背景:颌骨畸形诊断需要客观检查。目前的方法,比如头颅测量,有局限性。然而,最近的研究表明,机器学习可以在两个维度上诊断颌骨畸形。因此,我们假设多层感知器(MLP)可以在三维(3D)中准确诊断颌骨畸形。
目的:通过关注下颌位置异常来检查该假设。我们的目标是:(1)创建一个机器学习模型来诊断下颌后颌和前颌;(2)将其性能与传统的头颅测量方法进行比较。
■对去识别的回顾性数据进行计算机模拟实验。这项研究是在休斯顿卫理公会研究所和伦斯勒理工学院进行的。包括颌骨畸形的患者记录和术前3D面部模型。排除明显下颌不对称的患者。
方法:用于诊断下颌前后位置的测试是:(1)SNB角度;(2)面部角度;(3)下颌单位长度(MdUL);和(4)MLP模型。
方法:结果诊断:正常,前颌,或者回颌。
■无。
方法:一位高级外科医生将患者的下颌骨标记为前颌,正常,或者回颌,创造黄金标准。伦斯勒理工学院的科学家开发了一种MLP模型,使用50个地标的3D坐标来诊断下颌前颌和回颌。将MLP模型的性能与三种传统的头颅测量进行了比较:(1)SNB,(2)面部角度,和(3)MdUL。用于评估性能的主要指标是诊断准确性。McNemar的精确测试测试了传统头颅测量与MLP之间的差异。科恩的Kappa衡量了每种方法与黄金标准之间的评分者之间的一致性。
结果:样本包括101名患者。SNB的诊断准确性,面部角度,MdUL,MLP分别为74.3、74.3、75.3和85.2%,分别。McNemar的测试表明,我们的MLP表现明显优于SNB(P=.027),面部角度(P=.019),和MdUL(P=0.031)。传统的头颅测量与外科医生的诊断之间的一致性是公平的。相比之下,MLP和外科医生之间的一致性中等.
结论:MLP的性能明显优于传统的头颅测量。
Jaw deformity diagnosis requires objective tests. Current methods, like cephalometry, have limitations. However, recent studies have shown that machine learning can diagnose jaw deformities in two dimensions. Therefore, we hypothesized that a multilayer perceptron (MLP) could accurately diagnose jaw deformities in three dimensions (3D).
Examine the hypothesis by focusing on anomalous mandibular position. We aimed to: (1) create a machine learning model to diagnose mandibular retrognathism and prognathism; and (2) compare its performance with traditional cephalometric methods.
An in-silico experiment on deidentified retrospective data. The study was conducted at the Houston Methodist Research Institute and Rensselaer Polytechnic Institute. Included were patient records with jaw deformities and preoperative 3D facial models. Patients with significant jaw asymmetry were excluded.
The tests used to diagnose mandibular anteroposterior position are: (1) SNB angle; (2) facial angle; (3) mandibular unit length (MdUL); and (4) MLP model.
The resultant diagnoses: normal, prognathic, or retrognathic.
None.
A senior surgeon labeled the patients\' mandibles as prognathic, normal, or retrognathic, creating a gold standard. Scientists at Rensselaer Polytechnic Institute developed an MLP model to diagnose mandibular prognathism and retrognathism using the 3D coordinates of 50 landmarks. The performance of the MLP model was compared with three traditional cephalometric measurements: (1) SNB, (2) facial angle, and (3) MdUL. The primary metric used to assess the performance was diagnostic accuracy. McNemar\'s exact test tested the difference between traditional cephalometric measurement and MLP. Cohen\'s Kappa measured inter-rater agreement between each method and the gold standard.
The sample included 101 patients. The diagnostic accuracy of SNB, facial angle, MdUL, and MLP were 74.3, 74.3, 75.3, and 85.2%, respectively. McNemar\'s test shows that our MLP performs significantly better than the SNB (P = .027), facial angle (P = .019), and MdUL (P = .031). The agreement between the traditional cephalometric measurements and the surgeon\'s diagnosis was fair. In contrast, the agreement between the MLP and the surgeon was moderate.
The performance of the MLP is significantly better than that of the traditional cephalometric measurements.