SSG13-06

Evaluation of the Performance of Deep Learning Models Trained on a Combination of Major Abnormal Patterns on Chest Radiographs for Major Chest Diseases at International Multi-Centers

Tuesday, Dec. 3 11:20AM - 11:30AM Room: S502AB



Participants
Woong Bae, Seoul, Korea, Republic Of (Presenter) Nothing to Disclose
Beomhee Park, Seoul , Korea, Republic Of (Abstract Co-Author) Nothing to Disclose
Minki Jung, Seoul, Korea, Republic Of (Abstract Co-Author) Nothing to Disclose
Jin-Kyeong Sung, MD, Seoul, Korea, Republic Of (Abstract Co-Author) Employee, VUNO Inc
Kyu-Hwan Jung, PhD, Seoul, Korea, Republic Of (Abstract Co-Author) Employee, VUNO Inc
Sang Min Lee, MD, Seoul, Korea, Republic Of (Abstract Co-Author) Nothing to Disclose
Joon Beom Seo, MD, PhD, Seoul, Korea, Republic Of (Abstract Co-Author) Stockholder, Coreline Soft, Co Ltd;

PURPOSE

To evaluate the abnormal classification performance for major chest diseases using a deep learning model that was trained on a combination of major abnormal patterns on chest radiographs.

METHOD AND MATERIALS

We experimented with the abnormal classification performance for a deep learning model for major diseases (tuberculosis and pneumonia) that was trained on a combination of different patterns (nodule, consolidation and interstitial opacity) on CRs. To evaluate the effect of each pattern combination on performance for major diseases, we tested five cases of patterns, which is composed of the nodule case, the consolidation case, the interstitial opacity case, the combination of consolidation and interstitial opacity case, and the combination of all three cases. When training each case, all normal data was used for training. CRs with three abnormal patterns and normal patterns were used as training datasets, which were received from two hospitals and consisted of 2095, 2401, 1290, and 3000 images for nodule, consolidation, interstitial opacity, and normal patterns, respectively. And all abnormal CRs were clinically confirmed by CT scans. For an explicit evaluation, the public dataset was used as the test dataset, which consists of the Shenzhen (normal: 326, tuberculosis: 336) and PadChest (normal: 300, pneumonia: 127, randomly selected) dataset, which was used to evaluate tuberculosis and pneumonia, respectively.

RESULTS

In the test dataset, for tuberculosis and pneumonia, the classification performance of the models trained with the five cases of patterns showed AUC 0.58 / 0.69 for nodule case, 0.76 / 0.82 for consolidation, 0.52 / 0.76 for interstitial opacity case, 0.79 / 0.83 for combination of consolidation and interstitial opacity case, 0.79 / 0.82 for combination of all three case, respectively.

CONCLUSION

We have shown through experimentations that the deep learning model trained from data with major patterns (nodule, consolidation, interstitial opacity) can classify major diseases (tuberculosis, pneumonia) as abnormal. Also, consolidation was highly correlated with tuberculosis and pneumonia. On the other hand, interstitial opacity and nodule were more correlated with pneumonia, tuberculosis, respectively.

CLINICAL RELEVANCE/APPLICATION

The diagnosis based on the patterns of abnormal findings allows detection of various diseases.

Printed on: 12/06/19