An artificial intelligence-based noninvasive solution to estimate pulmonary artery pressure

Zheng, Jianmin; Abudayyeh, Islam; Mladenov, G.; Struppa, Daniele C.; Fu, Guohua; Rakovski, Cyril

doi:10.3389/fcvm.2022.855356

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Moreover, research by Zhigang Liu emphasizes that fluctuations in TT levels could affect lipid metabolism, including the synthesis and breakdown of triglycerides [53]. SBP, the maximum pressure exerted by blood against vessel walls during heart contraction, can influence the blood supply to pelvic organs, including the uterus [54]. Insufficient blood supply may lead to hypoxia and malnutrition in endometrial tissue, affecting the progression of EMS.…”

Section: Discussionmentioning

confidence: 99%

Genetic association of serum lipids and lipid-modifying targets with endometriosis: Trans-ethnic Mendelian-randomization and mediation analysis

Zhang,

Fan,

et al. 2024

PLoS ONE

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Background Prior observational research identified dyslipidemia as a risk factor for endometriosis (EMS) but the causal relationship remains unestablished due to inherent study limitations. Methods Genome-wide association study data for high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), and total cholesterol (TC) from European (EUR) and East Asian (EAS) ancestries were sourced from the Global Lipids Genetics Consortium. Multi-ancestry EMS data came from various datasets. Univariable Mendelian randomization (MR) examined causal links between serum lipids and EMS. Multivariable and mediation MR explored the influence of seven confounding factors and mediators. Drug-target MR investigates the association between lipid-lowering target genes identified in positive results and EMS. The primary method was inverse-variance weighted (IVW), with replication datasets and meta-analyses reinforcing causal associations. Sensitivity analyses included false discovery rate (FDR) correction, causal analysis using summary effect estimates (CAUSE), and colocalization analysis. Results IVW analysis in EUR ancestry showed a significant causal association between TG and increased EMS risk (OR = 1.112, 95% CI 1.033–1.198, P = 5.03×10−3, PFDR = 0.03), supported by replication and meta-analyses. CAUSE analysis confirmed unbiased results (P < 0.05). Multivariable and mediation MR revealed that systolic blood pressure (Mediation effect: 7.52%, P = 0.02) and total testosterone (Mediation effect: 10.79%, P = 0.01) partly mediated this relationship. No causal links were found between other lipid traits and EMS (P > 0.05 & PFDR > 0.05). In EAS ancestry, no causal relationships with EMS were detected (P > 0.05 & PFDR > 0.05). Drug-target MR indicated suggestive evidence for the influence of ANGPTL3 on EMS mediated through TG (OR = 0.798, 95% CI 0.670–0.951, P = 0.01, PFDR = 0.04, PP.H4 = 0.85%). Conclusions This MR study in EUR ancestry indicated an increased EMS risk with higher serum TG levels.

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Section: Discussionmentioning

confidence: 99%

Genetic association of serum lipids and lipid-modifying targets with endometriosis: Trans-ethnic Mendelian-randomization and mediation analysis

Zhang,

Fan,

et al. 2024

PLoS ONE

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“…Recent AI models for clinically significant portal hypertension use CT imaging-based auto machine-learning and compare the results to standard imaging and serum-based tools, with the proposed method outperforming the latter [ 226 ]. Similarly, although AI models exist for prediction of central venous pressures that utilize RHC data [ 227 ], the proposed noninvasive models have not been shown to outperform the gold standard of invasive monitoring. An ANN model constructed by Moinadini et al in 2019 used several clinical parameters, including the heart rate, SBP, caval index, lactate clearance and shock index, all of which showed an individual positive correlation with invasive CVP monitoring measurements.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives

Manga

Muthavarapu

Redij

et al. 2023

Sensors

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The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.

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“…The wavelet scattering network [19,28] is a type of deep neural network that can extract features from ECG signals by decomposing them into a series of wavelet coefficients. The wavelet scattering network consists of a series of wavelet transforms and non-linear operations, which can capture complex patterns in ECG signals [29]. For example, Fig.…”

Section: Wavelet Scattering Network For Feature Extractionmentioning

confidence: 99%

CardioGPT: An ECG Interpretation Generation Model

Fu,

Zheng,

Abudayyeh

et al. 2024

IEEE Access

Self Cite

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Objective: Numerous supervised learning models aimed at classifying 12-lead electrocardiograms into different groups have shown impressive performance by utilizing deep learning algorithms. However, few studies are dedicated to applying the Generative Pre-trained Transformer (GPT) model in interpreting electrocardiogram (ECG) using natural language. Thus, we are pioneering the exploration of this uncharted territory by employing the CardioGPT model to tackle this challenge. Methods: We used a dataset of ECGs (standard 10s, 12-channel format) from adult patients, with 60 distinct rhythms or conduction abnormalities annotated by board-certified, actively practicing cardiologists. The ECGs were collected from The First Affiliated Hospital of Ningbo University and Shanghai East Hospital. The dataset is partitioned into training (80%), validation (10%), and test (10%) cohorts for comprehensive evaluation. Each cohort contains ECGs from distinct patients, considering some patients took repeated ECG measurements. The proposed algorithm is evaluated in two levels, self-performance measurement and comparison with the residual neural network classification model. Two scores are used for self-performance measurement, including Bilingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE). To compare the performance of the proposed model with the residual neural network model, we assessed the F1 score and area under the receiver operating characteristic curve (AUC). Results: We have observed promising performance metrics across multiple evaluation criteria through an extensive evaluation of a large 12-lead ECG database comprising 1,128,553 ECG readings from 754,920 patients. The CardioGPT model exhibited high BLEU and ROUGE scores with 0.68 (95% CI: 0.66, 0.71) and 0.81 (95% CI: 0.79, 0.84). Furthermore, in the classification performance measurement setting, the CardioGPT achieved an average F1-score of 0.91(95% CI: 0.89, 0.93) and AUC of 0.82(95% CI: 0.79, 0.84) and has higher scores than that of the convolutional neural network model, indicating its proficiency in accurately classifying ECG recordings. Conclusion: By leveraging the power of transformer structure model and natural language processing, the GPT model addresses the challenge of imbalanced learning commonly encountered in ECG classification tasks. The results indicate that the GPT model can accurately interpret ECG using natural language, providing valuable insights into the underlying patterns and abnormalities present in the data. Significance: The pioneering application of the GPT model for interpreting ECGs with natural language demonstrates its potential to address ECG classification challenges and offer valuable insights into cardiac health.

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An artificial intelligence-based noninvasive solution to estimate pulmonary artery pressure

Cited by 4 publications

References 32 publications

Genetic association of serum lipids and lipid-modifying targets with endometriosis: Trans-ethnic Mendelian-randomization and mediation analysis

Genetic association of serum lipids and lipid-modifying targets with endometriosis: Trans-ethnic Mendelian-randomization and mediation analysis

Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives

CardioGPT: An ECG Interpretation Generation Model

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