Review Article
Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review
Table 3
Overview of documents using deep learning techniques for LVO detection.
| References | Study objective | Date published | DL-based approaches | Optimal results | Clinical implications | Limitation |
| Chatterjee et al. [88] | LVO detection | 2019 | CNN | Sensitivity (82%), specificity (94%), PPV (77%), and NPV (95%) | The first AI algorithm for detecting intracranial LVOs, improving EVT rates | Difficult to detect anatomic variations such as tortuosity and MCA-M2. | Shaham and R L R [89] | LVO detection | 2019 | RRCNN | AUC (0.914) for original brain CTA volumes, AUC (0.899) for brain tissue images | Automated detection of AIS with CTA images | Larger number of datasets should be considered to improve the performance of the model. | Yu et al. [91] | LVO detection | 2020 | DCNN | AUC (0.847) | Automated detection of AIS with CTA images, improving prehospital triage systems | The NCCT brain scans are thick-cut and lack prospective validation and angiogram within the acute setting. | McLouth et al. [92] | LVO detection | 2021 | CNNs, CINA v1.0 device (Avicenna.ai, La Ciotat, France) | Accuracy (98.1%), sensitivity (98.1%), and specificity (98.2%) | Automated detection of AIS with CTA, improving EVT rates | Not differentiate acute and nonacute LVO etiologies; not evaluate occlusions in the anterior cerebral arteries or posterior circulation. |
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