FIGURE 1.
Evolution of PE detection granularity in CAD. Study-level classification provides binary prediction for the entire CTPA volume, enabling rapid triage. Slice-level detection identifies specific axial slices containing PE. Object detection localizes individual emboli through bounding boxes. Voxel-level segmentation enables precise delineation of emboli boundaries and quantitative analysis of clot burden. This progression demonstrates increasing granularity and clinical utility, from initial triage to detailed morphological analysis. Images adapted from the RSPECT dataset.22
TABLE 1.
Publicly available PE datasets. The progression from study-level to voxel-level annotations shows the inverse relationship between annotation detail and dataset size, reflecting the increased annotation effort required for more detailed labels.
TABLE 2.
Evolution of study-level PE detection method performance (2002–2024). The progression shows significant improvement from early approaches with high false positive rates to modern AI systems achieving better balance between sensitivity and specificity.
| Author | Year | Sensitivity (%) | Specificity (%) | False positive PEs/case | PPV (%) | F1 (%) | Train size (scans) | Test size (scans) |
|---|---|---|---|---|---|---|---|---|
| Masutani et al.26 | 2002 | 100.0/85.0* | – | 7.7/2.6 | 11 | 19.8 | 11 | 19 |
| Pichon et al.12 | 2004 | 86 | – | 6.3 | – | – | 6 | |
| Maizlin et al.33 | 2007 | 53.3 | 77.5 | 1 | 28.5 | 37.4 | – | 104 |
| Engelke et al.27 | 2008 | 30.7 | – | 4.1 | – | – | 56 | |
| Das et al.35 | 2008 | 83 | 80 | 4 | – | – | 43 | |
| Zhou et al.36 | 2009 | 80 | – | 18.9 | 59 | 6 | ||
| Wittenberg et al.34 | 2010 | 94 | 21 | 4.7 | – | 292 | ||
| Tajbakhsh et al.28 | 2015 | 83.4 | – | 2 | – | 121 | ||
| Huang et al.29 | 2020 | 75 | 81 | – | 77 | 75.9 | 1461 | 369 |
| Weikert et al.31 | 2020 | 92.7 | 95.5 | 0.12 | 86 | 28,000 | 1,465 | |
| Ma et al.37 | 2022 | 86 | 85 | – | – | – | 5,292 | 1,000 |
| Condrea et al.32 | 2024 | 92.9 | 96.1 | 0.15 | – | 91 | 6,133 | 836 |
| Doğan et al.38 | 2024 | 96.2 | 93.4 | – | – | – | 38 | 12 |
TABLE 3.
Performance comparison of slice-level PE detection methods. The table summarizes sensitivity, specificity, and AUC values reported in recent studies, showing the evolution of detection capabilities across different architectures and datasets.
TABLE 4.
Bounding box detection performance for PE detection. Results reported at mAP at 0.5 IoU. Due to the very small amount of data available and the granularity of the task, dataset sizes are reported in number of annotated images.
TABLE 5.
Per-embolus localization performance in PE detection. Note: Direct comparison between methods should be made with caution due to varying evaluation protocols and matching criteria between predicted and ground truth emboli.
| Author | Year | Recall | PPV | F1 | Train size (scans) | Test size (scans) |
|---|---|---|---|---|---|---|
| Özkan et al.44 | 2014 | 95.1 | 52.6 | 67.7 | 142 | 33 |
| Tajbakhsh et al.28 | 2015 | 83.4 | 47.2 | 60.3 | 121 | 20 |
| Tajbakhsh et al.45 | 2019 | 32.9 | 98.6 | 49.4 | 121 | 20 |
| Weikert et al.31 | 2020 | 82.2 | 86.8 | 85.8 | 30,000 | 1,465 |
| Xu et al.42 | 2023 | 93.2 | 51.2 | 66.1 | 113 | – |
| Pu et al.46 WSL* | 2023 | 61.8 | 78.2 | 69.1 | 6,415 | 91 |
| Zhu et al.47 | 2024 | 86 | 61.3 | 71.6 | 142 | 410 |
| Condrea et al.48 WSL* | 2024 | 66.9 | 77.0 | 71.6 | 11329 | 445 |
| Condrea et al.48 Finetune** | 2024 | 73.9 | 77.5 | 75.5 | 111 | 334 |
TABLE 6.
Performance of PE segmentation Methods. Results show the progression of segmentation accuracy using the DSC and other relevant metrics.