Predicting adverse cardiac events in sarcoidosis: deep learning from automated characterization of regional myocardial remodeling

Chen, Lu; Wang, Yi Grace; Zaman, Fahim; Wu, Xiaodong; Adhaduk, Mehul; Chang, Amanda; Ji, Jiansong; Wei, Tiemin; Suksaranjit, Promporn; Christodoulidis, Georgios; Scalzetti, Ernest M.; Han, Yuchi; Feiglin, David; Liu, Kan

doi:10.1007/s10554-022-02564-5

Cited by 12 publications

(2 citation statements)

References 47 publications

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“…Lu et al . [29 ▪ ] used a CNN to evaluate cardiac magnetic resonance (CMR) data in 117 patients with sarcoidosis. The algorithm discriminated significantly between patient sub-cohorts with higher and lower prevalence of subsequent cardiac events, justifying further research into this technique in cardiac sarcoidosis risk stratification.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the diagnostic accuracy of CT in pulmonary sarcoidosis has not been rigorously examined in individual CT phenotypes. Although recognizing exceptional scenarios, expert groups have tended to continue to advocate a histologic diagnosis in most patients with suspected pulmonary sarcoidosis [29 ▪ ], but a significant proportion of patients decline bronchoscopic diagnostic procedures. Deep learning might, in principle, be used to identify phenotypic CT separations in pulmonary sarcoidosis more accurately than can be achieved by human observers.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative computed tomography and machine learning: recent data in fibrotic interstitial lung disease and potential role in pulmonary sarcoidosis

Wells

Walsh

2022

Current Opinion in Pulmonary Medicine

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Purpose of reviewThe aim of this study was to summarize quantitative computed tomography (CT) and machine learning data in fibrotic lung disease and to explore the potential application of these technologies in pulmonary sarcoidosis.Recent findingsRecent data in the use of quantitative CT in fibrotic interstitial lung disease (ILD) are covered. Machine learning includes deep learning, a branch of machine learning particularly suited to medical imaging analysis. Deep learning imaging biomarker research in ILD is currently undergoing accelerated development, driven by technological advances in image processing and analysis. Fundamental concepts and goals related to deep learning imaging research in ILD are discussed. Recent work highlighted in this review has been performed in patients with idiopathic pulmonary fibrosis (IPF). Quantitative CT and deep learning have not been applied to pulmonary sarcoidosis, although there are recent deep learning data in cardiac sarcoidosis.SummaryPulmonary sarcoidosis presents unsolved problems for which quantitative CT and deep learning may provide unique solutions: in particular, the exploration of the long-standing question of whether sarcoidosis should be viewed as a single disease or as an umbrella term for disorders that might usefully be considered as separate diseases.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%