Clinical application of convolutional neural network lung nodule detection software: An Australian quaternary hospital experience.

Journal: Journal of medical imaging and radiation oncology

PMID: 39123308

Abstract

INTRODUCTION: Early-stage lung cancer diagnosis through detection of nodules on computed tomography (CT) remains integral to patient survivorship, promoting national screening programmes and diagnostic tools using artificial intelligence (AI) convolutional neural networks (CNN); the software of AI-Rad Companion™ (AIRC), capable of self-optimising feature recognition. This study aims to demonstrate the practical value of AI-based lung nodule detection in a clinical setting; a limited body of research.

Authors

Peter Mark

Department of Radiology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia.
Isabella Papalia

Department of Radiology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia.
Jeffrey Kc Lai

Department of Radiology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia.
Diane M Pascoe

Department of Radiology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia.

Keywords

Adult Aged Aged, 80 and over Australia Female Humans Lung Neoplasms Male Middle Aged Neural Networks, Computer Prospective Studies Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity Software Solitary Pulmonary Nodule Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (39123308)

Clinical application of convolutional neural network lung nodule detection software: An Australian quaternary hospital experience.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals