Search Results (112)

As part of our technology-enhanced learning (TEL) strategy, we developed a field-based augmented reality (AR) learning system for biodiversity education among senior elementary school students. Using a 2D illustration style to present the appearance of the species and a situational interactive design, the AR app focused on common wild animals in Taiwan. They also gained insight into wild animal species in outdoor settings, gained knowledge about the phenomenon of roadkill and the rescue of wild animals, and promoted their awareness of ecological conservation. Using the design-based research (DBR) method, we integrated user-oriented design processes and iteratively modified the system functions and interface through expert review and field usability testing. During this activity, 26 senior elementary school students were recruited to participate in an interactive AR game designed for a single player. As part of the learning content, students must collect images of species, recognize roadkill, and learn about wildlife rescue. To evaluate the effect of the activity on knowledge learning and the app’s usability, data were collected through pre- and post-test paper tests, questionnaires, and so on. Based on the research results, this system can significantly enhance students’ learning interests and contextual understanding of biodiversity topics as an effective technology-assisted learning tool. Students reported high levels of immersion and learning motivation, and the teachers agreed that it promoted inquiry-based and independent learning. The results of this study contribute to the field of educational and environmental education. Consequently, context-aware AR tools may enhance students’ situational learning experience and environmental literacy. In addition, it provides a practical design reference for future AR educational applications, demonstrating that gamification and outdoor learning can enhance the learning outcomes of traditional science education. Full article

The Indian Diabetic Retinopathy Image Dataset (IDRiD) has been widely adopted for DR lesion segmentation research. However, it contains annotation gaps for proliferative DR lesions and labeling errors that limit its utility for comprehensive automated screening systems. We present Refined IDRiD, an enhanced version that addresses these limitations through (1) expert ophthalmologist validation and correction of labeling errors in original annotations for four non-proliferative lesions (microaneurysms, hemorrhages, hard exudates, cotton-wool spots), (2) the addition of three critical proliferative DR lesion annotations (neovascularization, vitreous hemorrhage, intraretinal microvascular abnormalities), and (3) the integration of comprehensive anatomical context (optic disc, fovea, blood vessels, retinal region). A team of three ophthalmologists (one senior specialist with >10 years’ experience, two expert fundus image annotators) conducted systematic annotation refinement, achieving an inter-rater agreement F1-score of 0.9012. The enhanced dataset comprises 81 high-resolution fundus images with pixel-level annotations for seven DR lesion types and four anatomical structures. All images were cropped to the retinal region of interest and resized to 1024 × 1024 pixels, with annotations stored as unified grayscale masks containing 12 classes enabling efficient multi-task learning. Refined IDRiD enables training of comprehensive DR screening systems capable of detecting both non-proliferative and proliferative stages while reducing false positives through anatomical context awareness. Full article

Background/Objectives: AI models such as ChatGPT-4 have shown strong performance in dermatology; however, the diagnostic value of AI-generated clinical image descriptions remains underexplored. This study assesses whether ChatGPT-4’s image descriptions can support accurate dermatologic diagnosis and evaluates their potential integration into the Electronic Medical Record (EMR) system. Materials & Methods: In this Exploratory cross-sectional study, we analyzed images and descriptions from teledermatology consultations conducted between December 2023 and February 2024. ChatGPT-4 generated clinical descriptions for each image, which two senior dermatologists then used to formulate differential diagnoses. Diagnoses based on ChatGPT-4’s output were compared to those derived from the original clinical notes written by teledermatologists. Concordance was categorized as Top1 (exact match), Top3 (correct within top three), Partial, or No match. Results: The study included 154 image descriptions from 67 male and 87 female patients, aged 0 to 93 years. ChatGPT-4 descriptions averaged 74.3 ± 33.1 words, compared to 7.9 ± 3.0 words for teledermatologists. At least one of the two dermatologists achieved a Top 3 concordance rate of 82.5% using ChatGPT-4’s descriptions and 85.3% with teledermatologist descriptions. Conclusions: Preliminary findings highlight the potential integration of ChatGPT-4-generated descriptions into EMRs to enhance documentation. Although AI descriptions were longer, they did not enhance diagnostic accuracy, and expert validation remained essential. Full article

