{Reference Type}: Journal Article
{Title}: Automatic detection of pulmonary embolism on computed tomography pulmonary angiogram scan using a three-dimensional convolutional neural network.
{Author}: Zhu H;Tao G;Jiang Y;Sun L;Chen J;Guo J;Wang N;Wei H;Liu X;Chen Y;Yan Z;Chen Q;Sun X;Yu H;
{Journal}: Eur J Radiol
{Volume}: 177
{Issue}: 0
{Year}: 2024 Jun 21
{Factor}: 4.531
{DOI}: 10.1016/j.ejrad.2024.111586
{Abstract}: <B>OBJECTIVE: </B>To propose a convolutional neural network (EmbNet) for automatic pulmonary embolism detection on computed tomography pulmonary angiogram (CTPA) scans and to assess its diagnostic performance.<BR><B>METHODS: </B>305 consecutive CTPA scans between January 2019 and December 2021 were enrolled in this study (142 for training, 163 for internal validation), and 250 CTPA scans from a public dataset were used for external validation. The framework comprised a preprocessing step to segment the pulmonary vessels and the EmbNet to detect emboli. Emboli were divided into three location-based subgroups for detailed evaluation: central arteries, lobar branches, and peripheral regions. Ground truth was established by three radiologists.<BR><B>RESULTS: </B>The EmbNet's per-scan level sensitivity, specificity, positive predictive value (PPV), and negative predictive value were 90.9%, 75.4%, 48.4%, and 97.0% (internal validation) and 88.0%, 70.5%, 42.7%, and 95.9% (external validation). At the per-embolus level, the overall sensitivity and PPV of the EmbNet were 86.0% and 61.3% (internal validation), and 83.5% and 57.5% (external validation). The sensitivity and PPV of central emboli were 89.7% and 52.0% (internal validation), and 94.4% and 43.0% (external validation); of lobar emboli were 95.2% and 76.9% (internal validation), and 93.5% and 72.5% (external validation); and of peripheral emboli were 82.6% and 61.7% (internal validation), and 80.2% and 59.4% (external validation). The average false positive rate was 0.45 false emboli per scan (internal validation) and 0.69 false emboli per scan (external validation).<BR><B>CONCLUSIONS: </B>The EmbNet provides high sensitivity across embolus locations, suggesting its potential utility for initial screening in clinical practice.