Evidence map›Paper›PMID 39214877›Full record

ArticleSurgical endoscopy2024

Automated surgical skill assessment in colorectal surgery using a deep learning-based surgical phase recognition model.

Kei Nakajima, Daichi Kitaguchi, Shin Takenaka, Atsuki Tanaka, Kyoko Ryu, Nobuyoshi Takeshita, Yusuke Kinugasa, Masaaki Ito

Abstract read
PubMed Publisher
In one paragraph

Article in Surgical endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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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

8 authors.

Kei NakajimaDepartment 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.
Shin TakenakaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Atsuki TanakaDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Kyoko RyuDepartment 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.
Yusuke KinugasaDepartment of Gastrointestinal Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-Ku, Tokyo, 113-8510, Japan.
Masaaki ItoDepartment for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan. maito@east.ncc.go.jp.ORCID 0000-0002-1101-2707

Funding

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

Abstract

backgroundThere is an increasing demand for automated surgical skill assessment to solve issues such as subjectivity and bias that accompany manual assessments. This study aimed to verify the feasibility of assessing surgical skills using a surgical phase recognition model.

methodsA deep learning-based model that recognizes five surgical phases of laparoscopic sigmoidectomy was constructed, and its ability to distinguish between three skill-level groups-the expert group, with a high Endoscopic Surgical Skill Qualification System (ESSQS) score (26 videos); the intermediate group, with a low ESSQS score (32 videos); and the novice group, with an experience of < 5 colorectal surgeries (27 videos)-was assessed. Furthermore, 1 272 videos were divided into three groups according to the ESSQS score: ESSQS-high, ESSQS-middle, and ESSQS-low groups, and whether they could be distinguished by the score calculated by multiple regression analysis of the parameters from the model was also evaluated.

resultsThe time for mobilization of the colon, time for dissection of the mesorectum plus transection of the rectum plus anastomosis, and phase transition counts were significantly shorter or less in the expert group than in the intermediate (p = 0.0094, 0.0028, and < 0.001, respectively) and novice groups (all p < 0.001). Mesorectal excision time was significantly shorter in the expert group than in the novice group (p = 0.0037). The group with higher ESSQS scores also had higher AI scores.

conclusionThis model has the potential to be applied to automated skill assessments.

Indexed as

Clinical CompetenceColorectal SurgeryDeep LearningLaparoscopyAdultColectomyColon, SigmoidFeasibility StudiesFemaleHumansMaleMiddle AgedOperative TimeAutomatedDeep learningESSQS scoreSkill assessment

Identifiers

PMID39214877

What OpenQuestion holds

Textmetadata
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Registered trials

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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.