Application of Artificial Intelligence Techniques in Bronchopulmonary dysplasia in neonates

What is it about?

Provides an overview of bronchopulmonary dysplasia, its definitions, and their shortcomings. Explores the areas where machine learning may be used to further our understanding of bronchopulmonary dysplasia. Presents current research conducted in bronchopulmonary dysplasia and elucidates unexplored directions.

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Photo by Alexander Grey on Unsplash

Photo by Alexander Grey on Unsplash

Why is it important?

Bronchopulmonary dysplasia (BPD) is the most common long-term morbidity among premature infants, with an incidence inversely related to gestational age and birth weight. Though these clinical treatment definitions of BPD are easy to use, there is increasing effort to devise more robust ways of identifying and categorizing the disease. Artificial Intelligence (AI) and Machine Learning (ML) present promising avenues for exploration of this disease. As computing power has increased over the past decade, techniques in these areas have found more practical uses in healthcare. Researchers have used these techniques to shed deep insights into various diseases, but applications in the field of BPD have been limited. We present an overview of challenges in defining a continuously evolving disease such as BPD and a potential role of AI in meeting this challenge.