Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
PLoS One. 2023 Aug 9;18(8):e0287081. doi: 10.1371/journal.pone.0287081. eCollection 2023.
Digital twins derived from 3D scanning data were developed to measure soft tissue deformation in head and neck surgery by an artificial intelligence approach. This framework was applied suggesting feasibility of soft tissue shift detection as a hitherto unsolved problem. In a pig head cadaver model 104 soft tissue resection had been performed. The surface of the removed soft tissue (RTP) and the corresponding resection cavity (RC) was scanned (N = 416) to train an artificial intelligence (AI) with two different 3D object detectors (HoloLens 2; ArtecEva). An artificial tissue shift (TS) was created by changing the tissue temperature from 7,91±4,1°C to 36,37±1,28°C. Digital twins of RTP and RC in cold and warm conditions had been generated and volumes were calculated based on 3D surface meshes. Significant differences in number of vertices created by the different 3D scanners (HoloLens2 51313 vs. ArtecEva 21694, p<0.0001) hence result in differences in volume measurement of the RTC (p = 0.0015). A significant TS could be induced by changing the temperature of the tissue of RC (p = 0.0027) and RTP (p = <0.0001). RC showed more correlation in TS by heating than RTP with a volume increase of 3.1 μl or 9.09% (p = 0.449). Cadaver models are suitable for training a machine learning model for deformable registration through creation of a digital twin. Despite different point cloud densities, HoloLens and ArtecEva provide only slightly different estimates of volume. This means that both devices can be used for the task.TS can be simulated and measured by temperature change, in which RC and RTP react differently. This corresponds to the clinical behaviour of tumour and resection cavity during surgeries, which could be used for frozen section management and a range of other clinical applications.
基于人工智能的方法，从 3D 扫描数据中开发出数字孪生体，以测量头颈部手术中的软组织变形。该框架应用于软组织移位检测，该检测是一个迄今尚未解决的问题。在猪头部尸体模型中进行了 104 次软组织切除术。对切除的软组织 (RTP) 和相应的切除腔 (RC) 的表面进行了扫描 (N=416)，以使用两种不同的 3D 物体探测器 (HoloLens 2; ArtecEva) 对人工智能进行训练。通过将组织温度从 7.91±4.1°C 改变到 36.37±1.28°C，创建了人工组织移位 (TS)。生成了冷、热条件下 RTP 和 RC 的数字孪生体，并根据 3D 表面网格计算了体积。不同 3D 扫描仪生成的顶点数量存在显著差异 (HoloLens2 51313 与 ArtecEva 21694，p<0.0001)，因此导致 RTC 体积测量存在差异 (p=0.0015)。通过改变 RC (p=0.0027) 和 RTP (p=<0.0001) 组织的温度可以诱导明显的 TS。与 RTP 相比，RC 通过加热显示出更多的 TS 相关性，体积增加 3.1 μl 或 9.09% (p=0.449)。尸体模型适合通过创建数字孪生体来训练可变形配准的机器学习模型。尽管点云密度不同，但 HoloLens 和 ArtecEva 提供的体积估计值仅略有不同。这意味着这两种设备都可用于该任务。通过温度变化可以模拟和测量 TS，RC 和 RTP 的反应不同。这与肿瘤和切除腔在手术中的临床行为相对应，可用于冷冻切片管理和一系列其他临床应用。
