Performing a Research Study Using Open-Source Deep Learning Models

Overview

Journal Korean J Radiol

Specialty Radiology

Date 2024 Jan 18

PMID 38238013

Authors

Hyungjin Kim

Affiliations

Soon will be listed here.

References

Huh J, Lee J, Hwang E, Park C . Effects of Expert-Determined Reference Standards in Evaluating the Diagnostic Performance of a Deep Learning Model: A Malignant Lung Nodule Detection Task on Chest Radiographs. Korean J Radiol. 2023; 24(2):155-165. PMC: 9892220. DOI: 10.3348/kjr.2022.0548. View

Pickhardt P, Summers R, Garrett J . Automated CT-Based Body Composition Analysis: A Golden Opportunity. Korean J Radiol. 2021; 22(12):1934-1937. PMC: 8628162. DOI: 10.3348/kjr.2021.0775. View

Lee M, Zea R, Garrett J, Graffy P, Summers R, Pickhardt P . Abdominal CT Body Composition Thresholds Using Automated AI Tools for Predicting 10-year Adverse Outcomes. Radiology. 2022; 306(2):e220574. PMC: 9885340. DOI: 10.1148/radiol.220574. View

Moons K, Altman D, Reitsma J, Ioannidis J, Macaskill P, Steyerberg E . Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015; 162(1):W1-73. DOI: 10.7326/M14-0698. View

Lu M, Raghu V, Mayrhofer T, Aerts H, Hoffmann U . Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model. Ann Intern Med. 2020; 173(9):704-713. PMC: 9200444. DOI: 10.7326/M20-1868. View

Lee J, Choi S, Jung K, Goo J, Yoon S . High visceral fat attenuation and long-term mortality in a health check-up population. J Cachexia Sarcopenia Muscle. 2023; 14(3):1495-1507. PMC: 10235877. DOI: 10.1002/jcsm.13226. View

Lee Y, Hong N, Witanto J, Choi Y, Park J, Decazes P . Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment. Clin Nutr. 2021; 40(8):5038-5046. DOI: 10.1016/j.clnu.2021.06.025. View

Weiss J, Raghu V, Bontempi D, Christiani D, Mak R, Lu M . Deep learning to estimate lung disease mortality from chest radiographs. Nat Commun. 2023; 14(1):2797. PMC: 10188525. DOI: 10.1038/s41467-023-37758-5. View

Hwang E, Goo J, Nam J, Park C, Hong K, Kim K . Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial. Korean J Radiol. 2023; 24(3):259-270. PMC: 9971841. DOI: 10.3348/kjr.2022.0651. View

10.

Yoo H, Kim E, Kim H, Choi Y, Kim M, Hwang S . Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort. Korean J Radiol. 2022; 23(10):1009-1018. PMC: 9523233. DOI: 10.3348/kjr.2022.0189. View

11.

Lee J, Lee D, Lu M, Raghu V, Park C, Goo J . Deep Learning to Optimize Candidate Selection for Lung Cancer CT Screening: Advancing the 2021 USPSTF Recommendations. Radiology. 2022; 305(1):209-218. DOI: 10.1148/radiol.212877. View