems offer efficiency and automation, the accuracy and clinical reliability of AI-generated measurements and diagnostic assessments remain unclear. Therefore, to use AI systems safely and effectively in clinical orthodontics, it is important to check their results by comparing them with those of experienced orthodontists. Methods: Digital models of 48 patients were analyzed by the Orthodontist group and two AI platforms: Titan (full) and SoftSmile (Bolton only). Three orthodontists independently measured all variables using 3Shape OrthoAnalyzer, and group means were used for comparison. A subset of models was reanalyzed after two weeks to assess consistency. Data distribution was evaluated, and appropriate statistical tests were applied. Reliability was assessed using intraclass correlation coefficients (ICC) and Cohen’s kappa. Results: Almost perfect agreement was observed between the orthodontists and Titan AI in molar classification (κ = 0.955 right, κ = 0.900 left; p < 0.001), with perfect agreement reported across all groups—including between the orthodontists themselves—for Angle classification (κ = 1.00). In anterior and overall Bolton analyses, no meaningful agreement was found between the orthodontists and AI platforms. However, in a subset of patients where all three methods identified the tooth size discrepancy in the same arch (either maxilla or mandible), no significant differences were found in anterior (p = 0.226) or overall Bolton values (p = 0.795). Overjet, overbite, and space analysis values showed significant differences between the orthodontist and Titan groups (p < 0.001). ICC analysis indicated good to excellent intra- and inter-rater reliability within the orthodontist group (≥0.77), while both AI systems demonstrated excellent internal consistency, with ICC values exceeding 0.95. Conclusions: AI-based platforms showed high agreement with orthodontists only in Angle classification. While their performance in Bolton analysis was limited, significant differences were observed in other linear measurements, indicating the need for further refinement before clinical use. Full article

Background: Diabetic retinopathy (DR) is a leading cause of visual impairment worldwide. Manual grading of fundus images is the gold standard in DR screening, although it is time-consuming. Artificial intelligence (AI)-based algorithms offer a faster alternative, though concerns remain about their diagnostic reliability. Methods: A cross-sectional pilot study among patients (≥18 years) with diabetes was established for DR and diabetic macular edema (DME) screening at the Oslo University Hospital (OUH), Department of Ophthalmology, and the Norwegian Association of the Blind and Partially Sighted (NABP). The aim of the study was to evaluate the validity (accuracy, sensitivity, specificity) and reliability (inter-rater agreement) of automated AI-based compared to manual consensus (MC) grading of DR and DME, performed by a multidisciplinary team of healthcare professionals. Grading of DR and DME was performed manually and by EyeArt (Eyenuk) software version v2.1.0, based on the International Clinical Disease Severity Scale (ICDR) for DR. Agreement was measured by Quadratic Weighted Kappa (QWK) and Cohen’s Kappa (κ). Sensitivity, specificity, and diagnostic test accuracy (Area Under the Curve (AUC)) were also calculated. Results: A total of 128 individuals (247 eyes) (51 women, 77 men) were included, with a median age of 52.5 years. Prevalence of any vs. referable DR (RDR) was 20.2% vs. 11.7%, while sensitivity was 94.0% vs. 89.7%, specificity was 72.6% was 83.0%, and AUC was 83.5% vs. 86.3%, respectively. DME was detected only in one eye by both methods. Conclusions: AI-based grading offered high sensitivity and acceptable specificity for detecting DR, showing moderate agreement with manual assessments. Such grading may serve as an effective screening tool to support clinical evaluation, while ongoing training of human graders remains essential to ensure high-quality reference standards for accurate diagnostic accuracy and the development of AI algorithms. Full article

