Deep Learning-Based Estimated Pulmonary Biological Age From Chest Computed Tomography Images in Healthy Adults: Model Development and Validation Study.

Liping Zuo, Na Zhu, Bowen Wang, Donglai Li, Jinlei Fan, Zhaolei Fan, Yongsheng Shang, Yongxiang Wang, Lei Xu, Peng Zhou, Wangshu Cai, Dexin Yu

Department of Radiology, Qilu Hospital of Shandong University, 107 Wenhua Xilu, Lixia District, Jinan 250012, Shandong, China, 86 18560081629.Department of Radiology, Qilu Hospital of Shandong University, 107 Wenhua Xilu, Lixia District, Jinan 250012, Shandong, China, 86 18560081629. · pubmed

Paper

Abstract

Estimated pulmonary biological age (ePBA) has emerged as a more reliable indicator for disease progression and mortality than chronological age, with chest computed tomography (CT) as a promising tool for calculating ePBA. However, the lack of models trained and validated with large-scale healthy adults hinders the generalizability of the CT-based ePBA.

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Research summary

The paper claims to develop and validate a model for estimating pulmonary biological age using chest CT images. This research is relevant as it addresses biological age, which is a more reliable indicator of health and longevity than chronological age, potentially contributing to our understanding of aging processes.

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