PLOS digital health最新文献

Dental age (DA) estimation is a key diagnostic tool in pediatric dentistry, particularly when birth records are unavailable or unreliable. It guides decisions on growth assessment, orthodontic planning, and timing of interventions such as space maintenance or extractions. Unlike skeletal maturity, dental development is less affected by nutritional and environmental factors, making it a reliable marker of biological age. Conventional methods require expert interpretation and are prone to variability. There is growing interest in automated, objective approaches to streamline this process and enhance clinical utility. A total of 550 panoramic radiographs from children aged 3-14 years were labeled into 11 dental age groups based on the AAPD reference chart by two experienced pediatric dentists. Images with poor quality were excluded. The dataset was divided into training (80%) and validation (20%) sets, with data augmentation applied to the training set. The YOLOv11n-cls model, consisting of 86 layers and 1.54 million parameters, was trained for 30 epochs using the Ultralytics engine and AdamW optimizer. Model performance was evaluated using Top-1 and Top-5 accuracy on the validation set and tested on an independent set of 203 images. Grad-CAM was used for model interpretability. The model achieved 92.6% Top-1 and 99.5% Top-5 accuracy on the validation set. Performance on the test set remained high, with most misclassifications occurring between adjacent age groups. Grad-CAM visualizations showed attention to clinically relevant areas like erupting molars and root development. The findings support the high performance of DL, through YOLOv11 for pediatric age prediction. The AI tool enabled fast, accurate, and interpretable DA classification, making it a strong candidate for clinical integration as an adjunct tool into pediatric dental practice.

The availability of medical imaging data is indispensable for medical advancements such as the development of new diagnostic tools, improved surgical navigation systems, and profiling for personalized medicine through imaging biomarkers. A central challenge in data governance is balancing the need to protect patient privacy with the necessity of promoting scientific innovation. Restrictive data governance policies could limit access to the large, high-quality datasets needed for such advancements. Conversely, lenient policies could compromise patient trust and lead to potential misuse of sensitive information. We call for a deliberate and well-considered approach to data governance, highlighting important factors that patients and healthcare organizations should consider when making imaging data governance decisions around data sharing.

Food preferences differ among individuals, and these variations reflect underlying personalities or mental tendencies. However, capturing and predicting these individual differences remains challenging. Here, we propose a novel method to predict individual food preferences by using CLIP (Contrastive Language-Image Pre-Training), which can capture both visual and semantic features of food images. By applying this method to food image rating data obtained from human subjects, we demonstrated our method's prediction capability, which achieved better scores compared to methods using pixel-based embeddings or label text-based embeddings. Our method can also be used to characterize individual traits as characteristic vectors in the embedding space. By analyzing these individual trait vectors, we captured the tendency of the trait vectors of the high picky-eater group. In contrast, the group with relatively high levels of general psychopathology did not show any bias in the distribution of trait vectors, but their preferences were significantly less well-represented by a single trait vector for each individual. Our results demonstrate that CLIP embeddings, which integrate both visual and semantic features, not only effectively predict food image preferences but also provide valuable representations of individual trait characteristics, suggesting potential applications for understanding and addressing food preference patterns in both research and clinical contexts.

Earthquakes and other disasters often cause substantial damage to health facilities, impacting short-term response capacity and long-term health system needs. Identifying health facility damage following disasters is therefore crucial for coordinating response, but ground-based evaluations require substantial time and labor. Artificial intelligence (AI) models trained on satellite imagery can estimate building damage and could be used to generate rapid health facility damage reports. There is little published about methods of generating these estimates, testing real-world accuracy, or exploring error. This study presents a novel method of overlaying model damage outputs with health facility location data to generate health facility damage estimates following the February 2023 earthquake in Turkey. Two models were compared for agreement, accuracy, and errors. Building-level damage estimates were obtained for Model A (Microsoft neural network model), and Model B (Google AI model), and overlaid with health facility location data to identify facilities with significant damage. Model agreement, sensitivity and specificity for damage detection were calculated. A descriptive review of common error sources based on selected satellite imagery was conducted. A spatially aggregated damage estimation, based on proportion of buildings damaged in a 0.125km2 area, was also generated and assessed for each model. Twenty-five hospitals, 13 dialysis facilities, and 454 pharmacies were evaluated across three cities. Estimated damage was higher for Model A (10.4%) than Model B (4.3%). Cohen's kappa was 0.32, indicating fair agreement. Sensitivity was low for both models at 42.9%, while specificity was high (A:93.6%, B:96.8%). Agreement and sensitivity were best for hospitals. Common errors included building identification and underestimation of damage for destroyed buildings. Spatially aggregated damage estimates yielded higher sensitivity (A:71.4%, B:57.1%) and agreement (Cohen's kappa 0.38). Leveraging remote-sensing models for health facility damage assessment is feasible but currently lacks the sensitivity to replace ground evaluations. Improving building identification, damage detection for destroyed buildings, and spatially aggregating results may improve the performance and utility of these models for use in disaster response settings.