This study presents a GIS-based Weighted Overlay Process (WOP) for Small Hill Reservoir Best Siting (SHRBS) in the Umbria region (central Italy), with a focus on supporting regional-scale planning rather than site-specific engineering design. The WOP incorporated commonly adopted SHRBS criteria, with suitability scores defined through two approaches: Model A, based on scoring scales from the literature, and Model B, which assigns scores based on the frequency distribution of the various attributes observed in a database of over 3000 existing SHRs in the region. The comparison between the models revealed significant differences, particularly in the scores assigned to texture, precipitation, and contributing area. Models A and B, tested on the existing SHRs, indicated quite different average suitability values (2.68 and 3.30, respectively, on a 5-point scale) and only a slight agreement (weighted Cohen’s kappa Kw ≤ 0.13). Both models also showed poor agreement (Kw < 0) when compared with a third suitability model based solely on sedimentation risk, which was developed using the Sediment Delivery Ratio from the InVEST suite. This indicates that many sites considered highly suitable by models A and B were also highly susceptible to sedimentation. Given the economic and environmental implications of sedimentation, this study recommends explicitly incorporating sedimentation risk criteria into SHRBS methodologies to enhance the effectiveness of siting decisions. Full article

Background/Objectives: Deep learning (DL) has shown strong potential in analyzing food images, but few studies have directly predicted mass, energy, and macronutrient content from images. In addition to the importance of high-quality data, differences in country-specific food composition databases (FCDBs) can hinder model generalization. Methods: We assessed the performance of several standard DL models using four ground truth datasets derived from Nutrition5k—the largest image–nutrition dataset with ~5000 complex US cafeteria dishes. In light of developing an Italian dietary assessment tool, these datasets varied by FCDB alignment (Italian vs. US) and data curation (ingredient–mass correction and frame filtering on the test set). We evaluated combinations of four feature extractors [ResNet-50 (R50), ResNet-101 (R101), InceptionV3 (IncV3), and Vision Transformer-B-16 (ViT-B-16)] with two regression networks (2+1 and 2+2), using IncV3_2+2 as the benchmark. Descriptive statistics (percentages of agreement, unweighted Cohen’s kappa, and Bland–Altman plots) and standard regression metrics were used to compare predicted and ground truth nutritional composition. Dishes mispredicted by ≥7 algorithms were analyzed separately. Results: R50, R101, and ViT-B-16 consistently outperformed the benchmark across all datasets. Specifically, when replacing it with these top algorithms, reductions in median Mean Absolute Percentage Errors were 6.2% for mass, 6.4% for energy, 12.3% for fat, and 33.1% and 40.2% for protein and carbohydrates. Ingredient–mass correction substantially improved prediction metrics (6–42% when considering the top algorithms), while frame filtering had a more limited effect (<3%). Performance was consistently poor across most models for complex salads, chicken-based or eggs-based dishes, and Western-inspired breakfasts. Conclusions: The R101 and ViT-B-16 architectures will be prioritized in future analyses, where ingredient–mass correction and automated frame filtering methods will be considered. Full article

Background/Objectives: This study aims to evaluate the response accuracy and quality of three AI chatbots—GPT-4.0, Claude-2, and Llama-2—in answering multiple-choice questions in prosthodontic and restorative dentistry. Methods: A total of 191 text-based multiple-choice questions were selected from the prosthodontic and restorative dentistry sections of the United States Integrated National Board Dental Examination (INBDE) (n = 80) and the United Kingdom Overseas Registration Examination (ORE) (n = 111). These questions were inputted into the chatbots, and the AI-generated answers were compared with the official answer keys to determine their accuracy. Additionally, two dental specialists independently evaluated the rationales accompanying each chatbot response for accuracy, relevance, and comprehensiveness, categorizing them into four distinct ratings. Chi-square and post hoc Z-tests with Bonferroni adjustment were used to analyze the responses. The inter-rater reliability for evaluating the quality of the rationale ratings among specialists was assessed using Cohen’s Kappa (κ). Results: GPT-4.0 (65.4%; n = 125/191) demonstrated a significantly higher proportion of correctly answered multiple-choice questions when compared to Claude-2 (41.9%; n = 80/191) (p < 0.017) and Llama-2 (26.2%; n = 50/191) (p < 0.017). Significant differences were observed in the answer accuracy among all of the chatbots (p < 0.001). In terms of the rationale quality, GPT-4.0 (58.1%; n = 111/191) had a significantly higher proportion of “Correct Answer, Correct Rationale” than Claude-2 (37.2%; n = 71/191) (p < 0.017) and Llama-2 (24.1%; n = 46/191) (p < 0.017). Significant differences were observed in the rationale quality among all of the chatbots (p < 0.001). The inter-rater reliability was very high (κ = 0.83). Conclusions: GPT-4.0 demonstrated the highest accuracy and quality of reasoning in responding to prosthodontic and restorative dentistry questions. This underscores the varying efficacy of AI chatbots within specialized dental contexts. Full article

