Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants †
Abstract
:1. Introduction
2. Methods
2.1. Study Design and Participants
2.2. Data Collection
2.3. Signal Processing
2.4. Extracting Prolonged Relative Desaturations
2.5. Feature Extraction
2.6. Machine Learning Models
2.7. Combining Models
2.8. Statistical Analysis
3. Results
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
rcSO2 | Regional cerebral oxygen saturation |
SpO2 | Peripheral oxygen saturation |
NIRS | Near-infrared spectroscopy |
IVH | Intraventricular haemorrhage |
PRD | Prolonged relative desaturation |
US | Ultrasound |
AUC | Area under the curve |
References
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Clinical Features | Brain Injury (n = 12) | No/Mild Injury (n = 34) |
---|---|---|
Sex (male) (% of the total number of infants in the group) | 8 (67%) | 17 (50%) |
Hypotension (% of the total number of infants in the group) | 7 (58%) | 13 (38%) |
Inotrope administration (% of the total number of infants in the group) | 3 (25%) | 6 (18%) |
Chorioamnionitis (% of the total number of infants in the group) | 0 (0%) | 5 (15%) |
Gestational age (days) Mean (SD) | 178.5 (7.5) | 181.3 (8.8) |
Birth weight (g) Mean (SD) | 769.2 (173.7) | 766.8 (151.5) |
APGAR score (5 min) Mean (SD) | 7.0 (2.0) | 6.9 (1.9) |
Head circumference (cm) Mean (SD) | 23.3 (1.5) | 23.3 (1.5) |
Model | AUC | 95% Confidence Interval |
---|---|---|
Clinical data | 0.575 | 0.390–0.759 |
rcSO2 | 0.532 | 0.312–0.740 |
rcSO2 without PRDs | 0.542 | 0.331–0.741 |
rcSO2 PRDs | 0.846 * | 0.720–0.948 |
rcSO2 and clinical data | 0.571 | 0.390–0.756 |
rcSO2 without PRDs and clinical data | 0.591 | 0.396–0.777 |
rcSO2 PRDs and clinical data | 0.860 * | 0.742–0.957 |
SpO2 | 0.198 | 0.000–0.459 |
SpO2 without PRDs | 0.261 | 0.061–0.545 |
SpO2 PRDs | 0.493 | 0.264–0.741 |
SpO2 and clinical data | 0.324 | 0.133–0.546 |
SpO2 without PRDs and clinical data | 0.348 | 0.151–0.579 |
SpO2 PRDs and clinical data | 0.454 | 0.235–0.697 |
rcSO2 time spent below 63% | 0.593 | 0.399–0.775 |
SpO2 time spent below 85% | 0.522 | 0.277–0.759 |
Feature | AUC | 95% Confidence Interval |
---|---|---|
Frequency of the transients per hour | 0.409 | 0.201–0.619 |
Total power | 0.368 | 0.191–0.560 |
Envelope (mean) | 0.397 | 0.214–0.592 |
Envelope (standard deviation) | 0.539 | 0.352–0.738 |
Hjorth parameters (activity) | 0.603 | 0.399–0.802 |
Hjorth parameters (mobility) | 0.409 | 0.214–0.602 |
Hjorth parameters (complexity) | 0.569 | 0.353–0.775 |
Inter-spike interval (mean) | 0.532 | 0.306–0.748 |
Inter-spike interval (standard deviation) | 0.505 | 0.282–0.714 |
Time spent below 63% | 0.691 | 0.494–0.861 |
Nadir amplitude of PRDs (mean) | 0.733 * | 0.530–0.919 |
Slope down (mean) | 0.689 | 0.474–0.883 |
Slope up (mean) | 0.669 | 0.477–0.838 |
Duration of the PRDs (mean) | 0.603 | 0.422–0.770 |
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Ashoori, M.; O’Toole, J.M.; O’Halloran, K.D.; Naulaers, G.; Thewissen, L.; Miletin, J.; Cheung, P.-Y.; EL-Khuffash, A.; Van Laere, D.; Straňák, Z.; et al. Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants. Children 2023, 10, 917. https://doi.org/10.3390/children10060917
Ashoori M, O’Toole JM, O’Halloran KD, Naulaers G, Thewissen L, Miletin J, Cheung P-Y, EL-Khuffash A, Van Laere D, Straňák Z, et al. Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants. Children. 2023; 10(6):917. https://doi.org/10.3390/children10060917
Chicago/Turabian StyleAshoori, Minoo, John M. O’Toole, Ken D. O’Halloran, Gunnar Naulaers, Liesbeth Thewissen, Jan Miletin, Po-Yin Cheung, Afif EL-Khuffash, David Van Laere, Zbyněk Straňák, and et al. 2023. "Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants" Children 10, no. 6: 917. https://doi.org/10.3390/children10060917
APA StyleAshoori, M., O’Toole, J. M., O’Halloran, K. D., Naulaers, G., Thewissen, L., Miletin, J., Cheung, P. -Y., EL-Khuffash, A., Van Laere, D., Straňák, Z., Dempsey, E. M., & McDonald, F. B. (2023). Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants. Children, 10(6), 917. https://doi.org/10.3390/children10060917