Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors’ evaluation in lung cancer screening

Sep 21, 2026

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Evaluate how consistently an AI computer-aided diagnostic system matches doctors when analysing and diagnosing pulmonary nodules, and whether nodule characteristics derived from both approaches agree for interpreting carcinomatous nodules. Retrospective analysis covers participants aged 40–74 from 2011–2013 using low-dose chest CT, expert panel evaluation, and a 3D CNN DenseNet CAD model. Agreement tests and ROC-based accuracy are applied, with logistic regression to compare predictive factors for cancerous nodules.

LanguageEnglish

CategoryHealthcare

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Pages8

Reading Time20 min

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Abstract

Objective

Materials and Methods

Results

Conclusion

Introduction

Lung cancer screening and CT assessment

Gao et al. BMC Medical Imaging (2024) 24:141 [https://doi.org/10.1186/s12880-024-01288-3](https://doi.org/10.1186/s12880-024-01288-3) BMC Medical Imaging RESEARCH Open Access Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors’ evaluation in lung cancer screening Shan Gao1,2†, Zexuan Xu2†, Wanli Kang2, Xinna Lv2, Naihui Chu1,2*, Shaofa Xu2* and Dailun Hou2* Abstract Objective To evaluate the consistency between doctors and artificial intelligence (AI) software in analysing and diagnosing pulmonary nodules, and assess whether the characteristics of pulmonary nodules derived from the two methods are consistent for the interpretation of carcinomatous nodules. Materials and Methods This retrospective study analysed participants aged 40–74 in the local area from 2011 to 2013. Pulmonary nodules were examined radiologically using a low-dose chest CT scan, evaluated by an expert panel of doctors in radiology, oncology, and thoracic departments, as well as a computer-aided diagnostic(CAD) system based on the three-dimensional(3D) convolutional neural network (CNN) with DenseNet architecture(InferRead CT Lung, IRCL) . Consistency tests were employed to assess the uniformity of the radiological characteristics of the pulmonary nodules. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic accuracy. Logistic regression analysis is utilized to determine whether the two methods yield the same predictive factors for cancerous nodules. Results A total of 570 subjects were included in this retrospective study. The AI software demonstrated high consistency with the panel’s evaluation in determining the position and diameter of the pulmonary nodules (kappa = 0 . 883, concordance correlation coefficient (CCC) = 0 . 809, p = 0 . 000) . The comparison of the solid nodules’ attenuation characteristics also showed acceptable consistency (kappa = 0 . 503) . In patients diagnosed with lung cancer, the area under the curve (AUC) for the panel and AI were 0.873 (95%CI: 0 .829–0. 909) and 0.921 (95%CI: 0 .884–0. 949), respectively. However, there was no significant difference (p = 0 . 0950) . The maximum diameter, solid nodules, subsolid nodules were the crucial factors for interpreting carcinomatous nodules in the analysis of expert panel and IRCL pulmonary nodule characteristics. †Shan Gao and Zexuan Xu contributed equally to this work. *Correspondence: Naihui Chu [dongchu1994@sina.com](dongchu1994@sina.com)[ ](dongchu1994@sina.com)Shaofa Xu [xushaofa@263.net](xushaofa@263.net)[ ](xushaofa@263.net)Dailun Hou [hou.dl@mail.ccmu.edu.cn](hou.dl@mail.ccmu.edu.cn) Full list of author information is available at the end of the article © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[. The Creative Commons Public Domain Dedication waiver (](http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ()[http://creativecom](http://creativecom)[mons.org/publicdomain/zero/1.0/](mons.org/publicdo

FAQs

What is the study objective regarding AI and doctors?

To assess the consistency between doctors and an AI software system when analysing and diagnosing pulmonary nodules, and to determine whether derived nodule characteristics match for interpreting carcinomatous nodules.

How were pulmonary nodules evaluated in the study?

Nodules were examined using low-dose chest CT, evaluated by an expert doctor panel, and also assessed by a computer-aided diagnostic system based on a 3D convolutional neural network with DenseNet architecture.

What were the main findings about diagnostic consistency?

The AI showed high consistency with the panel for the position and diameter of nodules and acceptable consistency for solid nodule attenuation characteristics, with comparable AUC values in patients diagnosed with lung cancer.

Sep 21, 2026

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