From the Department of Plastic Surgery, Albacete University Hospital.
Department of Computer Engineering, University of Castilla-La Mancha.
Plast Reconstr Surg. 2024 Feb 1;153(2):273e-280e. doi: 10.1097/PRS.0000000000010603. Epub 2023 Apr 26.
In plastic surgery, evaluation of breast symmetry is an important aspect of clinical practice. Computer programs have been developed for this purpose, but most of them require operator input. Artificial intelligence has been introduced into many aspects of medicine. In plastic surgery, automated neural networks for breast evaluation could improve quality of care. In this work, the authors evaluate the identification of breast features with an ad hoc trained neural network.
An ad hoc convolutional neural network was developed on the YOLOV3 platform to detect key features of the breast that are commonly used in plastic surgery for symmetry evaluation. The program was trained with 200 frontal photographs of patients who underwent breast surgery and was tested on 47 frontal images of patients who underwent breast reconstruction after breast cancer surgery.
The program was able to detect key features in 97.74% of cases (boundaries of the breast in 94 of 94 cases, the nipple-areola complex in 94 of 94 cases, and the suprasternal notch in 41 of 47 cases). Mean time of detection was 0.52 seconds.
The ad hoc neural network was successful in localizing key breast features, with a total detection rate of 97.74%. Neural networks and machine learning have the potential to improve the evaluation of breast symmetry in plastic surgery by automated and quick detection of features used by surgeons in practice. More studies and development are needed to further knowledge in this area.
在整形外科学中，乳房对称性的评估是临床实践的重要方面。为此目的已经开发了计算机程序，但大多数程序都需要操作员的输入。人工智能已被引入到医学的许多方面。在整形外科学中，用于乳房评估的自动化神经网络可以提高护理质量。在这项工作中，作者评估了使用专门训练的神经网络识别乳房特征的能力。
在 YOLOV3 平台上开发了一个专门的卷积神经网络，用于检测乳房的关键特征，这些特征在用于乳房对称性评估的整形手术中很常见。该程序使用 200 名接受过乳房手术的患者的正面照片进行训练，并在 47 名接受过乳腺癌手术后乳房重建的患者的正面图像上进行了测试。
该程序能够在 97.74%的病例中检测到关键特征（94 例中的 94 例乳房边界，94 例中的 94 例乳头乳晕复合体，47 例中的 41 例胸骨上切迹）。检测时间的平均值为 0.52 秒。
专门的神经网络成功地定位了关键的乳房特征，总检测率为 97.74%。神经网络和机器学习有可能通过自动快速检测外科医生在实践中使用的特征来改善整形手术中乳房对称性的评估。需要进一步的研究和开发来进一步了解这一领域。
