PENet—a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging

TLDR

Pulmonary embolism is a life‑threatening condition whose prompt detection by computed tomography pulmonary angiography is critical, yet it is frequently missed or delayed. This study develops PENet, a deep‑learning model that automatically detects pulmonary embolism on volumetric CTPA scans as an end‑to‑end solution. PENet is a 77‑layer 3D CNN pretrained on Kinetics‑600 and fine‑tuned on a retrospective CTPA dataset, evaluated on hold‑out internal and external institutional data to assess generalizability. PENet achieved AUROCs of 0.84 on the internal test set and 0.85 on an external set, outperforming existing 3D CNNs, and demonstrates that an end‑to‑end model can triage clinically important PEs without intensive preprocessing.

Abstract

Abstract Pulmonary embolism (PE) is a life-threatening clinical problem and computed tomography pulmonary angiography (CTPA) is the gold standard for diagnosis. Prompt diagnosis and immediate treatment are critical to avoid high morbidity and mortality rates, yet PE remains among the diagnoses most frequently missed or delayed. In this study, we developed a deep learning model—PENet, to automatically detect PE on volumetric CTPA scans as an end-to-end solution for this purpose. The PENet is a 77-layer 3D convolutional neural network (CNN) pretrained on the Kinetics-600 dataset and fine-tuned on a retrospective CTPA dataset collected from a single academic institution. The PENet model performance was evaluated in detecting PE on data from two different institutions: one as a hold-out dataset from the same institution as the training data and a second collected from an external institution to evaluate model generalizability to an unrelated population dataset. PENet achieved an AUROC of 0.84 [0.82–0.87] on detecting PE on the hold out internal test set and 0.85 [0.81–0.88] on external dataset. PENet also outperformed current state-of-the-art 3D CNN models. The results represent successful application of an end-to-end 3D CNN model for the complex task of PE diagnosis without requiring computationally intensive and time consuming preprocessing and demonstrates sustained performance on data from an external institution. Our model could be applied as a triage tool to automatically identify clinically important PEs allowing for prioritization for diagnostic radiology interpretation and improved care pathways via more efficient diagnosis.