Retinopathy of prematurity (ROP) is the leading cause of blindness and visual impairment in children world-wide. Identification of risk factors to predict the infants at risk of developing ROP is vital and has been a major research topic for many years. Known risk factors are mainly related to birth characteristics and environmental condition. Some infants develop ROP despite not having any of the known risk factors, and identification of these infants is also important. In this context, determination of biomarkers (in the broadest term) is an important tool. During the last decade new innovative technologies have emerged that identify and quantify new (previously unknown) biomarkers based on high-resolution imaging and ‘omics’ techniques. In addition, artificial intelligence (deep learning) approaches for detection of ROP biomarkers are slowly appearing with the potential to combine more biomarkers to improve risk stratification of ROP development eventually leading to advances in precision medicine in preterm infant.
As ROP is the leading cause of blindness and visual impairment in children world-wide, identification of infants at risk of ROP is important. ROP experts are commonly agreeing on only a few strong risk factors (such as low GA, born small for gestational age, male, and excessive oxygen supplementation). As some infants develop ROP despite having any of these known risk factors, identification of other biomarkers for ROP development is crucial. Determination of important biomarkers will help us stratify the risk of ROP development with increased precision for each individual infant. The identification of valid biomarkers will also help detect disease earlier than we do now and allow to monitor disease progression in more detail, as well as detection of key ‘target’ molecules to which new therapies can be directed. Finally, new innovative approaches such as artificial intelligence (deep learning) that have the potential to combine various biomarkers for ROP development will advance development of risk stratification and advance precision medicine in preterm infants.
The goal of this Research Topic is to investigate and determine new biomarkers for ROP development and explore the opportunities to integrate artificial intelligence (such as deep learning) approaches to combine biomarkers to improve precision medicine in preterm infants.
In this context, biomarkers (or biological markers) are defined as a molecules, genes, or characteristics that either predicts disease, characterize the disease course, or show response mechanisms related to treatments.
Herein, biomarkers include in the broadest possible term everything from morphological signs of disease to expression of disease-related molecules in different body biofluids or ocular tissue.
The scope of the research topic:
a) To investigate/identify new predictive biomarkers to stratify the risk of ROP development in preterm infants.
b) To investigate/identify new biomarkers to monitor disease progression in preterm infants.
c) To investigate/identify new key ‘target’ molecules part of pathological processes towards which new therapies should be directed.
d) New mathematical and data science (such as artificial intelligence) approaches to combine input from several biomarkers to improve risk stratification of ROP development, and precision of diagnosis and monitorization of disease in each individual preterm infant.
Retinopathy of prematurity (ROP) is the leading cause of blindness and visual impairment in children world-wide. Identification of risk factors to predict the infants at risk of developing ROP is vital and has been a major research topic for many years. Known risk factors are mainly related to birth characteristics and environmental condition. Some infants develop ROP despite not having any of the known risk factors, and identification of these infants is also important. In this context, determination of biomarkers (in the broadest term) is an important tool. During the last decade new innovative technologies have emerged that identify and quantify new (previously unknown) biomarkers based on high-resolution imaging and ‘omics’ techniques. In addition, artificial intelligence (deep learning) approaches for detection of ROP biomarkers are slowly appearing with the potential to combine more biomarkers to improve risk stratification of ROP development eventually leading to advances in precision medicine in preterm infant.
As ROP is the leading cause of blindness and visual impairment in children world-wide, identification of infants at risk of ROP is important. ROP experts are commonly agreeing on only a few strong risk factors (such as low GA, born small for gestational age, male, and excessive oxygen supplementation). As some infants develop ROP despite having any of these known risk factors, identification of other biomarkers for ROP development is crucial. Determination of important biomarkers will help us stratify the risk of ROP development with increased precision for each individual infant. The identification of valid biomarkers will also help detect disease earlier than we do now and allow to monitor disease progression in more detail, as well as detection of key ‘target’ molecules to which new therapies can be directed. Finally, new innovative approaches such as artificial intelligence (deep learning) that have the potential to combine various biomarkers for ROP development will advance development of risk stratification and advance precision medicine in preterm infants.
The goal of this Research Topic is to investigate and determine new biomarkers for ROP development and explore the opportunities to integrate artificial intelligence (such as deep learning) approaches to combine biomarkers to improve precision medicine in preterm infants.
In this context, biomarkers (or biological markers) are defined as a molecules, genes, or characteristics that either predicts disease, characterize the disease course, or show response mechanisms related to treatments.
Herein, biomarkers include in the broadest possible term everything from morphological signs of disease to expression of disease-related molecules in different body biofluids or ocular tissue.
The scope of the research topic:
a) To investigate/identify new predictive biomarkers to stratify the risk of ROP development in preterm infants.
b) To investigate/identify new biomarkers to monitor disease progression in preterm infants.
c) To investigate/identify new key ‘target’ molecules part of pathological processes towards which new therapies should be directed.
d) New mathematical and data science (such as artificial intelligence) approaches to combine input from several biomarkers to improve risk stratification of ROP development, and precision of diagnosis and monitorization of disease in each individual preterm infant.
