Frontiers in digital health最新文献

Digital biomarkers are revolutionizing medicine in ways that were unimaginable a few years ago. Consequently, precision medicine approaches now realistically can promise personalization, i.e., the right treatments for the right patients at the right time, including earlier, targeted interventions which lead to a major paradigm shift in how medicine is practiced from reactive to preventive action. Although the scientific evidence is clear on the power of digital biomarkers, there is an unmet need for translating these findings into actionable insights in clinical practice. In this paper, we focus on Alzheimer's disease and related dementias (ADRD), and how digital biomarkers could empower clinical decision making in its preclinical stages. We argue that a new all-encompassing score is needed, akin to a BrainHealth Index linked to the established and validated risk stratifications frameworks and is directed at the prevention of ADRD. Specifically, we propose the new concept "Digital Neuro Fingerprint (DNF)", built with simultaneous collection of multimodal digital biomarkers (speech, gait, eye movements etc.) from smartphone based augmented reality or virtual reality while an individual is immersed in activities of daily living. Fusing the captured multimodal digital biomarkers, data is automatically analyzed with custom combinations of machine- and deep-learning approaches and enhanced with explainable artificial intelligence (XAI) and uncertainty quantifications. We argue that DNF is useful for capturing ADRD progression and should supersede the biomarkers that are invasive and expensive to obtain, offering a sensitive and highly specific score that measures meaningful aspects of health for the patients in high-frequency intervals.

Assistive technologies can enhance safety, independence, and quality of life for people with blindness and low vision. Despite their benefits, abandonment of these technologies remains widespread, and recent research on this issue is limited. In this Perspective article, we draw on both professional experiences and relevant scientific literature to examine adoption and abandonment in the context of new artificial intelligence-powered applications. We highlight risks arising from misaligned design, inconsistent industry support, and inadequate user training. We synthesize existing knowledge on factors that influence abandonment and propose three priorities to realign assistive technology development: participatory and transdisciplinary research, integrated technology ecosystems, and socially supported engagement. Taken collectively, these priorities ensure that emerging assistive technologies better align with the needs of people with blindness and low vision, promoting lasting adoption rather than abandonment.

Background: Audiovisual tools are increasingly used in healthcare to improve patient education and engagement. However, few studies, particularly in dermatology, have evaluated their effectiveness in enhancing patient understanding during the informed consent process. This study aimed to compare the effectiveness of a motion-graphic educational video with conventional verbal consent for patients undergoing platelet-rich plasma (PRP) therapy for androgenetic alopecia (AGA).

Methods: In this randomized controlled trial, participants aged 18-55 years with AGA were recruited at the Dermatology Clinic, Walailak University Hospital, between December 2024 and March 2025. Participants were randomized to receive informed consent through either an educational video (Group A) or a conventional verbal explanation (Group B). Pre- and post-intervention knowledge and anxiety levels were assessed, and satisfaction was evaluated in Group A.

Results: Thirty-four participants completed the study (73.5% male; median age: 39.5 years, IQR: 23.0). Median baseline knowledge and anxiety scores were 0.0 (IQR: 2.0) and 6.0 (IQR: 3.0), respectively. Post-intervention knowledge scores increased significantly in both groups (Group A: 9.0, IQR: 1.0; Group B: 7.0, IQR: 2.0; p < 0.001), with a greater knowledge gain in Group A (8.0, IQR: 3.0) compared to Group B (6.0, IQR: 2.0; p = 0.009). Anxiety scores remained unchanged in both groups. Group A reported a high usefulness score for the video (median, 10.0; IQR, 1.0). No significant correlations were found between demographic factors and baseline knowledge or anxiety.

Conclusions: The motion-graphic educational video improved patient knowledge compared with conventional verbal explanations, without reducing anxiety. Participants reported high satisfaction, supporting the use of audiovisual media as an effective adjunct to the informed consent process.

Clinical trial registration: https://www.thaiclinicaltrials.org/show/TCTR20241222004, identifier TCTR20241222004.

