Timing the Ischemic Stroke by Multiparametric Quantitative Magnetic Resonance Imaging

McGarry, Bryony L.; Kauppinen, Risto A.

doi:10.36255/exonpublications.stroke.timingischemicstroke.2021

Cited by 5 publications

(2 citation statements)

References 52 publications

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“…Abi sekos lyginamos kartu -ADC ir difuzijos restrikcijos sekų radiniai geriausiai išryškėja praėjus 2-4 valandoms po insulto. Vertinant FLAIR seką hiperūmiu (hyperacute) laikotarpiu, jokių specifinių radinių nebūna, tuo tarpu difuzijos restrikcijos sekoje randami pokyčiai, kurie gali padėti nustatyti, kad insultas įvyko mažiau nei prieš 4,5 valandos, kai nėra žinoma insulto pradžia [13]. Kai kuriais atvejais FLAIR sekoje praėjus 3-6 valandoms gali būti randama parenchimos pokyčių, todėl gali būti sudėtinga parinkti tinkamą gydymo taktiką [14].…”

Section: Išeminio Insulto Diagnostika Magnetinio Rezonanso Tyrimuunclassified

Išeminio Insulto Diagnostika Magnetinio Rezonanso Tyrimu

Valentinaitė,

Galiniatytė,

Budrys

2023

Health Sciences

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Pagrindinis išeminio insulto diagnostikos metodas yra kompiuterinė tomografija (KT). Tobulėjant tyrimo metodams, vis dažniau praktikoje naudojamas magnetinio rezonanso tomografijos (MRT) tyrimas, kuris padeda nustatyti tikslesnį išemijos laiką ir parinkti tinkamiausią gydymo taktiką. Tyrimo tikslas − išanalizuoti ir apžvelgti įrodymais grįstoje mokslinėje literatūroje išeminio insulto diagnostiką MRT tyrimu.

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Section: Išeminio Insulto Diagnostika Magnetinio Rezonanso Tyrimuunclassified

Išeminio Insulto Diagnostika Magnetinio Rezonanso Tyrimu

Valentinaitė,

Galiniatytė,

Budrys

2023

Health Sciences

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“…MRI-based techniques have been adopted to stratify stroke patients with an unknown time of onset. However, image evaluation generally relies on clinical experience, which is inevitably subjective [11]. Existing evidence has shown that the performance of imaging in estimating stroke onset time needs to be improved [12].…”

Section: Introductionmentioning

confidence: 99%

The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis

Feng,

Zhang,

et al. 2023

J Med Internet Res

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Background Machine learning is a potentially effective method for identifying and predicting the time of the onset of stroke. However, the value of applying machine learning in this field remains controversial and debatable. Objective We aimed to assess the value of applying machine learning in predicting the time of stroke onset. Methods PubMed, Web of Science, Embase, and Cochrane were comprehensively searched. The C index and sensitivity with 95% CI were used as effect sizes. The risk of bias was evaluated using PROBAST (Prediction Model Risk of Bias Assessment Tool), and meta-analysis was conducted using R (version 4.2.0; R Core Team). Results Thirteen eligible studies were included in the meta-analysis involving 55 machine learning models with 41 models in the training set and 14 in the validation set. The overall C index was 0.800 (95% CI 0.773-0.826) in the training set and 0.781 (95% CI 0.709-0.852) in the validation set. The sensitivity and specificity were 0.76 (95% CI 0.73-0.80) and 0.79 (95% CI 0.74-0.82) in the training set and 0.81 (95% CI 0.68-0.90) and 0.83 (95% CI 0.73-0.89) in the validation set, respectively. Subgroup analysis revealed that the accuracy of machine learning in predicting the time of stroke onset within 4.5 hours was optimal (training: 0.80, 95% CI 0.77-0.83; validation: 0.79, 95% CI 0.71-0.86). Conclusions Machine learning has ideal performance in identifying the time of stroke onset. More reasonable image segmentation and texture extraction methods in radiomics should be used to promote the value of applying machine learning in diverse ethnic backgrounds. Trial Registration PROSPERO CRD42022358898; https://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=358898

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The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis (Preprint)

Feng,

Zhang,

et al. 2022

Preprint

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BACKGROUND Machine learning is a potentially effective method for identifying and predicting the time of the onset of stroke. However, the value of applying machine learning in this field remains controversial and debatable. OBJECTIVE We aimed to assess the value of applying machine learning in predicting the time of stroke onset. METHODS PubMed, Web of Science, Embase, and Cochrane were comprehensively searched. The C index and sensitivity with 95% CI were used as effect sizes. The risk of bias was evaluated using PROBAST (Prediction Model Risk of Bias Assessment Tool), and meta-analysis was conducted using R (version 4.2.0; R Core Team). RESULTS Thirteen eligible studies were included in the meta-analysis involving 55 machine learning models with 41 models in the training set and 14 in the validation set. The overall C index was 0.800 (95% CI 0.773-0.826) in the training set and 0.781 (95% CI 0.709-0.852) in the validation set. The sensitivity and specificity were 0.76 (95% CI 0.73-0.80) and 0.79 (95% CI 0.74-0.82) in the training set and 0.81 (95% CI 0.68-0.90) and 0.83 (95% CI 0.73-0.89) in the validation set, respectively. Subgroup analysis revealed that the accuracy of machine learning in predicting the time of stroke onset within 4.5 hours was optimal (training: 0.80, 95% CI 0.77-0.83; validation: 0.79, 95% CI 0.71-0.86). CONCLUSIONS Machine learning has ideal performance in identifying the time of stroke onset. More reasonable image segmentation and texture extraction methods in radiomics should be used to promote the value of applying machine learning in diverse ethnic backgrounds. CLINICALTRIAL PROSPERO CRD42022358898; https://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=358898

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Timing the Ischemic Stroke by Multiparametric Quantitative Magnetic Resonance Imaging

Cited by 5 publications

References 52 publications

Išeminio Insulto Diagnostika Magnetinio Rezonanso Tyrimu

Išeminio Insulto Diagnostika Magnetinio Rezonanso Tyrimu

The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis

The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis (Preprint)

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