Background: Despite the availability of contouring guidelines and advanced imaging modalities, interobserver variability (IOV) in the delineation of the planning target volume and organs at risk remains a critical factor influencing treatment quality in radiotherapy. The aim of this study was to examine variations in contour delineation with respect to anatomical landmarks, as well as differences in the inclusion of lymph node levels within the PTV. Methods: Ten senior radiation oncologists from six different institutions participated in the study and contoured PTV1, PTV2 and 16 OARs in a patient with oropharyngeal carcinoma. Interobserver variation was quantified by volume statistics such as mean, standard deviation (SD) and ranges, as well as using coefficient of variance (CoV) and conformity index (CI). Results: High agreement was observed in the inclusion of the ipsilateral lymph node levels Ib–IVa and VIIa+b, whereas notable discrepancies were identified in the delineation inclusion of the cervical triangle group and lateral supraclavicular nodes. Regarding OARs, the greatest variability was observed in the delineation of the left and right inner ear, with volume ranges of 0.12–2.84 cm³ and 0.11–2.38 cm³, respectively. Conclusions: This study reaffirms the presence of significant interobserver variability in the delineation of PTVs and OARs in patients with oropharyngeal carcinoma. Especially inclusion of elective lymph node levels and definition of margins around the gross tumor volume are substantial factors for IOV. By emphasizing structured anatomical assessment as a standard approach, variability can be minimized, treatment consistency enhanced, and ultimately, patient outcomes improved. Full article

Background: To investigate the performance of radiologists in characterizing and diagnosing hepatic lesions with and without the assistance of deep learning-based artificial intelligence (AI). Methods: This retrospective study included 83 nodules/masses from 69 patients who underwent dynamic contrast-enhanced CT of the liver. Image assessments were conducted by 20 radiologists. grouped according to their level of experience (10 senior and 10 junior). Each radiologist determined the probability of eight characteristics based on enhancement patterns and the diagnosis with and without AI attached to the SYNAPSE SAI viewer (FUJIFILM Corporation, Minato-ku, Japan). The reference standard for comparison was established as follows: final diagnoses were based on pathology for 39 lesions and expert imaging consensus for the remainder, while image characteristics for all lesions were determined by expert imaging consensus. Areas under the receiver operating characteristic curves (AUCs) were analyzed using the multireader multicase method. Results: Using AI significantly improved the overall AUCs for both the characterization and the diagnosis of liver lesions. Improvement was suggested for specific items, including the characterization of enhancement, nonperipheral washout, and delayed enhancement, and the diagnosis of hepatocellular carcinoma. The utilization of AI system also suggested potential improvements in the AUCs for image characterization in both the senior and junior groups. Conclusions: Using AI improved the radiologists’ performance in characterizing and diagnosing hepatic lesions. In terms of their capacity to assess imaging characteristics, improvements were observed regardless of their level of experience. Full article

Background: Degenerative cervical spondylosis (DCS) is a common and potentially debilitating condition, with surgery indicated in selected patients. Deep learning models (DLMs) can improve consistency in grading DCS neural stenosis on magnetic resonance imaging (MRI), though existing models focus on axial images, and comparisons are mostly limited to radiologists. Methods: We developed an enhanced transformer-based DLM that trains on sagittal images and optimizes axial and foraminal classification using a maximized dataset. DLM training utilized 648 scans, with internal testing on 75 scans and external testing on an independent 75-scan dataset. Performance of the DLM, spine surgeons, and radiologists of varying subspecialities/seniority were compared against a consensus reference standard. Results: On internal testing, the DLM achieved high agreement for all-class classification: axial spinal canal κ = 0.80 (95%CI: 0.72–0.82), sagittal spinal canal κ = 0.83 (95%CI: 0.81–0.85), and neural foramina κ = 0.81 (95%CI: 0.77–0.84). In comparison, human readers demonstrated lower levels of agreement (κ = 0.60–0.80). External testing showed modestly degraded model performance (κ = 0.68–0.77). Conclusions: These results demonstrate the utility of transformer-based DLMs in multiplanar MRI interpretation, surpassing spine surgeons and radiologists on internal testing and highlighting its potential for real-world clinical adoption. Full article

