Development and Validation of Artificial Intelligence Models for Prognosis Prediction of Juvenile Myoclonic Epilepsy with Clinical and Radiological Features
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data and Participants
2.2. Image Acquisition and Processing
2.3. Machine Learning Models
3. Results
3.1. Clinical Characteristics
3.2. Radiological Characteristics
3.3. Performances of Machine Learning Models
3.4. Feature Importances
4. Discussion
- The volumes of the left amygdala and right hippocampus, along with male gender, were associated with prognosis;The XGBoost model demonstrated a performance of approximately 0.700 in predicting prognosis, with higher performance observed when combining the clinical and radiological variables;
- The cerebellum, thalamus, and globus pallidus were crucial for the machine learning model’s prediction of prognosis.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Favorable Prognosis (n = 85) | Poor Prognosis (n = 40) | p-Value | |
---|---|---|---|
Age (years) | 23.5 ± 8.7 | 23.2 ± 7.7 | 0.843 |
Male sex, n (%) | 51 (60.0) | 16 (40.0) | 0.036 |
Onset age (years) | 15.2 ± 4.5 | 15.2 ± 4.8 | 0.991 |
Epilepsy duration (years) | 8.3 ± 9.4 | 8.0 ± 8.2 | 0.888 |
Family history, n (%) | 16 (18.8) | 4 (10.0) | 0.209 |
Febrile seizure history *, n (%) | 9 (10.6) | 5 (12.5) | 0.767 |
Absence seizure, n (%) | 31 (36.5) | 14 (35.0) | 0.873 |
Treated, n (%) | 44 (51.8) | 24 (60.0) | 0.388 |
Number of ASMs | 2 (1–3) | 2 (1–3) | 0.577 |
VPA, n (%) | 65 (76.5) | 30 (75.0) | 0.857 |
LTG, n (%) | 39 (45.9) | 21 (52.5) | 0.490 |
LEV, n (%) | 40 (47.1) | 24 (60.0) | 0.177 |
TPM, n (%) | 17 (20.0) | 6 (15.0) | 0.501 |
Follow-up duration (years) | 13.9 ± 6.7 | 11.1 ± 7.2 | 0.178 |
Favorable Prognosis (n = 85) | Poor Prognosis (n = 40) | p-Value | |
---|---|---|---|
Left | |||
Thalamus | 8054.0 ± 844.4 | 7748.2 ± 1393.7 | 0.131 |
Caudate | 3569.1 ± 433.8 | 3458.2 ± 643.6 | 0.259 |
Putamen | 5075.5 ± 619.2 | 4902.2 ± 895.1 | 0.211 |
Pallidum | 2073.8 ± 244.5 | 2017.6 ± 353.9 | 0.303 |
Hippocampus | 4180.6 ± 415.2 | 4027.1 ± 630.2 | 0.107 |
Amygdala | 1739.9 ± 263.4 | 1601.7 ± 358.2 | 0.017 |
Nucleus accumbens | 511.4 ± 98.5 | 286.6 ± 133.8 | 0.246 |
Ventral diencephalon | 4175.7 ± 457.7 | 4003.7 ± 621.1 | 0.084 |
Choroid plexus | 437.0 ± 163.7 | 427.5 ± 159.2 | 0.765 |
Cerebellum–cortex | 56,595.1 ± 5734.4 | 54,113.8 ± 8452.0 | 0.057 |
Cerebellum–white matter | 14,800.9 ± 1846.9 | 14,133.2 ± 2293.1 | 0.084 |
Right | |||
Thalamus | 7574.4 ± 775.1 | 7219.0 ± 1275.0 | 0.056 |
Caudate | 3637.9 ± 444.1 | 3544.3 ± 567.1 | 0.318 |
Putamen | 5130.4 ± 617.7 | 4985.8 ± 809.3 | 0.273 |
Pallidum | 1977.6 ± 238.8 | 1942.0 ± 284.8 | 0.467 |
Hippocampus | 4396.6 ± 417.7 | 4128.8 ± 825.7 | 0.017 |
Amygdala | 1848.7 ± 279.3 | 1744.6 ± 361.0 | 0.080 |
Nucleus accumbens | 577.2 ± 105.1 | 554.5 ± 117.2 | 0.280 |
Ventral diencephalon | 4173.3 ± 444.1 | 4017.0 ± 562.6 | 0.095 |
Choroid plexus | 430.6 ± 154.9 | 422.9 ± 188.5 | 0.809 |
Cerebellum–cortex | 56,266.6 ± 5838.5 | 53,800.4 ± 8392.4 | 0.059 |
Cerebellum–white matter | 14,242.4 ± 2001.1 | 13,540.7 ± 2200.4 | 0.079 |
Midline | |||
Brainstem | 21,182.7 ± 2266.9 | 20,565.8 ± 3631.8 | 0.248 |
Optic-chiasm | 154.3 ± 58.7 | 140.1 ± 60.8 | 0.213 |
Corpus callosum | |||
Anterior | 862.7 ± 141.4 | 836.8 ± 158.5 | 0.360 |
Mid-anterior | 669.4 ± 181.2 | 666.1 ± 171.8 | 0.922 |
Central | 688.8 ± 172.1 | 660.2 ± 171.5 | 0.388 |
Mid-posterior | 552.8 ± 103.9 | 560.6 ± 128.1 | 0.720 |
Posterior | 990.4 ± 178.0 | 982.0 ± 205.2 | 0.815 |
Total intracranial volume | 1,581,418.5 ± 175,060.8 | 1,502,151.6 ± 250,957.1 | 0.077 |
Models | Accuracy | Precision | Recall | F1-Score | AUROC |
---|---|---|---|---|---|
Logistic Regression | 0.600 | 0.560 | 0.600 | 0.565 | 0.431 |
Random Forest | 0.680 | 0.664 | 0.680 | 0.652 | 0.580 |
XGBoost | 0.680 | 0.816 | 0.680 | 0.712 | 0.700 |
Light GBM | 0.560 | 0.486 | 0.560 | 0.505 | 0.618 |
SVM | 0.640 | 0.410 | 0.640 | 0.500 | 0.500 |
ANN | 0.600 | 0.400 | 0.600 | 0.480 | 0.425 |
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Kim, K.M.; Choi, B.K.; Ha, W.-S.; Cho, S.; Chu, M.K.; Heo, K.; Kim, W.-J. Development and Validation of Artificial Intelligence Models for Prognosis Prediction of Juvenile Myoclonic Epilepsy with Clinical and Radiological Features. J. Clin. Med. 2024, 13, 5080. https://doi.org/10.3390/jcm13175080
Kim KM, Choi BK, Ha W-S, Cho S, Chu MK, Heo K, Kim W-J. Development and Validation of Artificial Intelligence Models for Prognosis Prediction of Juvenile Myoclonic Epilepsy with Clinical and Radiological Features. Journal of Clinical Medicine. 2024; 13(17):5080. https://doi.org/10.3390/jcm13175080
Chicago/Turabian StyleKim, Kyung Min, Bo Kyu Choi, Woo-Seok Ha, Soomi Cho, Min Kyung Chu, Kyoung Heo, and Won-Joo Kim. 2024. "Development and Validation of Artificial Intelligence Models for Prognosis Prediction of Juvenile Myoclonic Epilepsy with Clinical and Radiological Features" Journal of Clinical Medicine 13, no. 17: 5080. https://doi.org/10.3390/jcm13175080