Evidence map›Paper›PMID 40065440›Full record

ArticleBreast cancer research : BCR2025

Development of a deep learning-based model for guiding a dissection during robotic breast surgery.

Jeea Lee, Sungwon Ham, Namkug Kim, Hyung Seok Park

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Breast cancer research : BCR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. 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

4 authors.

Jeea Lee *Department of Surgery, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu-si, Gyeonggi-do, South Korea.
Sungwon Ham *Healthcare Readiness Institute for Unified Korea, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City, 15355, Gyeonggi-do, South Korea.
Namkug KimDepartment of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea. namkugkim@gmail.com.
Hyung Seok ParkDepartment of Surgery, Yonsei University College of Medicine, 50-1, Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea. imgenius@yuhs.ac.

Funding

Ministry of Science and ICT, South Korea 2022R1C1C1010441Ministry of Science and ICT, South Korea 2022R1I1A1A01071908
6 · The paper itself

Abstract

backgroundTraditional surgical education is based on observation and assistance in surgical practice. Recently introduced deep learning (DL) techniques enable the recognition of the surgical view and automatic identification of surgical landmarks. However, there was no previous studies have conducted to develop surgical guide for robotic breast surgery. To develop a DL model for guiding the dissection plane during robotic mastectomy for beginners and trainees.

methodsTen surgical videos of robotic mastectomy procedures were recorded. Video frames taken at 1-s intervals were converted to PNG format. The ground truth was manually delineated by two experienced surgeons using ImageJ software. The evaluation metrics were the Dice similarity coefficient (DSC) and Hausdorff distance (HD).

resultsA total of 8,834 images were extracted from ten surgical videos of robotic mastectomies performed between 2016 and 2020. Skin flap dissection during the robotic mastectomy console time was recorded. The median age and body mass index of the patients was 47.5 (38-52) years and 22.00 (19.30-29.52) kg/m

conclusionDL can serve as a surgical guide for beginners and trainees, and can be used as a training tool to enhance surgeons' surgical skills.

Indexed as

Breast NeoplasmsDeep LearningDissectionMastectomyRobotic Surgical ProceduresAdultFemaleHumansMiddle AgedVideo RecordingArtificial intelligenceBreast neoplasmsDeep learningMinimally invasive surgical proceduresRobotic mastectomy

Identifiers

PMID40065440
PMCPMC11895239

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LicenceCC BY
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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.