Detection and position evaluation of chest percutaneous drainage catheter on chest radiographs using deep learning.

PURPOSE

This study aimed to develop an algorithm for the automatic detecting chest percutaneous catheter drainage (PCD) and evaluating catheter positions on chest radiographs using deep learning.

METHODS

This retrospective study included 1,217 chest radiographs (proper positioned: 937; malpositioned: 280) from a total of 960 patients underwent chest PCD from October 2017 to February 2023. The tip location of the chest PCD was annotated using bounding boxes and classified as proper positioned and malpositioned. The radiographs were randomly allocated into the training, validation sets (total: 1,094 radiographs; proper positioned: 853 radiographs; malpositioned: 241 radiographs), and test datasets (total: 123 radiographs; proper positioned: 84 radiographs; malpositioned: 39 radiographs). The selected AI model was used to detect the catheter tip of chest PCD and evaluate the catheter's position using the test dataset to distinguish between properly positioned and malpositioned cases. Its performance in detecting the catheter and assessing its position on chest radiographs was evaluated by per radiographs and per instances. The association between the position and function of the catheter during chest PCD was evaluated.

RESULTS

In per chest radiographs, the selected model's accuracy was 0.88. The sensitivity and specificity were 0.86 and 0.92, respectively. In per instance, the selected model's the mean Average Precision 50 (mAP50) was 0.86. The precision and recall were 0.90 and 0.79 respectively. Regarding the association between the position and function of the catheter during chest PCD, its sensitivity and specificity were 0.93 and 0.95, respectively.

CONCLUSION

The artificial intelligence model for the automatic detection and evaluation of catheter position during chest PCD on chest radiographs demonstrated acceptable diagnostic performance and could assist radiologists and clinicians in the early detection of catheter malposition and malfunction during chest percutaneous catheter drainage.