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Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database
- Source :
- Biomechanics and Modeling in Mechanobiology
- Publication Year :
- 2021
-
Abstract
- This study presents an application of machine learning (ML) methods for detecting the presence of stenoses and aneurysms in the human arterial system. Four major forms of arterial disease -- carotid artery stenosis (CAS), subclavian artery stenosis (SAC), peripheral arterial disease (PAD), and abdominal aortic aneurysms (AAA) -- are considered. The ML methods are trained and tested on a physiologically realistic virtual patient database (VPD) containing 28,868 healthy subjects, which is adapted from the authors previous work and augmented to include the four disease forms. Six ML methods -- Naive Bayes, Logistic Regression, Support Vector Machine, Multi-layer Perceptron, Random Forests, and Gradient Boosting -- are compared with respect to classification accuracies and it is found that the tree-based methods of Random Forest and Gradient Boosting outperform other approaches. The performance of ML methods is quantified through the F1 score and computation of sensitivities and specificities. When using all the six measurements, it is found that maximum F1 scores larger than 0.9 are achieved for CAS and PAD, larger than 0.85 for SAS, and larger than 0.98 for both low- and high-severity AAAs. Corresponding sensitivities and specificities are larger than 90% for CAS and PAD, larger than 85% for SAS, and larger than 98% for both low- and high-severity AAAs. When reducing the number of measurements, it is found that the performance is degraded by less than 5% when three measurements are used, and less than 10% when only two measurements are used for classification. For AAA, it is shown that F1 scores larger than 0.85 and corresponding sensitivities and specificities larger than 85% are achievable when using only a single measurement. The results are encouraging to pursue AAA monitoring and screening through wearable devices which can reliably measure pressure or flow-rates<br />Changelog: Added a bullet point in the discussion (end of section 3.3)
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Databases, Factual
Arterial disease
Subclavian Artery
02 engineering and technology
computer.software_genre
Severity of Illness Index
Machine Learning (cs.LG)
030218 nuclear medicine & medical imaging
Machine Learning
User-Computer Interface
0302 clinical medicine
Virtual patient
Subclavian artery stenosis
Carotid Stenosis
Stenosis
Database
Healthy subjects
3. Good health
Peripheral
medicine.anatomical_structure
Modeling and Simulation
Screening
Algorithms
Biotechnology
Artery
0206 medical engineering
Pulse wave haemodynamics
FOS: Physical sciences
Machine learning
Models, Biological
Sensitivity and Specificity
Peripheral Arterial Disease
03 medical and health sciences
Aneurysm
medicine
Humans
Original Paper
business.industry
Mechanical Engineering
medicine.disease
020601 biomedical engineering
Physics - Medical Physics
Virtual patients
Regional Blood Flow
Neural Networks, Computer
Medical Physics (physics.med-ph)
Artificial intelligence
business
computer
Aortic Aneurysm, Abdominal
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Biomechanics and Modeling in Mechanobiology
- Accession number :
- edsair.doi.dedup.....6fb9c4817e260880a6750ff16fe6bc4d