Gener José Avilés-Rodríguez

Objectives: Digital fundoscopy is an ophthalmological technique used to evaluate the posterior segment of the eye, including the retina and its anatomical structures [1]. This technique acquires fundus images, visually documenting the aforementioned structures. This dataset was generated from a subset of images from the public Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset [2], these were manually annotated to generate segmentation masks of anatomical fundus structures, specifically the vascular arcade and the optic nerve head. We contribute to the development and evaluation of artificial intelligence models under comparable conditions by generating data to serve as input for segmentation models, thereby supporting the design of more efficient algorithms for the automated identification of ophthalmological structures [3, 4]. Data description: This dataset consists of 500 masks generated from 500 images randomly selected from the APTOS 2019 BD dataset [2]. These are non-homogeneous color fundus images containing global labels for pathological alterations, illumination, as well as varying image resolutions. Image dimensions vary from 474 × 358 pixels to 4288 × 2848 pixels. This dataset provides manual masks for specific ophthalmic structures.

Background/Objectives: Clinical foundation models (CFMs) are increasingly being applied to analyze unstructured clinical text, supporting tasks such as clinical reasoning, clinical documentation generation, and information extraction. However, their evaluation remains limited and lacks standardization, which represents a significant challenge for their safe integration into healthcare systems. This scoping review aimed to examine how performance, clinical utility, safety, and generalization are assessed in current evaluation frameworks and methodologies for CFMs applied to electronic health record text and to identify existing gaps and challenges in their evaluation. Methods: This scoping review was reported in accordance with PRISMA-ScR guidance, and the study selection process was summarized using a PRISMA flow diagram. A total of 448 records were identified in the initial search, of which 16 met the eligibility criteria and were included in the final synthesis. Results: The findings were organized into three categories: methodological frameworks, performance and clinical utility, and ethical and safety considerations. The results show progress in structured methods, evaluation schemes, and simulated environments. Nevertheless, inconsistencies persist in operational robustness, particularly regarding clinical reasoning and hallucinations. The literature also highlights important ethical concerns, including potential biases and regulatory gaps. Conclusions: Although CFMs show strong potential to transform clinical text analysis, no comprehensive framework currently guides their evaluation and safe use in real clinical settings. These findings highlight the need for a structured conceptual framework that integrates methodological, technical, clinical, and ethical criteria to support responsible implementation in clinical environments.

Urban mobility is increasingly conceptualized as a multidimensional, user-centered domain of transport system evaluation with potential implications for population health. This study examined the association between user-reported urban mobility experiences and perceived stress among adults using a high-intensity corridor in Nogales, Sonora, Mexico. A quantitative cross-sectional analytical study was conducted with 423 participants using the Urban Mobility Experiences Scale (UMES) and the Perceived Stress Scale (PSS-14). Spearman’s correlation analyses showed inverse associations between perceived stress and several mobility dimensions, although only Sustainability and Urban Environment remained statistically significant after Bonferroni correction (ρ = −0.266; p < 0.001). In multivariate analysis, Sustainability and Urban Environment, Accessibility and Connectivity, and Travel Time and Efficiency were retained as significant predictors, jointly explaining 14.1% of the variance in perceived stress (R² = 0.141; f² = 0.152). These findings suggest that multidimensional urban mobility experiences, particularly environmental and accessibility conditions, are associated with perceived stress beyond traditional operational indicators in high-intensity urban corridors.

Urban mobility plays a key role in territorial equity, access to services, and population well-being, as unfavorable mobility experiences are associated with stress and physical and mental deterioration. However, in Latin American and border cities, validated instruments for comprehensively assessing these experiences remain scarce. This study aimed to design and evaluate the psychometric properties of the Urban Mobility Experiences Scale [UMES]. A cross-sectional study was conducted with 423 adults from Nogales, Sonora, Mexico, selected through convenience sampling. The initial UMES consisted of 24 items distributed across five conceptual dimensions. Content validity was assessed by nine experts using Aiken’s V coefficient, while construct validity was examined through exploratory factor analysis with principal axis factoring and PROMAX rotation. Data adequacy was verified using the Kaiser–Meyer–Olkin index and Bartlett’s test of sphericity. Internal consistency was estimated using McDonald’s Omega. All items demonstrated adequate content validity (V ≥ 0.80). Five factors were identified, explaining 53.6% of the total variance, with factor loadings above 0.40. Reliability was acceptable across all dimensions (ω ≥ 0.70), and overall internal consistency was high (ω = 0.912). The UMES is a valid and reliable instrument for assessing urban mobility experiences in intermediate and border cities and may inform evidence-based policies promoting equity, sustainability, and urban well-being.

