A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms

Abe, Daijiro; Inaji, Motoki; Hase, Takeshi; Takahashi, Shinichi; Sakai, Ryosuke; Ayabe, Fuga; Tanaka, Yoji; Otomo, Yasuhiro; Maehara, Taketoshi

doi:10.1001/jamanetworkopen.2022.16393

Cited by 23 publications

(21 citation statements)

References 44 publications

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“…Thirdly, whereas machine learning-based models frequently rely on a large number of variables the present models rely exclusively on a few, rapidly and easily available pre-hospital variables in daily practice and make it suitable for a decision-making tool. Fourthly, our results are consistent with a recent study by Abe et al In this study, ML algorithms have achieved the same performance as our model (AUC: 0.78) in detecting intracerebral hemorrhage after traumatic brain injury [ 18 ]. Finally, this study provides information on the “black box” of the model through Shapley values in order to ensure explainability for clinicians and increase acceptance.…”

Section: Discussionsupporting

confidence: 92%

Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury

et al. 2022

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Background Rapid referral of traumatic brain injury (TBI) patients requiring emergency neurosurgery to a specialized trauma center can significantly reduce morbidity and mortality. Currently, no model has been reported to predict the need for acute neurosurgery in severe to moderate TBI patients. This study aims to evaluate the performance of Machine Learning-based models to establish to predict the need for neurosurgery procedure within 24 h after moderate to severe TBI. Methods Retrospective multicenter cohort study using data from a national trauma registry (Traumabase®) from November 2011 to December 2020. Inclusion criteria correspond to patients over 18 years old with moderate or severe TBI (Glasgow coma score ≤ 12) during prehospital assessment. Patients who died within the first 24 h after hospital admission and secondary transfers were excluded. The population was divided into a train set (80% of patients) and a test set (20% of patients). Several approaches were used to define the best prognostic model (linear nearest neighbor or ensemble model). The Shapley Value was used to identify the most relevant pre-hospital variables for prediction. Results 2159 patients were included in the study. 914 patients (42%) required neurosurgical intervention within 24 h. The population was predominantly male (77%), young (median age 35 years [IQR 24–52]) with severe head injury (median GCS 6 [3–9]). Based on the evaluation of the predictive model on the test set, the logistic regression model had an AUC of 0.76. The best predictive model was obtained with the CatBoost technique (AUC 0.81). According to the Shapley values method, the most predictive variables in the CatBoost were a low initial Glasgow coma score, the regression of pupillary abnormality after osmotherapy, a high blood pressure and a low heart rate. Conclusion Machine learning-based models could predict the need for emergency neurosurgery within 24 h after moderate and severe head injury. Potential clinical benefits of such models as a decision-making tool deserve further assessment. The performance in real-life setting and the impact on clinical decision-making of the model requires workflow integration and prospective assessment.

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Section: Discussionsupporting

confidence: 92%

Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury

et al. 2022

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“…Following the primary search, 1,405 studies were recognized after removing duplicated studies. At last, after screening the title, abstract, and full paper, twenty-six retrospective and three prospective, and two retrospective/prospective studies were included [ 20 – 50 ]; then, twenty-nine studies were included in the final quantitative analysis, and the other studies were excluded because no diagnostic accuracy was reported ( Fig. 1 ) [ 20 – 26 , 28 – 46 , 48 – 52 ].…”

Section: Resultsmentioning

confidence: 99%

“…At last, after screening the title, abstract, and full paper, twenty-six retrospective and three prospective, and two retrospective/prospective studies were included [ 20 – 50 ]; then, twenty-nine studies were included in the final quantitative analysis, and the other studies were excluded because no diagnostic accuracy was reported ( Fig. 1 ) [ 20 – 26 , 28 – 46 , 48 – 52 ]. The machine learning networks were classified into, Support vector machine (SVM), Random Forest (RF), k-nearest neighbors’ algorithm (k-NN), VGG-16, Logistic Regression (LR), ResNet-18, AlexNet, DenseNet-121, eXtreme Gradient Boosting (XGBoost), Decision Tree (DT), and Deep Learning (DL) included Convolutional Neural Network (CNN); ResNet34, ResNet50, ResNet18, ResNet-v2, GoogleNet ( Table 1 ) .…”

