Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs

Age and sex estimation in cephalometric radiographs based on multitask convolutional neural networks

OBJECTIVES

Age and sex characteristics are evident in cephalometric radiographs (CRs), yet their accurate estimation remains challenging due to the complexity of these images. This study aimed to harness deep learning to automate age and sex estimation from CRs, potentially simplifying their interpretation.

STUDY DESIGN

We compared the performance of 4 deep learning models (SVM, R-net, VGG16-SingleTask, and our proposed VGG16-MultiTask) in estimating age and sex from the testing dataset, utilizing a VGG16-based multitask deep learning model on 4,557 CRs. Gradient-weighted class activation mapping (Grad-CAM) was incorporated to identify sex. Performance was assessed using mean absolute error (MAE), specificity, sensitivity, F1 score, and the area under the curve (AUC) in receiver operating characteristic analysis.

RESULTS

The VGG16-MultiTask model outperformed the others, with the lowest MAE (0.864±1.602) and highest sensitivity (0.85), specificity (0.88), F1 score (0.863), and AUC (0.93), demonstrating superior efficacy and robust performance.

CONCLUSIONS

The VGG multitask model demonstrates significant potential in enhancing age and sex estimation from cephalometric analysis, underscoring the role of AI in improving biomedical interpretations.

Deep learning-based age estimation from chest CT scans

PURPOSE

Medical imaging can be used to estimate a patient's biological age, which may provide complementary information to clinicians compared to chronological age. In this study, we aimed to develop a method to estimate a patient's age based on their chest CT scan. Additionally, we investigated whether chest CT estimated age is a more accurate predictor of lung cancer risk compared to chronological age.

METHODS

To develop our age prediction model, we utilized composite CT images and Inception-ResNet-v2. The model was trained, validated, and tested on 13,824 chest CT scans from the National Lung Screening Trial, with 91% for training, 5% for validation, and 4% for testing. Additionally, we independently tested the model on 1849 CT scans collected locally. To assess chest CT estimated age as a risk factor for lung cancer, we computed the relative lung cancer risk between two groups. Group 1 consisted of individuals assigned a CT age older than their chronological age, while Group 2 comprised those assigned a CT age younger than their chronological age.

RESULTS

Our analysis revealed a mean absolute error of 1.84 years and a Pearson's correlation coefficient of 0.97 for our local data when comparing chronological age with the estimated CT age. The model showed the most activation in the area associated with the lungs during age estimation. The relative risk for lung cancer was 1.82 (95% confidence interval, 1.65-2.02) for individuals assigned a CT age older than their chronological age compared to those assigned a CT age younger than their chronological age.

CONCLUSION

Findings suggest that chest CT age captures some aspects of biological aging and may be a more accurate predictor of lung cancer risk than chronological age. Future studies with larger and more diverse patients are required for the generalization of the interpretations.

Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.

Patient Reidentification from Chest Radiographs: An Interpretable Deep Metric Learning Approach and Its Applications

Purpose

To train an explainable deep learning model for patient reidentification in chest radiograph datasets and assess changes in model-perceived patient identity as a marker for emerging radiologic abnormalities in longitudinal image sets.

Materials and Methods

This retrospective study used a set of 1 094 537 frontal chest radiographs and free-text reports from 259 152 patients obtained from six hospitals between 2006 and 2019, with validation on the public ChestX-ray14, CheXpert, and MIMIC-CXR datasets. A deep learning model was trained for patient reidentification and assessed on patient identity confirmation, retrieval of patient images from a database based on a query image, and radiologic abnormality prediction in longitudinal image sets. The representation learned was incorporated into a generative adversarial network, allowing visual explanations of the relevant features. Performance was evaluated with sensitivity, specificity, F1 score, Precision at 1, R-Precision, and area under the receiver operating characteristic curve (AUC) for normal and abnormal prediction.

Results

Patient reidentification was achieved with a mean F1 score of 0.996 ± 0.001 (2 SD) on the internal test set (26 152 patients) and F1 scores of 0.947-0.993 on the external test data. Database retrieval yielded a mean Precision at 1 score of 0.976 ± 0.005 at 299 × 299 resolution on the internal test set and Precision at 1 scores between 0.868 and 0.950 on the external datasets. Patient sex, age, weight, and other factors were identified as key model features. The model achieved an AUC of 0.73 ± 0.01 for abnormality prediction versus an AUC of 0.58 ± 0.01 for age prediction error.

