Diagnostics最新文献

Background/Objectives: Intraoral scanners (IOSs) are essential tools in digital dentistry; however, their accuracy remains influenced by clinical conditions such as restricted access, patient movement, or intraoral moisture. Intraoral scanning is performed within a confined space that restricts scanner motion, potentially influencing maneuverability during data acquisition and, consequently, IOS performance. This study investigated the impact of maneuverability constraints on the trueness accuracy and efficiency of IOS under clinically representative intraoral conditions. Methods: Fifteen participants with no previous experience in intraoral scanning or device operation were recruited. Each participant scanned a maxillary full-dentition typodont model and a mandibular implant-containing typodont model using the Aoralscan 3 IOS. Scans were performed under two conditions: constrained intraoral scanning within a manikin and open-vision extraoral scanning on a bench-top. Trueness accuracy was evaluated using three parameters: the root mean square (RMS) deviation of the maxillary dentition, discrepancies in inter-scan body distances, and angular deviations of the scan bodies, each calculated by comparison with reference data obtained from an industrial-grade scanner. Scan time was recorded to assess time-based efficiency. Results: No significant differences were observed in RMS trueness, inter-implant distances, or implant angular deviations between intraoral and extraoral scans. Extraoral scanning significantly reduced scan times for both maxillary and mandibular models (p < 0.0001). Conclusions: Within the limitations of this study, maneuverability constraints alone may not significantly affect IOS trueness accuracy compared with open bench-top scanning. However, scanning efficiency was reduced under intraoral scanning constraints, with longer scan times observed among inexperienced operators. The potential influence of intraoral factors other than maneuverability on IOS accuracy under clinical conditions warrants further investigation.

Background/Objectives: Bloodstream infections (BSIs) are a global healthcare issue associated with high mortality rates. Rapid diagnosis is of importance for the early selection of targeted therapy to improve patient outcomes. The use of rapid molecular assays with positive blood culture (BC) allows the identification (ID) of pathogens and the most relevant resistance determinants (RDs) in a shorter turnaround time, compared to standard culture. In this study, the performances of a new syndromic panel to determine the IDs and RDs of Gram-negative (GN) and Gram-positive (GP) bacteria were investigated in comparison with a standard-of-care (SoC) workflow. Methods: Two hospitals processed residual positive BC samples from non-replicated patients using Molecular Mouse (MM) Sepsis panels (Alifax, Padova, Italy) for GP ID, GN ID and RD detection. Results were compared with an SOC workflow based on subculture, ID by MALDI-ToF mass spectrometry, phenotypic antibiogram, and real-time PCRs for RDs from isolated colonies. Results: A total of 140 and 136 residual positive BC samples were found to be valid for MM-ID and RD, respectively, yielding 76 GN and 76 GP species. Overall ID agreement at the species level was 136/152 (89%). RD agreement was 144/146 (99%). Regarding GN and GP species, ID agreement was 68/76 (89%) and 70/76 (92%), respectively. Conclusions: MM showed high sensitivity in RD detection; however, some discrepancies with results of the SoC workflow were observed, represented by reduced sensitivity for some species-specific IDs. Panel size and compact instrument dimension can be seen as the principal advantage of this modular molecular assay for the rapid detection of pathogens responsible for BSIs.

Infants may place various objects in their mouths during the developmental process, which can sometimes involve life-threatening risks, such as choking. We describe the case of a 1-year 3-month-old female with a foreign body in the oral cavity. She was referred to our hospital with chief complaints of suspected supernumerary teeth and blisters, and the initial examination revealed blister-like swelling and a white swelling on the hard palate. Intraoral photographs were obtained and examined from multiple angles, revealing findings that resembled a character. Careful re-examination showed that a three-dimensional sticker was attached to the hard palate, which could be removed in one piece. It is important for dental professionals to conduct intraoral examinations of pediatric patients with the understanding that unexpected findings may be present, and think about a foreign body in palatal lesions. In addition, this report highlights a new risk for caregivers supervising infants, as seemingly harmless stickers can remain in the mouth for extended periods.