Section: Resultsmentioning

confidence: 99%

Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study

Maghami,

Sattari,

Tahmasbi

et al. 2023

BioMed Eng OnLine

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Background This systematic review and meta-analysis were conducted to objectively evaluate the evidence of machine learning (ML) in the patient diagnosis of Intracranial Hemorrhage (ICH) on computed tomography (CT) scans. Methods Until May 2023, systematic searches were conducted in ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE for studies that evaluated the diagnostic precision of ML model-assisted ICH detection. Patients with and without ICH as the target condition who were receiving CT-Scan were eligible for the research, which used ML algorithms based on radiologists' reports as the gold reference standard. For meta-analysis, pooled sensitivities, specificities, and a summary receiver operating characteristics curve (SROC) were used. Results At last, after screening the title, abstract, and full paper, twenty-six retrospective and three prospective, and two retrospective/prospective studies were included. The overall (Diagnostic Test Accuracy) DTA of retrospective studies with a pooled sensitivity was 0.917 (95% CI 0.88–0.943, I2 = 99%). The pooled specificity was 0.945 (95% CI 0.918–0.964, I2 = 100%). The pooled diagnostic odds ratio (DOR) was 219.47 (95% CI 104.78–459.66, I2 = 100%). These results were significant for the specificity of the different network architecture models (p-value = 0.0289). However, the results for sensitivity (p-value = 0.6417) and DOR (p-value = 0.2187) were not significant. The ResNet algorithm has higher pooled specificity than other algorithms with 0.935 (95% CI 0.854–0.973, I2 = 93%). Conclusion This meta-analysis on DTA of ML algorithms for detecting ICH by assessing non-contrast CT-Scans shows the ML has an acceptable performance in diagnosing ICH. Using ResNet in ICH detection remains promising prediction was improved via training in an Architecture Learning Network (ALN).

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“…Shapley values have been adopted to interpret feature importance 20 , 22 , 29 , 38 and feature importance based on the XGBoost model was ranked with the aid of Shapley values. For easy and ready usage in clinical practice, previous studies have built machine learning models using only several top features 30 , 39 , 40 . For our XGBoost model, the top 4 features are CCM volume, presence of ICH, CCM at brain stem, and age at diagnosis, with which we try to build 4-Elements model.…”

Section: Discussionmentioning

confidence: 99%

Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning

Li,

Jones,

Zhao

2024

Sci Rep

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The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary aim for CCM management. However, accurately identifying the potential (re)hemorrhage among sporadic CCM patients in advance remains a challenge. This study aims to develop machine learning models to detect potential (re)hemorrhage in sporadic CCM patients. This study was based on a dataset of 731 sporadic CCM patients in open data platform Dryad. Sporadic CCM patients were followed up 5 years from January 2003 to December 2018. Support vector machine (SVM), stacked generalization, and extreme gradient boosting (XGBoost) were used to construct models. The performance of models was evaluated by area under receiver operating characteristic curves (AUROC), area under the precision-recall curve (PR-AUC) and other metrics. A total of 517 patients with sporadic CCM were included (330 female [63.8%], mean [SD] age at diagnosis, 42.1 [15.5] years). 76 (re)hemorrhage (14.7%) occurred during follow-up. Among 3 machine learning models, XGBoost model yielded the highest mean (SD) AUROC (0.87 [0.06]) in cross-validation. The top 4 features of XGBoost model were ranked with SHAP (SHapley Additive exPlanations). All-Elements XGBoost model achieved an AUROCs of 0.84 and PR-AUC of 0.49 in testing set, with a sensitivity of 0.86 and a specificity of 0.76. Importantly, 4-Elements XGBoost model developed using top 4 features got a AUROCs of 0.83 and PR-AUC of 0.40, a sensitivity of 0.79, and a specificity of 0.72 in testing set. Two machine learning-based models achieved accurate performance in identifying potential (re)hemorrhages within 5 years in sporadic CCM patients. These models may provide insights for clinical decision-making.

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A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms

Cited by 23 publications

References 44 publications

Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury

Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury

Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study

Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning

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