Collaborating Authors


Retinal age gap as a predictive biomarker for mortality risk


Aim To develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk. Methods A total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality. Results The DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality. Conclusions Our findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions. Data are available in a public, open access repository.

A nomogram based on CT deep learning signature


Xianyue Quan Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, People's Republic of China Tel/Fax 86-2061643114 Email Purpose: To develop and further validate a deep learning signature-based nomogram from computed tomography (CT) images for prediction of the overall survival (OS) in resected non-small cell lung cancer (NSCLC) patients. Patients and Methods: A total of 1792 deep learning features were extracted from non-enhanced and venous-phase CT images for each NSCLC patient in training cohort (n 231). Then, a deep learning signature was built with the least absolute shrinkage and selection operator (LASSO) Cox regression model for OS estimation. At last, a nomogram was constructed with the signature and other independent clinical risk factors. The performance of nomogram was assessed by discrimination, calibration and clinical usefulness.

MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models Artificial Intelligence

Self-supervised approaches such as Momentum Contrast (MoCo) can leverage unlabeled data to produce pretrained models for subsequent fine-tuning on labeled data. While MoCo has demonstrated promising results on natural image classification tasks, its application to medical imaging tasks like chest X-ray interpretation has been limited. Chest X-ray interpretation is fundamentally different from natural image classification in ways that may limit the applicability of self-supervised approaches. In this work, we investigate whether MoCo-pretraining leads to better representations or initializations for chest X-ray interpretation. We conduct MoCo-pretraining on CheXpert, a large labeled dataset of X-rays, followed by supervised fine-tuning experiments on the pleural effusion task. Using 0.1% of labeled training data, we find that a linear model trained on MoCo-pretrained representations outperforms one trained on representations without MoCo-pretraining by an AUC of 0.096 (95% CI 0.061, 0.130), indicating that MoCo-pretrained representations are of higher quality. Furthermore, a model fine-tuned end-to-end with MoCo-pretraining outperforms its non-MoCo-pretrained counterpart by an AUC of 0.037 (95% CI 0.015, 0.062) with the 0.1% label fraction. These AUC improvements are observed for all label fractions for both the linear model and an end-to-end fine-tuned model with the greater improvements for smaller label fractions. Finally, we observe similar results on a small, target chest X-ray dataset (Shenzhen dataset for tuberculosis) with MoCo-pretraining done on the source dataset (CheXpert), which suggests that pretraining on unlabeled X-rays can provide transfer learning benefits for a target task. Our study demonstrates that MoCo-pretraining provides high-quality representations and transferable initializations for chest X-ray interpretation.

Deep Learning for Automated Classification of Tuberculosis-Related Chest X-Ray: Dataset Specificity Limits Diagnostic Performance Generalizability Machine Learning

Machine learning has been an emerging tool for various aspects of infectious diseases including tuberculosis surveillance and detection. However, WHO provided no recommendations on using computer-aided tuberculosis detection software because of the small number of studies, methodological limitations, and limited generalizability of the findings. To quantify the generalizability of the machine-learning model, we developed a Deep Convolutional Neural Network (DCNN) model using a TB-specific CXR dataset of one population (National Library of Medicine Shenzhen No.3 Hospital) and tested it with non-TB-specific CXR dataset of another population (National Institute of Health Clinical Centers). The findings suggested that a supervised deep learning model developed by using the training dataset from one population may not have the same diagnostic performance in another population. Technical specification of CXR images, disease severity distribution, overfitting, and overdiagnosis should be examined before implementation in other settings.