A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.
A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.
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DOI：
10.1371/journal.pone.0259724
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Cavallaro G
中科院分区：
文献类型：
--
作者：
Amodeo I;De Nunzio G;Raffaeli G;Borzani I;Griggio A;Conte L;Macchini F;Condò V;Persico N;Fabietti I;Ghirardello S;Pierro M;Tafuri B;Como G;Cascio D;Colnaghi M;Mosca F;Cavallaro G
先天性腹股沟疝（CDH）患者的结局预测在产前估计出生后肺动脉高压（PH）方面仍有一定的局限性。我们建议将机器学习（ML）和深度学习（DL）方法应用于患有CDH的胎儿和新生儿，以基于临床数据的综合分析在产前时期开发预测模型，以提供新生儿PH作为第一个结果，并且可能：对胎儿内窥镜气管闭塞（FETO）的有利反应，需要体外膜氧合（ECMO），ECMO生存和死亡。此外，我们计划在磁共振成像（MRI）中产生一个（半）自动胎儿肺分割系统，这将在项目实施过程中非常有用，但也将是一个重要的工具，以标准化CDH胎儿的肺体积测量。将入组来自单胎妊娠的孤立性CDH患者，从妊娠第30周开始，在Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico（米兰，意大利）的胎儿手术室进行产前检查。将对2012年1月1日至2020年12月31日期间出生的合格患者的新生儿和母亲临床记录中的临床和放射学变量进行回顾性数据收集。将收集来自胎儿磁共振成像（MRI）的天然序列。来自不同来源的数据将使用ML和DL进行整合和分析，并为每个结果开发预测算法。将采用数据扩充和降维方法（特征选择和提取）来增加样本量并避免过度拟合。基于DL 3D U-NET方法，还将开发用于MRI中自动胎儿肺体积分割的软件系统。这项回顾性研究获得了当地伦理委员会（米兰2区，意大利）的批准。CDH结局预测模型的开发将为疾病预测、早期靶向干预和个性化管理做出关键贡献，并全面改善护理质量、资源分配、医疗保健和家庭储蓄。我们的研究结果将在未来的前瞻性多中心队列研究中得到验证。该研究在ClinicalTrials.gov上注册，标识符为NCT 04609163。
Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical data, to provide neonatal PH as the first outcome and, possibly: favorable response to fetal endoscopic tracheal occlusion (FETO), need for Extracorporeal Membrane Oxygenation (ECMO), survival to ECMO, and death. Moreover, we plan to produce a (semi)automatic fetus lung segmentation system in Magnetic Resonance Imaging (MRI), which will be useful during project implementation but will also be an important tool itself to standardize lung volume measures for CDH fetuses. Patients with isolated CDH from singleton pregnancies will be enrolled, whose prenatal checks were performed at the Fetal Surgery Unit of the Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico (Milan, Italy) from the 30th week of gestation. A retrospective data collection of clinical and radiological variables from newborns’ and mothers’ clinical records will be performed for eligible patients born between 01/01/2012 and 31/12/2020. The native sequences from fetal magnetic resonance imaging (MRI) will be collected. Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. A software system for automatic fetal lung volume segmentation in MRI based on the DL 3D U-NET approach will also be developed. This retrospective study received approval from the local ethics committee (Milan Area 2, Italy). The development of predictive models in CDH outcomes will provide a key contribution in disease prediction, early targeted interventions, and personalized management, with an overall improvement in care quality, resource allocation, healthcare, and family savings. Our findings will be validated in a future prospective multicenter cohort study. The study was registered at ClinicalTrials.gov with the identifier NCT04609163.
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DOI：
10.1002/bdra.23093
发表时间：
2013-01-01
影响因子：
--
作者：
Beurskens, Leonardus W. J. E.;Schrijver, Lieske H.;Steegers-Theunissen, Regine P. M.
通讯作者：
Steegers-Theunissen, Regine P. M.
影响因子：
3.6
作者：
Amodeo, Ilaria;Borzani, Irene;Cavallaro, Giacomo
通讯作者：
Cavallaro, Giacomo
DOI：
10.1016/j.bpobgyn.2018.12.010
发表时间：
2019-07-01
影响因子：
5.5
作者：
Basurto, David;Russo, Francesca Maria;Deprest, Jan
通讯作者：
Deprest, Jan
影响因子：
7.1
作者：
Cruz-Martinez, R.;Castanon, M.;Gratacos, E.
通讯作者：
Gratacos, E.
影响因子：
5.3
作者：
Au-Yong-Oliveira M;Pesqueira A;Sousa MJ;Dal Mas F;Soliman M
通讯作者：
Soliman M