Harnessing Infant Cry for swift, cost-effective Diagnosis of Perinatal Asphyxia in low-resource settings
This addresses the critical issue of early detection of a leading cause of infant mortality in developing countries, though it is an incremental application of existing methods to a new domain.
The researchers tackled the problem of diagnosing perinatal asphyxia in low-resource settings by developing a machine learning system that analyzes infant cries, achieving a prediction accuracy of 88.85% in laboratory tests.
Perinatal Asphyxia is one of the top three causes of infant mortality in developing countries, resulting to the death of about 1.2 million newborns every year. At its early stages, the presence of asphyxia cannot be conclusively determined visually or via physical examination, but by medical diagnosis. In resource-poor settings, where skilled attendance at birth is a luxury, most cases only get detected when the damaging consequences begin to manifest or worse still, after death of the affected infant. In this project, we explored the approach of machine learning in developing a low-cost diagnostic solution. We designed a support vector machine-based pattern recognition system that models patterns in the cries of known asphyxiating infants (and normal infants) and then uses the developed model for classification of `new' infants as having asphyxia or not. Our prototype has been tested in a laboratory setting to give prediction accuracy of up to 88.85%. If higher accuracies can be obtained, this research may be a key contributor to the 4th Millennium Development Goal (MDG) of reducing mortality in under-five children.