Convolutional neural network performance compared to radiologists in detecting intracranial hemorrhage from brain computed tomography: A systematic review and meta-analysis

Jørgensen, Mia Daugaard; Antulov, Ronald; Hess, Søren; Lysdahlgaard, Simon

doi:10.1016/j.ejrad.2021.110073

Cited by 25 publications

(8 citation statements)

References 38 publications

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“…The pooled sensitivity of this study was higher than our analysis (96 vs 88.8), but its specificity was lower than ours (97.2% vs 97%). It is worth mentioning that the Jorgensen et al study included 380 382 non-contrast CT scans, whereas our study included 1,997,749 patients and 16 more studies 29. Another systematic review and meta-analysis has been carried out on the application of AI, including DL, neural networks, and unsupervised algorithms for detecting intracerebral hemorrhage.…”

Section: Discussionmentioning

confidence: 97%

Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis

Abdollahifard

Farrokhi

Mowla

2022

J NeuroIntervent Surg

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BackgroundThis study aimed to investigate the application of deep learning (DL) models for the detection of subdural hematoma (SDH).MethodsWe 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).ResultsWe 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.ConclusionDL models might be an appropriate tool for detecting SDHs with a reasonably high sensitivity and specificity.

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Section: Discussionmentioning

confidence: 97%

Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis

Abdollahifard

Farrokhi

Mowla

2022

J NeuroIntervent Surg

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“…However, this observation might be confounded by the usage of public datasets. Deep neural networks and derivatives thereof were the most frequently applied ML algorithms, which might be due to their proven high performance and robust feature input methods [ 47 ]. All studies published in clinical journals used private datasets with larger patient populations.…”

Section: Discussionmentioning

confidence: 99%

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review

et al. 2022

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We conducted a systematic review of the current status of machine learning (ML) algorithms’ ability to identify multiple brain diseases, and we evaluated their applicability for improving existing scan acquisition and interpretation workflows. PubMed Medline, Ovid Embase, Scopus, Web of Science, and IEEE Xplore literature databases were searched for relevant studies published between January 2017 and February 2022. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The applicability of ML algorithms for successful workflow improvement was qualitatively assessed based on the satisfaction of three clinical requirements. A total of 19 studies were included for qualitative synthesis. The included studies performed classification tasks (n = 12) and segmentation tasks (n = 7). For classification algorithms, the area under the receiver operating characteristic curve (AUC) ranged from 0.765 to 0.997, while accuracy, sensitivity, and specificity ranged from 80% to 100%, 72% to 100%, and 65% to 100%, respectively. For segmentation algorithms, the Dice coefficient ranged from 0.300 to 0.912. No studies satisfied all clinical requirements for successful workflow improvements due to key limitations pertaining to the study’s design, study data, reference standards, and performance reporting. Standardized reporting guidelines tailored for ML in radiology, prospective study designs, and multi-site testing could help alleviate this.

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“…There has been an explosive increase in interest in arti cial intelligence (AI) algorithms within recent clinical research, which has led to a rapid surge in their applications, including the detection and validation of AIH on brain CT scans [20][21][22][23][24][25][26][27] . However, to our knowledge, there has been no study differentiating CCM and AIH using an AI algorithm on brain CT images.…”

Section: Introductionmentioning

confidence: 99%

Improved Differentiation of Cavernous Malformation and Acute Intraparenchymal Hemorrhage on CT Using an AI Algorithm

Kim,

Choi,

Kim

et al. 2024

Preprint

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This study aimed to evaluate the utility of an artificial intelligence (AI) algorithm in differentiating between cerebral cavernous malformation (CCM) and acute intraparenchymal hemorrhage (AIH) on brain computed tomography (CT). A retrospective, multireader, randomized study was conducted to validate the performance of an AI algorithm (SK Inc. C&C Medical Insight+ Brain Hemorrhage) in differentiating AIH from CCM on brain CT. CT images of CM and AIH (< 3cm) were identified from the database. Six blinded reviewers, including two neuroradiologists, two radiology residents, and two emergency department physicians, evaluated CT images from 288 patients (CCM, n = 173; AIH, n = 115) with and without AI assistance, comparing diagnostic performance. Brain CT interpretation with AI assistance resulted in significantly higher diagnostic accuracy than without (86.92% vs. 79.86%, p < 0.001). Radiology residents and emergency department physicians showed significantly improved accuracy of CT interpretation with AI assistance than without (84.21% vs 75.35%, 80.73% vs. 72.57%; respectively, p < 0.05). Neuroradiologists showed a trend of higher accuracy with AI assistance in the interpretation but lacked statistical significance (95.83% vs. 91.67%). The use of an AI algorithm can enhance the differentiation of AIH from CCM in brain CT interpretation, particularly for nonexperts in neuroradiology.

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Convolutional neural network performance compared to radiologists in detecting intracranial hemorrhage from brain computed tomography: A systematic review and meta-analysis

Cited by 25 publications

References 38 publications

Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis

Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review

Improved Differentiation of Cavernous Malformation and Acute Intraparenchymal Hemorrhage on CT Using an AI Algorithm

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