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• W4366998084 abstract "Abstract Objectives To develop a deep learning-based algorithm that automatically and accurately classifies patients as either having pulmonary emboli or not in CT pulmonary angiography (CTPA) examinations with high accuracy. Materials and Methods Seven hundred CTPA examinations from 652 patients (median age 72 years, range 16-100 years; interquartile range 18 years; 353 women) performed at a single institution between 2014 and 2018, of which 149 examinations contained PE, were used for model development. The nnU-Net deep learning-based segmentation framework was trained using 5-fold cross-validation. To enhance classification, we applied logical rules based on PE volume and probability thresholds. External model evaluation was performed in 770 and 34 CTPAs from two independent datasets. Results A total of 1483 CTPA examinations were evaluated. In internal cross-validation and test set, the trained model correctly classified 123 of 128 examinations as positive for PE (sensitivity 96.1%; 95% C.I. 91-98%; P < .05) and 521 of 551 as negative (specificity 94.6%; 95% C.I. 92-96%; P < .05). In the first external test dataset, the trained model correctly classified 31 of 32 examinations as positive (sensitivity 96.9%; 95% C.I. 84-99%; P < .05) and 2 of 2 as negative (specificity 100%; 95% C.I. 34-100%; P < .05). In the second external test dataset, the trained model correctly classified 379 of 385 examinations as positive (sensitivity 98.4%; 95% C.I. 97-99%; P < .05) and 346 of 385 as negative (specificity 89.9%; 95% C.I. 86-93%; P < .05). Conclusion Beyond state-of-art classification for PE in CTPA was achieved using nnU-Net for deep learning-based segmentation in combination with volume-and probability-based classification. Clinical relevance statement A rapid and fully automated classification of patients as having or not having PE in CTPA examinations using deep learning techniques can help prioritize patients with PE for rapid review in emergency radiology. Key Points An nnU-Net segmentation framework was applied to patient-level classification in CTPA examinations. The proposed algorithm achieved an accuracy of 94.6% with a sensitivity of 96.1% and a specificity of 94.6% in the internal dataset (n=679). The proposed algorithm showed remarkable performance on both internal and two publicly available external testing datasets (AUC, 98.3%; n=1355.)" @default.
• W4366998084 created "2023-04-27" @default.
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• W4366998084 date "2023-04-25" @default.
• W4366998084 modified "2023-09-29" @default.
• W4366998084 title "High Performance Embolism Detection in Real-World CT Pulmonary Angiography Examinations Using Deep Learning" @default.
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• W4366998084 doi "https://doi.org/10.1101/2023.04.21.23288861" @default.
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