A deep learning-based algorithm using nnU-Net accurately classified pulmonary embolism in CT pulmonary angiography, achieving an AUROC of 96.4% (95% CI 79-99%) in internal testing.
Observational (n=1,483)
Yes
Does a deep learning-based segmentation algorithm accurately detect pulmonary embolism in CT pulmonary angiography?
A deep learning-based segmentation algorithm using nnU-Net demonstrated high sensitivity and specificity for detecting pulmonary embolism on CT pulmonary angiography across internal and external datasets.
Effect estimate: AUROC (95% CI 79-99)
p-value: p=< .05
Purpose: 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. Materials and methods: For model development, 700 CTPA examinations from 652 patients performed at a single institution were used, of which 149 examinations contained 1497 PE traced by radiologists. 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), achieving an area under the receiver operating characteristic (AUROC) of 96.4 % (95 % C.I. 79-99 %; 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), achieving an AUROC of 98.6 % (95 % C.I. 83-100 %; 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), achieving an AUROC of 98.5 % (95 % C.I. 83-100 %; P < .05). Conclusion: Our automatic pipeline achieved beyond state-of-the-art diagnostic performance of PE in CTPA using nnU-Net for segmentation and volume- and probability-based post-processing for classification.
Kahraman et al. (Thu,) conducted a observational in Pulmonary embolism (n=1,483). nnU-Net deep learning-based segmentation framework was evaluated on Classification of pulmonary emboli in CTPA examinations (AUROC, 95% CI 79-99, p=< .05). A deep learning-based algorithm using nnU-Net accurately classified pulmonary embolism in CT pulmonary angiography, achieving an AUROC of 96.4% (95% CI 79-99%) in internal testing.