Evidence map›Paper›PMID 39812861›Full record

ArticleLangenbeck's archives of surgery2025

Objective surgical skill assessment based on automatic recognition of dissection and exposure times in robot-assisted radical prostatectomy.

Kodai Sato, Shin Takenaka, Daichi Kitaguchi, Xue Zhao, Atsushi Yamada, Yuto Ishikawa, Nobushige Takeshita, Nobuyoshi Takeshita, Shinichi Sakamoto, Tomohiko Ichikawa and 1 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Langenbeck's archives of surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
–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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Review
  5. Article
  6. 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

11 authors.

Kodai SatoDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.ORCID http://orcid.org/0009-0003-7573-9547
Shin TakenakaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Daichi KitaguchiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Xue ZhaoDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Atsushi YamadaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Yuto IshikawaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Nobushige TakeshitaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Nobuyoshi TakeshitaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Shinichi SakamotoDepartment of Urology, Graduate School of Medicine, Chiba University, Chiba, Japan.
Tomohiko IchikawaDepartment of Urology, Graduate School of Medicine, Chiba University, Chiba, Japan.
Masaaki ItoDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan. matio@east.ncc.go.jp.

Funding

National Cancer Center Japan Research and Development Fund 2021-A-9
6 · The paper itself

Abstract

purposeAssessing surgical skills is vital for training surgeons, but creating objective, automated evaluation systems is challenging, especially in robotic surgery. Surgical procedures generally involve dissection and exposure (D/E), and their duration and proportion can be used for skill assessment. This study aimed to develop an AI model to acquire D/E parameters in robot-assisted radical prostatectomy (RARP) and verify if these parameters could distinguish between novice and expert surgeons.

methodsThis retrospective study used 209 RARP videos from 18 Japanese institutions. Dissection time was defined as the duration of forceps energy activation, and exposure time as the combined duration of manipulating the third arm and camera. To measure these times, an AI-based interface recognition model was developed to automatically extract instrument status from the da Vinci Surgical System

resultsThe overall accuracies of the UI recognition model for recognizing the forceps type, energy activation status, and camera usage status were 0.991, 0.998, and 0.991, respectively. Dissection time was 45.2 vs. 35.1 s (novice vs. expert, p = 0.374), exposure time was 195.7 vs. 89.7 s (novice vs. expert, p < 0.001), and the D/E ratio was 0.174 vs. 0.315 (novice vs. expert, p = 0.003).

conclusionsWe successfully developed a model to automatically acquire dissection and exposure parameters for RARP. Exposure time may serve as an objective parameter to distinguish between novices and experts in RARP, and automated technical evaluation in RARP is feasible. TRIAL REGISTRATION NUMBER AND DATE: This study was approved by the Institutional Review Board of the National Cancer Center Hospital East (No.2020 - 329) on January 28, 2021.

Indexed as

Clinical CompetenceDissectionProstatectomyRobotic Surgical ProceduresArtificial IntelligenceHumansJapanMaleOperative TimeProstatic NeoplasmsRetrospective StudiesAutomationDissectionExposureProstatectomyRobotic surgical proceduresSurgical skill assessment

Identifiers

PMID39812861
PMCPMC11735544

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.