The research article is based on a background that differential diagnosis of children’s respiratory disorders is challenging and inefficient. Current diagnostic criteria are often associated with high error rates, improper medication, and mortality rates. In addition, the article provides the background that recent advancements in technology, such as artificial intelligence engineering based on sound analysis, are significant advancements in accurately diagnosing respiratory disorders. The multicentre design study contrasts the use of a cough sound assessment technique to clinical diagnosis in identifying pediatric respiratory diseases. The study comprised a sample size of 585 participants between 2015 and 2018. The inclusion criteria of the study were children between the ages of 29 days and 12 years with atleast a cough, wheeze, stridor, and shortness of breath. The exclusion criteria included an absence of a respiratory condition, structural airways diseases, mechanical ventilation and lack of consent. The study involved simultaneous clinical evaluations and cough recordings. In addition, the researchers collected cough, medical history, demographic data, and investigation findings without interfering with the clinical care provided to patients’ health professionals. The study developed an algorithm by employing mathematical feature extraction from cough samples, tested and applied an optimized diagnostic algorithm. Lastly, the researchers derived power calculations using statistical analysis of the collected data to obtain results. The study demonstrates that automated analysis of cough is highly accurate at diagnosing respiratory illnesses in children. In conclusion, the article is valuable in emphasizing the importance of technology in the provision of quality healthcare by highlighting that technological trends in sound analysis result in accurate identification and diagnosis of respiratory disorders. Therefore, the technology reduces the need for diagnostic support services and clinical knowledge.
Reference
Porter, P., Abeyratne, U., Swarnkar, V., Tan, J., Ng, T. W., Brisbane, J. M., & Della, P. (2019). A prospective multicentre study testing the diagnostic accuracy of an automated cough sound-centred analytic system for the identification of common respiratory disorders in children. Respiratory Research, 20(1). Web.