Clinical validity and precision of deep learning-based cone-beam computed tomography automatic landmarking algorithm.

IF 1.7 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Imaging Science in Dentistry Pub Date : 2024-09-01 Epub Date: 2024-08-12 DOI:10.5624/isd.20240009
Jungeun Park, Seongwon Yoon, Hannah Kim, Youngjun Kim, Uilyong Lee, Hyungseog Yu
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引用次数: 0

Abstract

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared.

Materials and methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which were determined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method.

Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The time required to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually, compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz).

Conclusion: Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculate these measurements, the efficiency of diagnosis and treatment may be improved.

基于深度学习的锥束计算机断层扫描自动标记算法的临床有效性和精确性。
目的:本研究旨在评估基于深度学习的锥束计算机断层扫描(CBCT)自动标记算法的临床有效性和准确性。比较了通过手动和自动标记获得的三维(3D)CBCT 头部测量结果:总共 80 张 CBCT 扫描图像被分为 3 组:非手术组(39 例);不含硬件(即手术板和微型螺钉)的手术组(9 例);含硬件的手术组(32 例)。对每张 CBCT 扫描图像进行分析,以获得 53 个测量值,包括 27 个长度、21 个角度和 5 个比率,这些测量值是根据使用手动或三维自动地标检测方法识别的 65 个地标确定的:结果:在比较人工和人工智能地标的测量值时,有 6 个项目显示出显著差异:R U6CP-L U6CP、R L3CP-L L3CP、S-N、Or_R-R U3CP、L1L to Me-GoL和GoR-Gn/S-N(PConclusion:使用基于深度学习的 CBCT 自动地标算法获得的测量值在准确性上与人工确定的点得出的值相似。通过缩短计算这些测量值所需的时间,可以提高诊断和治疗的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Imaging Science in Dentistry
Imaging Science in Dentistry DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
2.90
自引率
11.10%
发文量
42
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