Development and Validation of a Modified Three-Dimensional U-Net Deep-Learning Model for Automated Detection of Lung Nodules on Chest CT Images From the Lung Image Database Consortium and Japanese Datasets
| Autor: | Shigeki Aoki, Kazuhiro Suzuki, Yukihiro Nomura, Ryohei Kuwatsuru, Yujiro Otsuka, Kanako K. Kumamaru |
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| Rok vydání: | 2022 |
| Předmět: |
Nodule detection
Lung Neoplasms Chest ct 030218 nuclear medicine & medical imaging 03 medical and health sciences Deep Learning 0302 clinical medicine Japan medicine Humans Radiology Nuclear Medicine and imaging Lung Bootstrapping (statistics) Retrospective Studies business.industry Deep learning External validation Confidence interval medicine.anatomical_structure Image database 030220 oncology & carcinogenesis Artificial intelligence Tomography X-Ray Computed Nuclear medicine business |
| Zdroj: | Academic Radiology. 29:S11-S17 |
| ISSN: | 1076-6332 |
| DOI: | 10.1016/j.acra.2020.07.030 |
| Popis: | Rationale and Objectives A more accurate lung nodule detection algorithm is needed. We developed a modified three-dimensional (3D) U-net deep-learning model for the automated detection of lung nodules on chest CT images. The purpose of this study was to evaluate the accuracy of the developed modified 3D U-net deep-learning model. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant, Institutional Review Board-approved retrospective study, the 3D U-net based deep-learning model was trained using the Lung Image Database Consortium and Image Database Resource Initiative dataset. For internal model validation, we used 89 chest CT scans that were not used for model training. For external model validation, we used 450 chest CT scans taken at an urban university hospital in Japan. Each case included at least one nodule of >5 mm identified by an experienced radiologist. We evaluated model accuracy using the competition performance metric (CPM) (average sensitivity at 1/8, 1/4, 1/2, 1, 2, 4, and 8 false-positives per scan). The 95% confidence interval (CI) was computed by bootstrapping 1000 times. Results In the internal validation, the CPM was 94.7% (95% CI: 89.1%–98.6%). In the external validation, the CPM was 83.3% (95% CI: 79.4%–86.1%). Conclusion The modified 3D U-net deep-learning model showed high performance in both internal and external validation. |
| Databáze: | OpenAIRE |
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