This study introduces an explainable neuro-symbolic and large language model (LLM)-driven framework for intelligent interpretation of corneal topography and precision surgical decision support. In a prospective cohort of 20 eyes, comprehensive IOLMaster 700 reports were analyzed through a four-stage pipeline: (1) automated extraction of key parameters—including corneal curvature, pachymetry, and axial biometry; (2) mapping of these quantitative features onto a curated corneal disease and refractive-surgery knowledge graph; (3) Bayesian probabilistic inference to evaluate early keratoconus and surgical eligibility; and (4) explainable multi-model LLM reporting, employing DeepSeek and GPT-4.0, to generate bilingual physician- and patient-facing narratives. By transforming complex imaging data into transparent reasoning chains, the pipeline delivered case-level outputs within ~95 ± 12 s. When benchmarked against independent evaluations by two senior corneal specialists, the framework achieved 92 ± 4% sensitivity, 94 ± 5% specificity, 93 ± 4% accuracy, and an AUC of 0.95 ± 0.03 for early keratoconus detection, alongside an F1 score of 0.90 ± 0.04 for refractive surgery eligibility. The generated bilingual reports were rated ≥4.8/5 for logical clarity, clinical usefulness, and comprehensibility, with representative cases fully concordant with expert judgment. Comparative benchmarking against baseline CNN and ViT models demonstrated superior diagnostic accuracy (AUC = 0.95 ± 0.03 vs. 0.88 and 0.90, p < 0.05), confirming the added value of the neuro-symbolic reasoning layer. All analyses were executed on a workstation equipped with an NVIDIA RTX 4090 GPU and implemented in Python 3.10/PyTorch 2.2.1 for full reproducibility. By explicitly coupling symbolic medical knowledge with advanced language models and embedding explainable artificial intelligence (XAI) principles throughout data processing, reasoning, and reporting, this framework provides a transparent, rapid, and clinically actionable AI solution. The approach holds significant promise for improving early ectatic disease detection and supporting individualized refractive surgery planning in routine ophthalmic practice. Full article

Background: Ovarian cancer is the deadliest female reproductive malignancy. Accurate preoperative differentiation of benign and malignant ovarian masses is critical for appropriate treatment. O-RADS category 4 lesions present a wide range of malignant risk, challenging radiologists. Ultrasonic images are the primary focus of current deep learning models, with no consideration for clinical data. Methods: We proposed OTC-NET, a model that uses multimodal data for classification, which combines ultrasound images and clinical information to improve the classification ability of O-RADS 4 ovarian masses. Results: OTC-NET outperforms seven deep learning models and three radiologists of varying experience, with AUC significantly higher than junior (p < 0.001), intermediate (p < 0.01), and senior (p < 0.05) radiologists. Additionally, OTC-NET–assisted diagnosis notably improves AUC and accuracy of junior and senior radiologists (p < 0.05). Conclusions: These results indicate that OTC-NET provides superior diagnostic accuracy and has strong potential for clinical application. Full article

Background: In healthcare institutions, radiologists play an essential role in patients’ care, enabling them to begin treatment and start their recoveries. However, data on the characteristics and distribution of the radiology workforce in Saudi Arabia are limited. Therefore, this study aimed to conduct a comprehensive analysis of the radiology workforce in SA based on national data and identify key distributional and specialty trends relevant to workforce planning and radiology service delivery. Methods: The following data were obtained from the Saudi Commission for Health Specialties (SCFHS) Registry: total number of registered radiologists, age, subspecialty, professional classification, place of qualification, and geographical location. Descriptive statistics were used for data analysis. Additionally, the findings were compared with those of published international benchmarks. Results: There were 5150 radiologists registered with SCFHS in SA, which corresponded to 147 radiologists per 1,000,000 inhabitants. The mean age was 40.8 years (standard deviation [SD] 9.8), with 60% of them being aged 30–44 years. Most of the radiologists specialised in general diagnostic radiology (83.7%), with few of them specialising in interventional radiology (1.8%), paediatric radiology (1.1%), and breast imaging (0.9%). The workforce mainly comprised consultants (35.0%), followed by registrars (29.7%) and senior registrars (22.7%). Two-thirds (65.0%) of the radiologists had obtained their qualifications abroad. More than half of the radiologists resided in three provinces: Riyadh (29%), Mecca (23%), and the Eastern Region (15%), while several provinces had fewer than 2% of the available workforce. Conclusions: The radiology workforce in SA is relatively young and has a higher density than the average in the European Union. Further, most of the radiologists are professionally classified as consultants or registrars. However, there is a clear imbalance in their geographic distribution, which is consistent with the population sizes of the respective cities. Targeted training expansion and reduced reliance on foreign-trained professionals are warranted to meet future service demands in line with the Vision 2030 objectives. Full article

