A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.

<h4>Introduction</h4>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 newbo...

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Autores principales:	Ilaria Amodeo, Giorgio De Nunzio, Genny Raffaeli, Irene Borzani, Alice Griggio, Luana Conte, Francesco Macchini, Valentina Condò, Nicola Persico, Isabella Fabietti, Stefano Ghirardello, Maria Pierro, Benedetta Tafuri, Giuseppe Como, Donato Cascio, Mariarosa Colnaghi, Fabio Mosca, Giacomo Cavallaro
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fd3fe0cd7fc949528acd0f5cf4d51352
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spelling	oai:doaj.org-article:fd3fe0cd7fc949528acd0f5cf4d513522021-12-02T20:07:41ZA maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.1932-620310.1371/journal.pone.0259724https://doaj.org/article/fd3fe0cd7fc949528acd0f5cf4d513522021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259724https://doaj.org/toc/1932-6203<h4>Introduction</h4>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.<h4>Methods and analytics</h4>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.<h4>Ethics and dissemination</h4>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.<h4>Registration</h4>The study was registered at ClinicalTrials.gov with the identifier NCT04609163.Ilaria AmodeoGiorgio De NunzioGenny RaffaeliIrene BorzaniAlice GriggioLuana ConteFrancesco MacchiniValentina CondòNicola PersicoIsabella FabiettiStefano GhirardelloMaria PierroBenedetta TafuriGiuseppe ComoDonato CascioMariarosa ColnaghiFabio MoscaGiacomo CavallaroPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0259724 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ilaria Amodeo Giorgio De Nunzio Genny Raffaeli Irene Borzani Alice Griggio Luana Conte Francesco Macchini Valentina Condò Nicola Persico Isabella Fabietti Stefano Ghirardello Maria Pierro Benedetta Tafuri Giuseppe Como Donato Cascio Mariarosa Colnaghi Fabio Mosca Giacomo Cavallaro A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.
description	<h4>Introduction</h4>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.<h4>Methods and analytics</h4>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.<h4>Ethics and dissemination</h4>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.<h4>Registration</h4>The study was registered at ClinicalTrials.gov with the identifier NCT04609163.
format	article
author	Ilaria Amodeo Giorgio De Nunzio Genny Raffaeli Irene Borzani Alice Griggio Luana Conte Francesco Macchini Valentina Condò Nicola Persico Isabella Fabietti Stefano Ghirardello Maria Pierro Benedetta Tafuri Giuseppe Como Donato Cascio Mariarosa Colnaghi Fabio Mosca Giacomo Cavallaro
author_facet	Ilaria Amodeo Giorgio De Nunzio Genny Raffaeli Irene Borzani Alice Griggio Luana Conte Francesco Macchini Valentina Condò Nicola Persico Isabella Fabietti Stefano Ghirardello Maria Pierro Benedetta Tafuri Giuseppe Como Donato Cascio Mariarosa Colnaghi Fabio Mosca Giacomo Cavallaro
author_sort	Ilaria Amodeo
title	A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.
title_short	A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.
title_full	A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.
title_fullStr	A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.
title_full_unstemmed	A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.
title_sort	machine and deep learning approach to predict pulmonary hypertension in newborns with congenital diaphragmatic hernia (clannish): protocol for a retrospective study.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/fd3fe0cd7fc949528acd0f5cf4d51352
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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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