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Pediatric sepsis prediction: Human in the loop framework. 儿童脓毒症预测:人在循环框架。
IF 7.7 Pub Date : 2025-11-04 eCollection Date: 2025-11-01 DOI: 10.1371/journal.pdig.0001045
Radha Nagarajan, Sandip A Godambe, Raina Paul, Ryan Tennant, Kanwaljeet J S Anand, Emma Sandhu, Nicole Abrahamson, David Gibbs, Charles Golden, Leo Anthony Celi, Steven Martel
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引用次数: 0
Video consent is preferred over written informed consent in pediatric rheumatology research. 在儿童风湿病研究中,视频同意优于书面知情同意。
IF 7.7 Pub Date : 2025-11-03 eCollection Date: 2025-11-01 DOI: 10.1371/journal.pdig.0001067
Nicholas C Chan, Amalia R Silberman, Megan K Robertson, Angela R De Castro, Marie P Lauro, Susheen Mahmood, Tamar A Tabrizi, Hannah Nguyen, Brian M Feldman, Y Ingrid Goh

The goal of this study was to determine the difference in participant understanding, satisfaction, timing and, preference between video consent and written informed consent in a pediatric rheumatology research setting. Participants were randomized to receive either video consent or written informed consent for a registry study. After completing the first consent method, they completed a comprehension and satisfaction questionnaire. Then they received the alternate consent method and completed a second set of questionnaires. Bayesian non-parametric tests determined the difference in comprehension, satisfaction, timing and preference between video consent and written informed consent. Ninety-nine caregivers and 76 patients were randomized into video consent (n = 88) and written informed consent (n = 87) groups. Comprehension (Max = 12) and satisfaction (Max = 5) were high in both groups. There was moderate evidence supporting no difference in comprehension (medianvideo consent = 11 and medianwritten informed consent = 10) and satisfaction (medianvideo consent = 4 and medianwritten informed consent = 5) between video consent and written informed consent (BF10 = 0.225 and 0.32, respectively). The median time to complete video consent and written informed consent was 408 (95% Credible Interval (CrI): 397-412) and 360 (95% CrI: 329-391) seconds, respectively. There was decisive evidence that video consent increased the time of consent (in our sample by 48 seconds) compared to written informed consent (BF10 = 713). There was decisive evidence for participants preferring video consent over written informed consent (BF10 = 2.307x1011) as they thought it was easier to follow. Overall, participant understanding and satisfaction were comparable between video consent and written informed consent. Even though video consent was slightly less time efficient compared to written informed consent, video consent was highly preferred by caregivers and patients, supporting its use to obtain informed consent.

本研究的目的是确定在儿童风湿病研究环境中,视频同意和书面知情同意在参与者理解、满意度、时间和偏好方面的差异。在注册研究中,参与者被随机分配接受视频同意书或书面知情同意书。在完成第一同意法后,他们完成了一份理解和满意度问卷。然后,他们接受了另一种同意法,并完成了第二套问卷。贝叶斯非参数测试确定视频同意和书面知情同意在理解、满意度、时间和偏好方面的差异。99名护理人员和76名患者随机分为视频知情同意书组(n = 88)和书面知情同意书组(n = 87)。两组学生的理解力(Max = 12)和满意度(Max = 5)均较高。中度证据支持视频同意和书面知情同意在理解(媒体视频同意= 11,媒体书面知情同意= 10)和满意度(媒体视频同意= 4,媒体书面知情同意= 5)方面无差异(BF10分别= 0.225和0.32)。完成视频同意书和书面知情同意书的中位时间分别为408秒(95%可信区间(CrI): 397-412)和360秒(95%可信区间:329-391)。有决定性的证据表明,与书面知情同意(BF10 = 713)相比,视频同意增加了同意时间(在我们的样本中增加了48秒)。有决定性的证据表明,参与者更喜欢视频同意而不是书面知情同意(BF10 = 2.307 × 1011),因为他们认为更容易遵循。总体而言,参与者的理解和满意度在视频同意和书面知情同意之间具有可比性。尽管与书面知情同意书相比,视频同意书的时间效率略低,但护理人员和患者非常喜欢视频同意书,支持使用视频同意书来获得知情同意书。
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引用次数: 0
Personalized decision-making through AI solutions in pediatric emergency medicine: Focusing on febrile children. 基于AI解决方案的儿科急诊个性化决策:以发热儿童为重点
IF 7.7 Pub Date : 2025-11-03 eCollection Date: 2025-11-01 DOI: 10.1371/journal.pdig.0001080
Lina Jankauskaite, Urte Oniunaite, Rimantas Kevalas

