Seminars in Fetal & Neonatal Medicine最新文献

英文中文

Special Issue on artificial intelligence in neonatology 新生儿人工智能特刊。

IF 2.8 3区医学 Q1 PEDIATRICS

Seminars in Fetal & Neonatal Medicine

Pub Date : 2026-02-01 Epub Date: 2026-01-14 DOI: 10.1016/j.siny.2026.101699

Gianluca Lista , Istvan Seri

引用次数: 0

A systematic review on the use of artificial intelligence in the neonatal intensive care unit: far beyond the potential impact 在新生儿重症监护病房使用人工智能的系统综述：远远超出潜在的影响。

IF 2.8 3区医学 Q1 PEDIATRICS

Seminars in Fetal & Neonatal Medicine

Pub Date : 2026-02-01 Epub Date: 2025-11-24 DOI: 10.1016/j.siny.2025.101690

Antonio Martínez Millana , Álvaro Solaz-García , Andrea García Montaner , María Portolés-Morales , Longwei Xiao , Yan Sun , Vicente Traver , Máximo Vento , Pilar Sáenz-González

Objectives

To explore the applicability of artificial intelligence (AI) in neonatal intensive care units (NICUs), identifying key trends in AI-driven technologies and their roles in the prognosis, classification, monitoring and forecasting of neonatal conditions.

Methods

A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-guided systematic review was conducted across MEDLINE, EMBASE, Cochrane, and IEEE Xplore, covering studies published between January 2013 and December 2023. A total of 318 studies were initially retrieved. After removing 61 duplicates and screening 257 articles by eligibility criteria, 64 studies were assessed for full-text eligibility, leading to the final inclusion of 41 studies.

Results

The predominant AI application referred to conditions in the following systems: cardiovascular (n = 9, 21.9 %), neural/brain (n = 8, 19.5 %), respiratory (n = 8, 19.5 %), immune (infections) (n = 6, 14.6 %), gastrointestinal (n = 2, 4.9 %), and microvascular diseases (n = 1, 2.4 %). Additionally, six studies focused on monitoring systems or body positioning (categorized as "Not Disease"), and one study (2.4 %) addressed mortality prediction. Regarding the purposes of AI application, prognosis (n = 23, 56.1 %) was the most common, followed by classification (n = 14, 34.1 %), monitoring (n = 5, 12.2 %), and symptom forecasting (n = 1, 2.4 %). More than 70 % of studies (n = 29, 70.7 %) lacked a validation procedure, highlighting a critical gap in methodological rigor.

Conclusions

Our findings underscore the potential benefits of the use of AI in neonatology, possibly resulting in improved patient outcomes and enhanced operational efficiency. However, data privacy, algorithm interpretability, and ethical considerations must be addressed for responsible AI deployment in neonatal care. We highlight future directions, emphasizing interdisciplinary collaboration, adherence to reporting guidelines, and the need for further research to enhance AI reproducibility and clinical integration in the NICUs. The findings of this study support AI's potential for shaping neonatal health care.

目的：探讨人工智能（AI）在新生儿重症监护病房（NICUs）的适用性，识别AI驱动技术的主要趋势及其在新生儿病情预后、分类、监测和预测中的作用。方法：采用PRISMA （Preferred Reporting Items for Systematic Reviews and meta - analysis）指导的系统评价方法，对MEDLINE、EMBASE、Cochrane和IEEE explore进行系统评价，涵盖2013年1月至2023年12月间发表的研究。最初共检索了318项研究。在根据入选标准剔除61项重复和筛选257篇文章后，64项研究被评估为符合全文入选资格，最终纳入41项研究。结果：人工智能主要应用于以下系统：心血管（n = 9, 21.9%）、神经/脑（n = 8, 19.5%）、呼吸（n = 8, 19.5%）、免疫（感染）（n = 6, 14.6%）、胃肠道（n = 2, 4.9%）和微血管疾病（n = 1, 2.4%）。此外，六项研究侧重于监测系统或身体定位（归类为“非疾病”），一项研究（2.4%）涉及死亡率预测。人工智能应用的目的以预后（n = 23, 56.1%）最为常见，其次是分类（n = 14, 34.1%）、监测（n = 5, 12.2%）和症状预测（n = 1, 2.4%）。超过70%的研究（n = 29, 70.7%）缺乏验证程序，突出了方法学严谨性的关键差距。结论：我们的研究结果强调了在新生儿中使用人工智能的潜在益处，可能会改善患者的预后并提高操作效率。然而，为了在新生儿护理中负责任地部署人工智能，必须解决数据隐私、算法可解释性和伦理考虑。我们强调了未来的发展方向，强调跨学科合作，遵守报告指南，以及进一步研究的必要性，以提高人工智能在新生儿重症监护病房的可重复性和临床整合。这项研究的结果支持人工智能在塑造新生儿保健方面的潜力。

