AI-based computer-aided diagnostic system of chest digital tomography synthesis: Demonstrating comparative advantage with X-ray-based AI systems. (arXiv:2206.13504v1 [eess.IV])

Compared with chest X-ray (CXR) imaging, which is a single image projected
from the front of the patient, chest digital tomosynthesis (CDTS) imaging can
be more advantageous for lung lesion detection because it acquires multiple
images projected from multiple angles of the patient. Various clinical
comparative analysis and verification studies have been reported to demonstrate
this, but there were no artificial intelligence (AI)-based comparative analysis
studies. Existing AI-based computer-aided detection (CAD) systems for lung
lesion diagnosis have been developed mainly based on CXR images; however,
CAD-based on CDTS, which uses multi-angle images of patients in various
directions, has not been proposed and verified for its usefulness compared to
CXR-based counterparts. This study develops/tests a CDTS-based AI CAD system to
detect lung lesions to demonstrate performance improvements compared to
CXR-based AI CAD. We used multiple projection images as input for the
CDTS-based AI model and a single-projection image as input for the CXR-based AI
model to fairly compare and evaluate the performance between models. The
proposed CDTS-based AI CAD system yielded sensitivities of 0.782 and 0.785 and
accuracies of 0.895 and 0.837 for the performance of detecting tuberculosis and
pneumonia, respectively, against normal subjects. These results show higher
performance than sensitivities of 0.728 and 0.698 and accuracies of 0.874 and
0.826 for detecting tuberculosis and pneumonia through the CXR-based AI CAD,
which only uses a single projection image in the frontal direction. We found
that CDTS-based AI CAD improved the sensitivity of tuberculosis and pneumonia
by 5.4% and 8.7% respectively, compared to CXR-based AI CAD without loss of
accuracy. Therefore, we comparatively prove that CDTS-based AI CAD technology
can improve performance more than CXR, enhancing the clinical applicability of



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