Pediatric emergency medicine (PEM) presents unique challenges due to the diverse developmental stages and medical conditions of young patients. The increasing patient load and nonurgent referrals to pediatric emergency departments (PEDs) emphasize the need for personalized decision-making approaches. These approaches must accommodate the complexities of pediatric care while fostering collaboration between healthcare providers and families. Integrating artificial intelligence (AI) into healthcare settings can transform PEM by enhancing diagnostic accuracy, customizing treatments, and optimizing resource allocation. AI technologies leverage vast datasets, including electronic health records and genetic profiles, to generate personalized diagnostic and treatment plans. Machine learning algorithms can identify patterns in complex data, facilitating early disease detection and precise interventions. This literature review analyzes the role of AI in supporting pediatric emergency care through diagnostic assistance, predictive modeling for febrile disease progression, and outcome optimization. It also highlights the challenges of applying AI in PEM, including data limitations and the need for algorithmic transparency. By addressing these challenges, AI has the potential to revolutionize personalized care in pediatric emergency settings, ultimately improving patient outcomes and care delivery.

由于年轻患者的不同发育阶段和医疗条件,儿科急诊医学(PEM)提出了独特的挑战。不断增加的病人负荷和非紧急转介到儿科急诊科(PEDs)强调需要个性化的决策方法。这些方法必须适应儿科护理的复杂性,同时促进医疗保健提供者和家庭之间的合作。将人工智能(AI)集成到医疗保健设置中可以通过提高诊断准确性、定制治疗和优化资源分配来改变PEM。人工智能技术利用庞大的数据集,包括电子健康记录和基因档案,来生成个性化的诊断和治疗计划。机器学习算法可以识别复杂数据中的模式,促进早期疾病检测和精确干预。本文献综述分析了人工智能通过诊断辅助、发热性疾病进展的预测建模和结果优化在支持儿科急诊护理中的作用。它还强调了在PEM中应用人工智能的挑战,包括数据限制和对算法透明度的需求。通过应对这些挑战,人工智能有可能彻底改变儿科急诊环境中的个性化护理,最终改善患者的治疗效果和护理服务。
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引用次数: 0
Risk factors and predictive performance for first healthcare encounter indicating homelessness using administrative data among Calgary residents diagnosed with addiction or mental health conditions. 根据卡尔加里被诊断为成瘾或精神健康状况的居民的行政数据,第一次医疗保健就诊的风险因素和预测表现表明无家可归。
IF 7.7 Pub Date : 2025-10-31 eCollection Date: 2025-10-01 DOI: 10.1371/journal.pdig.0001064
Faezehsadat Shahidi, M Ethan MacDonald, Dallas Seitz, Rebecca Barry, Geoffrey Messier

Individuals diagnosed with addiction or mental health (AMH) conditions are more likely to experience potentially adverse outcomes of homelessness. Despite their link to later outcomes, research on initial episodes of AMH outcomes is limited. This study aims to use administrative data to identify the factors associated with the first healthcare encounters with indicators of homelessness (FHE-H) for individuals diagnosed with AMH. We assessed logistic regression and compared its performance with machine learning models, including random forests and extreme gradient boosting (XGBoost). We conducted a retrospective cohort study linking several administrative datasets for 232,253 individuals with Alberta health insurance in Calgary, Canada, who were aged between 18 and 65 and diagnosed with AMH between April 1, 2013, and March 31, 2018. We assessed outcomes in two years following cohort entry. Individuals with episodes of FHE-H (2,606 individuals) before the index date were excluded. Multivariable logistic regression models were used to identify factors associated with outcomes by estimating adjusted odds ratios (AORs) with 95% confidence intervals. Among 229,647 individuals diagnosed with AMH, 1,886 (0.82%) experienced FHE-H during the follow-up period. Mental health emergency visits (AORs=5.28 [95% CI: 4.41, 6.33]), substance misuse (AORs=3.87 [95% CI: 3.28, 4.56], substance use disorders (AORs=2.03 [95% CI: 1.64, 2.50]), and male sex (AORs=1.28 [95% CI: 1.14, 1.44]) were associated with FHE-H. XGBoost performance improved over logistic regression, with increases in area under the curve (AUC) by 1% and precision by 2%. Overall, several AMH features were associated with FHE-H, and machine learning models outperformed logistic regression, although to a small degree.