Background/Objectives: Thymomas are the most common tumors of the anterior mediastinum. While early-stage disease often has a favorable prognosis, therapeutic options in advanced stages remain limited. Moreover, the molecular profile of thymomas is still poorly characterized. In the present study, we explored the presence of targetable mutations and programmed death-ligand 1 (PD-L1) expression in a cohort of surgically resected thymomas. Furthermore, we investigated the correlation between PD-L1 expression, histological subtype, and risk of recurrence in patients who underwent curative-intent thymectomy. Methods: Mutational profiling was performed using a DNA-based NGS Cancer Panel of 16 genes. PD-L1 expression was evaluated via Tumor Proportion Score (TPS), and thymomas with TPS ≥ 50% were identified as high expressors. The associations with histological subtype and disease-free survival (DFS) were analyzed using logistic regression, Cox proportional hazards models, and Kaplan-Meier survival curves. Results: In our study, 2/37 (5.4%) of tested neoplasms (type AB and B2 thymoma) reported as a PIK3CA mutation; no other targetable mutations were observed. Moreover, high PD-L1 expression (≥50%) was reported in (15/37) 40.5% of patients and was significantly associated with aggressive histological subtypes (B2 and B3) (p < 0.001). Logistic regression analysis showed that high PD-L1 expression was a significant predictor of aggressive histology (McFadden's R² = 0.268, p < 0.001), with an odds ratio of 15.5 (95% CI: 2.9-83.4; p = 0.001). During follow-up, 5/37 (13.5%) of patients experienced disease recurrence; however, no significant difference in DFS was found between high and low PD-L1 expression groups. Conclusions: Our data confirm the presence of PIK3CA mutations in thymomas and encourage the exploration the potential role of molecular target therapy in this setting. Moreover, we underlined that high PD-L1 expression level is associated with more aggressive thymoma subtypes and may have a role as a prognostic biomarker. These findings support the need for further studies on the potential role of molecular and predictive pathology in thymic epithelial tumors.

Background/Objectives: Adequate bowel preparation (BP) is crucial for effective colorectal cancer (CRC) surveillance in ulcerative colitis (UC). While active inflammation is known to negatively impact cleansing, data regarding predictors of BP quality specifically in UC patients with inactive disease remain limited. This study aimed to investigate risk factors for inadequate BP in UC patients in clinical/endoscopic remission and to compare the efficacy of 1L-PEG-ASC versus 2L-PEG regimens. Methods: A multicentric, retrospective, cohort study was conducted across eight Italian centers. Consecutive adult outpatients with UC undergoing colonoscopy between January-2021 and December-2022 who were in endoscopic and clinical remission were included. Boston Bowel Preparation Scale (BBPS) was assessed in patients undergoing 1L-PEG-ASC or 2L-PEG bowel preparation. Univariable and multivariable logistic regression analyses were performed to identify risk factors for inadequate BP and compare outcomes between PEG regimens. Results: A total of 379 patients were included (58% M, mean age 52.3 ± 15.4 years). The overall rate of adequate BP was 90.5%. Traditional risk factors, including demographic, clinical, and endoscopic characteristics, were not predictive of inadequate preparation in this remission cohort. Comparing regimens, 1L-PEG-ASC yielded significantly higher median total BBPS scores compared to 2L-PEG (8 [IQR 7-9] vs. 6 [IQR 6-8]; p < 0.001) and a higher exam completion rate (99.5% vs. 95.7%; p = 0.02), although the difference in adequate BP rates did not reach statistical significance (92.6% vs. 87.7%; p = 0.12). Multivariable analysis confirmed that 2L-PEG was independently associated with lower odds of achieving higher BBPS scores (OR 0.30; 95% CI 0.20-0.45). Conclusions: In UC patients with clinical and endoscopic remission, BP adequacy rates are high and comparable to the general population, suggesting that traditional IBD-related risk factors are less relevant in the absence of active inflammation. However, the 1L-PEG-ASC regimen demonstrated superior cleansing quality and exam completion rates compared to 2L-PEG. These findings support the prioritization of 1L-PEG-ASC to optimize mucosal visualization during CRC surveillance in this population.

