基于智能手机的唇腭裂新生儿腭部模型扫描:三维图像捕捉和机器学习平板工具展望。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-09-22 DOI:10.1111/ocr.12859
José Wittor de Macêdo Santos, Andreas Albert Mueller, Benito K Benitez, Yoriko Lill, Prasad Nalabothu, Francisco Wilker Mustafa Gomes Muniz
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引用次数: 0

摘要

目的评估智能手机扫描应用程序(App)在获取腭裂模型三维网格方面的性能。其次,验证用于计算自动术前平板(PSP)的机器学习(ML)工具:我们在 15 个腭裂模型上对两款应用程序进行了比较分析:5 个单侧唇腭裂 (UCLP)、5 个双侧唇腭裂 (BCLP) 和 5 个孤立性腭裂 (ICP)。扫描时使用和不使用镜子,以模拟口内采集。三维重建结果与使用开源软件的专业口内扫描仪获得的对照重建结果进行了比较:每款应用程序均采集了 30 次三维扫描,共计 60 次扫描。主要结果显示,在 UCLP 样本中,不带镜子的 KIRI 扫描(0.22 ± 0.03 毫米)性能良好,与地面真实值(0.14 ± 0.13 毫米)的偏差与对照组相当(p = .653)。在所有样本中,带镜子的扫描宇宙精确度最低。除 ICP 模型外,ML 工具能够预测地标并自动生成平板。KIRI 扫描板在有镜子(0.22 ± 0.06 毫米)和无镜子(0.18 ± 0.05 毫米)时表现更好,与对照组(0.16 ± 0.08 毫米)相当(p = .954 和 p = .439):结论:KIRI 引擎在扫描没有镜子的 UCLP 模型时表现更好。ML 工具在形态识别和自动生成 PSP 方面表现出很高的能力。
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Smartphone-based scans of palate models of newborns with cleft lip and palate: Outlooks for three-dimensional image capturing and machine learning plate tool.

Objectives: To evaluate the performance of smartphone scanning applications (apps) in acquiring 3D meshes of cleft palate models. Secondarily, to validate a machine learning (ML) tool for computing automated presurgical plate (PSP).

Materials and methods: We conducted a comparative analysis of two apps on 15 cleft palate models: five unilateral cleft lip and palate (UCLP), five bilateral cleft lip and palate (BCLP) and five isolated cleft palate (ICP). The scans were performed with and without a mirror to simulate intraoral acquisition. The 3D reconstructions were compared to control reconstructions acquired using a professional intraoral scanner using open-source software.

Results: Thirty 3D scans were acquired by each app, totalling 60 scans. The main findings were in the UCLP sample, where the KIRI scans without a mirror (0.22 ± 0.03 mm) had a good performance with a deviation from the ground truth comparable to the control group (0.14 ± 0.13 mm) (p = .653). Scaniverse scans with a mirror showed the lowest accuracy of all the samples. The ML tool was able to predict the landmarks and automatically generate the plates, except in ICP models. KIRI scans' plates showed better performance with (0.22 ± 0.06 mm) and without mirror (0.18 ± 0.05 mm), being comparable with controls (0.16 ± 0.08 mm) (p = .954 and p = .439, respectively).

Conclusions: KIRI Engine performed better in scanning UCLP models without a mirror. The ML tool showed a high capability for morphology recognition and automated PSP generation.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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