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引用次数: 0

Application of AI in neonatal gastroenterology and nutrition 人工智能在新生儿胃肠病学和营养学中的应用。

IF 2.8 3区医学 Q1 PEDIATRICS

Seminars in Fetal & Neonatal Medicine

Pub Date : 2026-02-01 Epub Date: 2025-11-18 DOI: 10.1016/j.siny.2025.101689

Wissam Shalish , Josef Neu , Guilherme Sant’Anna

Optimizing neonatal nutrition and diagnosing serious gastrointestinal diseases remains a challenge, as traditional guideline-based approaches often fail to address the individualized needs of preterm and term infants. Advances in artificial intelligence and machine learning provide opportunities for precision diagnostics and therapeutics by incorporating multiomic data and clustering infants based on risk factors and metabolic profiles. For example, machine learning is redefining necrotizing enterocolitis as a spectrum of intestinal injuries rather than a single disease, while digital twin models offer the potential for real-time personalized nutrition optimization. Moreover, integration of advanced gastrointestinal monitoring methods using novel biomarkers and sensor technologies may further enhance early detection and intervention strategies. Altogether, these digital technological advancements may lead to identification of early predictors of nutritional deficiencies and prompt recognition of gastrointestinal pathologies, thereby allowing for proactive interventions and potentially improved outcomes in the neonatal population.

优化新生儿营养和诊断严重胃肠道疾病仍然是一个挑战，因为传统的基于指南的方法往往不能解决早产儿和足月儿的个性化需求。人工智能和机器学习的进步通过结合多组学数据和基于危险因素和代谢特征的婴儿聚类，为精确诊断和治疗提供了机会。例如，机器学习正在将坏死性小肠结肠炎重新定义为一系列肠道损伤，而不是单一疾病，而数字孪生模型提供了实时个性化营养优化的潜力。此外，使用新型生物标志物和传感器技术的先进胃肠道监测方法的整合可能进一步增强早期检测和干预策略。总之，这些数字技术的进步可能会导致识别营养缺乏的早期预测因素，并迅速识别胃肠道疾病，从而允许积极干预，并可能改善新生儿群体的预后。

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引用次数: 0

Artificial intelligence in neonatal sepsis: Scope, challenges, and potential solutions! 新生儿败血症中的人工智能：范围、挑战和潜在解决方案！

IF 2.8 3区医学 Q1 PEDIATRICS

Seminars in Fetal & Neonatal Medicine

Pub Date : 2026-02-01 Epub Date: 2025-11-19 DOI: 10.1016/j.siny.2025.101687

Deepika Kainth, Ramesh Agarwal

Neonatal sepsis remains a major cause of neonatal deaths globally. Despite advances, accurate and timely diagnosis is hindered by the limited performance of the current clinical approaches, imperfect laboratory biomarkers, and long turnaround time of blood cultures. Artificial intelligence (AI), with its ability to identify patterns and learn continuously (machine learning), seems promising. Basic steps in model development include data filtration, train: test split, feature selection, choosing appropriate algorithms, and evaluating performance using a reference standard. In neonatal sepsis, the role of AI spans from predicting sepsis and related outcomes to formulating an individualized treatment approach for the neonate. Existing models, largely from high-income countries, report encouraging diagnostic accuracy but face methodological limitations, lack external validation, and remain somewhat distant from bedside application. Additional barriers to their generalizability include lack of uniform definition of sepsis, variations in disease and pathogen profiles in different settings (particularly in developing countries), availability of electronic health data, tweaks in feature selection, and ethical and legal challenges. This review synthesizes current evidence, highlights gaps, and outlines priorities for future research. We call for a collaborative effort from AI and neonatal experts to devise robust, context-specific solutions.