Background: Knee osteoarthritis (KOA) is a chronic joint disorder that significantly affects the quality of life in the older adult. It is primarily characterized by notable knee pain following activity, which typically alleviates with rest. With the rapid growth of the internet, people increasingly rely on social media to obtain health-related information. Short-form videos, as an emerging format, play an important role in information dissemination. TikTok is currently the world's most downloaded application platform primarily dedicated to short-form video content. Against this backdrop, we observed a substantial number of KOA-related videos on TikTok, the quality and reliability of which have not yet been systematically evaluated.

Objective: To assess the quality and reliability of KOA-related videos available on the domestic TikTok platform.

Methods: A total of 100 KOA-related videos were retrieved and screened from TikTok. Basic metadata were extracted, and video content and format were categorized through coding. The source of each video was also documented. Two independent raters evaluated video quality using the DISCERN instrument, the Journal of the American Medical Association (JAMA) benchmark criteria, and the Global Quality Score (GQS).

Results: Of 100 analyzed videos, 96 were posted by medical staff and 4 by science communicators. Eighty videos were audio-based (41% outpatient daily, 39% general science popularization), with others using graph-text formats. The video content is divided into 7 groups: disease prevention, diagnosis, symptoms, description, life-style and therapy, among which the video related to disease description is the most. The average DISCERN, JAMA and GQS scores of videos were 36.29, 1.24 and 2.45, respectively, and the overall quality was low. Further analysis shows that there are significant differences in video quality between science communicators and medical staff. The number of likes, comments, collections, and shares are strongly positively correlated with each other, and they are weakly positively correlated with the number of upload days and DISCERN scores.

Conclusion: KOA-related content on TikTok demonstrates concerning quality limitations, with significant variation across source types. Given TikTok's expanding influence in health communication, urgent improvements and standardized quality control measures are needed.

Introduction: Hereditary angioedema (HAE) is a rare disease imposing a significant quality of life burden. Affect monitoring via Ecological Momentary Assessment (EMA) could offer personalized psychological support by collecting repeated, ecological data in real-life, overcoming the limitations of traditional methods. This study assessed the feasibility and acceptability of an EMA protocol for affect monitoring in HAE patients vs. healthy controls (CTR).

Methods: HAE patients and CTR were recruited for a 16-week EMA study. Participants received weekly EMA surveys assessing affect via REDCap™. Feasibility was evaluated through recruitment, response, and completion rates. Acceptability was assessed via a post-study questionnaire through a visual analogue scale ranging from 1 to 100.

Results: Twenty-eight Caucasian subjects were contacted, 12 HAE [median age: 50 (22) years, 5 males] and 14 CTR [age: 30 (32) years, 6 males] agreed to participate, resulting in a recruitment rate of 93%. Response and completion rates were ≥92% and ≥96% respectively in both groups. Completion time was brief and did not differ between groups [HAE: 1' 28″ (29″) vs. CTR: 1' 15' (15″), P = 0.274]. The protocol was considered acceptable by both groups [HAE: rate 83.5 (18.8) vs. CTR: 72.0 (13.0), p = 0.27] with HAE rating the experience as helpful [79 (39.8)] and thought-provoking [67 (33)].

Conclusion: EMA is a highly feasible and acceptable method for affect monitoring in HAE. The presence of a rare disease does not appear to be a barrier to its application, supporting its use in this clinical setting.

Background: Valvular heart disease and heart failure are major global health burdens, yet access to comprehensive echocardiography is often limited, particularly in resource-constrained settings. Artificial intelligence (AI) may enable rapid, point-of-care cardiac assessment using simplified imaging protocols.

Objectives: To evaluate whether a deep learning model can accurately detect significant valvular and ventricular dysfunction using only a single two-dimensional apical four-chamber echocardiographic view, including images acquired by non-cardiologists with handheld ultrasound devices.

Methods: We retrospectively analyzed 120,127 echocardiographic studies from a tertiary medical center to train and validate a deep learning model for identifying moderate-or-greater mitral or tricuspid regurgitation, right ventricular dysfunction, and reduced left ventricular ejection fraction (≤40%). A prospective cohort of 209 patients underwent handheld point-of-care cardiac ultrasound performed by non-cardiologist physicians, with same-hospitalization comprehensive echocardiography as the reference standard.