Studying for final exams is often regarded as difficult for nursing students, therefore, activities using game-based learning methods may increase student satisfaction. Therefore, this study aimed to understand the feasibility of a game-based learning activity on nursing students' learning and revision processes. A one-group pre and post-questionnaire design was undertaken to evaluate the effectiveness of a game-based learning activity. All nursing students found the game-based learning activity valuable when preparing for written exams. The learning activity increased the levels of knowledge retention and the final grades. Although two students found the activity somewhat distracting, most students believed that game-based learning should be embedded into the nursing curriculum. The game-based learning activity was well-accepted when revising for written exams in nursing. However, research at a larger scale is required to confirm the effectiveness of the activity on students' knowledge, grades and long-term retention.

Over the past few years, health delivery organizations (HDOs) have been adopting and integrating AI tools, including clinical tools for tasks like predicting risk of inpatient mortality and operational tools for clinical documentation, scheduling and revenue cycle management, to fulfill the quintuple aim. The expertise and resources to do so is often concentrated in academic medical centers, leaving patients and providers in lower-resource settings unable to fully realize the benefits of AI tools. There is a growing divide in HDO ability to conduct AI product lifecycle management, due to a gap in resources and capabilities (e.g., technical expertise, funding, data infrastructure) to do so. In previous technological shifts in the United States including electronic health record and telehealth adoption, there were similar disparities in rates of adoption between higher and lower-resource settings. The government responded to these disparities successfully by creating centers of excellence to provide technical assistance to HDOs in rural and underserved communities. Similarly, a hub-and-spoke network, connecting HDOs with technical, regulatory, and legal support services from vendors, law firms, other HDOs with more AI capabilities, etc. can enable all settings to be well equipped to adopt AI tools. Health AI Partnership (HAIP) is a multi-stakeholder collaborative seeking to promote the safe and effective use of AI in healthcare. HAIP has launched a pilot program implementing a hub-and-spoke network, but targeted public investment is needed to enable capacity building nationwide. As more HDOs are striving to utilize AI tools to improve care delivery, federal and state governments should support the development of hub-and-spoke networks to promote widespread, meaningful adoption of AI across diverse settings. This effort requires coordination among all entities in the health AI ecosystem to ensure these tools are implemented safely and effectively and that all HDOs realize the benefits of these tools.

Pregnancy involves rapid physiological and psychological changes that can increase vulnerability to health complications, underscoring the need for timely, individualized support. Mobile health (mHealth) tools offer a scalable way to capture repeated measures of health status throughout pregnancy, facilitating longitudinal assessment and the opportunity for timely intervention. This study leveraged mHealth technologies, including the Oura smart ring and ecological momentary assessment (EMA) via a mobile app, to examine how emotional distress affects the relationship between physical activity (PA) and heart rate variability (HRV), an indicator of physiological stress during pregnancy. Specifically, we examined whether emotional distress, measured via daily EMA surveys, moderates the association between physical activity and nighttime HRV, captured by continuous Oura ring data. Hence, this analysis integrated temporally aligned wearable and self-report data to investigate the interaction between subjective emotional states and objectively measured physical activity patterns. Consenting participants, aged 18-40 years, with a healthy singleton pregnancy in the second trimester, were enrolled in the study. Our findings revealed that on days with high emotional distress, each additional 1,000 steps was associated with a 3.5% increase in nighttime HRV (p-value < 0.001; 95% CI: 2.6%, 4.4%). In contrast, physical activity had little to no association with HRV on days with moderate distress (0.6%; 95% CI: -0.7%, 1.9%) and low distress (0.6%; 95% CI: -0.4%, 1.5%). These findings suggest that physical activity may be particularly beneficial on high-distress days, supporting the development of adaptive interventions that prioritize PA engagement during periods of elevated emotional distress. Based on our model-estimated moderation effects, we may recommend that a pregnant woman increase her physical activity on high-distress days due to a strong positive PA-HRV association, whereas for those who do not experience much emotional distress, the recommendation may be less emphasized, given the weaker observed association.