Background: Cardiotoxicity is a major limitation of chemotherapy and radiotherapy for thoracic and systemic cancers, contributing significantly to morbidity and mortality among survivors. Early prediction and prevention are critical to balance oncologic efficacy with cardiovascular safety. Artificial intelligence (AI) offers powerful tools to improve risk stratification, enable earlier detection of subclinical injury, and guide treatment planning in cardio-oncology. Methods: We performed a comprehensive review of the literature on AI applications for cancer therapy-related cardiotoxicity. Evidence was identified from PubMed, Scopus, and Web of Science, focusing on electrocardiography, biomarkers, proteomics, extracellular vesicles, genomics, advanced imaging (echocardiography, cardiac magnetic resonance, computed tomography, nuclear imaging), and radiotherapy dose modeling (dosiomics). Translational insights from animal models and in vitro systems were also included. Methodological quality was appraised with reference to TRIPOD-AI, PROBAST-AI, and CLAIM standards. Results: AI applications span multiple domains. Machine learning models integrating biomarkers, exosomes, and extracellular vesicles show promise for noninvasive early detection. Deep learning enables automated analysis of echocardiographic strain and cardiac MRI mapping, while radiomics and dosiomics approaches combine imaging with cardiac substructure dose maps to predict and prevent late radiation-induced injury. Preclinical studies demonstrate AI-driven advances in small-animal imaging, histopathology quantification, and multi-omics data integration, supporting the discovery of translational biomarkers. Despite encouraging performance, most models remain limited by small cohorts, methodological heterogeneity, and scarce external validation. Conclusions: AI has the potential to transform cardio-oncology by shifting from reactive detection to proactive prevention of cardiotoxicity. Future research should prioritize multimodal integration, harmonized multicenter datasets, prospective validation, and guideline-based clinical trials. As emerging data are incorporated, the field is expanding rapidly-dynamic, complex, and evolving.

Background/Objectives: Mental health screening in athletes is an essential process to support well-being and sustainable performance. The Athlete Psychological Strain Questionnaire (APSQ) represents the ten-item triage step of the Sport Mental Health Assessment Tool-1 (SMHAT-1), created by the International Olympic Committee. We aimed to gather relevant information concerning the validity of the APSQ in different cultural settings. Methods: The study was designed as a scoping review and included 19 articles from Scopus, PubMed, Embase, Web of Science, and Google Scholar databases. Articles were written in English and tested the APSQ validity. Results: Different studies used the original or the translated version of APSQ and tested its benchmarked validity against other validated questionnaires, ran confirmatory and exploratory analyses, test-retest stability, calculated diagnostic metrics, and internal consistency. Most studies agreed on the good internal consistency, with optimal Cronbach's alpha values, test-retest reliability, three-factor solution, convergent validity with scales assessing distress, divergent validity with well-being scales as demonstrated by significant correlation coefficients. The cut-off showed good accuracy for anxiety and depressive symptoms in terms of AUC, sensitivity, and specificity, but, in some cases, a limited ability (based on the AUC) to detect sleep concerns, alcohol misuse, substance use, and disordered eating (as measured by BEDA-Q). Some authors suggested that using different cut-offs, including all questionnaires from SMHAT-1 Step 2, or using a clinical interview, might mitigate these concerns. Conclusions: Different cultural environments might influence the validity of APSQ. A structured translation and validation study is advised before implementing APSQ in a different language.

