Evidence map›Paper›PMID 39419830›Full record

ArticleLangenbeck's archives of surgery2024

Deep learning-based surgical step recognition for laparoscopic right-sided colectomy.

Ryoya Honda, Daichi Kitaguchi, Yuto Ishikawa, Norihito Kosugi, Kazuyuki Hayashi, Hiro Hasegawa, Nobuyoshi Takeshita, Masaaki Ito

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In one paragraph

Article in Langenbeck's archives of surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ryoya Honda *Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, Chiba, Japan.
Daichi Kitaguchi *Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba, Japan.
Yuto IshikawaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, Chiba, Japan.
Norihito KosugiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, Chiba, Japan.
Kazuyuki HayashiDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, Chiba, Japan.
Hiro HasegawaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, Chiba, Japan.
Nobuyoshi TakeshitaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, Chiba, Japan.
Masaaki ItoDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, Chiba, Japan. maito@east.ncc.go.jp.

Funding

Japan Agency for Medical Research and Development 20he2302003h0002
6 · The paper itself

Abstract

purposeUnderstanding the complex anatomy and surgical steps involved in laparoscopic right-sided colectomy (LAP-RC) is essential for standardizing the surgical procedure. Deep-learning (DL)-based computer vision can achieve this. This study aimed to develop a step recognition model for LAP-RC using a dataset of surgical videos with annotated step information and evaluate its recognition performance.

methodsThis single-center retrospective study utilized a video dataset of laparoscopic ileocecal resection (LAP-ICR) and laparoscopic right-sided hemicolectomy (LAP-RHC) for right-sided colon cancer performed between January 2018 and March 2022. The videos were split into still images, which were divided into training, validation, and test sets using 66%, 17%, and 17% of the data, respectively. Videos were manually classified into eight main steps: 1) medial mobilization, 2) central vascular ligation, 3) dissection of the superior mesenteric vein, 4) retroperitoneal mobilization, 5) lateral mobilization, 6) cranial mobilization, 7) mesocolon resection, and 8) intracorporeal anastomosis. In a simpler version, consecutive surgical steps were combined, resulting in five steps. Precision, recall, F1 scores, and overall accuracy were assessed to evaluate the model's performance in the surgical step classification task.

resultsSeventy-eight patients were included; LAP-ICR and LAP-RHC were performed in 35 (44%) and 44 (56%) patients, respectively. The overall accuracy was 72.1% and 82.9% for the eight-step and combined five-step classification tasks, respectively.

conclusionsThe automatic surgical step-recognition model for LAP-RCs, developed using a DL algorithm, exhibited a fairly high classification performance. A model that understands the complex steps of LAP-RC will aid the standardization of the surgical procedure.

Indexed as

ColectomyColonic NeoplasmsDeep LearningLaparoscopyAgedFemaleHumansMaleMiddle AgedRetrospective StudiesVideo RecordingConvolutional neural networkDeep learningLaparoscopic right-sided colectomyPhase recognitionReal-time automatic recognitionStandardization

Identifiers

PMID39419830

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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.