Results: In retrospective testing, model areas under the curve (AUCs) were 0.883 for mitral regurgitation, 0.913 for tricuspid regurgitation, 0.940 for right ventricular dysfunction, and 0.982 for reduced ejection fraction. In the prospective cohort, AUCs were 0.72, 0.87, 0.95, and 0.97 for the same respective targets.

Conclusions: A single-view deep learning model demonstrated strong diagnostic accuracy for detecting significant valvular and ventricular dysfunction across both standard and handheld ultrasound acquisitions. This approach may facilitate rapid, scalable cardiac function screening by non-cardiologists in diverse clinical environments.

Clinical trial registration: identifier NCT05455541.

Introduction: The classification of lesion types in Digital Breast Tomosynthesis (DBT) images is crucial for the early diagnosis of breast cancer. However, the task remains challenging due to the complexity of breast tissue and the subtle nature of lesions. To alleviate radiologists' workload, computer-aided diagnosis (CAD) systems have been developed. The breast lesion regions vary in size and complexity, which leads to performance degradation.

Methods: To tackle this problem, we propose a novel DBT Dual-Net architecture comprising two complementary neural network branches that extract both low-level and high-level features. By fusing different-level feature representations, the model can better capture subtle structure. Furthermore, we introduced a pseudo-color enhancement procedure to improve the visibility of lesions on DBT. Moreover, most existing DBT classification studies rely on two-dimensional (2D) slice-level analysis, neglecting the rich three-dimensional (3D) spatial context within DBT volumes. To address this limitation, we used majority voting for image-level classification from predictions across slices.

Results: We evaluated our method on a public DBT dataset and compared its performance with several existing classification approaches. The results showed that our method outperforms baseline models.

Discussion: The use of pseudo-color enhancement, extracting high and low-level features and inter-slice majority voting proposed method is effective for lesion classification in DBT. The code is available at https://github.com/xiaoerlaigeid/DBT-Dual-Net.

Background: Sleep disturbances and autonomic dysregulation are common in autism spectrum disorder (ASD), yet few studies have examined long-term nocturnal heart rate variability (HRV) in home settings.

Objective: This study evaluated the feasibility of one-month home-based HRV monitoring using smart clothing in a preschooler with ASD, and explored whether nocturnal HRV predicts next-day problem behaviors.

Methods: HRV was recorded nightly for 25 valid days using a garment-type wearable ECG. Problem behaviors were reported daily by caregivers. HRV indices were compared between nights preceding days with and without problem behaviors using Wilcoxon signed-rank tests.

Results: No significant differences in total sleep time or HRV indices were found between the two day types.

Conclusion: Although HRV did not predict next-day behavior, the study demonstrates the feasibility and methodological transparency of long-term home-based physiological monitoring in young children with ASD.

Objective: While the integration of modern AI systems in clinical practice is currently in the process of transforming how medicine is being practiced, the focus of most research activities lies on AI-associated efficacy and safety. However, the interplay between human agents and AI systems will equally shape the actual impact of such systems.

Methods: This study simulated human decision-making using 27 agents characterized by varying levels of competence, certainty, and trust. Agents completed binary and three-option decision tasks, both with and without AI assistance. AI models varied in competence (0.3-0.9) and, in some simulations, included confidence signals to influence human trust dynamically. Each scenario involved 10,000 simulated decisions per agent. In AI-assisted conditions, decisions were modulated by the agent's baseline trust and, in the conditional trust setting, the AI's expressed confidence.

Results: AI support significantly improved decision accuracy for most agents, especially those with high competence but low certainty. In binary tasks, agents showed up to 150% relative improvement in decision accuracy with AI competence ≥0.6. In three-option tasks, even lower-performing AI (e.g., 0.4 competence) enhanced decision results. Conditional trust simulations showed further gains, particularly among agents with moderate baseline trust, as dynamic trust adjustments based on AI confidence reduced over-reliance on poor AI recommendations.

Discussion: Results demonstrate that AI assistance, particularly when paired with confidence calibration, enhances human decision-making, especially for uncertain or moderately skilled users. However, over-trusting low-competence AI can impair outcomes for high-performing agents. Tailored AI-human collaboration strategies are essential for optimizing clinical decision support.