Performance validation of a closed loop fully automated AI model for lung nodule stratification in screening cases.

A Taha¹, M S Muneer², A Kalra³, M Muelly³, J Reicher³

¹Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford Medicine, Stanford, CA, United States. Electronic address: ataha1@stanford.edu.

²Division of Radiology, Stanford Medicine, Stanford, CA, United States.

³Imvaria Inc., Berkeley, CA, United States.

PubMed DOI

Abstract

BACKGROUND: Several limitations hinder the effectiveness of human-based lung cancer screening (LCS): high false-positive rates leading to unnecessary follow-up imaging, procedures, and surgeries; inter-reader variability; inconsistent Lung-RADS adherence; and fatigue-related diagnostic errors. Additionally, most artificial intelligence (AI) models address only one task (nodule detection or risk st…

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