Fractional Dynamics Foster Deep Learning of COPD Stage Prediction

Yin, Chenzhong; Udrescu, Mihai; Gupta, Gaurav; Cheng, Mingxi; Lihu, Andrei; Udrescu, Lucreţia; Bogdan, Paul; Mannino, David M.; Mihăicuţă, Ştefan

doi:10.1002/advs.202203485

Cited by 17 publications

(5 citation statements)

References 91 publications

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“… 74 , 75 Our subsequent research will also focus on applying ML to the early differentiation of COPD phenotypes and assessing the sensitivity to glucocorticoid therapy in COPD patients with different phenotypes. At present, deep learning has been applied to drug development and vaccine design, 76 and we should also keep an eye on this in order to find potential drugs that can benefit COPD patients. Finally, although we have discovered that CLEC5A, FTL, and SLC2A3 help to identify COPD, the specific mechanisms by which these three genes impact COPD remain to be explored.…”

Section: Discussionmentioning

confidence: 99%

A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments

Zhang,

Yu,

Wang

et al. 2023

JIR

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Background This study aims to investigate the association between immune cells and the development of COPD, while providing a new method for the diagnosis of COPD according to the changes in immune microenvironment. Methods In this study, the “CIBERSORT” algorithm was used to estimate the tissue infiltration of 22 types of immune cells in GSE20257 and GSE10006. The “limma” package was used for differentially expressed analysis. The key modules associated with vital immune cells were identified using WGCNA. GO and KEGG enrichment analysis revealed the biological functions of the candidate genes. Ultimately, a novel diagnostic prediction model was constructed via machine learning methods and multivariate logistic regression analysis based on GSE20257. Furthermore, we examined the stability of the model on one internal test set (GSE10006), three external test sets (GSE8545, GSE57148 and GSE76925), one single-cell transcriptome dataset (GSE167295), macrophages (THP-M cells) and lung tissue from COPD patients. Results M0 macrophages (AUC > 0.7 in GSE20257 and GSE10006) were considered as the most important immune cells through exploring the immune microenvironment landscapes in COPD patients and healthy controls. The differentially expressed genes from GSE20257 and GSE10006 were divided into six and five modules via WGCNA, respectively. The green module in GSE20257 (cor = 0.41, P < 0.001) and the brown module in GSE10006 (cor = 0.67, P < 0.001) were highly correlated with M0 macrophages and were selected as key modules. Forty-one intersected genes obtained from two modules were primarily involved in regulation of cytokine production, regulation of innate immune response, specific granule, phagosome, lysosome, ferroptosis, and other biological processes. On the basis of the candidate genetic markers further characterized via the “Boruta” and “LASSO” algorithm for COPD, a diagnostic model comprising CLEC5A, FTL and SLC2A3 was constructed, which could accurately distinguish COPD patients from healthy controls in multiple datasets. GSE20257 as the training set has an AUC of 0.916. The AUCs of the internal test set and three external test sets were 0.873, 0.932, 0.675 and 0.688, respectively. Single-cell sequencing analysis suggested that CLEC5A, FTL and SLC2A3 were expressed in macrophages from COPD patients. The expressions of CLEC5A, FTL and SLC2A3 were up-regulated in THP-M cells and lung tissue from COPD patients. Conclusion According to the variations of immune microenvironment in COPD patients, we constructed and validated a novel macrophage M0-associated diagnostic model with satisfactory predictive value. CLEC5A, FTL and SLC2A3 are expected to be promising targets of immunotherapy in COPD.

