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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
- Source :
- PLoS ONE, Vol 16, Iss 11, p e0259724 (2021), PLoS ONE, Vol 16, Iss 11 (2021), PLoS ONE
- Publication Year :
- 2021
-
Abstract
- Introduction 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. Methods and analytics 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. Ethics and dissemination 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. Registration The study was registered at ClinicalTrials.gov with the identifier NCT04609163.
- Subjects :
- Pediatrics
medicine.medical_treatment
retrospective study
Diagnostic Radiology
Cohort Studies
Study Protocol
Mathematical and Statistical Techniques
Pregnancy
Medicine and Health Sciences
Lung volumes
Multidisciplinary
medicine.diagnostic_test
Radiology and Imaging
Statistics
Software Engineering
Magnetic Resonance Imaging
Pulmonary Imaging
machine learning
Obstetric Procedures
Physical Sciences
Engineering and Technology
Medicine
Female
Cohort study
Computer and Information Sciences
medicine.medical_specialty
Imaging Techniques
Hypertension, Pulmonary
Science
Surgical and Invasive Medical Procedures
Research and Analysis Methods
Pulmonary hypertension
Computer Software
Diagnostic Medicine
Artificial Intelligence
Congenital Diaphragmatic Hernia, Pulmonary Ipertension, Deep Learning, protocol
medicine
Extracorporeal membrane oxygenation
Humans
Hernia
Statistical Methods
Retrospective Studies
Fetal surgery
business.industry
diaphragmatic hernia
segmentation
Infant, Newborn
Biology and Life Sciences
Neonates
Congenital diaphragmatic hernia
deep learning
Retrospective cohort study
Magnetic resonance imaging
medicine.disease
Settore FIS/07 - Fisica Applicata(Beni Culturali, Ambientali, Biol.e Medicin)
Hernias, Diaphragmatic, Congenital
business
Mathematics
Developmental Biology
Forecasting
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- PLoS ONE, Vol 16, Iss 11, p e0259724 (2021), PLoS ONE, Vol 16, Iss 11 (2021), PLoS ONE
- Accession number :
- edsair.doi.dedup.....65d0eac06cdda354ab3d721d5727c12b