Development of Deep Learning Models for Real-Time Thoracic Ultrasound Image Interpretation
Abstract
1. Introduction
- Additional swine data curation was implemented for a more robust dataset compared to previous studies.
- The filtering methods of the dataset were designed from the results of image preprocessing and analysis to correct data distribution shifts.
- A multi-class DL CNN was developed and optimized to classify ultrasound images as uninjured, HTX, or PTX.
- The model performance was evaluated with real-time data captures to highlight how the advancements improved performance when compared to previous studies.
2. Related Work
3. Materials and Methods
3.1. Readying the Image Dataset
3.1.1. Dataset Preparation
3.1.2. Data Preprocessing
3.2. DL Model Training and Evaluation
3.2.1. Hyperparameter Tuning
3.2.2. Real-Time Testing
4. Results
4.1. Cluster Analysis Results
4.2. AI Model Development
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Filter | Weighted Loss | Validation Patience | Weighted Decay | Balanced Accuracy AVG (%) | Balanced Accuracy STD (%) | Global Accuracy AVG (%) | Global Accuracy STD (%) |
---|---|---|---|---|---|---|---|
No Filter | None | None | None | 88.17 | 7.05 | 86.63 | 7.97 |
No Filter | None | 10 | None | 84.64 | 4.14 | 84.64 | 5.08 |
No Filter | Balanced | None | None | 76.86 | 8.91 | 76.86 | 10.91 |
No Filter | Balanced | 10 | None | 79.64 | 4.72 | 79.64 | 5.79 |
No Filter | Balanced | 10 | 1 × 10−4 | 80.8 | 4.85 | 80.67 | 6.04 |
No Filter | Balanced | 10 | 1 × 10−5 | 79.7 | 4.76 | 79.7 | 5.83 |
C vs. B | None | None | None | 82.89 | 2.16 | 87.8 | 0.95 |
C vs. B | None | 10 | None | 79.55 | 14.32 | 81.16 | 7.6 |
C vs. B | Balanced | None | None | 80.27 | 3.15 | 75.34 | 5.85 |
C vs. B | Balanced | 10 | None | 86.75 | 2.47 | 84.96 | 11.29 |
C vs. B | Balanced | 10 | 1 × 10−4 | 78.39 | 6.18 | 78.75 | 12.91 |
C vs. B | Balanced | 10 | 1 × 10−5 | 90.49 | 4.51 | 84.74 | 8.18 |
K vs. B | None | None | None | 85.17 | 7.23 | 84.87 | 3.05 |
K vs. B | Balanced | 10 | None | 81.26 | 8 | 81.26 | 9.8 |
K vs. B | Balanced | 10 | 1 × 10−5 | 76.83 | 7.75 | 81.15 | 12.31 |
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Ruiz, A.J.; Hernández Torres, S.I.; Snider, E.J. Development of Deep Learning Models for Real-Time Thoracic Ultrasound Image Interpretation. J. Imaging 2025, 11, 222. https://doi.org/10.3390/jimaging11070222
Ruiz AJ, Hernández Torres SI, Snider EJ. Development of Deep Learning Models for Real-Time Thoracic Ultrasound Image Interpretation. Journal of Imaging. 2025; 11(7):222. https://doi.org/10.3390/jimaging11070222
Chicago/Turabian StyleRuiz, Austin J., Sofia I. Hernández Torres, and Eric J. Snider. 2025. "Development of Deep Learning Models for Real-Time Thoracic Ultrasound Image Interpretation" Journal of Imaging 11, no. 7: 222. https://doi.org/10.3390/jimaging11070222
APA StyleRuiz, A. J., Hernández Torres, S. I., & Snider, E. J. (2025). Development of Deep Learning Models for Real-Time Thoracic Ultrasound Image Interpretation. Journal of Imaging, 11(7), 222. https://doi.org/10.3390/jimaging11070222