Department of Surgery, University of Vermont, Burlington, Vermont.
McGowan Institute of Regenerative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
J Surg Res. 2023 Nov;291:683-690. doi: 10.1016/j.jss.2023.07.017. Epub 2023 Aug 8.
The clinical characterization of the functional status of active wounds in terms of their driving cellular and molecular biology remains a considerable challenge that currently requires excision via a tissue biopsy. In this pilot study, we use convolutional Siamese neural network (SNN) architecture to predict the functional state of a wound using digital photographs of wounds in a canine model of volumetric muscle loss (VML).
Digital images of VML injuries and tissue biopsies were obtained in a standardized fashion from an established canine model of VML. Gene expression profiles for each biopsy site were obtained using RNA sequencing. These profiles were converted to functional profiles by a manual review of validated gene ontology databases in which we determined a hierarchical representation of gene functions based on functional specificity. An SNN was trained to regress functional profile expression values, informed by an image segment showing the surface of a small tissue biopsy.
The SNN was able to predict the functional expression of a range of functions based with error ranging from ∼5% to ∼30%, with functions that are most closely associated with the early state of wound healing to be those best-predicted.
These initial results suggest promise for further research regarding this novel use of machine learning regression on medical images. The regression of functional profiles, as opposed to specific genes, both addresses the challenge of genetic redundancy and gives a deeper insight into the mechanistic configuration of a region of tissue in wounds.
从其驱动细胞和分子生物学的角度对活跃伤口的功能状态进行临床描述仍然是一个相当大的挑战，目前需要通过组织活检切除。在这项初步研究中，我们使用卷积 Siamese 神经网络（SNN）架构，通过犬容积性肌肉损失（VML）模型中的伤口的数字照片来预测伤口的功能状态。
以标准化的方式从已建立的犬容积性肌肉损失模型中获得 VML 损伤和组织活检的数字图像。使用 RNA 测序获得每个活检部位的基因表达谱。通过手动审查验证的基因本体数据库来将这些谱转换为功能谱，其中我们根据功能特异性确定了基因功能的层次表示。使用 SNN 来回归功能谱表达值，由显示小组织活检表面的图像段提供信息。
SNN 能够以约 5%至约 30%的误差范围预测一系列功能的功能表达，与伤口愈合的早期状态最密切相关的功能是预测效果最好的功能。
这些初步结果表明，关于机器学习回归在医学图像上的这种新用途的进一步研究具有前景。功能谱的回归，而不是特定基因的回归，既解决了遗传冗余的挑战，又深入了解了伤口组织区域的机械配置。
