Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage

Trevisi, Gianluca; Caccavella, Valerio Maria; Scerrati, Alba; Signorelli, Francesco; Salamone, Giuseppe Giovanni; Orsini, Klizia; Fasciani, Christian; D’Arrigo, Sonia; Auricchio, Anna Maria; D'Onofrio, Ginevra; Salomi, Francesco; Albanese, Alessio; Bonis, Pasquale De; Mangiola, Annunziato; Sturiale, Carmelo Lucio

doi:10.1007/s10143-022-01802-7

Cited by 9 publications

(12 citation statements)

References 44 publications

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“…The overall DTA of the 26 retrospective studies and 904,755 scans was estimated using a univariate meta-analysis with a pooled sensitivity was 0.917 (95% CI 0.88 to 0.943, I 2 = 99%) ( Fig. 4 ) [ 20 – 26 , 28 – 43 , 45 , 46 , 48 – 50 ]. The pooled specificity was 0.945 (95% CI 0.918 to 0.964, I 2 = 100%) ( Fig.…”

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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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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“…In a retrospective study by Guo et al, 9 ML-based models slightly outperformed traditional statistical analysis and the ICH score in prediction of mortality. Another RF model to predict outcome in elderly ICH patients by Trevisi et al 33 achieved an area under the curve (AUC) of 0.96, 0.89, and 0.93 for dead, poor, and good outcome classes, respectively, demonstrating high discriminative ability. ML-based models have also been use to predict hematoma expansion and outcome from CT images of patients with a high degree of accuracy.…”

Section: Results and Validationmentioning

confidence: 99%

Development of an Artificial Intelligence-Based Machine Tool to Predict Outcome in Intracerebral Hemorrhage

Menon

Gajula

et al. 2022

Journal of Stroke Medicine

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Introduction: Spontaneous intracerebral hemorrhage is the second most common type of stroke with high morbidity and mortality. Outcome prediction is very important in this disease, to enable us tailor treatment strategies especially in a low- and middle-income countries. Today, prediction is predominantly limited to few clinical factors and may not be very accurate. We explore the application of an artificial intelligence-based platform for outcome prediction with a combination of clinical, radiological, and biochemical parameters. Methods: Data from our prospectively maintained stroke register was cleaned and processed using the XGBoost machine learning (ML) algorithm to predict outcome at discharge and 90 days using the modified Rankin scale. A total of 1,000 patients were included in the study, 129 variables were pruned to 19 significant features during the phase of preprocessing. Results: The data set was split 9:1 with 900 cases being used for training and the remaining 100 for validation. The models were evaluated based on the mean absolute error (MAE). Model-1 trained for predicting “mRS_Discharge” had a MAE of 0.34 and model-2 trained for predicting “mRS_3months” had a MAE of 0.63. Conclusion: ML algorithms can be successfully applied for the prediction of outcome in intracerebral hemorrhage.

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“…Some studies have utilized logistic regression to develop prognostication tools specific to geriatric TBI, based on multiple factors such as age, GCS, hypotension, Charlson Comorbidity Index and ISS [8,22,28]. Previous studies found machine learning algorithms-based models performed well on the prediction of prognosis in many kinds of neurosurgical patients, such as aneurysmal subarachnoid hemorrhage, and intracerebral hemorrhage [45][46][47]. Additionally, some studies exploring the prognostic value of machine learning in pediatric TBI found machine learning performed better than conventional statistical models and CT scores in predicting outcomes [48,49], while there is still no study exploring the prognostic value of machine learning algorithms in geriatric TBI patients.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Mortality in Geriatric Traumatic Brain Injury Patients Using Machine Learning Algorithms

et al. 2023

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Background: The number of geriatric traumatic brain injury (TBI) patients is increasing every year due to the population’s aging in most of the developed countries. Unfortunately, there is no widely recognized tool for specifically evaluating the prognosis of geriatric TBI patients. We designed this study to compare the prognostic value of different machine learning algorithm-based predictive models for geriatric TBI. Methods: TBI patients aged ≥65 from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for this study. To develop and validate machine learning algorithm-based prognostic models, included patients were divided into a training set and a testing set, with a ratio of 7:3. The predictive value of different machine learning based models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy and F score. Results: A total of 1123 geriatric TBI patients were included, with a mortality of 24.8%. Non-survivors had higher age (82.2 vs. 80.7, p = 0.010) and lower Glasgow Coma Scale (14 vs. 7, p < 0.001) than survivors. The rate of mechanical ventilation was significantly higher (67.6% vs. 25.9%, p < 0.001) in non-survivors while the rate of neurosurgical operation did not differ between survivors and non-survivors (24.3% vs. 23.0%, p = 0.735). Among different machine learning algorithms, Adaboost (AUC: 0.799) and Random Forest (AUC: 0.795) performed slightly better than the logistic regression (AUC: 0.792) on predicting mortality in geriatric TBI patients in the testing set. Conclusion: Adaboost, Random Forest and logistic regression all performed well in predicting mortality of geriatric TBI patients. Prognostication tools utilizing these algorithms are helpful for physicians to evaluate the risk of poor outcomes in geriatric TBI patients and adopt personalized therapeutic options for them.

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Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage

Cited by 9 publications

References 44 publications

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

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

Development of an Artificial Intelligence-Based Machine Tool to Predict Outcome in Intracerebral Hemorrhage

Prediction of Mortality in Geriatric Traumatic Brain Injury Patients Using Machine Learning Algorithms

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