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Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis
Saeed Abdollahifard1,2, Amirmohammad Farrokhi1,2, Ashkan Mowla3
1Medical School, Shiraz University of Medical Sciences, Shiraz, Iran.
Background:
This study aimed to investigate the application of deep learning (DL) models for the detection of subdural hematoma (SDH).
Methods:
We conducted a comprehensive search using relevant keywords. Articles extracted were original studies in which sensitivity and/or specificity were reported. Two different approaches of frequentist and Bayesian inference were applied. For quality and risk of bias assessment we used Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2).
Results:
We analyzed 22 articles that included 1,997,749 patients. In the first step, the frequentist method showed a pooled sensitivity of 88.8% (95% confidence interval (CI): 83.9% to 92.4%) and a specificity of 97.2% (95% CI 94.6% to 98.6%). In the second step, using Bayesian methods including 11 studies that reported sensitivity and specificity, a sensitivity rate of 86.8% (95% CI: 77.6% to 92.9%) at a specificity level of 86.9% (95% CI: 60.9% to 97.2%) was achieved. The risk of bias assessment was not remarkable using QUADAS-2.
Conclusion:
DL models might be an appropriate tool for detecting SDHs with a reasonably high sensitivity and specificity.
