Open Access
CC BY 4.0 · Arch Plast Surg 2024; 51(01): 030-035
DOI: 10.1055/a-2190-5781
Breast/Trunk
Original Article

A Novel, Deep Learning-Based, Automatic Photometric Analysis Software for Breast Aesthetic Scoring

Joseph Kyu-hyung Park 
1   Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnamsi, Gyeonggi-do, Republic of Korea
,
Seungchul Baek 
1   Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnamsi, Gyeonggi-do, Republic of Korea
,
Chan Yeong Heo 
1   Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnamsi, Gyeonggi-do, Republic of Korea
,
Jae Hoon Jeong 
1   Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnamsi, Gyeonggi-do, Republic of Korea
,
Yujin Myung 
1   Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnamsi, Gyeonggi-do, Republic of Korea
› Author Affiliations
Funding None.
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Abstract

Background Breast aesthetics evaluation often relies on subjective assessments, leading to the need for objective, automated tools. We developed the Seoul Breast Esthetic Scoring Tool (S-BEST), a photometric analysis software that utilizes a DenseNet-264 deep learning model to automatically evaluate breast landmarks and asymmetry indices.

Methods S-BEST was trained on a dataset of frontal breast photographs annotated with 30 specific landmarks, divided into an 80–20 training–validation split. The software requires the distances of sternal notch to nipple or nipple-to-nipple as input and performs image preprocessing steps, including ratio correction and 8-bit normalization. Breast asymmetry indices and centimeter-based measurements are provided as the output. The accuracy of S-BEST was validated using a paired t-test and Bland–Altman plots, comparing its measurements to those obtained from physical examinations of 100 females diagnosed with breast cancer.

Results S-BEST demonstrated high accuracy in automatic landmark localization, with most distances showing no statistically significant difference compared with physical measurements. However, the nipple to inframammary fold distance showed a significant bias, with a coefficient of determination ranging from 0.3787 to 0.4234 for the left and right sides, respectively.

Conclusion S-BEST provides a fast, reliable, and automated approach for breast aesthetic evaluation based on 2D frontal photographs. While limited by its inability to capture volumetric attributes or multiple viewpoints, it serves as an accessible tool for both clinical and research applications.

Keywords

breast cancer - aesthetics - deep learning

Authors' Contributions

Conceptualization: J.K-h.P., C.Y.H., J.H.J., Y.M.


Data curation: S.B.


Formal analysis: J.K-h.P., S.B.


Writing—original draft: J.K-h.P.


Writing—review and editing: S.B., C.Y.H., J.H.J., Y.M.


Ethical Approval

The study was performed in accordance with the principles of the Declaration of Helsinki. The study was approved by the Institutional Review Board of Seoul National University Bundang Hospital (IRB#B-2207-770-101).


Patient Consent

Written informed consent was obtained from the author.




Publication History

Received: 02 May 2023

Accepted: 26 September 2023

Accepted Manuscript online:
12 October 2023

Article published online:
07 February 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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