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Section: Discussionmentioning

confidence: 99%

A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments

Zhang,

Yu,

Wang

et al. 2023

JIR

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“…It is also worth noting that recent literature has showcased the potential of leveraging fractional dynamics to capture long-range memory in ECG data analysis, offering valuable insights for addressing temporal dependencies in the field 40,41 . In this study, we implemented various models including pure RNN, LSTM, and Transformers [42][43][44] .…”

Section: Discussionmentioning

confidence: 99%

Externally validated deep learning model to identify prodromal Parkinson’s disease from electrocardiogram

Karabayir

Gunturkun

Butler

et al. 2023

Sci Rep

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Little is known about electrocardiogram (ECG) markers of Parkinson’s disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case–control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). Cases and controls were matched according to specific characteristics (date, age, sex and race). Clinical data were available from May, 2014 onward at LUC and from January, 2015 onward at MLH, while the ECG data were available as early as 1990 in both institutes. PD was denoted by at least two primary diagnostic codes (ICD9 332.0; ICD10 G20) at least 30 days apart. PD incidence date was defined as the earliest of first PD diagnostic code or PD-related medication prescription. ECGs obtained at least 6 months before PD incidence date were modeled to predict a subsequent diagnosis of PD within three time windows: 6 months–1 year, 6 months–3 years, and 6 months–5 years. We applied a novel deep neural network using standard 10-s 12-lead ECGs to predict PD risk at the prodromal phase. This model was compared to multiple feature engineering-based models. Subgroup analyses for sex, race and age were also performed. Our primary prediction model was a one-dimensional convolutional neural network (1D-CNN) that was built using 131 cases and 1058 controls from MLH, and externally validated on 29 cases and 165 controls from LUC. The model was trained on 90% of the MLH data, internally validated on the remaining 10% and externally validated on LUC data. The best performing model resulted in an external validation AUC of 0.67 when predicting future PD at any time between 6 months and 5 years after the ECG. Accuracy increased when restricted to ECGs obtained within 6 months to 3 years before PD diagnosis (AUC 0.69) and was highest when predicting future PD within 6 months to 1 year (AUC 0.74). The 1D-CNN model based on raw ECG data outperformed multiple models built using more standard ECG feature engineering approaches. These results demonstrate that a predictive model developed in one cohort using only raw 10-s ECGs can effectively classify individuals with prodromal PD in an independent cohort, particularly closer to disease diagnosis. Standard ECGs may help identify individuals with prodromal PD for cost-effective population-level early detection and inclusion in disease-modifying therapeutic trials.

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“…While CT can provide detailed structural information with its high-resolution 3D imaging capabilities, other data, such as PFT, 39 clinical data, 40,41 chest x-ray, 42 and magnetic resonance imaging (MRI), 43 can also provide useful information. PFT is noninvasive, and allows for longitudinal tracking of lung function, but is limited to characterizing anatomical changes.…”

Section: Single-modality Versus Multimodalitymentioning

confidence: 99%

Computed tomography‐based artificial intelligence in lung disease—Chronic obstructive pulmonary disease

Wang,

Li,

Gao

et al. 2024

MedComm – Future Medicine

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Chronic obstructive pulmonary disease (COPD) stands as a global health crisis, responsible for substantial morbidity and mortality on a worldwide scale. Its insidious nature underscores the importance of early detection and accurate diagnosis. While spirometry has been the cornerstone for COPD diagnosis, the role of computed tomography (CT) imaging has evolved, offering a valuable avenue for early detection and subtype classification. Recently, the advent of artificial intelligence (AI) has brought forth the potential to revolutionize the accuracy and efficiency of COPD diagnosis, with a specific focus on CT images. This intersection of healthcare and technology signifies a paradigm shift in the way we approach COPD management. The transformative capacity of AI positions it as a vital instrument for early detection and precise subtype classification of COPD. Moreover, the synergistic relationship between medical imaging and AI paves the way for more precise and efficient disease management. Therefore, in this perspective, we tend to offer a comprehensive exploration of the latest breakthroughs in the field of CT‐based AI in COPD diagnosis, aiming to demonstrate the promise and potential of AI in refining the accuracy of COPD classification and to illuminate the evolving landscape of AI's impact on COPD management.

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Fractional Dynamics Foster Deep Learning of COPD Stage Prediction

Cited by 17 publications

References 91 publications

A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments

A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments

Externally validated deep learning model to identify prodromal Parkinson’s disease from electrocardiogram

Computed tomography‐based artificial intelligence in lung disease—Chronic obstructive pulmonary disease

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