Key result
Bioelectrical impedance analysis using machine learning models successfully detected abdominal aortic aneurysms, with the best-performing model achieving 100% sensitivity and 100% specificity.
Why the study?
Current imaging for abdominal aortic aneurysm screening has limitations such as examiner dependency and ionizing radiation, prompting investigation into whether bioelectrical impedance analysis could detect aneurysms.
Does bioelectrical impedance analysis combined with machine learning accurately detect abdominal aortic aneurysms?
Cross-Sectional (n=61)
No
Does bioelectrical impedance analysis combined with machine learning accurately detect abdominal aortic aneurysms?
Bioelectrical impedance analysis combined with machine learning is a technically feasible and promising non-invasive tool for detecting abdominal aortic aneurysms.
Answers are grounded in this paper and cite their sources.
May support BIA-ML as a feasible non-invasive AAA detection tool; hypothesis-generating and requires prospective validation before clinical adoption.
Screening and diagnosing abdominal aortic aneurysms (AAA) are currently dependent on imaging studies such as ultrasound or computed tomography angiography. All imaging studies offer distinct advantages but also suffer from inherent limitations such as examiner dependency or ionizing radiation. Bioelectrical impedance analysis has previously been investigated with respect to its use in the detection of several cardiovascular and renal pathologies. The present pilot study assessed the feasibility of AAA detection based on bioimpedance analysis. In this single-center exploratory pilot study, measurements were conducted among three different cohorts: patients with AAA, end-stage renal disease patients without AAA, and healthy controls. The device used in the study, CombynECG, is an open-market accessible device for segmental bioelectrical impedance analysis. The data was preprocessed and used to train four different machine learning models on a randomized training sample (80% of the full dataset). Each model was then evaluated on a test set (20% of the full dataset). The total sample included 22 patients with AAA, 16 chronic kidney disease patients, and 23 healthy controls. All four models showed strong predictive performance in the test partitions. Specificity ranged from 71.4 to 100%, while sensitivity ranged from 66.7 to 100%. The best-performing model had 100% accuracy for classification when applied to the test sample. Additionally, an exploratory analysis to approximate the maximum AAA diameter was conducted. An association analysis revealed several impedance parameters that might possess predictive ability with respect to aneurysm size. AAA detection via bioelectrical impedance analysis is technically feasible and appears to be a promising technology for large-scale clinical studies and routine clinical screening assessments.
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Hofmann et al. (2023) conducted a cross-sectional in Abdominal Aortic Aneurysm (n=61). Bioelectrical impedance analysis (CombynECG) vs. Non-AAA controls was evaluated on Detection of abdominal aortic aneurysm (sensitivity and specificity) (100% sensitivity and 100% specificity (Model 2)). Bioelectrical impedance analysis using machine learning models successfully detected abdominal aortic aneurysms, with the best-performing model achieving 100% sensitivity and 100% specificity.
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