Sensitivity and Specificity of Artificial Intelligence for Chest Diagnostic Radiology in Lung Cancer
Keywords:
Sensitivity, Specificity, Artificial intelligence, Chest radiograph, Lung cancerAbstract
Background : AIChest4All is the model development for screening abnormalities in chest radiograph and classification as normal, suspected active TB, suspected lung malignancy, abnormal heart and great vessels, intrathoracic abnormal findings and extrathoracic abnormal findings. The purpose of this artificial intelligence (AI) was to aid radiologists and clinicians, especially in the rural areas.
Objectives : To analyze the sensitivity and specificity of artificial intelligence for chest radiograph in diagnosis of lung malignancy.
Methods : The pathological and cytological reports were retrospectively reviewed. 800 patients of malignancy and 716 patients of non malignancy were randomly selected. The chest radiographs in a 3-month period before the procedures were collected. The chest radiographs were reviewed by three radiologists and classified as lung malignancy and non lung malignancy groups. The same radiographs were evaluated by AI and reported as percent of probability. The cut point for detected lung cancer was analyzed. The sensitivity and specificity of lung cancer diagnosis by radiologist and AI were calculated.
Results : The sensitivity and specificity in diagnosis of lung malignancy by radiologist was 67.5% and 83.1%, respectively. The sensitivity and specificity of AI to detected lung malignancy in chest radiographs was 50.0% and 84.8%, respectively. The cut point of probability percent which presented by AI for appropriated sensitivity and specificity was 52.5.
Conclusions : The sensitivity of AI in diagnosis of lung malignancy from chest radiographs was slightly less than radiologist. The specificity of AI was comparable to the diagnosis by radiologist.
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