BJU international · 2026
AI-based anatomical recognition for bladder neck dissection in robot-assisted radical prostatectomy.
СтатьяНазвание и аннотация: Перевод в очереди
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Fujiwara Shinnosuke, Fukumoto Keishiro, Takeuchi Masashi, Konnai Yuichiro, Yasumizu Yota, Tanaka Nobuyuki, Takeda Toshikazu, Matsumoto Kazuhiro, Kosaka Takeo, Kawakubo Hirofumi, Ishida Masaru, Kitagawa Yuko, Oya Mototsugu
Аннотация
OBJECTIVE: To develop an artificial intelligence (AI) system for anatomical recognition that can automatically identify key anatomical structures during bladder neck dissection in robot-assisted radical prostatectomy (RARP), a critical yet technically demanding step.
MATERIALS AND METHODS: We constructed an AI model and evaluated its accuracy using 210 RARP videos from two institutions. A total of 1353 frames of bladder neck dissection were extracted from 25 videos, and the boundaries of the prostate, bladder and retrotrigonal layer were annotated. The DeepLabV3+ model was employed in developing the anatomical recognition AI model. An active learning approach was applied by iteratively retraining the model using frames with poor initial performance. The model was further evaluated using 202 independent test frames derived from seven surgeons. Segmentation performance was evaluated using the intersection over union (IoU) and Dice similarity coefficient.
RESULTS: The IoU of the initial AI model was 0.41 for the prostate and 0.44 for the bladder. The retrained model demonstrated improved segmentation performance, with final IoU values of 0.75 and 0.68, respectively. In an independent test dataset derived from seven surgeons, segmentation performance for the prostate and bladder was maintained.
CONCLUSION: We developed an AI system for anatomical recognition and improved its accuracy by using retraining processes. The retrained AI model demonstrated high accuracy, and its use is anticipated to support surgeons and aid in the education of novice surgeons.
MATERIALS AND METHODS: We constructed an AI model and evaluated its accuracy using 210 RARP videos from two institutions. A total of 1353 frames of bladder neck dissection were extracted from 25 videos, and the boundaries of the prostate, bladder and retrotrigonal layer were annotated. The DeepLabV3+ model was employed in developing the anatomical recognition AI model. An active learning approach was applied by iteratively retraining the model using frames with poor initial performance. The model was further evaluated using 202 independent test frames derived from seven surgeons. Segmentation performance was evaluated using the intersection over union (IoU) and Dice similarity coefficient.
RESULTS: The IoU of the initial AI model was 0.41 for the prostate and 0.44 for the bladder. The retrained model demonstrated improved segmentation performance, with final IoU values of 0.75 and 0.68, respectively. In an independent test dataset derived from seven surgeons, segmentation performance for the prostate and bladder was maintained.
CONCLUSION: We developed an AI system for anatomical recognition and improved its accuracy by using retraining processes. The retrained AI model demonstrated high accuracy, and its use is anticipated to support surgeons and aid in the education of novice surgeons.