Evidence map›Paper›PMID 40304828›Full record

ArticleJournal of robotic surgery2025

Pioneering AI-guided fluorescence-like navigation in urological surgery: real-time ureter segmentation during robot-assisted radical cystectomy using convolutional neural network.

Wei Chen, Shohei Fukuda, Soichiro Yoshida, Nao Kobayashi, Kyohei Fukada, Munenori Fukunishi, Yuhi Otani, Shunya Matsumoto, Masaki Kobayashi, Yuki Nakamura and 7 more

Abstract read
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Article in Journal of robotic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Wei ChenDepartment of Urology, Institute of Science Tokyo, Tokyo, Japan.
Shohei FukudaDepartment of Urology, Institute of Science Tokyo, Tokyo, Japan. shouro@tmd.ac.jp.
Soichiro YoshidaDepartment of Urology, Institute of Science Tokyo, Tokyo, Japan.
Nao KobayashiAnaut Inc., Tokyo, Japan.
Kyohei FukadaAnaut Inc., Tokyo, Japan.
Munenori FukunishiAnaut Inc., Tokyo, Japan.
Yuhi OtaniAnaut Inc., Tokyo, Japan.
Shunya MatsumotoDepartment of Urology, Institute of Science Tokyo, Tokyo, Japan.
Masaki KobayashiDepartment of Urology, Institute of Science Tokyo, Tokyo, Japan.
Yuki NakamuraDepartment of Urology, Institute of Science Tokyo, Tokyo, Japan.
Bo FanDepartment of Urology, Institute of Science Tokyo, Tokyo, Japan.
Yudai IshikawaDepartment of Urology, Institute of Science Tokyo, Tokyo, Japan.
Hiroshi FukushimaDepartment of Urology, Institute of Science Tokyo, Tokyo, Japan.
Guangqing FuDepartment of Urology, Zigong Fourth People's Hospital, Sichuan, China.
Yuma WasedaDepartment of Urology, Institute of Science Tokyo, Tokyo, Japan.
Hajime TanakaDepartment of Urology, Institute of Science Tokyo, Tokyo, Japan.
Yasuhisa FujiiDepartment of Urology, Institute of Science Tokyo, Tokyo, Japan.

Funding

Science and Technology Project of Sichuan Health Commission 2024CXTD06Sichuan Province Science and Technology Support Program 2024JDHJ0045
6 · The paper itself

Abstract

Artificial intelligence (AI)-driven intraoperative navigation in urological surgery can enhance surgical precision through real-time structure identification and tracking. This study describes a novel AI solution that enables real-time fluorescence-like navigation (FLN) for robot-assisted radical cystectomy (RARC) with an initial focus on ureter, potentially enhancing outcomes and training efficacy. We established a new AI model using convolutional neural network (CNN) to achieve real-time intraoperative ureter recognition using 730 images from 17 RARC cases. Quantitative and qualitative analyses were performed for each procedure (phases I-V: identifying, exposing, elevating, retracting, distal separation). For quantitative evaluation, we calculated precision, recall, intersection over union (IoU), and Dice coefficients by comparing AI-inferred images with surgeons' annotations on 41 test images. In addition, 18 surgeons participated in a qualitative assessment, answering questions on identification, misidentification, and clinical utility. The CNN-based prediction model has been successfully established and validated. The AI model achieved a Dice score of 0.71 (phases I-V, respectively: 0.75/0.69/0.71/0.69/0.72), IoU of 0.55 (0.60/0.53/0.55/0.52/0.57), recall of 0.90 (0.91/0.92/0.92/0.89/0.89), precision of 0.60 (0.67/0.55/0.58/0.61/0.64), and accuracy of 0.99 (1.00/0.99/0.99/0.99/0.99). For identification performance, the AI system scored an average of 4.74 on a scale of 0 to 5 (4.94/4.61/4.94/4.89/4.33), indicating that most images achieved > 80% recognition accuracy. The average score for misidentification of other tissues as ureter was low at 0.60 on a scale of 0 to 5 (phases I-V: 0.11/0.56/0.39/0.44/1.50). In the clinical utility assessment, 62.22% (60.00%-64.44%) of the AI-inferred images were correctly distinguished from the ground truth. Our AI model reliably annotated the ureter in real-time during RARC, achieving high accuracy and acceptable precision. This technology has the potential to reduce the risk of ureter misrecognition by surgeons, thereby enhancing surgical accuracy and safety.

Indexed as

Artificial IntelligenceCystectomyNeural Networks, ComputerRobotic Surgical ProceduresSurgery, Computer-AssistedUreterUrologic Surgical ProceduresConvolutional Neural NetworksHumansMaleArtificial intelligenceConvolutional neural networkMuscle invasive bladder cancerRobot-assisted radical cystectomySurgical navigation

Identifiers

PMID40304828

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.