Evidence map›Paper›PMID 31797047›Full record

Observational studySurgical endoscopy2020

Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach.

Daichi Kitaguchi, Nobuyoshi Takeshita, Hiroki Matsuzaki, Hiroaki Takano, Yohei Owada, Tsuyoshi Enomoto, Tatsuya Oda, Hirohisa Miura, Takahiro Yamanashi, Masahiko Watanabe and 4 more

Registry-linked trialAbstract readObservational Study
PubMed Publisher
In one paragraph

Observational study in Surgical endoscopy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06864702 (The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging), which is not on this map. Cited by 83 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
83citing papers in PubMed, 7 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.

NCT06864702 naenrolling by invitationnot on this mapstarted 2023, after this paper: background citation

The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging: A Retrospective Case Analysis Clinical Study

TypeinterventionalSponsorZhujiang HospitalRan2023 to 2025Enrolled220ConditionsArtificial Intelligence (AI), Deep Learning, Laparoscopic SurgeryArmsWhether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominal
3 · Its place in the literature

Who cites it

83 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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23 more citing papers are in PubMed but not listed here.

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

14 authors.

Daichi KitaguchiDepartment of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan.
Nobuyoshi TakeshitaDepartment of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan. ntakeshi@east.ncc.go.jp.
Hiroki MatsuzakiSurgical Device Innovation Office, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan.
Hiroaki TakanoSurgical Device Innovation Office, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan.
Yohei OwadaDepartment of Gastrointestinal and Hepato-Biliary-Pancreatic Surgery, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, 305-8575, Japan.
Tsuyoshi EnomotoDepartment of Gastrointestinal and Hepato-Biliary-Pancreatic Surgery, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, 305-8575, Japan.
Tatsuya OdaDepartment of Gastrointestinal and Hepato-Biliary-Pancreatic Surgery, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, 305-8575, Japan.
Hirohisa MiuraDepartment of Surgery, Kitasato University School of Medicine, Kitasato 1-15-1, Minami-ku, Sagamihara, 252-0374, Japan.
Takahiro YamanashiDepartment of Surgery, Kitasato University School of Medicine, Kitasato 1-15-1, Minami-ku, Sagamihara, 252-0374, Japan.
Masahiko WatanabeDepartment of Surgery, Kitasato University School of Medicine, Kitasato 1-15-1, Minami-ku, Sagamihara, 252-0374, Japan.
Daisuke SatoMICIN, Inc, Nihon Bldg 13F, Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan.
Yusuke SugomoriMICIN, Inc, Nihon Bldg 13F, Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan.
Seigo HaraMICIN, Inc, Nihon Bldg 13F, Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan.
Masaaki ItoDepartment of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa-City, Chiba, 277-8577, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAutomatic surgical workflow recognition is a key component for developing the context-aware computer-assisted surgery (CA-CAS) systems. However, automatic surgical phase recognition focused on colorectal surgery has not been reported. We aimed to develop a deep learning model for automatic surgical phase recognition based on laparoscopic sigmoidectomy (Lap-S) videos, which could be used for real-time phase recognition, and to clarify the accuracies of the automatic surgical phase and action recognitions using visual information.

methodsThe dataset used contained 71 cases of Lap-S. The video data were divided into frame units every 1/30 s as static images. Every Lap-S video was manually divided into 11 surgical phases (Phases 0-10) and manually annotated for each surgical action on every frame. The model was generated based on the training data. Validation of the model was performed on a set of unseen test data. Convolutional neural network (CNN)-based deep learning was also used.

resultsThe average surgical time was 175 min (± 43 min SD), with the individual surgical phases also showing high variations in the duration between cases. Each surgery started in the first phase (Phase 0) and ended in the last phase (Phase 10), and phase transitions occurred 14 (± 2 SD) times per procedure on an average. The accuracy of the automatic surgical phase recognition was 91.9% and those for the automatic surgical action recognition of extracorporeal action and irrigation were 89.4% and 82.5%, respectively. Moreover, this system could perform real-time automatic surgical phase recognition at 32 fps.

conclusionsThe CNN-based deep learning approach enabled the recognition of surgical phases and actions in 71 Lap-S cases based on manually annotated data. This system could perform automatic surgical phase recognition and automatic target surgical action recognition with high accuracy. Moreover, this study showed the feasibility of real-time automatic surgical phase recognition with high frame rate.

Indexed as

Deep LearningColectomyColon, SigmoidComputer SystemsHumansLaparoscopyOperative TimeRetrospective StudiesSurgery, Computer-AssistedWorkflowConvolutional neural networkDeep learningLaparoscopic sigmoidectomyPhase recognitionReal-time automatic recognitionSurgical action recognition

Identifiers

PMID31797047

What OpenQuestion holds

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Registered trials

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.