Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Editorial
. 2024 Sep;6(5):e240426.
doi: 10.1148/ryai.240426.

Unveiling Disease Progression in Chest Radiographs through AI

Natália Alves  1 , Kiran Vaidhya Venkadesh  1
Affiliations
Editorial

Unveiling Disease Progression in Chest Radiographs through AI

Natália Alves et al. Radiol Artif Intell. 2024 Sep.
No abstract available

PubMed Disclaimer

Conflict of interest statement

Disclosures of conflicts of interest: N.A. Funding from Siemens Healthineers in the context of an internship; funding from Radboud University Medical Center. K.V.V. Author received a financial settlement from Predible Health following its acquisition by Nference.

Figures

Natália Alves, MSc, is a PhD candidate at the Diagnostic Image Analysis Group at Radboud University Medical Center in Nijmegen, the Netherlands. Her research interests include deep learning for cancer detection and AI uncertainty quantification. She is a member of the 2023–2025 trainee editorial board of Radiology: Artificial Intelligence and the Young Club Committee in the European Society of Medical Imaging Informatics.
Natália Alves, MSc, is a PhD candidate at the Diagnostic Image Analysis Group at Radboud University Medical Center in Nijmegen, the Netherlands. Her research interests include deep learning for cancer detection and AI uncertainty quantification. She is a member of the 2023–2025 trainee editorial board of Radiology: Artificial Intelligence and the Young Club Committee in the European Society of Medical Imaging Informatics.
Kiran Vaidhya Venkadesh, PhD, is a postdoctoral researcher at Radboud University Medical Center in Nijmegen, the Netherlands. He is part of the Diagnostic Image Analysis Group, specializing in the development and validation of deep learning algorithms for radiology. His research over the past 10 years has focused extensively on radiology AI algorithms, with a special emphasis on chest CT image analysis.
Kiran Vaidhya Venkadesh, PhD, is a postdoctoral researcher at Radboud University Medical Center in Nijmegen, the Netherlands. He is part of the Diagnostic Image Analysis Group, specializing in the development and validation of deep learning algorithms for radiology. His research over the past 10 years has focused extensively on radiology AI algorithms, with a special emphasis on chest CT image analysis.
See this image and copyright information in PMC

Comment on

References

    1. Mahesh M , Ansari AJ , Mettler FA Jr . Patient exposure from radiologic and nuclear medicine procedures in the United States and worldwide: 2009-2018 . Radiology 2023. ; 307 ( 1 ): e221263 . - PMC - PubMed
    1. Radiology facing a global shortage . RSNA News . https://www.rsna.org/news/2022/may/Global-Radiologist-Shortage. Accessed July 10, 2024 .
    1. Ahn JS , Ebrahimian S , McDermott S , et al. . Association of artificial intelligence–aided chest radiograph interpretation with reader performance and efficiency . JAMA Netw Open 2022. ; 5 ( 8 ): e2229289 . - PMC - PubMed
    1. Ginsburg AS , McCollum ED . Artificial intelligence and pneumonia: a rapidly evolving frontier . Lancet Glob Health 2023. ; 11 ( 12 ): e1849 – e1850 . - PubMed
    1. Venkadesh KV , Aleef TA , Scholten ET , et al. . Prior CT improves deep learning for malignancy risk estimation of screening-detected pulmonary nodules . Radiology 2023. ; 308 ( 2 ): e223308 . - PubMed

MeSH terms

LinkOut - more resources