[HTML][HTML] Preliminary study on artificial intelligence diagnosis of pulmonary embolism based on computer in-depth study
X Li, X Wang, X Yang, Y Lin… - Annals of Translational …, 2021 - ncbi.nlm.nih.gov
X Li, X Wang, X Yang, Y Lin, Z Huang
Annals of Translational Medicine, 2021•ncbi.nlm.nih.govBackground Objective to preliminarily verify the feasibility of AI intelligent diagnosis of
pulmonary embolism by using a new artificial intelligence (AI) computer-aided diagnosis
system (CAD) to localize and quantitatively diagnose pulmonary embolism in pulmonary
artery CT angiography (CTA). Methods Computed tomography angiography (CTA) data of
85 patients with PE in our hospital from January 2017 to May 2018 were retrospectively
collected and randomly allocated to2 groups: computer depth learning group (n= 43) and …
pulmonary embolism by using a new artificial intelligence (AI) computer-aided diagnosis
system (CAD) to localize and quantitatively diagnose pulmonary embolism in pulmonary
artery CT angiography (CTA). Methods Computed tomography angiography (CTA) data of
85 patients with PE in our hospital from January 2017 to May 2018 were retrospectively
collected and randomly allocated to2 groups: computer depth learning group (n= 43) and …
Abstract
Background
Objective to preliminarily verify the feasibility of AI intelligent diagnosis of pulmonary embolism by using a new artificial intelligence (AI) computer-aided diagnosis system (CAD) to localize and quantitatively diagnose pulmonary embolism in pulmonary artery CT angiography (CTA).
Methods
Computed tomography angiography (CTA) data of 85 patients with PE in our hospital from January 2017 to May 2018 were retrospectively collected and randomly allocated to2 groups: computer depth learning group (n= 43) and experimental group (n= 42). For the training set (13,144 sheets) and the test set (313 sheets), the auxiliary diagnosis method was obtained and applied to the experimental group.
Results
Among the participants, a good sensitivity of 90.9% and an average false positive of 2.0 were obtained by using the deep learning detection method proposed in this paper, and the detection rate was positively correlated with arterial grade.
Conclusions
The computer-aided diagnostic method proposed in this paper can effectively improve the detection rate of PE, especially for the detection of intra-arterial embolism above grade 3. However, because of the high misdetection rate, more in-depth learning datasets are needed for the detection of embolism below grade 3.
ncbi.nlm.nih.gov
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