Predicting the Severity of Neurological Impairment Caused by Ischemic Stroke Using Deep Learning Based on Diffusion-Weighted Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Patients
2.2. MRI Scanning Protocols
2.3. Classification
2.4. Image Preprocessing
2.5. Convolutional Neural Network Construction
2.6. Statistical Analysis
3. Results
3.1. Subjects’ Clinical Information
3.2. Classification at Admission
3.3. Classification on Day 7 of Hospitalization
3.4. Classification of IS Based on Different Circulations
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Model | Type | Filter Size | Number of Filters | Stride |
---|---|---|---|---|
Layer 1 | Conv1 + Maximum Pooling | 3 × 3 × 3 | 16 | (1, 1, 1) |
Layer 2 | Conv2 + Maximum Pooling | 3 × 3 × 3 | 32 | (2, 2, 2) |
Layer 3 | Conv3 | 3 × 3 × 3 | 64 | (1, 1, 1) |
Layer 4 | Conv4 + Maximum Pooling | 3 × 3 × 3 | 64 | (2, 2, 2) |
Layer 5 | Conv6 | 3 × 3 × 3 | 96 | (1, 1, 1) |
Layer 6 | Conv6 + Maximum Pooling | 3 × 3 × 3 | 96 | (2, 2, 2) |
Layer 7 | Conv7 | 3 × 3 × 3 | 128 | (1, 1, 1) |
Layer 8 | Conv8 + Maximum Pooling | 3 × 3 × 3 | 128 | (2, 2, 2) |
Layer 9 | FC1 | - | - | - |
Layer 10 | FC2 | - | - | - |
Layer 11 | FC3 (SoftMax) | - | - | - |
Predicted NIHSS Stage | Normalization | Voxels | |
---|---|---|---|
Model A | Admission | Maximum–minimum | 128 × 128 × 32 |
Model B | Admission | Maximum–minimum | 256 × 256 × 64 |
Model C | Admission | Z-score | 128 × 128 × 32 |
Model D | Admission | Z-score | 256 × 256 × 64 |
Model E | Hospital Day 7 | Maximum–minimum | 128 × 128 × 32 |
Model F | Hospital Day 7 | Maximum–minimum | 256 × 256 × 64 |
Model G | Hospital Day 7 | Z-score | 128 × 128 × 32 |
Model H | Hospital Day 7 | Z-score | 256 × 256 × 64 |
Characteristics | Training and Validation Sets | Test Set | p-Value |
---|---|---|---|
Sample capacity | 711 | 140 | |
Age (years) a | 66.02 ± 11.22 | 65.00 ± 10.26 | 0.31 |
Women (%) b | 33.1 (237) | 35.7 (50) | 0.65 |
Anterior circulation (%) | 80.9 (538) | 82.9 (113) | 0.08 |
Posterior circulation (%) | 25.2 (173) | 25.0 (27) | 0.83 |
NIHSS (0 days) <5 (%) | 55.3 (393) | 34.2 (48) | <0.01 |
NIHSS (0 days) ≥5 (%) | 44.7 (318) | 65.7 (92) | <0.01 |
NIHSS (7 days) <5 (%) | 62.7 (445) | 67.1 (94) | 0.35 |
NIHSS (7 days) ≥5 (%) | 37.3 (268) | 32.9 (46) | 0.32 |
Model | AUC | Sensitivity | Specificity |
---|---|---|---|
Model A | 0.842 (0.771–0.898) | 71.7% (64.1–80.6%) | 77.1% (62.7–88.0%) |
Model B | 0.821 (0.747–0.881) | 71.7% (61.4–80.6%) | 79.2% (65.0–98.5%) |
Model C | 0.809 (0.734–0.871) | 59.8% (49.0–64.8%) | 93.7% (82.8–98.7%) |
Model D | 0.846 (0.776–0.902) | 60.9% (50.1–70.9%) | 97.9% (88.9–99.9%) |