Adults with spinal cord injury/ dysfunction (SCI/D) commonly take multiple medications for a variety of secondary conditions, and have described challenges with medication self-management. To help support medication self-management, a web-based toolkit, MedManageSCI, was co-designed by our team of researchers and adults with SCI/D, caregivers, and healthcare providers (www.medmanagesci.ca). Together, we co-developed the content areas to include in MedManageSCI, along with the design and brand considerations, to create an initial prototype of the toolkit. To finalize the prototype prior to implementation, the primary objective of this qualitative study was to further refine MedManageSCI by examining the clarity, comprehensiveness, relevance, and delivery of the toolkit modules. Cognitive interviews were conducted virtually between July 2024 and September 2024 with adults with SCI/D (N = 16). A concurrent verbal probing approach using scripted and spontaneous probes was followed. Data were coded using a pre-established coding matrix that aligned with the scripted probes. Participants provided 193 specific modifications to improve the clarity, comprehensiveness, relevance, or delivery of the MedManageSCI toolkit, which were categorized as: Comprehension, Design, and Web-based Delivery. The Comprehension category contained three subcategories: Written Refinements, Ensuring Accessibility, and Revamping Resources. The Design category contained three subcategories: Formatting Content, Streamlining Function, and Enhancing Visuals. Participants perceived the website as an ideal way to deliver the toolkit, noting several benefits of a web-based delivery in comparison to a paper-based toolkit. Overall, participants found the modules to be comprehensive and highly relevant. Further, we discuss the application of cognitive interviews for further refining the MedManageSCI prototype, recommendations to improve the comprehensibility, and the advantages of a web-based toolkit for the SCI/D population. Involving individuals with SCI/D in the development and refinement of self-management materials will help ensure that the content and resources are tailored and appropriate; thereby elevating its likelihood of uptake and dissemination during implementation.

The successful implementation of artificial intelligence-assisted diagnostic system (AIADS) in pathology relies not only on the maturity of AI technology but also on pathologists' cognition and acceptance of AI. However, research on pathologists' perceptions towards AIADS is limited. This study aims to explore pathologists' knowledge, attitudes, and practice toward AIADS and identify key factors influencing their willingness to use it, providing insights for the effective integration of AI technology in pathology. An online, nationwide, cross-sectional survey is to investigate pathologists' knowledge, attitudes and behavioral intention/practice regarding AIADS with a 5-point Likert scale. Descriptive analysis is used to present the results, while logistic regression examines factors influencing AIADS adoption. The mediating effect of attitude in the association between knowledge and behavioral intention is also explored. A total of 224 pathologists were surveyed, with 85 (37.9%) having used AIADS and 139 (62.1%) not using it. The mean scores for knowledge, attitude, and behavioral intention were 3.42 ± 0.97, 3.48 ± 0.44, and 3.47 ± 0.44, respectively. Pathologists who had used AIADS scored higher in knowledge, attitude, and behavioral intention, with clearer attitudes toward AIADS. Over 80% of pathologists supported the use of AIADS in clinical diagnostics, citing improved diagnostic speed and reduced workload as key reasons. The main concerns about AIADS were its diagnostic accuracy. Logistic regression analysis indicated that a greater likelihood of willingness to use AIADS was associated with not having used it before (OR=2.462, 95%CI 1.087-5.573), as well as with higher knowledge scores (OR=1.140, 95%CI 1.076-1.208) and more positive attitude scores (OR=1.119, 95%CI 1.053-1.189). Mediation analysis indicated an indirect path from knowledge to behavioral intention through attitude among individuals who have used AIADS, with the mediation effect accounting for 59.4%. In conclusion, most pathologists support the use of AIADS in clinical practice, but improvements in diagnostic performance are necessary. Enhancing pathologists' knowledge, attitudes, and user experience is crucial for the broader adoption of AIADS.