Tree species classification provides invaluable information across various sectors, from forest management to conservation. This task is most commonly performed using remote sensing; however, this method is prone to classification errors, which modern computational approaches aim to minimize. Convolutional neural networks (CNNs) used to model tabular data have recently gained popularity as a highly efficient classification tool. In the present study, a variation of this method is used to classify satellite multispectral data from the Sentinel-2 mission to distinguish between 18 common Polish tree species. The novel model is trained and tested on data from species-homogeneous forest stands. The data form a multi-seasonal time series and cover five years of observations. The model achieved an overall accuracy of 80% and Cohen Kappa of 0.80 of the raw output and increased to 93% with post-processing procedures. Considering the large number of species classified, this is a promising and encouraging result. The presented results indicate the importance of early vegetation season reflectance data in model training. The spectral bands representing the infrared, red-edge and green wavelengths had the greatest impact on the model. Full article

Cosmetic restorative dentistry focuses on restoring teeth affected by caries or trauma using materials that mimic natural teeth in shape, texture, and color. Composite resins, particularly nanofilled composites, are widely used due to their superior mechanical and aesthetic properties. Accurate tooth color selection is crucial, and methods include visual (shade guides) and instrumental (spectrophotometers, colorimeters, and intraoral scanners). Newer biomimetic composites, such as Admira Fusion 5 and Clearfil Majesty ES-2 Universal, simplify shade selection through advanced optical technologies. A randomized clinical study involving 30 patients compared the color-matching accuracy of two biomimetic composite resins: Admira Fusion 5 (Voco) and Clearfil Majesty ES-2 Universal (Kuraray). The study utilized the Vita Easyshade spectrophotometer and the Primescan intraoral scanner. Patients were treated following standardized protocols, and shade accuracy was evaluated pre- and post-restoration using Cohen’s Kappa index. Admira Fusion 5 showed higher shade-matching accuracy, with good agreement between pre- and post-restoration measurements using both instruments. Clearfil Majesty ES-2 Universal demonstrated lower reproducibility in shade matching, particularly in posterior teeth, with lower agreement in pre- and post-tests. Instrumentation Comparison: Primescan showed slightly better performance than Easyshade, but both provided comparable results. In conclusions, universal composites may not always achieve optimal shade matching in posterior teeth. Layered composites provide better color adaptability. While digital instruments enhance shade accuracy, combining them with visual methods yields the best clinical outcomes. Further research with larger sample sizes is needed to improve shade-matching techniques in aesthetic restorative dentistry. Full article

Equine Infectious Anemia Virus (EIAV), a lentivirus marked by considerable genetic variability, poses significant diagnostic challenges. Existing diagnostic tools encompass the Agar Gel Immunodiffusion Assay (AGID), enzyme-linked immunosorbent assay (ELISA), and Western blotting (WB). ELISA and AGID mainly utilize the p26 capsid protein, often sourced from the Wyoming reference strain. To broaden the range of viral proteins and strains employed in these immunoassays, we previously developed a novel p26/double-strain gp45 indirect ELISA. In this study, we evaluated the performance of this ELISA in comparison to two commercial EIAV ELISAs using Cohen’s Kappa test and Bayesian Latent Class Analysis (BLCA), a statistical method that estimates test performance without requiring a perfect reference standard. A comparison with the official classification of the sera by the Italian Veterinary Service was also performed. A total of 372 serum samples, including 96 that were positives by all three tests, were analyzed. Results from both Cohen’s Kappa test and BLCA, alongside comparison with official classifications, affirm the diagnostic reliability of the two commercial ELISAs and suggest that the novel ELISA, with its enhanced antigenic diversity, could offer an accurate and reliable diagnostic option for EIAV. This novel assay enhances existing commercial ELISAs and has the potential to strengthen routine diagnostic workflows. Full article

