Accuracy of gray-scale coding in lung sound mapping

Abstract

Stethoscope evaluation of the lungs is widely accepted and practiced; however, there are some widely recognized, major limitations with its use. A safe device that helped solve these limitations by translating sound into objective, quantifiable images would have clinical utility. Translating lung sounds into quantifiable images in which regional differences or asymmetry in intensities of breath sounds are presented as gradients in gray-scale is not a trivial process. Healthy lungs and lung pathology are characterized by different patterns of regional breath sound distribution and, therefore, the accuracy of mapping gray-scale images must be ensured in a controlled systematic fashion prior to clinical use of such a technique. Vibration response imaging (VRI) maps lung sounds from 40 sensors to a two-dimensional gray-scale image. To assess mapping accuracy, a simulated lung sound map with uniform signals was compared to modified maps where sound signals were reduced (1–3 db) at one sensor. Also, 8 readers evaluated the gray-scale images. The computer algorithm accurately displayed gray-scale coding changes in correct locations in 97% of images. There was 95 ± 4% accuracy rate by readers to correctly identify gray-scale changes. In addition, quantitative data at different stages of signal processing were investigated in a LSM of a subject with asthma. Signal processing was 97% accurate overall in that the gray-scale values from which the image was derived corresponded with intensity values from recorded signals. These results suggest VRI accurately maps acoustic signals to a gray-scale image and that trained readers can detect small changes.