Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects
Abstract
:1. Introduction
2. Materials and Methods
2.1. Surgical Methods
2.2. Right Ventricle Video Recording
- Maximum contraction velocity: estimates the instantaneous maximal velocity of the cardiac tissue during systole;
- Force: estimates the instantaneous acceleration;
- Energy: estimates the kinetic energy during cardiac cycles;
- Perimeter: estimates the ventricular compliance.
2.3. Features/Predictors
2.4. Models’ Training and Optimization
- Optimizable KNN (k-nearest neighbor classifier), via the “fitcknn” function (https://it.mathworks.com/help/stats/fitcknn.html, accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features;
- Optimizable SVM (support vector machine classifier), via the “fitcsvm” function (https://it.mathworks.com/help/stats/fitcsvm.html, accessed on 1 August 2021) with a total of 100 optimization iterations and no standardization of the input features.
2.5. Decision Surface
2.6. Statistical Analysis
3. Results
3.1. Selected Models
3.2. Optimized Model Training
- The MATLAB®’s script of the optimized model;
- The MATLAB®’s optimized model (as a saved workspace structure array) to employ in the operating room to classify the current patient’s heart movement as unhealthy or healthy.
3.3. Classifiers’ Prediction Ability Tested via Two Additional Patients with Different Outcomes
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature/Predictor | MATLAB® Function |
---|---|
Band power [pixel]: it returns the ‘average power’ or average l2 norm (average Euclidean norm) of the input signal [pixel] | bandpower https://it.mathworks.com/help/signal/ref/bandpower.html |
Power bandwidth [Hz]: it returns the 3 dB (half-power) bandwidth of the input signal | powerbw https://it.mathworks.com/help/signal/ref/powerbw.html |
Occupied bandwidth [Hz]: it returns the 99% occupied bandwidth of the input signal | obw https://it.mathworks.com/help/signal/ref/obw.html |
Spurious free dynamic range [dB]: it returns the SFDR of the real sinusoidal-like input signal | sfdr https://it.mathworks.com/help/signal/ref/sfdr.html |
Signal to noise and distortion ratio [dB]: it returns the SINAD of the real sinusoidal-like input signal | sinad https://it.mathworks.com/help/signal/ref/sinad.html |
Signal to noise ratio [dB]: it returns the SNR of the input signal | SNR https://it.mathworks.com/help/signal/ref/snr.html |
Spectral entropy (information content) of the input signal | pentropy https://it.mathworks.com/help/signal/ref/pentropy.html |
Classifier | Accuracy (%) |
---|---|
Boosted trees (ensemble of trees using the AdaBoost (Adaptive Boosting) algorithm) [32] | 46.5 |
RUSBoosted trees (ensemble of trees using the RUSBoost (Random Undersampling Boosting) algorithm) [33] | 46.5 |
Linear discriminant [34] | 66.3 |
Kernel naïve Bayes [35] | 68.6 |
Gaussian naïve Bayes [36] | 69.8 |
Fine Gaussian (Radial Basis) support vector machine (SVM) [37] | 79.1 |
Fine k-nearest neighbor (KNN) [38] | 86.0 |
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Lo Muzio, F.P.; Rozzi, G.; Rossi, S.; Luciani, G.B.; Foresti, R.; Cabassi, A.; Fassina, L.; Miragoli, M. Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects. J. Clin. Med. 2021, 10, 5330. https://doi.org/10.3390/jcm10225330
Lo Muzio FP, Rozzi G, Rossi S, Luciani GB, Foresti R, Cabassi A, Fassina L, Miragoli M. Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects. Journal of Clinical Medicine. 2021; 10(22):5330. https://doi.org/10.3390/jcm10225330
Chicago/Turabian StyleLo Muzio, Francesco Paolo, Giacomo Rozzi, Stefano Rossi, Giovanni Battista Luciani, Ruben Foresti, Aderville Cabassi, Lorenzo Fassina, and Michele Miragoli. 2021. "Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects" Journal of Clinical Medicine 10, no. 22: 5330. https://doi.org/10.3390/jcm10225330