Computer-aided detection for the automated evaluation of pulmonary embolism

Li, Yan; Dai, Yongliang; Deng, Lei; Ye, Nan; Guo, Youmin

doi:10.3233/thc-171315

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…In this study, PE was identified automatically based on the "digital lung" detection system [19,20], by which the emboli could be automatically segmented, extracted, and quantitatively analyzed. Our results indicated that the computer-aided interpretation was better than manual interpretation in reducing timecosts, which was consistent with other previous studies [12,21].…”

Section: Discussionmentioning

confidence: 99%

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Assessment of Acute Pulmonary Embolism by Computer-Aided Technique: A Reliability Study

et al. 2020

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Background:Acute pulmonary embolism is one of the most common cardiovascular diseases. Computer-aided technique is widely used in chest imaging, especially for assessing pulmonary embolism. The reliability and quantitative analyses of computer-aided technique are necessary. This study aimed to evaluate the reliability of geometrybased computer-aided detection and quantification for emboli morphology and severity of acute pulmonary embolism. Material/Methods:Thirty patients suspected of acute pulmonary embolism were analyzed by both manual and computer-aided interpretation of vascular obstruction index and computer-aided measurements of emboli quantitative parameters. The reliability of Qanadli and Mastora scores was analyzed using computer-aided and manual interpretation. Results:The time costs of manual and computer-aided interpretation were statistically different (374.90±150.16 versus 121.07±51.76, P<0.001). The difference between the computer-aided and manual interpretation of Qanadli score was 1.83±2.19, and 96.7% (29 out of 30) of the measurements were within 95% confidence interval (intraclass correlation coefficient, ICC=0.998). The difference between the computer-aided and manual interpretation of Mastora score was 1.46±1.62, and 96.7% (29 out of 30) of the measurements were within 95% confidence interval (ICC=0.997). The emboli quantitative parameters were moderately correlated with the Qanadli and Mastora scores (all P<0.001). Conclusions:Computer-aided technique could reduce the time costs, improve the and reliability of vascular obstruction index and provided additional quantitative parameters for disease assessment.

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Section: Discussionmentioning

confidence: 99%

“…Computer-aided quantitative method could not only obtain the location and embolism severity information but also quantify the volume and length of emboli accurately. The quantitative outcomes using CAT are perceptible and measurable units, which are more reliable and repeatable than those using manual interpretation [19,20].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Acute Pulmonary Embolism by Computer-Aided Technique: A Reliability Study

et al. 2020

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“…Compared with human readers, CAD is more sensitive in the detection of peripheral emboli, particularly for inexperienced readers [104,106]. CAD algorithms can also be useful in PE risk stratification by automatic computation of the RV/LV ratio as a measure of right ventricular dysfunction [107,108]. Figure 9.…”

Section: Computed Tomography Pulmonary Angiography (Ctpa)mentioning

confidence: 99%

From Early Morphometrics to Machine Learning—What Future for Cardiovascular Imaging of the Pulmonary Circulation?

Gopalan

Gibbs

2020

Diagnostics

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Imaging plays a cardinal role in the diagnosis and management of diseases of the pulmonary circulation. Behind the picture itself, every digital image contains a wealth of quantitative data, which are hardly analysed in current routine clinical practice and this is now being transformed by radiomics. Mathematical analyses of these data using novel techniques, such as vascular morphometry (including vascular tortuosity and vascular volumes), blood flow imaging (including quantitative lung perfusion and computational flow dynamics), and artificial intelligence, are opening a window on the complex pathophysiology and structure–function relationships of pulmonary vascular diseases. They have the potential to make dramatic alterations to how clinicians investigate the pulmonary circulation, with the consequences of more rapid diagnosis and a reduction in the need for invasive procedures in the future. Applied to multimodality imaging, they can provide new information to improve disease characterization and increase diagnostic accuracy. These new technologies may be used as sophisticated biomarkers for risk prediction modelling of prognosis and for optimising the long-term management of pulmonary circulatory diseases. These innovative techniques will require evaluation in clinical trials and may in themselves serve as successful surrogate end points in trials in the years to come.

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“…A wide range of Natural Language Processing (NLP) methods have been used for years to automatically analyze unstructured CT reports and detect reported pathological findings [7] , [8] , [9] , [10] , [11] , [12] . This includes rule-based, machine learning-based, and hybrid algorithms.…”

Section: Introductionmentioning

confidence: 99%

BERT-based natural language processing analysis of French CT reports: Application to the measurement of the positivity rate for pulmonary embolism

Jupin-Delevaux¹,

Djahnine²,

Talbot³

et al. 2023

Research in Diagnostic and Interventional Imaging

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Computer-aided detection for the automated evaluation of pulmonary embolism

Abstract: We found strong correlation between RHF and the severity of PE evaluated by CAD, and the method was found to be simple and reliable.

Cited by 7 publications

References 17 publications

Assessment of Acute Pulmonary Embolism by Computer-Aided Technique: A Reliability Study

Assessment of Acute Pulmonary Embolism by Computer-Aided Technique: A Reliability Study

From Early Morphometrics to Machine Learning—What Future for Cardiovascular Imaging of the Pulmonary Circulation?

BERT-based natural language processing analysis of French CT reports: Application to the measurement of the positivity rate for pulmonary embolism

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