新生儿败血症仍然是全球新生儿死亡的一个主要原因。尽管取得了进步，但由于目前临床方法的性能有限，实验室生物标志物不完善以及血液培养的周转时间长，阻碍了准确和及时的诊断。人工智能（AI）具有识别模式和持续学习（机器学习）的能力，似乎很有前景。模型开发的基本步骤包括数据过滤、训练：测试分割、特征选择、选择适当的算法，以及使用参考标准评估性能。在新生儿败血症中，人工智能的作用从预测败血症和相关结果到为新生儿制定个性化治疗方法。现有的模型，主要来自高收入国家，报告了令人鼓舞的诊断准确性，但面临方法上的限制，缺乏外部验证，离临床应用还有一定距离。影响其推广的其他障碍包括：缺乏对败血症的统一定义、不同环境（特别是在发展中国家）疾病和病原体概况的差异、电子卫生数据的可用性、特征选择的调整以及伦理和法律挑战。这篇综述综合了目前的证据，突出了差距，并概述了未来研究的重点。我们呼吁人工智能和新生儿专家共同努力，制定稳健的、针对具体情况的解决方案。

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引用次数: 0

Neonatal artificial intelligence and machine learning mortality prediction modeling: A systematic review for risk adjustment in the neonatal intensive care unit 新生儿人工智能和机器学习死亡率预测模型：新生儿重症监护病房风险调整的系统回顾。

IF 2.8 3区医学 Q1 PEDIATRICS

Seminars in Fetal & Neonatal Medicine

Pub Date : 2026-02-01 Epub Date: 2025-11-19 DOI: 10.1016/j.siny.2025.101688

Chelsea K. Bitler , C. Briana Bertoni , Brian C. King , Thomas A. Hooven , Christopher M. Horvat

Mortality remains a key indicator for the assessment of care quality in medicine. In neonatology, mortality rates are highly variable, both across units and over time. Comparison of crude mortality rates, however, are insufficient for benchmarking, as they fail to account for differences in population case mix and severity of illness. Risk adjustment using artificial intelligence (AI) and machine learning (ML) has emerged as a promising tool to facilitate meaningful comparisons and drive improvement. This review seeks to examine the state of the current literature on the use of AI/ML-based models to predict mortality in the neonatal intensive care unit (NICU). We identified 37 studies describing 242 models. Most studies developed models using single-center data and frequently lacked external validation. Similarly, reporting of performance metrics was heterogenous, limiting evaluation. As a result, further work is necessary before AI/ML-enabled risk adjustment is feasible.

死亡率仍然是评估医疗保健质量的一个关键指标。在新生儿学中，不同单位和不同时间的死亡率变化很大。然而，粗死亡率的比较不足以作为基准，因为它们不能说明人口病例组合和疾病严重程度的差异。使用人工智能（AI）和机器学习（ML）进行风险调整已经成为一种有前途的工具，可以促进有意义的比较并推动改进。本综述旨在研究当前关于使用基于AI/ ml的模型预测新生儿重症监护病房（NICU）死亡率的文献状况。我们确定了37项研究，描述了242种模型。大多数研究使用单中心数据建立模型，经常缺乏外部验证。类似地，性能指标的报告是异质的，限制了评估。因此，在启用AI/ ml的风险调整可行之前，还需要进一步的工作。

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引用次数: 0

Leveraging Artificial Intelligence for decision support in neonatal and pediatric pharmacotherapy: A scoping review 利用人工智能在新生儿和儿童药物治疗中的决策支持：范围综述。

IF 2.8 3区医学 Q1 PEDIATRICS

Seminars in Fetal & Neonatal Medicine

Pub Date : 2026-02-01 Epub Date: 2025-11-19 DOI: 10.1016/j.siny.2025.101691

Luana Conte , Nunzia Decembrino , Cristina Arribas , Federico Cucci , Giorgio De Nunzio , Ilaria Amodeo , Genny Raffaeli , Roberta Leonardi , Donato Cascio , Felipe Garrido , Giacomo Cavallaro

The use of Artificial Intelligence (AI) has the potential to transform healthcare in part by enhancing the accuracy of drug dosing and improving patient safety. However, its use in neonatology and pediatrics has just been started, with limited research exploring its full potential. This scoping review systematically maps the literature on AI applications in pediatric and neonatal pharmacology, analyzing studies published between 2004 and 2024. Searches in databases including MEDLINE, Scopus, and IEEE Xplore identified 412 records, of which 33 met the inclusion criteria. These included neonates (n = 8) and older pediatric patients (n = 25), encompassing 58,864 patients and utilizing various Machine-Learning techniques. The use of AI has demonstrated significant potential for precision dosing, predicting drug efficacy, and decreasing the occurrence of adverse events. Despite these promising findings, however, more rigorous, large-scale studies are essential to validate the results. Future research should prioritize real-world applications and address integration barriers, ensuring safe and effective use of AI in neonatal and pediatric clinical practice.