This study presents a comprehensive and critical review of segmentation algorithms applied to digital fundus images, aiming to identify computational strategies that balance diagnostic accuracy with practical feasibility in clinical environments. A systematic search following PRISMA guidelines was conducted for studies published between 2014 and 2025, encompassing deep learning, classical machine learning, hybrid, and semi-supervised approaches. The review examines how each methodological family performs in segmenting key anatomical structures such as blood vessels, the optic disc, and the fovea, considering both algorithmic and clinical metrics. Findings reveal that advanced deep learning models—particularly U-Net and CNN-based architectures—achieve superior accuracy in delineating complex and low-contrast structures but demand high computational resources. In contrast, traditional and hybrid methods offer efficient alternatives for real-time or low-resource settings, maintaining acceptable precision while minimizing cost. Importantly, the analysis underscores the persistent gap between methodological innovation and clinical translation, emphasizing the need for lightweight, clinically interpretable models that integrate algorithmic performance with medical relevance.

This literature review examines the integration of digital health record systems in the management of diabetes mellitus (DM), particularly type 2 diabetes, and highlights the urgent need for improved patient access to health information through technology. The use of information technology (IT) in the management of diabetes has shown remarkable results, such as increased medication adherence (12.8%–39%), lower HbA1c (0.49%–8%) levels, and lower blood pressure (47.2%–30.8%) levels. It also explores the benefits of mobile health (mHealth) applications, electronic health records (EHR), and personal health records (PHR) in improving self-management and healthcare support via social networks. It emphasizes the need for regular monitoring and communication facilitated by robust IT solutions that enable patients to access and share their health data, ensure effective communication, and support health monitoring. Unlike other reviews, we focus exclusively on proposals that facilitate interaction with medical records for automatic access to patient data. Our main contribution focuses on identifying critical needs to improve diabetes management through technology.

Securing access to quality healthcare services, particularly for essential screening tests like mammograms, presents a significant challenge in developing nations. Women often encounter extensive waiting periods, sometimes extending for several months, to undergo a mammogram. This crucial test plays a pivotal role in the early detection of breast cancer, where timely diagnosis is crucial for effective treatment and enhanced survival prospects. Delays in obtaining a diagnosis can significantly impact the health of patients and their well-being, underscoring the importance of early detection. Compounding these challenges is the scarcity of resources and healthcare professionals, which hinders swift and efficient access to preventive care. Such constraints underscore the pressing need for improvements in the interpretation of radiological studies and a reduction in the workload of imaging specialists. These improvements would not only optimize interdisciplinary collaboration but also enhance patient care, particularly for critical screenings like mammograms (3). Technological advancements, particularly in artificial intelligence (AI), have significantly influenced numerous sectors, including healthcare (4). Yet, in areas such as Latin America, where the healthcare infrastructure and resources may not fully support the integration of advanced diagnostic techniques based on informatic tools, the potential of these innovations seems to be underutilized. A promising application of technology is in Computer-Aided Diagnosis (CAD) for breast cancer screening with mammograms. CAD leverages vast datasets and pattern recognition algorithms to detect anomalies within mammograms, potentially facilitating the early identification of lesions. Given that breast cancer ranks among the most prevalent diseases affecting women globally, early detection is vital for enhancing patient survival rates and quality of life. Given these challenges, our study aims to examine the latest developments in artificial intelligence technology as a supplementary tool in CAD strategies for mammograms. Our goal is to foster better interdisciplinary collaboration among clinicians, radiologists, and pathologists through a state of the art (SOTA) review of the topic. By doing so, we anticipate streamlining the diagnostic workflow and elevating the efficiency of breast cancer detection and treatment processes.

Coronavirus disease (COVID-19) represents one of the greatest challenges to public health in modern history. As the disease continues to spread globally, medical and allied healthcare professionals have become one of the most affected sectors. Stress and anxiety are indirect effects of the COVID-19 pandemic. Therefore, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. Here, we propose a computer-based method to better understand stress in healthcare workers facing COVID-19 at the beginning of the pandemic. We based our study on a representative sample of healthcare professionals attending to COVID-19 patients in the northeast region of Mexico, at the beginning of the pandemic. We used a machine learning classification algorithm to obtain a visualization model to analyze perceived stress. The C5.0 decision tree algorithm was used to study datasets. We carried out an initial preprocessing statistical analysis for a group of 101 participants. We performed chi-square tests for all questions, individually, in order to validate stress level calculation (p < 0.05) and a calculated Cronbach’s alpha of 0.94 and McDonald’s omega of 0.95, demonstrating good internal consistency in the dataset. The obtained model failed to classify only 6 out of the 101, missing two cases for mild, three for moderate and one for severe (accuracy of 94.1%). We performed statistical correlation analysis to ensure integrity of the method. In addition, based on the decision tree model, we concluded that severe stress cases can be related mostly to high levels of xenophobia and compulsive stress. Thus, showing that applied machine learning algorithms represent valuable tools in the assessment of perceived stress, which can potentially be adapted to other areas of the medical field.