Clinical Application Of Deep Learning-assisted Needles Reconstruction In Prostate Ultrasound Brachytherapy.

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High dose rate (HDR) prostate brachytherapy (BT) procedure requires image-guided needle insertion. Given that general anesthesia is often employed during the procedure, minimizing overall planning time is crucial. In this study, we explore the clinical feasibility and time-saving potential of artificial intelligence (AI)-driven auto-reconstruction of transperineal needles in the context of US-guided prostate BT planning. This study included a total of 102 US-planned BT images from a single institution and split into three groups: 50 for model training and validation, 11 to evaluate reconstruction accuracy (test set), and 41 to evaluate the AI tool in a clinical implementation (clinical set). Reconstruction accuracy for the test set was evaluated by comparing the performance of AI-derived and manually reconstructed needles from 5 medical physicists on the 3D-US scans after treatment. The needle total reconstruction time for the clinical set was defined as the timestamp difference from scan acquisition to the start of dose calculations and was compared to values recorded before the clinical implementation of the AI-assisted tool. A mean error of (0.44±0.32)mm was found between the AI-reconstructed and the human consensus needle positions in the test set, with 95.0% of AI needle points falling below 1mm from their human-made counterparts. Post-hoc analysis showed only one of the human observers' reconstructions were significantly different from the others including the AI's. In the clinical set, the AI algorithm achieved a true positive reconstruction rate of 93.4% with only 4.5% of these needles requiring manual corrections from the planner before dosimetry. Total time required to perform AI-assisted catheter reconstruction on clinical cases was on average 15.2min lower (p < 0.01) compared to procedure without AI assistance. This study demonstrates the feasibility of an AI-assisted needle reconstructing tool for 3D-US based HDR prostate BT. This is a step toward treatment planning automation and increased efficiency in HDR prostate BT.