人工智能（AI）的使用有可能通过提高药物剂量的准确性和改善患者安全来改变医疗保健。然而，它在新生儿和儿科的应用才刚刚开始，探索其全部潜力的研究有限。本综述系统地绘制了人工智能在儿科和新生儿药理学应用方面的文献，分析了2004年至2024年间发表的研究。在MEDLINE、Scopus和IEEE Xplore等数据库中检索到412条记录，其中33条符合纳入标准。其中包括新生儿（n = 8）和老年儿科患者（n = 25），共58,864名患者，并利用各种机器学习技术。人工智能的使用在精确给药、预测药物疗效和减少不良事件发生方面显示出巨大的潜力。尽管有这些有希望的发现，然而，更严格的，大规模的研究是必要的，以验证结果。未来的研究应优先考虑实际应用并解决整合障碍，确保人工智能在新生儿和儿科临床实践中安全有效地使用。

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引用次数: 0

Artificial intelligence in neonatal hemodynamics: Cerebral autoregulation 新生儿血流动力学中的人工智能：大脑自动调节。

IF 2.8 3区医学 Q1 PEDIATRICS

Seminars in Fetal & Neonatal Medicine

Pub Date : 2026-02-01 Epub Date: 2025-11-18 DOI: 10.1016/j.siny.2025.101686

Piyawat Arichai , Tai-Wei Wu , Istvan Seri , Shahab Noori

引用次数: 0

Central line-associated blood stream infections in newborns: From vulnerability to prevention 新生儿中央静脉相关血流感染：从易感性到预防。

IF 2.8 3区医学 Q1 PEDIATRICS

Seminars in Fetal & Neonatal Medicine

Pub Date : 2025-12-01 Epub Date: 2025-09-25 DOI: 10.1016/j.siny.2025.101665

Varvara Dimopoulou , Kirsten Glaser , Eric Giannoni

Newborns, especially preterm infants, are vulnerable to invasive infections due to their developing immune system and frequent need for central venous catheters. Central line-associated bloodstream infections (CLABSI) are among the most common invasive infections in this population and represent the leading cause of neonatal bloodstream infection in many settings. Neonatal CLABSI is associated with substantial mortality, long-term morbidity, and increased healthcare costs. Most importantly, CLABSI is preventable. Bundles centered on rigorous hand hygiene combined with standardized practices for catheter insertion, maintenance and removal have proven effective in reducing infection rates in neonates. Benchmarking and quality improvement initiatives enable neonatal intensive care units (NICUs) to track progress and share best practices. While no novel prevention strategies with robust evidence have emerged, sustained declines in CLABSI rates in many NICUs and networks over the past decades highlight the importance of a comprehensive multidisciplinary approach to implement and maintain best practices.

新生儿，特别是早产儿，由于其免疫系统发育不全和经常需要中心静脉导管，容易受到侵袭性感染。中心线相关性血流感染（CLABSI）是这一人群中最常见的侵袭性感染之一，在许多情况下是新生儿血流感染的主要原因。新生儿CLABSI与大量死亡率、长期发病率和医疗费用增加有关。最重要的是，CLABSI是可以预防的。以严格的手部卫生为中心，结合导管插入、维护和取出的标准化做法，已被证明对降低新生儿感染率有效。标杆和质量改进举措使新生儿重症监护病房（nicu）能够跟踪进展并分享最佳做法。虽然没有新的预防策略出现强有力的证据，但在过去几十年中，许多新生儿重症监护室和网络的CLABSI率持续下降，这突出了采用综合多学科方法实施和保持最佳做法的重要性。

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引用次数: 0

Respiratory syncytial virus. What's new in prevention? 呼吸道合胞病毒。预防方面有什么新进展？

IF 2.8 3区医学 Q1 PEDIATRICS

Seminars in Fetal & Neonatal Medicine

Pub Date : 2025-12-01 Epub Date: 2025-09-25 DOI: 10.1016/j.siny.2025.101667

Nestor E. Vain , Paolo Manzoni , Kee Thai Yeo

Prevention of RSV lower respiratory tract infections (LRTI) in infants has been limited to general measures and palivizumab, a monoclonal antibody indicated for the highest risk groups. Recently developed RSV vaccines used during pregnancy generate antibodies that cross the placenta. Randomized controlled trials (RCT) and real-life monitoring have demonstrated their effectiveness in protecting newborns and infants during the first months of life. Likewise, novel extended half-life monoclonal antibodies, nirsevimab and the recently approved clesrovimab, opened the possibility of large-scale protection targeted to all infants born during the winter season and those <6 months at the beginning of it. Several RCTs and results from populations adopting nirsevimab prophylaxis demonstrated a large decrease in the incidence of RSV-LRTIs and a great impact in infant public health. Deployment of either strategies or in combination as part of immunization programs can be complement each other even as newer immunologic agents are being introduced.