The efficacy of new FMECA methods can be assessed through qualitative comparisons of the failure mode rankings. This approach is suitable for a few failure modes but can become impractical or lead to misleading results for more extensive problems. This fact motivated us to introduce an alternative approach for comparing different FMECA methods based on agreement coefficients, enabling a statistical comparison between rankings generated by independent raters. Despite its relevance, the application of agreement coefficients is limited in the FMECA context. Our proposed approach utilizes Cohen’s kappa coefficient to evaluate the agreement between six FMECA configurations based on a type-2 fuzzy inference system and a reference FMECA ranking. We conducted an FMECA on power transformers to test our approach, identifying fourteen potential failure modes. Results show that, based on the agreement coefficient, our proposed approach proves effective for a statistical comparison of different FMECA methods rather than a qualitative comparison between rankings. Full article

Multidisciplinary tumor boards (MTBs) are essential for optimizing cancer care through collaborative decision-making. However, the concordance between initial surgeons’ recommendations and MTB outcomes, particularly in resource-limited settings, remains underexplored. This study evaluates the agreement between treatment plans proposed initially by surgeons and those finalized through MTB discussions conducted at the same stage of patient evaluation, with a focus on changes in treatment intent between curative and palliative care. A retrospective analysis of 216 patients discussed at bi-weekly MTB meetings between January 2021 and December 2023 at a tertiary care hospital was conducted. Statistical tests, including kappa statistics and concordance analysis were applied to assess the interrater agreement between surgeon-recommended and MTB-finalized decisions and to evaluate changes in treatment intent. A p-value < 0.05 was considered statistically significant. Strong concordance and significant perfect agreement were observed between curative versus palliative decisions of surgeons and MTBs, (Cohen’s kappa = 0.89, p < 0.001). MTB recommendations were added to the surgeons’ suggested plans in 38.4% (n = 83) of cases and replaced them entirely in 25.0% (n = 54) of cases. Shifts in treatment intent from curative to palliative or vice versa were infrequent (2.31%, n = 5), specifically in esophageal and stomach cancers. MTB decisions achieved a 100% implementation rate. This study underscores the critical role of MTBs in collaborative decision-making and their value as an essential tool for consistent, individualized, and evidence-based cancer care. Full article

Background/Objectives: The objective of this study is to investigate the intra- and inter-explorer reliability of different lung ultrasound (LUS) scores in patients with suspected rheumatoid arthritis with associated interstitial lung disease (RA-ILD). Methods: Fourteen consecutive patients with suspected RA-ILD based on the presence of respiratory symptoms, lung function tests (LFTs) or imaging alterations were recruited. The screening protocol consisted of an LFT, a chest X-ray, and HRCT. LUS examinations of different B-line and pleural line scores including 14 intercostal spaces were performed by three experienced sonographers, guaranteeing blinding. Intra- and inter-explorer reliability were calculated for all LUS scores and at the intercostal space level by weighted Cohen’s kappa and Fleiss’ kappa, respectively, relying on absolute differences using Stata/IC 14.2 software^® (StataCorp, College Station, TX, USA). Results: Both global (ĸ = 0.73–0.82) and binary (ĸ = 0.80–0.90) scores of B lines showed substantial to excellent intra-explorer reliability, with slightly better results for the binary score. The inter-explorer reliability was equally excellent for the global score (ĸ = 0.93) and the binary score (ĸ = 0.90) of B lines. The intra-explorer reliability of the semiquantitative pleural score was excellent for all the sonographers (ĸ = 0.88–0.91), and the binary pleural score had slightly lower intra-explorer reliability (ĸ = 0.77–0.84). Regarding inter-explorer reliability, both semiquantitative and binary pleural scores were equally excellent (ĸ = 0.84). Good to excellent inter-explorer reliability was found in all the scanned areas. Conclusions: Substantial to excellent intra- and inter-explorer reliability of different feasible B-line and pleural LUS scores were found, adding evidence in favor of the potential implementation of LUS for RA-ILD diagnosis in clinical practice. Full article