Machine and Deep Learning for Congenital Diaphragmatic Hernia (CLANNISH)

Plain-language summary

Congenital Diaphragmatic Hernia (CDH) is characterized by an incomplete diaphragm formation, resulting in poor lung development (pulmonary hypoplasia), associated with altered vascularization of the lung (pulmonary hypertension), with respiratory and cardiovascular insufficiency at birth. Mortality and morbidity are extremely variable. Several efforts have been done to identify possible prenatal and postnatal indicators which could accurately predict patients' prognosis and to promote an individualized management. However, to date the accuracy of these factors with respect to the prediction of survival and disease severity still has limits. In the last years, there has been an impressive development of new research methodologies based on the artificial intelligence, also in the neonatal field. The Machine Learning (ML) method explores the possibility of building algorithms starting from the acquisition of relevant clinical data, and using them to make predictions or take decisions. Nevertheless, the ML method has never been applied to predict patient's outcome in newborns with CDH so far. Moreover, with the available tools, a reliable prediction on patient's risk of developing severe postnatal PH is not feasible. Our hypothesis is that the use of ML approach, based on multivariate analysis of different clinical pre- and postnatal variables, could allow the development of algorithms able to accurately predict patient's outcome.

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