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ECR 2019 / C-2065
Towards radiologist-level malignancy detection on chest CT scans: a comparative study of the performance of convolutional neural networks and four thoracic radiologists
Congress: ECR 2019
Poster No.: C-2065
Type: Scientific Exhibit
Keywords: Artificial Intelligence, Lung, CT, Computer Applications-Detection, diagnosis, Cancer
Authors: V. Venugopal1, A. VAIDYA2, A. AHUJA2, Y. Singh2, K. Vaidhya3, A. Raj3, V. Mahajan2, S. Vaidya4, A. Rangasai Devalla3; 1Aligarh/IN, 2New Delhi/IN, 3Bangalore/IN, 4Mumbai/IN
DOI:10.26044/ecr2019/C-2065

Results

The results of radiologists on the Likert scales 1 and 2 were considered as negative for malignancy and 3,4 and 5 were considered to be positive for the presence of malignancy. For the AI, a predicted probability > 0.25 was considered to be positive for the presence of malignancy.

 

On the 96 chest CT scans reviewed by the radiologists, they had AUCs of 0.82, 0.81, 0.83 and 0.83 for predicting the risk of malignancy whereas the AI had an AUC of 0.91. Individually, radiologists’ accuracy varied from 76 to 77% and AI’s accuracy was 83%. The difference in the radiologist’s interpretation was not found to be statistically significant as the one - way ANOVA revealed p-value is 0.77311. 

 

User

Accuracy

Sensitivity

Specificity

AI

83%

73%

87%

RAD1

76%

65%

86%

RAD2

76%

65%

86%

RAD3

77%

65%

88%

RAD4

76%

65%

86%

 

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