Conclusion

The image features used by a deep learning patient reidentification model for chest radiographs corresponded to intuitive human-interpretable characteristics, and changes in these identifying features over time may act as markers for an emerging abnormality.Keywords: Conventional Radiography, Thorax, Feature Detection, Supervised Learning, Convolutional Neural Network, Principal Component Analysis Supplemental material is available for this article. © RSNA, 2023See also the commentary by Raghu and Lu in this issue.

Chest radiography as a biomarker of ageing: artificial intelligence-based, multi-institutional model development and validation in Japan

BACKGROUND

Chest radiographs are widely available and cost-effective; however, their usefulness as a biomarker of ageing using multi-institutional data remains underexplored. The aim of this study was to develop a biomarker of ageing from chest radiography and examine the correlation between the biomarker and diseases.

METHODS

In this retrospective, multi-institutional study, we trained, tuned, and externally tested an artificial intelligence (AI) model to estimate the age of healthy individuals using chest radiographs as a biomarker. For the biomarker modelling phase of the study, we used healthy chest radiographs consecutively collected between May 22, 2008, and Dec 28, 2021, from three institutions in Japan. Data from two institutions were used for training, tuning, and internal testing, and data from the third institution were used for external testing. To evaluate the performance of the AI model in estimating ages, we calculated the correlation coefficient, mean square error, root mean square error, and mean absolute error. The correlation investigation phase of the study included chest radiographs from individuals with a known disease that were consecutively collected between Jan 1, 2018, and Dec 31, 2021, from an additional two institutions in Japan. We investigated the odds ratios (ORs) for various diseases given the difference between the AI-estimated age and chronological age (ie, the difference-age).

FINDINGS

We included 101 296 chest radiographs from 70 248 participants across five institutions. In the biomarker modelling phase, the external test dataset from 3467 healthy participants included 8046 radiographs. Between the AI-estimated age and chronological age, the correlation coefficient was 0·95 (99% CI 0·95-0·95), the mean square error was 15·0 years (99% CI 14·0-15·0), the root mean square error was 3·8 years (99% CI 3·8-3·9), and the mean absolute error was 3·0 years (99% CI 3·0-3·1). In the correlation investigation phase, the external test datasets from 34 197 participants with a known disease included 34 197 radiographs. The ORs for difference-age were as follows: 1·04 (99% CI 1·04-1·05) for hypertension; 1·02 (1·01-1·03) for hyperuricaemia; 1·05 (1·03-1·06) for chronic obstructive pulmonary disease; 1·08 (1·06-1·09) for interstitial lung disease; 1·05 (1·03-1·06) for chronic renal failure; 1·04 (1·03-1·06) for atrial fibrillation; 1·03 (1·02-1·04) for osteoporosis; and 1·05 (1·03-1·06) for liver cirrhosis.

INTERPRETATION

The AI-estimated age using chest radiographs showed a strong correlation with chronological age in the healthy cohorts. Furthermore, in cohorts of individuals with known diseases, the difference between estimated age and chronological age correlated with various chronic diseases. The use of this biomarker might pave the way for enhanced risk stratification methodologies, individualised therapeutic interventions, and innovative early diagnostic and preventive approaches towards age-associated pathologies.

FUNDING

None.

TRANSLATION

For the Japanese translation of the abstract see Supplementary Materials section.

Automatic sex estimation using deep convolutional neural network based on orthopantomogram images

Sex estimation is very important in forensic applications as part of individual identification. Morphological sex estimation methods predominantly focus on anatomical measurements. Based on the close relationship between sex chromosome genes and facial characterization, craniofacial hard tissues morphology shows sex dimorphism. In order to establish a more labor-saving, rapid, and accurate reference for sex estimation, the study investigated a deep learning network-based artificial intelligence (AI) model using orthopantomograms (OPG) to estimate sex in northern Chinese subjects. In total, 10703 OPG images were divided into training (80%), validation (10%), and test sets (10%). At the same time, different age thresholds were selected to compare the accuracy differences between adults and minors. The accuracy of sex estimation using CNN (convolutional neural network) model was higher for adults (90.97%) compared with minors (82.64%). This work demonstrated that the proposed model trained with a large dataset could be used in automatic morphological sex-related identification with favorable performance and practical significance in forensic science for adults in northern China, while also providing a reference for minors to some extent.

