Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis

Ping Hu; Tengfeng Yan; Bing Xiao; Hongxin Shu; Yilei Sheng; Yanze Wu; Lei Shu; Shigang Lv; Minhua Ye; Yanyan Gong; Miaojing Wu; Xingen Zhu

doi:10.1097/JS9.0000000000001266

Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis

Int J Surg. 2024 Mar 15. doi: 10.1097/JS9.0000000000001266. Online ahead of print.

Authors

Ping Hu^{1

2

3

4}, Tengfeng Yan^{1

2

3

4}, Bing Xiao¹, Hongxin Shu^{1

2

3

4}, Yilei Sheng^{1

2

3

4}, Yanze Wu^{1

2

3

4}, Lei Shu^{1

2

3

4}, Shigang Lv¹, Minhua Ye¹, Yanyan Gong¹, Miaojing Wu¹, Xingen Zhu^{1

2

3

4}

Affiliations

¹ Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University.
² Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases.
³ Jiangxi Health Commission Key Laboratory of Neurological Medicine.
⁴ Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People's Republic of China.

PMID: 38489547
DOI: 10.1097/JS9.0000000000001266

Abstract

Background: Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans are well-established, but evidence on this topic is lacking.

Materials and methods: PubMed and Embase databases were searched from their inception to November 2023 to identify related studies. The primary outcomes included sensitivity, specificity, and the Dice Similarity Coefficient (DSC); while the secondary outcomes were positive predictive value (PPV), negative predictive value (NPV), precision, area under the receiver operating characteristic curve (AUROC), processing time, and volume of bleeding. Random-effect model and bivariate model were used to pooled independent effect size and diagnostic meta-analysis data, respectively.

Results: A total of 36 original studies were included in this meta-analysis. Pooled results indicated that DL technologies have a comparable performance in intracranial hemorrhage detection and segmentation with high values of sensitivity (0.89, 95% CI: 0.88-0.90), specificity (0.91, 95% CI: 0.89-0.93), AUROC (0.94, 95% CI: 0.93-0.95), PPV (0.92, 95% CI: 0.91-0.93), NPV (0.94, 95% CI: 0.91-0.96), precision (0.83, 95% CI: 0.77-0.90), DSC (0.84, 95% CI: 0.82-0.87). There is no significant difference between manual labeling and DL technologies in hemorrhage quantification (MD 0.08, 95% CI: -5.45-5.60, P=0.98), but the latter takes less process time than manual labeling (WMD 2.26, 95% CI: 1.96-2.56, P=0.001).

Conclusion: This systematic review has identified a range of DL algorithms that the performance was comparable to experienced clinicians in hemorrhage lesions identification, segmentation, and quantification but with greater efficiency and reduced cost. It is highly emphasized that multicenter randomized controlled clinical trials will be needed to validate the performance of these tools in the future, paving the way for fast and efficient decision-making during clinical procedure in patients with acute hemorrhagic stroke.