Artificial intelligence model for automatic 3-dimensional reconstruction of ossicular chain and bony labyrinth from high-resolution CT.

Radiology advances Pub Date : 2025-01-28 eCollection Date: 2025-01-01 DOI:10.1093/radadv/umaf004
Mingwei Xie, Haonan Wang, Zehong Yang, Ming Gao, Guangzi Shi, Xingnan Liao, Zhongqiang Luo, Xiaomeng Li, Jun Shen
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引用次数: 0

Abstract

Background: Three-dimensional (3D) reconstruction of ossicular chain and bony labyrinth based on temporal bone high-resolution CT (HRCT) is useful for diagnosis and treatment guidance of middle and inner ear diseases. However, these structures are small and irregular, making manual reconstruction time-consuming.

Purpose: To develop and validate an artificial intelligence (AI) model based on semisupervised learning for automated 3D reconstruction of ossicular chain and bony labyrinth on HRCT images.

Methods: HRCT images from 304 ears of 152 consecutive patients retrospectively collected from a single center were randomly divided into training (246 ears), validation (28 ears), and internal test (30 ears) cohorts for model development. A novel semisupervised ear bone segmentation framework was used to train the AI model, and its performance was evaluated by Dice similarity coefficients. The trained algorithm was applied to a temporally independent test dataset of 30 ears of 15 patients from the same center for comparison with manual 3D reconstruction for processing time, target volume, and visual assessment of segmentation.

Results: The AI model demonstrated a Dice score of 0.948 (95% CI, 0.940-0.955) for the internal and 0.979 (95% CI, 0.973-0.986) for the temporally independent test sets. In the latter dataset, the AI model required 2% or less processing time of manual 3D reconstruction for each ear (17.7 seconds ± 10.1 vs 1080.5 seconds ± 149.8; P < .001) and had an accuracy comparable to human experts in the volume and visual assessment of segmentation targets (P = .237-1.000). In a subgroup analysis, the model achieved accurate segmentation (Dice scores of 0.98-0.99) across various diseases (eg, otitis media, mastoiditis, otosclerosis, middle and inner ear malformations, Ménière disease).

Conclusion: The AI model enables robust, efficient and accurate 3D reconstruction for the small structures such as ossicular chain and bony labyrinth on HRCT images.

Abstract Image

Abstract Image

Abstract Image

高分辨率CT听骨链和骨迷路三维自动重建的人工智能模型。
背景:颞骨高分辨率CT (HRCT)三维(3D)重建听骨链和骨迷路对中耳和内耳疾病的诊断和治疗指导有重要意义。然而,这些结构很小且不规则,使得人工重建非常耗时。目的:开发并验证一种基于半监督学习的人工智能(AI)模型,用于HRCT图像上听骨链和骨迷路的自动三维重建。方法:从单个中心连续收集152例患者304只耳的HRCT图像,随机分为训练组(246只)、验证组(28只)和内测组(30只)进行模型开发。采用一种新型的半监督耳骨分割框架对模型进行训练,并通过Dice相似系数对其性能进行评价。将训练好的算法应用于同一中心15例患者的30只耳朵的时间独立测试数据集,与手动三维重建进行处理时间、目标体积和分割视觉评估的比较。结果:AI模型显示,内部测试集的Dice得分为0.948 (95% CI, 0.940-0.955),时间独立测试集的Dice得分为0.979 (95% CI, 0.973-0.986)。在后者的数据集中,人工智能模型每只耳朵需要2%或更少的人工3D重建处理时间(17.7秒±10.1 vs 1080.5秒±149.8;P < .001),并且在分割目标的体积和视觉评估方面具有与人类专家相当的准确性(P = .237-1.000)。在亚组分析中,该模型实现了对各种疾病(如中耳炎、乳突炎、耳硬化、中耳和内耳畸形、msamuire病)的准确分割(Dice评分为0.98-0.99)。结论:人工智能模型能够在HRCT图像上对听骨链、骨迷路等小结构进行鲁棒、高效、准确的三维重建。
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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