Machine-Learning-Based Diagnostics of Cardiac Sarcoidosis Using Multi-Chamber Wall Motion Analyses

Author:

Eckstein Jan1ORCID,Moghadasi Negin2ORCID,Körperich Hermann1ORCID,Akkuzu Rehsan1,Sciacca Vanessa3ORCID,Sohns Christian3ORCID,Sommer Philipp3ORCID,Berg Julian4,Paluszkiewicz Jerzy5,Burchert Wolfgang1,Piran Misagh1ORCID

Affiliation:

1. Institute for Radiology, Nuclear Medicine and Molecular Imaging, Heart and Diabetes Center North Rhine Westphalia, Bad Oeynhausen, University of Bochum, 32545 Bochum, Germany

2. Department of Engineering Systems & Environment, University of Virginia, Charlottesville, VA 22904, USA

3. Clinic for Electrophysiology, Heart and Diabetes Center North-Rhine Westphalia, Ruhr-University of Bochum, 32545 Bad Oeynhausen, Germany

4. Clinic for Thoracic and Cardiovascular Surgery, Heart and Diabetes Center North-Rhine Westphalia, Ruhr-University of Bochum, 32545 Bad Oeynhausen, Germany

5. Cardiology Institute and Clinic, Poznan University of Medical Sciences, 61-701 Poznan, Poland

Abstract

Background: Hindered by its unspecific clinical and phenotypical presentation, cardiac sarcoidosis (CS) remains a challenging diagnosis. Objective: Utilizing cardiac magnetic resonance imaging (CMR), we acquired multi-chamber volumetrics and strain feature tracking for a support vector machine learning (SVM)-based diagnostic approach to CS. Method: Forty-five CMR-negative (CMR(−), 56.5(53.0;63.0)years), eighteen CMR-positive (CMR(+), 64.0(57.8;67.0)years) sarcoidosis patients and forty-four controls (CTRL, 56.5(53.0;63.0)years)) underwent CMR examination. Cardiac parameters were processed using the classifiers of logistic regression, KNN(K-nearest-neighbor), DT (decision tree), RF (random forest), SVM, GBoost, XGBoost, Voting and feature selection. Results: In a three-cluster analysis of CTRL versus vs. CMR(+) vs. CMR(−), RF and Voting classifier yielded the highest prediction rates (81.82%). The two-cluster analysis of CTRL vs. all sarcoidosis (All Sarc.) yielded high prediction rates with the classifiers logistic regression, RF and SVM (96.97%), and low prediction rates for the analysis of CMR(+) vs. CMR(−), which were augmented using feature selection with logistic regression (89.47%). Conclusion: Multi-chamber cardiac function and strain-based supervised machine learning provides a non-contrast approach to accurately differentiate between healthy individuals and sarcoidosis patients. Feature selection overcomes the algorithmically challenging discrimination between CMR(+) and CMR(−) patients, yielding high accuracy predictions. The study findings imply higher prevalence of cardiac involvement than previously anticipated, which may impact clinical disease management.

Publisher

MDPI AG

Subject

Clinical Biochemistry

Reference35 articles.

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Management of cardiac sarcoidosis;European Heart Journal;2024-06-26

2. Process Quality Assurance of Artificial Intelligence in Medical Diagnosis;2024 International Conference on Intelligent Systems and Computer Vision (ISCV);2024-05-08

3. Systems Modeling of Trust in AI-Enabled Medical Diagnosis;2024 IEEE International Systems Conference (SysCon);2024-04-15

4. Systems Analysis of Bias and Risk in AI-Enabled Medical Diagnosis;2023 IEEE Symposium Series on Computational Intelligence (SSCI);2023-12-05

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