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CC BY 4.0 · Indian J Med Paediatr Oncol
DOI: 10.1055/s-0044-1791954
Review Article

Deep Learning-Based Pulmonary Nodule Screening: A Narrative Review

Abhishek Mahajan 
1   Department of Imaging, The Clatterbridge Cancer Centre NHS Foundation Trust
,
Ujjwal Agarwal 
3   Department of Radiodiagnosis, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
,
Rajat Agrawal
3   Department of Radiodiagnosis, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
,
Aditi Venkatesh
3   Department of Radiodiagnosis, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
,
Shreya Shukla 
3   Department of Radiodiagnosis, Homi Bhabha National Institute, Tata Memorial Hospital, Mumbai, Maharashtra, India
,
K S. S. Bharadwaj
4   Endimension Technology Pvt. Ltd., Maharashtra, India
,
M L. V. Apparao
4   Endimension Technology Pvt. Ltd., Maharashtra, India
,
Vivek Pawar
4   Endimension Technology Pvt. Ltd., Maharashtra, India
,
Vivek Poonia
4   Endimension Technology Pvt. Ltd., Maharashtra, India
› Author Affiliations Funding None.
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Abstract

Given its capacity to generate three-dimensional pictures, computed tomography is the most effective means of detecting lung nodules with more excellent resolution of detected nodules. Small lung nodules can easily be overlooked on chest X-rays, making interpretation difficult. Artificial intelligence algorithms have recently demonstrated remarkable progress in medical imaging, especially with deep learning techniques such as convolutional neural networks (CNNs). CNN produces excellent results in natural image recognition and classification using abundant available data and the computational abilities of modern computers. It further reduces false-positive pulmonary nodules in medical image processing. This review article provides a detailed and inclusive review of recent advances, challenges, performance comparisons, and possible future directions for the problem of pulmonary nodule screening using deep learning methods.

Keywords

deep learning - lung neoplasms - screening - AI - diagnosis - treatment outcome - prognosis

Ethical Approval

Not applicable.


Consent to Participate

Not applicable.


Note

The article is not under consideration for publication elsewhere. Each author participated sufficiently for the work to be submitted. The publication is approved by all authors.


Authors' Contribution

A.M. and K.S.S.B. contributed to the conceptualization, and design of the work. A.M., U.A., R.A., A.V., S.S., K.S.S.B., M.L.V.A., V.P., and V.P. contributed to writing—original draft, and writing—review and editing.


Patient Consent

N/A




Publication History

Article published online:
04 November 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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