被诊断患有成瘾或精神健康(AMH)疾病的人更有可能经历无家可归的潜在不良后果。尽管它们与后来的结果有关,但对AMH最初发作结果的研究是有限的。本研究旨在使用管理数据来确定与首次医疗保健接触相关的因素,这些因素与被诊断患有AMH的个人的无家可归指标(FHE-H)有关。我们评估了逻辑回归,并将其与机器学习模型(包括随机森林和极端梯度增强(XGBoost))的性能进行了比较。我们进行了一项回顾性队列研究,将加拿大卡尔加里232253名阿尔伯塔省医疗保险患者的几个行政数据集联系起来,这些患者年龄在18至65岁之间,并在2013年4月1日至2018年3月31日期间被诊断为AMH。我们在队列进入后的两年内评估了结果。在索引日期之前有FHE-H发作的个体(2,606例)被排除在外。采用多变量logistic回归模型,以95%置信区间估计调整优势比(AORs),确定与结果相关的因素。在诊断为AMH的229,647人中,1,886人(0.82%)在随访期间经历了FHE-H。精神卫生急诊就诊(AORs=5.28 [95% CI: 4.41, 6.33])、药物滥用(AORs=3.87 [95% CI: 3.28, 4.56])、药物使用障碍(AORs=2.03 [95% CI: 1.64, 2.50])和男性(AORs=1.28 [95% CI: 1.14, 1.44])与FHE-H相关。与逻辑回归相比,XGBoost的性能有所提高,曲线下面积(AUC)增加了1%,精度提高了2%。总体而言,几个AMH特征与FHE-H相关,机器学习模型的表现优于逻辑回归,尽管程度较小。
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引用次数: 0
TELEHEALTH and digital health platforms in promoting access to sexual reproductive health self care among youth: A case of rocket health services in Uganda. 远程保健和数字保健平台促进青年获得性生殖健康自我保健:乌干达火箭保健服务的一个案例。
IF 7.7 Pub Date : 2025-10-31 eCollection Date: 2025-10-01 DOI: 10.1371/journal.pdig.0000770
Vincent Ssenfuka, John Mark Bwanika, Louis Henry Kamulegeya, Elizabeth Ekirapa Kiracho, Martha Akulume, Lynn Atuyambe

Sexual Reproductive Health (SRH) self-care offers a pathway for low income countries to advance towards Universal Health Coverage by empowering individuals, families, and communities to prioritize their SRH needs independently of healthcare providers. Facilitating access to SRH products is crucial for embracing self-care and digital health technologies hold promise for enhancing accessibility. This study explored the role played by rocket health digital platforms in enhancing accessibility to SRH self-care products among youth in Uganda. Employing a cross-sectional design with a mixed-method approach, the study involved key informant interviews with youth who had purchased SRH self-care products from Rocket Health in 2022, as well as key staff at Rocket Health. Quantitative data were extracted from Rocket Health's Electronic Medical Records covering the period from January 2022 to December 2022.More males (57%) utilized digital platforms for SRH compared to females (43%). The highest utilization was via the E-commerce platform (49%) while the least was via the voice call platforms (4%). A notable portion of youth (30%) still relied on visiting the pharmacy. Contraception products were predominantly consumed through digital platforms (44%), whereas self-testing were less frequently utilized (14%). The study also identified key resources such as the digital infrastructure that maximize the potential of digital health platforms in enhancing SRH self-care. By gaining insights into the digital infrastructure, preferences, barriers, and financial considerations associated with accessing SRH self-care services through digital platforms, targeted interventions such as access to contraceptives, awareness programs, prevention and treatment of Sexual Transmitted Infections can be developed to promote positive SRH outcomes among youth.