Background/Objectives: Bone age assessment is critical in pediatric endocrinology and forensic medicine. Although recently developed multimodal large language models (LLMs) show potential in medical imaging, their diagnostic performance in bone age determination has not been sufficiently evaluated. This study evaluates the performance of four multimodal LLMs (ChatGPT-5, Gemini 2.5 Pro, Grok-3, and Claude 4 Sonnet) in bone age determination using the Gilsanz-Ratib (GR) atlas. Methods: This retrospective study included 245 pediatric patients (109 male, 136 female) under the age of 18 who underwent left wrist radiography. Each model estimated bone age using the patient's radiograph and GR atlas as reference (atlas-assisted prompting). Bone age assessments made by an experienced radiologist using the GR atlas were evaluated as the reference standard. Performance was assessed using mean absolute error (MAE), intraclass correlation coefficient (ICC), and Bland-Altman analysis. Results: ChatGPT-5 demonstrated statistically superior performance, with an MAE of 1.46 years and ICC of 0.849, showing the highest alignment with the reference standard. Gemini 2.5 Pro showed moderate performance, with an MAE of 2.24 years; Grok-3 (MAE: 3.14 years) and Claude 4 Sonnet (MAE: 4.29 years) had error rates that were too high for clinical use. Conclusions: Significant performance differences exist among multimodal LLMs, despite atlas-supported prompting. Only ChatGPT-5 qualified as "clinically useful," demonstrating potential as an auxiliary tool or educational support under expert supervision. Other models' reliability remains insufficient.

Background/Objectives: The variable accuracy of middle ear disease diagnosis based on oto-endoscopy underscores the need for improved decision support. Although convolutional Neural Networks (CNNs) are currently a mainstay of computer-aided diagnosis (CAD), their constraints in global feature integration persist. We therefore systematically benchmarked state-of-the-art CNNs and Transformers to establish a performance baseline. Beyond this benchmark, our primary contribution is the development of a probability-guided Top-K clinical decision framework that balances high accuracy with complete case coverage for practical deployment. Methods: Using a multicenter dataset of 6361 images (five categories), we implemented a two-stage validation strategy (fixed-split followed by 5-fold cross-validation). A comprehensive comparison was performed among leading CNNs and Transformer variants assessed by accuracy and Macro-F1 score. Results: The Swin Transformer model demonstrated superior performance, achieving an accuracy of 95.53% and a Macro-F1 score of 93.37%. It exhibited exceptional stability (95.61% ± 0.38% in cross-validation) and inherent robustness to class imbalance. A probability-guided Top-2 decision framework was developed, achieving 93.25% accuracy with 100% case coverage. Conclusions: This rigorous benchmark established Swin Transformer as the most effective architecture. Consequently, this study delivers not only a performance benchmark but also a clinically actionable decision-support framework, thereby facilitating the deployment of AI-assisted diagnosis for chronic middle ear conditions in specialist otology.

Background/Objectives: Abdominal obesity, especially visceral adipose tissue (VAT), is an independent risk factor for coronary artery disease. This study aimed to investigate the association between single-slice CT-measured VAT and significant coronary artery stenosis and to establish an optimal VAT cut-off value for Taiwanese adults. Methods: Patients who underwent abdominal CT and coronary CT angiography (CTA) within 1 month of each other were enrolled in this retrospective study. Axial images of abdominal CT at the L4 pedicle level were selected for further VAT, subcutaneous adipose tissue, and paraspinal muscles analysis. Significant coronary artery stenosis was defined as any luminal stenosis of >50% of the diameter of the vessel that was measured in coronary CTA. Anthropometric and laboratory measurements, including height, weight, waist circumference (WC), blood pressure, blood glucose, and blood lipids, were also analyzed. Results: A total of 779 patients (300 females; 54.9 ± 9.96 years) were enrolled. Only VAT and systolic blood pressure correlated significantly with significant coronary artery stenosis. No significant differences were found in other demographic and anthropometric characteristics between the groups with and without significant coronary artery stenosis. Conclusions: Single-slice CT-measured VAT was associated with significant coronary artery stenosis, and a lower VAT cut-off is recommended for the Taiwanese population.