The present study undertakes an analytical investigation into the relationships between Corporate Social Responsibility (CSR), Compliance Programs, Reputational Risk Management, and Corporate Image. A survey was conducted among 154 senior professionals in companies across diverse sectors and sizes, using the Partial Least Squares Structural Equation Modeling (PLS-SEM) methodology with the aid of SmartPLS 4.0 software. The findings indicate that CSR exerts a substantial and immediate influence on both the management of reputational risk and the establishment of a robust corporate image. Furthermore, it has been observed that the adoption of Compliance Programs is driven by CSR, which also contributes, albeit to a lesser extent, to the strengthening of the external perception of the company. Conversely, proactive management of reputational risk has been demonstrated to enhance regulatory compliance and positively impact corporate image. The alignment of corporate social responsibility (CSR) with compliance initiatives has been demonstrated to engender sustainable competitive advantages within challenging regulatory contexts. In conclusion, the present paper puts forward the suggestion of conducting longitudinal studies in order to observe the evolution of the relationships under discussion over time. Full article

Surge capacity is the ability to manage sudden patient influxes beyond routine levels and can be evaluated using the 4S Framework: staff, stuff, system, and space. While low-resource settings like Rwanda face frequent mass casualty incidents (MCIs), most surge capacity research comes from high-resource settings and lacks generalisability. This study assessed Rwanda’s hospital surge capacity using a cross-sectional survey of emergency and surgical departments in all referral hospitals. Descriptive statistics, t-tests, Fisher’s exact test, ANOVA, and linear mixed-model regression were used to analyze responses. Of the 39 invited participants, 32 (82%) responded. On average, respondents believed that they could manage 13 MCI patients (95% CI: 10–16) while maintaining routine care, with significant differences between tertiary and secondary hospitals (11 vs. 22; p = 0.016). The intra-class correlation was poor for most variables except for CT availability and ICU beds. Surge capacity perception did not vary significantly by professional category, though less senior staff reported higher capacity. Significantly higher capacity was reported by those with continuous access to imaging (p < 0.01). Despite limited resources, Rwandan hospitals appear able to manage small to moderate MCIs. For larger incidents, patient distribution across facilities is recommended, with critical cases prioritized for tertiary hospitals. Full article

Background: Papilledema, an ophthalmic finding associated with increased intracranial pressure, is often induced by dermatological medications, including corticosteroids, isotretinoin, and tetracyclines. Early detection is crucial for preventing irreversible optic nerve damage, but access to ophthalmologic expertise is often limited in rural settings. Artificial intelligence (AI) may enable the automated and accurate detection of papilledema from fundus images, thereby supporting timely diagnosis and management. Objective: The primary objective of this study was to explore the diagnostic capability of ChatGPT-4o, a general large language model with multimodal input, in identifying papilledema from fundus photographs. For context, its performance was compared with a ResNet-based convolutional neural network (CNN) specifically fine-tuned for ophthalmic imaging, as well as with the assessments of two human ophthalmologists. The focus was on applications relevant to dermatological care in resource-limited environments. Methods: A dataset of 1094 fundus images (295 papilledema, 799 normal) was preprocessed and partitioned into a training set and a test set. The ResNet model was fine-tuned using discriminative learning rates and a one-cycle learning rate policy. GPT-4o and two human evaluators (a senior ophthalmologist and an ophthalmology resident) independently assessed the test images. Diagnostic metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and Cohen’s Kappa, were calculated for each evaluator. Results: GPT-4o, when applied to papilledema detection, achieved an overall accuracy of 85.9% with substantial agreement beyond chance (Cohen’s Kappa = 0.72), but lower specificity (78.9%) and positive predictive value (73.7%) compared to benchmark models. For context, the ResNet model, fine-tuned for ophthalmic imaging, reached near-perfect accuracy (99.5%, Kappa = 0.99), while two human ophthalmologists achieved accuracies of 96.0% (Kappa ≈ 0.92). Conclusions: This study explored the capability of GPT-4o, a large language model with multimodal input, for detecting papilledema from fundus photographs. GPT-4o achieved moderate diagnostic accuracy and substantial agreement with the ground truth, but it underperformed compared to both a domain-specific ResNet model and human ophthalmologists. These findings underscore the distinction between generalist large language models and specialized diagnostic AI: while GPT-4o is not optimized for ophthalmic imaging, its accessibility, adaptability, and rapid evolution highlight its potential as a future adjunct in clinical screening, particularly in underserved settings. These findings also underscore the need for validation on external datasets and real-world clinical environments before such tools can be broadly implemented. Full article

