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Estimation of age in unidentified patients via chest radiography using convolutional neural network regression

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Abstract

Purpose

Patient age has important clinical utility for refining a differential diagnosis in radiology. Here, we evaluate the potential for convolutional neural network models to predict patient age based on anterior-posterior chest radiographs for instances where patients may present for emergency services without the ability to provide this identifying information.

Methods

We used the CheXpert dataset of 224,316 chest radiographs from 65,240 patients to train CNN regression models with ResNet50 and DenseNet121 architectures for prediction of patient age based on anterior-posterior (AP) view chest radiographs. We evaluate these models on both the CheXpert validation dataset and a local hospital case in which a patient initially presented for emergency services intubated and without identification.

Results

Mean absolute error (MAE) for our ResNet50 model on the CheXpert dataset is 4.94 years for predicting patient age based on AP chest radiographs. MAE for our DenseNet121 model is 4.69 years. Both models have a correlation coefficient between true patient ages and predicted ages of 0.944. Wilcoxon rank-sum comparison between the two model architectures shows no significant difference (p = 0.33), but both show improvement over a baseline demographic-driven estimation (p < 0.001).

Conclusions

For circumstances in which patients present for healthcare services without readily accessible identification such as in the setting trauma or altered mental status, CNN regression models for age prediction have potential clinical utility for refining estimates related to this missing patient information.

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Data availability

MIMIC-CXR dataset is publicly available after credentialing at https://physionet.org/content/mimic-cxr/2.0.0/

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Code availability

Code usable to recreate reported convolutional neural network models and figures is available upon request to the corresponding author.

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Authors and Affiliations

  1. Department of Internal Medicine, University Hospital and Clinics, Lafayette, LA, USA

    Carl F. Sabottke & Marc A. Breaux

  2. Department of Radiology, Louisiana State University Health Sciences Center, New Orleans, LA, USA

    Carl F. Sabottke, Marc A. Breaux & Bradley M. Spieler

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  1. Carl F. Sabottke
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  2. Marc A. Breaux
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Correspondence to Carl F. Sabottke.

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The authors declare that they have no conflict of interest.

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HIPAA compliant. Research was originally approved under IRB no. 10333 with further notification that this research met Institutional Review Board criteria for self-regulation as, per IRB policy, detailed review was not considered necessary for utilization of less than five local hospital cases or use of publicly available data.

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Sabottke, C.F., Breaux, M.A. & Spieler, B.M. Estimation of age in unidentified patients via chest radiography using convolutional neural network regression. Emerg Radiol 27, 463–468 (2020). https://doi.org/10.1007/s10140-020-01782-5

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  • DOI: https://doi.org/10.1007/s10140-020-01782-5

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