Cardiovascular disease and all-cause mortality risk prediction from abdominal CT using deep learning

Elton, Daniel C.; Chen, Andy; Pickhardt, Perry J.; Summers, Ronald M.

doi:10.1101/2021.08.30.21262686

Cited by 2 publications

(2 citation statements)

References 32 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, deep learning models can be used to predict future development cancer. The concept of care gap is that eventually patients do routine scans or MRI for other conditions and some AI models already have been developed to predict disease, for instance cardiovascular scores from CT scans [ 32 , 33 ]. A study reported on the ability to predict a 5-year future breast cancer risk from normal mammograms using deep-learning CNNs [ 34 ].…”

Section: Artificial Intelligence For Cancer Imagingmentioning

confidence: 99%

An Overview of Artificial Intelligence in Oncology

et al. 2022

View full text Add to dashboard Cite

Cancer is associated with significant morbimortality globally. Advances in screening, diagnosis, management and survivorship were substantial in the last decades, however, challenges in providing personalized and data-oriented care remain. Artificial intelligence (AI), a branch of computer science used for predictions and automation, has emerged as potential solution to improve the healthcare journey and to promote precision in healthcare. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (eg., prediction of the association of multiple parameters and outcomes – prognosis and response) and better understanding of tumor molecular biology. In this review, we examine the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.

show abstract

Section: Artificial Intelligence For Cancer Imagingmentioning

confidence: 99%

An Overview of Artificial Intelligence in Oncology

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In parallel, there have been efforts in related all-cause mortality prediction problems to include medical imaging data. Elton et al 5 have successfully explored using abdominal CT scans for cardiovascular disease and five-year survival prediction, similarly, in another work focused on CT imaging, Yan and colleagues 6 used low-dose CT imaging to predict all-cause mortality for lung cancer subjects. However, such imaging-focused approaches have not yet been explored for general body-composition-based allcause mortality prediction models.…”

Section: Plain Language Summarymentioning

confidence: 99%

Deep learning predicts all-cause mortality from longitudinal total-body DXA imaging

et al. 2022

View full text Add to dashboard Cite

Background Mortality research has identified biomarkers predictive of all-cause mortality risk. Most of these markers, such as body mass index, are predictive cross-sectionally, while for others the longitudinal change has been shown to be predictive, for instance greater-than-average muscle and weight loss in older adults. And while sometimes markers are derived from imaging modalities such as DXA, full scans are rarely used. This study builds on that knowledge and tests two hypotheses to improve all-cause mortality prediction. The first hypothesis is that features derived from raw total-body DXA imaging using deep learning are predictive of all-cause mortality with and without clinical risk factors, meanwhile, the second hypothesis states that sequential total-body DXA scans and recurrent neural network models outperform comparable models using only one observation with and without clinical risk factors. Methods Multiple deep neural network architectures were designed to test theses hypotheses. The models were trained and evaluated on data from the 16-year-long Health, Aging, and Body Composition Study including over 15,000 scans from over 3000 older, multi-race male and female adults. This study further used explainable AI techniques to interpret the predictions and evaluate the contribution of different inputs. Results The results demonstrate that longitudinal total-body DXA scans are predictive of all-cause mortality and improve performance of traditional mortality prediction models. On a held-out test set, the strongest model achieves an area under the receiver operator characteristic curve of 0.79. Conclusion This study demonstrates the efficacy of deep learning for the analysis of DXA medical imaging in a cross-sectional and longitudinal setting. By analyzing the trained deep learning models, this work also sheds light on what constitutes healthy aging in a diverse cohort.

show abstract

Cardiovascular disease and all-cause mortality risk prediction from abdominal CT using deep learning

Cited by 2 publications

References 32 publications

An Overview of Artificial Intelligence in Oncology

An Overview of Artificial Intelligence in Oncology

Deep learning predicts all-cause mortality from longitudinal total-body DXA imaging

Contact Info

Product

Resources

About