性生殖健康(SRH)自我保健为低收入国家推进全民健康覆盖提供了一条途径,使个人、家庭和社区能够独立于卫生保健提供者优先考虑其性健康和生殖健康需求。促进获得性健康和生殖健康产品对于接受自我保健至关重要,数字卫生技术有望提高可及性。本研究探讨了火箭健康数字平台在提高乌干达青年获得SRH自我保健产品的可及性方面所发挥的作用。该研究采用混合方法的横截面设计,包括对2022年从Rocket Health购买SRH自我护理产品的年轻人以及Rocket Health的主要工作人员进行关键信息访谈。定量数据提取自Rocket Health的电子医疗记录,涵盖2022年1月至2022年12月。与女性(43%)相比,男性(57%)更多地利用数字平台进行性健康生殖健康。电子商务平台的使用率最高(49%),而语音通话平台的使用率最低(4%)。相当一部分年轻人(30%)仍然依赖于去药店。避孕产品主要通过数字平台消费(44%),而自检的使用频率较低(14%)。该研究还确定了数字基础设施等关键资源,这些资源可以最大限度地发挥数字健康平台在加强性健康和生殖健康自我保健方面的潜力。通过深入了解数字基础设施、偏好、障碍和与通过数字平台获得性健康和生殖健康自我保健服务相关的财务考虑,可以制定有针对性的干预措施,如获得避孕药具、意识项目、预防和治疗性传播感染,以促进青少年性健康和生殖健康的积极成果。
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引用次数: 0
Caregiver experiences of an integrative patient-centered digital health application for pediatric type 1 diabetes care: Findings from a pilot clinical trial. 儿童1型糖尿病护理中以患者为中心的综合数字健康应用程序的护理人员体验:一项试点临床试验的结果
IF 7.7 Pub Date : 2025-10-31 eCollection Date: 2025-10-01 DOI: 10.1371/journal.pdig.0000861
Shazhan Amed, Susan Pinkney, Fatema S Abdulhussein, Anila Virani, Carlie Zachariuk, Sukhpreet K Tamana, Shruti Muralidharan, Matthias Görges, Bonnie Barrett, Tibor van Rooij, Elizabeth M Borycki, Andre Kushniruk, Holly Longstaff, Alice Virani, Wyeth W Wasserman

Diabetes technology generates vital health data, but healthcare professionals (HCP) and patients must navigate multiple platforms to access it. We developed a digital health platform, co-designed with patients and families living with type 1 diabetes (T1D) and their HCPs, that aim to support a collaborative care experience through shared access to diabetes data, clinical recommendations, and resources. We describe caregivers' views on the platform's impact on clinic visits and child self-management in children with T1D. A six-month observational pilot study at BC Children's Hospital Diabetes Clinic in British Columbia, Canada, gathered data through surveys and interviews. Surveys were administered to caregivers and HCPs at different time points throughout the study; 18 qualitative interviews were conducted with caregivers at the conclusion of the study. Quantitative data were summarized descriptively. Interview data were transcribed, coded using open and systematic coding, and subsequent inductive thematic analysis. Eighteen caregivers completed the surveys, and 11 HCP participants submitted 41 surveys (approximately 3-4 each) after using the platform. Most caregivers (61%; 11/18) found the platform helpful, and 56% (10/18) reported that using the platform made their clinical visits and recommendations more personalized. Nearly all HCPs (90%; 37/41) were satisfied with the platform's ability to support clinical visits. Themes identified from caregiver qualitative interviews revealed that (1) the platform provided a convenient connection that improved preparedness and empowered caregivers in managing their child's T1D; (2) the platform's value was driven by the healthcare team's usage of it; and (3) caregivers felt hopeful that the platform could better support their child's T1D management. The platform could foster a collaborative and personalized care experience that enables caregivers to engage in diabetes self-management and feel connected to their healthcare team. These results will guide the future development, evaluation, and implementation of the platform.

