Nagle Matt, Broderick Hannah Conroy, Tepole Adrian Buganza, Fop Michael, Annaidh Aisling Ní
SFI Centre for Research Training in Foundations of Data Science, University College Dublin, Belfield, Dublin 4, Ireland.
School of Mechanical and Materials Engineering, University College Dublin, Belfield, Dublin 4, Ireland.
Res Sq. 2024 Apr 18:rs.3.rs-4246629. doi: 10.21203/rs.3.rs-4246629/v1.
Since their invention, tissue expanders, which are designed to trigger additional skin growth, have revolutionised many reconstructive surgeries. Currently, however, the sole quantitative method to assess skin growth requires skin excision. Thus, in the context of patient outcomes, a machine learning method which uses non-invasive measurements to predict skin growth and other skin properties, holds significant value. In this study, the finite element method was used to simulate a typical skin expansion protocol and to perform various simulated wave propagation experiments during the first few days of expansion on 1,000 individual virtual subjects. An artificial neural network trained on this dataset was shown to be capable of predicting the future skin growth at 7 days (avg. ) as well as the subject-specific shear modulus ( ), growth rate ( ), and natural pre-stretch ( ) with a very high degree of accuracy. The method presented here has implications for the real-time prediction of patient-specific skin expansion outcomes and could facilitate the development of patient-specific protocols.
自发明以来，旨在促使皮肤额外生长的组织扩张器彻底改变了许多重建手术。然而，目前评估皮肤生长的唯一定量方法需要进行皮肤切除。因此，从患者预后的角度来看，一种使用非侵入性测量来预测皮肤生长和其他皮肤特性的机器学习方法具有重要价值。在本研究中，有限元方法被用于模拟典型的皮肤扩张方案，并在扩张的头几天对1000个个体虚拟对象进行各种模拟波传播实验。在该数据集上训练的人工神经网络被证明能够以非常高的准确度预测7天时的未来皮肤生长情况（平均 ）以及个体特异性剪切模量（ ）、生长速率（ ）和自然预拉伸（ ）。这里提出的方法对患者特异性皮肤扩张结果的实时预测具有启示意义，并且可以促进患者特异性方案的制定。
