Evidence map›Paper›PMID 40779134›Full record

ArticleSurgical endoscopy2025

Automatic surgical skill assessment using a task classification model in laparoscopic sigmoidectomy.

Keisuke Obuchi, Shin Takenaka, Daichi Kitaguchi, Kei Nakajima, Yuto Ishikawa, Hiroki Mitarai, Kyoko Ryu, Nobuyoshi Takeshita, Akinobu Taketomi, Masaaki Ito

Abstract read
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In one paragraph

Article in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

10 authors.

Keisuke ObuchiDepartment of Colorectal Surgery, National Cancer Center Hospital East, 6‑5‑1, Kashiwanoha, Kashiwa‑City, Chiba, 277‑8577, Japan.
Shin TakenakaSurgical Device Innovation Office, National Cancer Center Hospital East, 6‑5‑1, Kashiwanoha, Kashiwa‑City, Chiba, 277‑8577, Japan.
Daichi KitaguchiDepartment of Colorectal Surgery, National Cancer Center Hospital East, 6‑5‑1, Kashiwanoha, Kashiwa‑City, Chiba, 277‑8577, Japan.
Kei NakajimaSurgical Device Innovation Office, National Cancer Center Hospital East, 6‑5‑1, Kashiwanoha, Kashiwa‑City, Chiba, 277‑8577, Japan.
Yuto IshikawaSurgical Device Innovation Office, National Cancer Center Hospital East, 6‑5‑1, Kashiwanoha, Kashiwa‑City, Chiba, 277‑8577, Japan.
Hiroki MitaraiSurgical Device Innovation Office, National Cancer Center Hospital East, 6‑5‑1, Kashiwanoha, Kashiwa‑City, Chiba, 277‑8577, Japan.
Kyoko RyuSurgical Device Innovation Office, National Cancer Center Hospital East, 6‑5‑1, Kashiwanoha, Kashiwa‑City, Chiba, 277‑8577, Japan.
Nobuyoshi TakeshitaSurgical Device Innovation Office, National Cancer Center Hospital East, 6‑5‑1, Kashiwanoha, Kashiwa‑City, Chiba, 277‑8577, Japan.
Akinobu TaketomiDepartment of Gastroenterological Surgery I, Graduate School of Medicine, Hokkaido University, Sapporo, Japan.
Masaaki ItoDepartment of Colorectal Surgery, National Cancer Center Hospital East, 6‑5‑1, Kashiwanoha, Kashiwa‑City, Chiba, 277‑8577, Japan. maito@east.ncc.go.jp.ORCID http://orcid.org/0000-0002-1101-2707

Funding

Japan Agency for Medical Research and Development JP21vk0124001
6 · The paper itself

Abstract

backgroundIn surgery, the dissection-exposure time ratio indicates surgery efficiency and relates to surgical proficiency in laparoscopic colorectal cancer surgery. This study aimed to develop an artificial intelligence (AI) model that automatically recognizes dissection and exposure times to explore surgical skill assessment.

methodsVideo datasets were constructed using laparoscopic sigmoidectomy (Lap-S) videos submitted to the Endoscopic Surgical Skill Qualification System (ESSQS). Videos were classified according to surgical skill levels into those with ESSQS total scores + 2 SD (standard deviations) above or - 2 SD below the average range: " + 2SD" and "- 2SD" groups, respectively. The times taken for dissection (D time), exposure (E time), invalid time (not contributing to the surgical progress) (I time), and Outside (the camera is outside the body cavity) were defined and annotated on the still images. The D/E ratio and D-E transition (number of times D and E are switched) were calculated. A convolutional neural network-based image classification model was developed, and each parameter was compared.

resultsOverall, 57 patients were included: + 2SD group, 26; - 2SD group, 31. The test data in both groups encompassed 386,721 frames: 223,954, 108,801, 35,304, and 18,212 in the D, E, I, and Outside times, respectively. The f1 scores for the DEI classification model were 0.92, 0.82, and 0.74 for D, E, and I. In the AI model, D time average was 3328 (± 739 SD) and 4073 (± 1018 SD) frames, and E time average was 1678 (± 681 SD) and 2748 (± 1337 SD) frames in the + 2SD and - 2SD groups (both p < .01). The mean D-E transition was 204 (± 96 SD) in the + 2SD group -significantly lower than that of the - 2SD group: 405 (± 188 SD; p < .01).

conclusionsNew AI model automatically classifies Lap-S videos according to surgical proficiency based on "DEI" parameters and may help improve surgical quality and education.

Indexed as

Artificial IntelligenceClinical CompetenceColectomyColon, SigmoidLaparoscopyAgedFemaleHumansMaleMiddle AgedNeural Networks, ComputerOperative TimeVideo RecordingArtificial intelligenceAutomatic surgical skill assessmentDEI classification modelLaparoscopic sigmoidectomy

Identifiers

PMID40779134

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.