糖尿病技术产生重要的健康数据,但医疗保健专业人员(HCP)和患者必须浏览多个平台才能访问这些数据。我们开发了一个数字健康平台,与1型糖尿病(T1D)患者和家庭及其HCPs共同设计,旨在通过共享糖尿病数据、临床建议和资源来支持协作式护理体验。我们描述了护理人员对该平台对门诊就诊和T1D儿童自我管理的影响的看法。在加拿大不列颠哥伦比亚省的BC儿童医院糖尿病诊所进行了为期六个月的观察性试点研究,通过调查和访谈收集了数据。在整个研究的不同时间点对护理人员和医护人员进行调查;在研究结束时,对护理人员进行了18次定性访谈。定量数据进行描述性总结。对访谈数据进行转录、编码,采用开放式系统编码,并进行归纳性专题分析。18名护理人员完成了调查,11名HCP参与者在使用平台后提交了41份调查(每人大约3-4份)。大多数护理人员(61%;11/18)认为该平台很有帮助,56%(10/18)的人报告说,使用该平台使他们的临床就诊和建议更加个性化。几乎所有的HCPs(90%; 37/41)对该平台支持临床就诊的能力感到满意。从护理人员定性访谈中确定的主题显示:(1)该平台提供了一个方便的连接,改善了准备工作,并赋予护理人员管理孩子的T1D的能力;(2)医疗团队对平台的使用驱动了平台的价值;(3)看护人希望平台能够更好地支持孩子的T1D管理。该平台可以促进协作和个性化的护理体验,使护理人员能够参与糖尿病的自我管理,并与他们的医疗团队建立联系。这些结果将指导该平台未来的开发、评估和实施。
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引用次数: 0
Correction: Radiomics analysis for the early diagnosis of common sexually transmitted infections and skin lesions. 更正:放射组学分析用于常见性传播感染和皮肤病变的早期诊断。
IF 7.7 Pub Date : 2025-10-31 eCollection Date: 2025-10-01 DOI: 10.1371/journal.pdig.0001079
Jiajun Sun, Zhen Yu, Yingping Li, Janet M Towns, Lin Zhang, Jason J Ong, Zongyuan Ge, Christopher K Fairley, Lei Zhang

[This corrects the article DOI: 10.1371/journal.pdig.0000926.].

[这更正了文章DOI: 10.1371/journal.pdig.0000926.]。
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引用次数: 0
Transfer learning for predicting acute myocardial infarction using electrocardiograms. 利用心电图预测急性心肌梗死的迁移学习。
IF 7.7 Pub Date : 2025-10-31 eCollection Date: 2025-10-01 DOI: 10.1371/journal.pdig.0001058
Axel Nyström, Anders Björkelund, Mattias Ohlsson, Jonas Björk, Ulf Ekelund, Jakob Lundager Forberg

At the emergency department, it is important to quickly and accurately identify patients at risk of acute myocardial infarction (AMI). One of the main tools for detecting AMI is the electrocardiogram (ECG), which can be difficult to interpret manually. There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-quality ECGs are difficult to obtain. Transfer learning has been a successful strategy for mitigating data requirements in other applications, but the benefits for predicting AMI are understudied. Here we show that a straightforward application of transfer learning leads to large improvements also in this domain. We pre-train models to classify sex and age using a collection of 840 k ECGs from non-chest-pain patients, and fine-tune the resulting models to predict AMI using 44 k ECGs from chest-pain patients. The results are compared with models trained without transfer learning. We find a considerable improvement from transfer learning, consistent across multiple state-of-the-art ResNet architectures and data sizes, with the best performing model improving from 0.79 AUC to 0.85 AUC. This suggests that even a simple form of transfer learning from a moderately sized dataset of non-chest-pain ECGs can lead to major improvements in predicting AMI.

