Performing a Research Study Using Open-Source Deep Learning Models

Abstract

External Validation of an Open-Source DL Model for CRsChest radiographs (CRs) are among the most widely used imaging examinations globally, and their extensive availability facilitates the early application of DL algorithms in this domain.Common DL models include segmentation and detection algorithms for lung nodules, masses, consolidations, pneumothorax etc. [2][3][4].However, CRs may contain prognostic information beyond the traditional diagnostic findings, and DL models can effectively quantify this prognostic signature.For instance, Lu et al. [5] recently developed a convolutional neural network capable of predicting the long-term incidence of lung cancer for up to 12 years using publicly available CRs from a large randomized controlled trial.Their objective was to identify high-risk smokers for lung cancer CT screening.The model exhibited superior discrimination performance compared with that of the Centers for Medicare and Medicaid eligibility criteria in independent test sets.Along with my colleagues, I conducted an external validation study of the model developed by Lu et al. [5] considering that the selection criteria for lung cancer CT screening are important in optimizing nationwide CT screening programs in Korea.The model was downloaded from the Github repository (https://github.com/vineet1992/ CXR-LC), and image preprocessing was performed for CRs in accordance with the authors' instructions.In a retrospective analysis of 19488 individuals undergoing health checkups in Korea, the model showed good discrimination performance, and we demonstrated its added value to the 2021 United States Preventive Services Task Force recommendations (i.e., an update of the Centers for Medicare and Medicaid eligibility criteria) [6].The model proved to be useful in reducing the number of screening candidates while

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