Pediatric age estimation from thoracic and abdominal CT scout views using deep learning

Age assessment is regularly used in clinical routine by pediatric endocrinologists to determine the physical development or maturity of children and adolescents. Our study investigates whether age assessment can be performed using CT scout views from thoracic and abdominal CT scans using a deep neural network. Hence, we retrospectively collected 1949 CT scout views from pediatric patients (acquired between January 2013 and December 2018) to train a deep neural network to predict the chronological age from CT scout views. The network was then evaluated on an independent test set of 502 CT scout views (acquired between January 2019 and July 2020). The trained model showed a mean absolute error of 1.18 ± 1.14 years on the test data set. A one-sided t-test to determine whether the difference between the predicted and actual chronological age was less than 2.0 years was statistically highly significant (p < 0.001). In addition, the correlation coefficient was very high (R = 0.97). In conclusion, the chronological age of pediatric patients can be assessed with high accuracy from CT scout views using a deep neural network.

The deep learning algorithm estimates chest radiograph-based sex and age as independent risk factors for future cardiovascular outcomes

Objectives

Chest X-rays (CXRs) convey much illegible physiological information that deep learning model (DLM) has been reported interpreting successfully. Since the electrocardiogram age established by DLM was revealed as a heart biological marker, we hypothesize that CXR age has similar potential to describe the heart and lung states. Therefore, we developed a DLM to predict sex and age through CXR and analyzed its relation with future cardiovascular diseases (CVD).

Methods

A total of 90,396 CXRs aged 20 to 90 were collected and separated into a development set with 53,102 CXRs and demographic information pairs, a tuning set with 7073 pairs, an internal validation set with 17,364 pairs, and an external validation set with 12,857 pairs. The study trained DLM with development set for estimating age and sex and compared them to actual information.

Results

The mean absolute errors of predicted age were 4.803 and 4.313 years in the internal and external validation sets, respectively. The area under the curve of sex analysis was 0.9993 and 0.9988 in the internal and external validation sets, respectively. Patients whose CXR age was 5 years older than chronologic age lead to higher risk of all-cause mortality (hazard ratio (HR): 2.42, 95% confidence interval (CI): 2.00-2.92), cardiovascular (CV)-cause mortality (HR: 7.57, 95% CI: 4.55-12.60), new-onset heart failure (HR: 2.07, 95% CI: 1.56-2.76), new-onset chronic kidney disease (HR: 1.73, 95% CI: 1.46-2.05), new-onset acute myocardial infarction (HR: 1.80, 95% CI: 1.12-2.92), new-onset stroke (HR: 1.45, 95% CI: 1.10-1.90), new-onset coronary artery disease (HR: 1.26, 95% CI: 1.04-1.52), and new-onset atrial fibrillation (HR: 1.43, 95% CI: 1.01-2.02).

Conclusions

Using DLM to predict CXR age provided additional information for future CVDs. Older CXR age is an accessible risk classification tool for clinician use.

Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis

BACKGROUND

In recent years, there has been considerable research on the use of artificial intelligence to estimate age and disease status from medical images. However, age estimation from chest X-ray (CXR) images has not been well studied and the clinical significance of estimated age has not been fully determined.

METHODS

To address this, we trained a deep neural network (DNN) model using more than 100,000 CXRs to estimate the patients' age solely from CXRs. We applied our DNN to CXRs of 1562 consecutive hospitalized heart failure patients, and 3586 patients admitted to the intensive care unit with cardiovascular disease.

RESULTS

The DNN's estimated age (X-ray age) showed a strong significant correlation with chronological age on the hold-out test data and independent test data. Elevated X-ray age is associated with worse clinical outcomes (heart failure readmission and all-cause death) for heart failure. Additionally, elevated X-ray age was associated with a worse prognosis in 3586 patients admitted to the intensive care unit with cardiovascular disease.

CONCLUSIONS

Our results suggest that X-ray age can serve as a useful indicator of cardiovascular abnormalities, which will help clinicians to predict, prevent and manage cardiovascular diseases.