Machine Learning for Early Detection of Hypoxic-Ischemic Brain Injury After Cardiac Arrest
- 06.12.2021
- Original work
Erschienen in:
- Verfasst von
- Ali Mansour
- Jordan D. Fuhrman
- Faten El Ammar
- Andrea Loggini
- Jared Davis
- Christos Lazaridis
- Christopher Kramer
- Fernando D. Goldenberg
- Maryellen L. Giger
- Erschienen in
- Neurocritical Care | Ausgabe 3/2022
Abstract
Background
Establishing whether a patient who survived a cardiac arrest has suffered hypoxic-ischemic brain injury (HIBI) shortly after return of spontaneous circulation (ROSC) can be of paramount importance for informing families and identifying patients who may benefit the most from neuroprotective therapies. We hypothesize that using deep transfer learning on normal-appearing findings on head computed tomography (HCT) scans performed after ROSC would allow us to identify early evidence of HIBI.
Methods
We analyzed 54 adult comatose survivors of cardiac arrest for whom both an initial HCT scan, done early after ROSC, and a follow-up HCT scan were available. The initial HCT scan of each included patient was read as normal by a board-certified neuroradiologist. Deep transfer learning was used to evaluate the initial HCT scan and predict progression of HIBI on the follow-up HCT scan. A naive set of 16 additional patients were used for external validation of the model.
Results
The median age (interquartile range) of our cohort was 61 (16) years, and 25 (46%) patients were female. Although findings of all initial HCT scans appeared normal, follow-up HCT scans showed signs of HIBI in 29 (54%) patients (computed tomography progression). Evaluating the first HCT scan with deep transfer learning accurately predicted progression to HIBI. The deep learning score was the most significant predictor of progression (area under the receiver operating characteristic curve = 0.96 [95% confidence interval 0.91–1.00]), with a deep learning score of 0.494 having a sensitivity of 1.00, specificity of 0.88, accuracy of 0.94, and positive predictive value of 0.91. An additional assessment of an independent test set confirmed high performance (area under the receiver operating characteristic curve = 0.90 [95% confidence interval 0.74–1.00]).
Conclusions
Deep transfer learning used to evaluate normal-appearing findings on HCT scans obtained early after ROSC in comatose survivors of cardiac arrest accurately identifies patients who progress to show radiographic evidence of HIBI on follow-up HCT scans.
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- Titel
- Machine Learning for Early Detection of Hypoxic-Ischemic Brain Injury After Cardiac Arrest
- Verfasst von
-
Ali Mansour
Jordan D. Fuhrman
Faten El Ammar
Andrea Loggini
Jared Davis
Christos Lazaridis
Christopher Kramer
Fernando D. Goldenberg
Maryellen L. Giger
- Publikationsdatum
- 06.12.2021
- Verlag
- Springer US
- Erschienen in
-
Neurocritical Care / Ausgabe 3/2022
Print ISSN: 1541-6933
Elektronische ISSN: 1556-0961 - DOI
- https://doi.org/10.1007/s12028-021-01405-y
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