Enhancing Lung Nodule Detection and Characterization Using NLST Data
To further evaluate and refine our models, Riverain will leverage the National Lung Screening Trial (NLST) dataset. This comprehensive collection of CT and X-ray images along with associated demographic and pathological information will provide a valuable resource for assessing model performance across a wide range of nodule types, ultimately improving diagnostic accuracy and clinical outcomes. In particular, the pathological assessments within the NLST data will allow Riverain to evaluate performance on three critical lung nodule categories: benign, benign biopsied, and malignant.
1- Assess the effectiveness of our algorithms in detecting and characterizing various types of lung nodules using CT data on nodules within the NLST data.
2- Assess the effectiveness of our algorithms in detecting and characterizing various types of lung nodules using X-ray data on nodules within the NLST data.
Jason Knapp, Chief Technology Officer, Riverain Technologies
Bo Gong, Senior Research Scientist, Riverain Technologies
Dennis Wu, Senior Research Scientist, Riverain Technologies
Jonathan Yerkins, Research Scientist, Riverain Technologies