In the global work culture of the 21st century, the image of female leaders is marginal. The stereotypical opinion links the terms leaders and managers to the male gender and predetermined gendered characteristics typical to men. The aim of this study is to redefine certain perspectives through empirical research based on previous literature regarding gender stereotypes in leadership and challenges facing female leaders. This paper looks at the main issues that concern female leaders throughout their careers and even after reaching a higher position and discusses the differences between male and female workers on the top levels of the organizational hierarchy. The research was conducted in the Western Transdanubia region of Hungary. The research was based on eight semi-structured interviews with mid- and senior managers, which were analyzed through thematic analysis to identify patterns and challenges. As an exploratory qualitative study with a region-specific sample, the findings provide valuable insights but should be interpreted cautiously as they cannot be extrapolated to a comparable situation. The findings of the study indicate that gender gaps in the corporate world can be attributed to the presence of stereotypes resulting from gender roles embedded in patriarchal societies, gender-based discrimination in the labor market. As the results suggest, these non-quantifiable problems are of great importance in terms of the position of women in the labor market and society. Full article

Background/Objectives: Head and neck cancer (HNC) is a heterogeneous group of malignancies in which staging plays a critical role in guiding treatment and prognosis. Large language models (LLMs) such as ChatGPT, DeepSeek, and Grok have emerged as potential tools in oncology, yet their clinical applicability in staging remains unclear. This study aimed to evaluate the accuracy and concordance of LLMs compared to clinician-assigned staging in patients with HNC. Methods: The medical records of 202 patients with HNC, who presented to our center between 1 January 2010 and 13 February 2025, were retrospectively reviewed. The information obtained from the hospital information system by a junior researcher was re-evaluated by a senior researcher, and standard staging was completed. Except for the stage itself, the data used for staging were provided to a blinded third researcher, who then entered them into the ChatGPT, DeepSeek, and Grok applications with a staging command. After all staging processes were completed, the data were compiled, and clinician-assigned stages were compared with those generated by the LLMs. Results: The majority of the patients had laryngeal (45.5%) and nasopharyngeal cancer (21.3%). Definitive surgery was performed in 39.6% of the patients. Stage 4 was the most common stage among the patients (54%). The overall concordance rates, Cohen’s kappa values, and F1 scores were 85.6%, 0.797, and 0.84 for ChatGPT; 67.3%, 0.522, and 0.65 for DeepSeek; and 75.2%, 0.614, and 0.72 for Grok, respectively, with no statistically significant differences between models. Pathological and surgical staging were found to be similar in terms of concordance. The concordance of assessments utilizing only imaging, only pathology notes, only physical examination notes, and comprehensive information was evaluated, revealing no significant differences. Conclusions: Large language models (LLMs) demonstrate relatively high accuracy in staging HNC. With careful implementation and with the consideration of prospective studies, these models have the potential to become valuable tools in oncology practice. Full article

This study aims to develop and validate a multisequence MRI-based radiomics approach for distinguishing metaplastic breast cancer (MBC) from non-metaplastic triple-negative breast cancer (TNBC) at the initial diagnosis, which could facilitate optimal treatment selection. In this retrospective study, we analyzed 105 patients (27 MBC, 78 non-metaplastic TNBC) who underwent standardized breast magnetic resonance imaging (MRI), which included T1-weighted contrast-enhanced (T1W-CE) and short-tau inversion recovery (STIR) sequences. Two radiologists performed ground truth lesion segmentation, verified by a senior radiologist. We extracted 214 radiomic features (using PyRadiomics) and used least absolute shrinkage and selection operator (LASSO) regression for feature selection. Seven machine learning classifiers were thoroughly evaluated using five-fold cross-validation, with performance assessed through ROC analysis and accuracy metrics. The combined T1W-CE and STIR analysis demonstrated superior diagnostic performance for distinguishing MBC from non-metaplastic TNBC (AUC = 0.845; accuracy = 81%) compared with either sequence alone (T1W only AUC = 0.805; accuracy = 80%; STIR only AUC:0.768; accuracy = 77%). Multisequence MRI radiomics can reliably distinguish between MBC and TNBC at the time of initial diagnosis. This could potentially facilitate the selection of more appropriate treatments and help avoid ineffective chemotherapy for MBC patients. Full article