Pectus excavatum diagnosis redefined: A fully automatic tool for batch evaluation of chest computed tomography images

Abstract

We aimed to implement a fully automatic computed tomography (CT) image-detection programming algorithm as a pectus excavatum (PE) diagnostic tool, facilitating comprehensive chest wall deformity evaluation. We developed our algorithm using MATLAB®, leveraging the Hounsfield unit threshold and region growing methods. The MATLAB graphical user interface enables the direct use of our program. We validated the model using CT images of anthropomorphic phantoms. The measurement values obtained by our algorithm demonstrated very small difference compared to known anthropomorphic phantom model data. For algorithm testing, 17,214 chest CT images of 57 patients were processed by algorithm and reviewed independently by a radiologist and a thoracic surgeon. The measurements of transverse, anteroposterior, and sternum-to-vertebral distance of the thoracic cavity, and the calculation data of four indices exhibited high positive correlation (0.94 ~ 0.99); asymmetry index and maximum anteroposterior hemithorax distance exhibited moderate correlation (0.40 ~ 0.83). Our automatic PE diagnostic tool demonstrated high accuracy; four chest wall deformity indices were obtained simultaneously without any initial manual marking, which correlated with manual measurements.