A new clinical scoring system for patients with cystic fibrosis is needed because of recent advances in diagnosis and treatment which have changed the course of this disease. Chest radiograph scoring is the best objective measure of pulmonary disease for longitudinal studies beginning with infants; however, based on pilot studies, previous scoring systems are not sensitive enough in discriminating between degrees of mild lung disease. Therefore, a new radiographic scoring system was developed with the goal of achieving both sensitivity and reproducibility. This objective was pursued by applying multiattribute utility theory, using a panel of interpreters with expertise in cystic fibrosis radiology, and employing mathematical modeling techniques to weight the various components. The system was developed and validated in three phases including comparison to the Brasfield method of quantitative radiology. The data demonstrate that the new system can be applied reliably and conveniently to generate reproducible scores of pulmonary disease severity. Evaluation of the scores by four independent raters using chest radiographs from 61 patients at an average age of 8.37 years revealed good agreement with a .714 Kendall coefficient of concordance. Assessment of serial changes over time was performed using a group of 176 chest radiographs from 25 patients ranging from 4 weeks to 6 years old; this showed that the Wisconsin system generates score differences that are greater in magnitude with disease progression compared with the Brasfield method. Therefore, the new method is more sensitive to progression of mild disease and should be superior to prior radiographic scoring systems for evaluating therapies designed to modify the early course of disease. The Wisconsin system is designed to be useful in longitudinal clinical studies involving young children with cystic fibrosis and is capable to detecting progression from normality to mild lung disease.
CNN-MoE based framework for classification of respiratory anomalies and lung disease detection
