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. 2025 Mar 10;27(1):34.
doi: 10.1186/s13058-025-01981-3.

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

Jeea Lee #  1   2 , Sungwon Ham #  3 , Namkug Kim  4   5 , Hyung Seok Park  6   7
Affiliations

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

Jeea Lee et al. Breast Cancer Res. .

Abstract

Background: Traditional 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.

Methods: Ten 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).

Results: A 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/m2, respectively, and the median console time was 32 (21-48) min. Among the 8,834 images, 428 were selected and divided into training, validation, and testing datasets at a ratio of 7:1:2. Two experts determined that the DSC of our model was 0.828[Formula: see text]5.28 and 0.818[Formula: see text]6.96, while the HDs were 9.80[Formula: see text]2.57 and 10.32[Formula: see text]1.09.

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

Keywords: Artificial intelligence; Breast neoplasms; Deep learning; Minimally invasive surgical procedures; Robotic mastectomy.

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Conflict of interest statement

Declarations. Ethical approval: This study was approved by Severance Hospital Institutional Review Board (approval no. 4-2021-0241). Written informed consent was obtained from the patient for publication of accompanying images videos. A copy of the written consent is available for review by the Editor-in-Chief of this journal on request. Competing of interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Schematic flow of the current study
Fig. 2
Fig. 2
Schematic structures of network architectures. (a) Modified EfficientDet. (b) YOLO v5. (c) RetinaNet
Fig. 3
Fig. 3
Process of learning the imaginary boxes and lines. (a) Generation of the bounding box. (b) Imaginary line connecting the boxes and extending the line to both ends of the image
Fig. 4
Fig. 4
Comparison of the labeling and prediction results of the two experts. (a) Expert (1) (b) Expert (2) (c) Prediction results
See this image and copyright information in PMC

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