婴儿RSV下呼吸道感染（LRTI）的预防仅限于一般措施和帕利珠单抗，这是一种单克隆抗体，适用于最高风险人群。最近开发的RSV疫苗在怀孕期间使用，产生抗体，穿过胎盘。随机对照试验（RCT）和实际监测证明了它们在生命最初几个月保护新生儿和婴儿方面的有效性。同样，新型延长半衰期的单克隆抗体nirsevimab和最近批准的clesrovimab，为所有在冬季和冬季出生的婴儿提供了大规模保护的可能性

引用次数: 0

Probiotic supplementation – does it prevent or cause neonatal sepsis? 益生菌补充-它是预防还是导致新生儿败血症？

IF 2.8 3区医学 Q1 PEDIATRICS

Seminars in Fetal & Neonatal Medicine

Pub Date : 2025-12-01 Epub Date: 2025-09-25 DOI: 10.1016/j.siny.2025.101668

Nicholas D. Embleton , Chris H.P. van den Akker , Belal N. Alshaikh

Probiotic supplementation in preterm infants is one of the most extensively studied interventions in neonatal medicine, with over 50 randomised controlled trials. This paper examines the relationship between probiotic supplementation and late onset sepsis (LOS), considering mechanistic pathways, clinical evidence, and safety profile.

Multiple systematic reviews and meta-analyses consistently show that probiotics reduce necrotising enterocolitis (NEC) incidence and all-cause mortality in preterm infants, establishing them as one of the most beneficial interventions in neonatology. Current evidence suggests modest effects on LOS, with Cochrane systematic reviews reporting relative risk 0.89 (95 % CI 0.82–0.97) but with low certainty. Mechanisms supporting LOS reduction include competitive pathogen exclusion, enhanced epithelial barrier function, improved immune responses, and reduced time to full enteral feeding with decreased intravenous access requirements.

The safety profile of probiotics is reassuring, with serious adverse events being exceptionally rare. Probiotic-induced sepsis probably occurs in less than 0.5 % of treated infants, representing a very low risk that must be weighed against the likely substantial benefits for NEC and mortality reduction. Product contamination and other quality issues exist but appear manageable with appropriate quality control.

Given the robust evidence for NEC and mortality reduction, probiotics represent a valuable intervention for preterm infants but may have limited, if any impact on sepsis. While their specific role in LOS prevention and impacts on the resistome requires further investigation, the overall benefit-risk profile strongly favors their use. Future research will further refine understanding of optimal strain selection and implementation strategies for maximizing clinical benefits while maintaining safety.

早产儿补充益生菌是新生儿医学中研究最广泛的干预措施之一，有50多个随机对照试验。本文探讨了益生菌补充与迟发性脓毒症（LOS）之间的关系，考虑了机制途径、临床证据和安全性。多个系统综述和荟萃分析一致表明，益生菌可以降低早产儿坏死性小肠结肠炎（NEC）的发病率和全因死亡率，使其成为新生儿学中最有益的干预措施之一。目前的证据表明对LOS的影响不大，Cochrane系统评价报告的相对风险为0.89 (95% CI 0.82-0.97)，但确定性较低。支持LOS减少的机制包括竞争性病原体排斥、上皮屏障功能增强、免疫反应改善、完全肠内喂养时间缩短和静脉通路需求减少。益生菌的安全性令人放心，严重的不良事件非常罕见。益生菌引起的脓毒症可能发生在不到0.5%的接受治疗的婴儿中，这代表了一个非常低的风险，必须与NEC和死亡率降低的可能实质性益处进行权衡。产品污染和其他质量问题存在，但似乎可以通过适当的质量控制。鉴于NEC和死亡率降低的有力证据，益生菌对早产儿来说是一种有价值的干预措施，但对败血症的影响可能有限。虽然它们在LOS预防中的具体作用和对抵抗组的影响需要进一步调查，但总体的利益-风险概况强烈支持它们的使用。未来的研究将进一步完善对最佳菌株选择的理解和实施策略，以最大限度地提高临床效益，同时保持安全性。

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