在急诊科,快速准确地识别有急性心肌梗死(AMI)危险的患者是非常重要的。检测急性心肌梗死的主要工具之一是心电图(ECG),这很难人工解释。将机器学习算法应用于心电图已有很长的历史,但这种算法非常需要数据,并且很难获得正确标记的高质量心电图。迁移学习是一种成功的策略,可以缓解其他应用程序中的数据需求,但是预测AMI的好处还没有得到充分的研究。在这里,我们表明迁移学习的直接应用也会在这一领域带来巨大的改进。我们使用来自非胸痛患者的840 k心电图对模型进行预训练,以分类性别和年龄,并对结果模型进行微调,以使用来自胸痛患者的44 k心电图预测AMI。结果与未经迁移学习训练的模型进行了比较。我们发现迁移学习有相当大的改进,跨多个最先进的ResNet架构和数据大小是一致的,表现最好的模型从0.79 AUC提高到0.85 AUC。这表明,即使是从中等大小的非胸痛心电图数据集中进行简单形式的迁移学习,也可以在预测AMI方面取得重大进展。
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引用次数: 0
Dental age prediction from panoramic radiographs using machine learning techniques. 利用机器学习技术从全景x光片预测牙齿年龄。
IF 7.7 Pub Date : 2025-10-30 eCollection Date: 2025-10-01 DOI: 10.1371/journal.pdig.0001077
Mehdi Salehizeinabadi, Nazila Ameli, Kasra Kouchehbaghi, Sara Arastoo, Saghar Neghab, Ida M Kornerup, Camila Pacheco-Pereira

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.

牙龄(DA)估计是一个关键的诊断工具在儿科牙科,特别是当出生记录不可用或不可靠。它指导有关生长评估、正畸计划和干预措施(如空间维护或拔牙)时机的决策。与骨骼成熟不同,牙齿发育受营养和环境因素的影响较小,使其成为生物年龄的可靠标志。传统方法需要专家解释,而且容易发生变化。人们对自动化、客观的方法越来越感兴趣,以简化这一过程,提高临床效用。由两名经验丰富的儿科牙医根据AAPD参考图将550张3-14岁儿童的全景x线片划分为11个牙龄组。质量差的图像被排除在外。将数据集分为训练集(80%)和验证集(20%),并对训练集进行数据增强。YOLOv11n-cls模型由86层和154万个参数组成,使用Ultralytics引擎和AdamW优化器进行了30次epoch的训练。在验证集上使用Top-1和Top-5精度评估模型性能,并在203个独立图像集上进行测试。采用Grad-CAM进行模型可解释性分析。该模型在验证集上的Top-1准确率为92.6%,Top-5准确率为99.5%。测试集的表现仍然很高,大多数错误分类发生在邻近年龄组之间。Grad-CAM可视化显示了对临床相关区域的关注,如臼齿和牙根发育。研究结果支持通过YOLOv11进行儿童年龄预测的DL的高性能。该人工智能工具实现了快速、准确和可解释的数据数据分类,使其成为儿科牙科实践中临床整合的辅助工具。
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引用次数: 0
Medical Imaging Data Calls for a Thoughtful and Collaborative Approach to Data Governance. 医学影像数据需要一种深思熟虑和协作的数据治理方法。
IF 7.7 Pub Date : 2025-10-28 eCollection Date: 2025-10-01 DOI: 10.1371/journal.pdig.0001046
Aline Lutz de Araujo, Jie Wu, Hugh Harvey, Matthew P Lungren, Mackenzie Graham, Tim Leiner, Martin J Willemink

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.

医学成像数据的可用性对于医学进步是不可或缺的,例如开发新的诊断工具,改进手术导航系统,以及通过成像生物标志物进行个性化医疗分析。数据治理的一个核心挑战是在保护患者隐私的需求与促进科学创新的必要性之间取得平衡。限制性数据治理策略可能会限制对此类进步所需的大型高质量数据集的访问。相反,宽松的政策可能会损害患者的信任,并导致敏感信息的潜在滥用。我们呼吁采用深思熟虑的数据治理方法,强调患者和医疗保健组织在围绕数据共享做出成像数据治理决策时应考虑的重要因素。
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引用次数: 0
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