Model E | 0.895 (0.832–0.940) | 95.7% (88.5–99.9%) | 67.0% (56.6–76.4%) |
Model F | 0.831 (0.759–0.889) | 76.1% (61.2–87.4%) | 79.8% (70.2–87.4%) |
Model G | 0.855 (0.785–0.908) | 76.1% (61.2–87.4%) | 88.3% (80.0–94.0%) |
Model H | 0.855 (0.758–0.909) | 82.6% (68.6–92.2%) | 78.7% (69.1–86.5%) |
Model | AUC | Sensitivity | Specificity |
---|---|---|---|
Model A | 0.815 (0.731–0.881) | 59.4% (46.9–71.1%) | 97.7% (88.0–99.9%) |
Model B | 0.793 (0.707–0.886) | 84.1% (77.3–91.8%) | 63.6% (47.8–77.6%) |
Model C | 0.798 (0.712–0.867) | 59.4% (46.9–71.1%) | 93.2% (81.3–98.6%) |
Model D | 0.815 (0.731–0.881) | 56.5% (44.0–68.4%) | 97.7% (88.0–99.9%) |
Model E | 0.905 (0.836–0.952) | 90.0% (73.5–97.9%) | 74.7% (64.0–83.6%) |
Model F | 0.821 (0.738–0.887) | 76.7% (57.7–90.1%) | 81.9% (72.0–89.5%) |
Model G | 0.899 (0.828–0.948) | 86.7% (69.3–96.2%) | 86.7% (77.5–93.2%) |
Model H | 0.878 (0.803–0.932) | 96.7% (82.8–99.9%) | 72.7% (39.0–94.0%) |
Model | AUC | Sensitivity | Specificity |
---|---|---|---|
Model A | 0.989 (0.853–1.000) | 78.3% (56.3–92.5%) | 100.0% (39.8–100.0%) |
Model B | 0.989 (0.853–1.000) | 95.6% (77.8–99.9%) | 100.0% (39.8–100.0%) |
Model C | 0.946 (0.785–0.996) | 78.3% (56.3–92.5%) | 100.0% (39.8–100.0%) |
Model D | 1.000 (0.872–1.000) | 100.0% (85.2–100.0%) | 100.0% (39.8–100.0%) |
Model E | 0.903 (0.727–0.983) | 93.7% (69.8–99.8%) | 81.8% (48.2–97.7%) |
Model F | 0.835 (0.643–0.949) | 56.2% (29.2–80.2%) | 100.0% (71.5–100.0%) |
Model G | 0.899 (0.828–0.948) | 86.7% (69.3–96.2%) | 72.7% (39.0–94.0%) |
Model H | 0.773 (0.572–0.910) | 75.0% (47.6–92.7%) | 72.7% (39.0–94.0%) |
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Zeng, Y.; Long, C.; Zhao, W.; Liu, J. Predicting the Severity of Neurological Impairment Caused by Ischemic Stroke Using Deep Learning Based on Diffusion-Weighted Images. J. Clin. Med. 2022, 11, 4008. https://doi.org/10.3390/jcm11144008
Zeng Y, Long C, Zhao W, Liu J. Predicting the Severity of Neurological Impairment Caused by Ischemic Stroke Using Deep Learning Based on Diffusion-Weighted Images. Journal of Clinical Medicine. 2022; 11(14):4008. https://doi.org/10.3390/jcm11144008
Chicago/Turabian StyleZeng, Ying, Chen Long, Wei Zhao, and Jun Liu. 2022. "Predicting the Severity of Neurological Impairment Caused by Ischemic Stroke Using Deep Learning Based on Diffusion-Weighted Images" Journal of Clinical Medicine 11, no. 14: 4008. https://doi.org/10.3390/jcm11144008
APA StyleZeng, Y., Long, C., Zhao, W., & Liu, J. (2022). Predicting the Severity of Neurological Impairment Caused by Ischemic Stroke Using Deep Learning Based on Diffusion-Weighted Images. Journal of Clinical Medicine, 11(14), 4008. https://doi.org/10.3390/jcm11144008