计算机断层扫描中蝶窦通气的深度学习自动预测。

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Ali Alamer, Omar Salim, Fawaz Alharbi, Fahd Alsaleem, Afnan Almuqbil, Khaled Alhassoon, Fahad Alsunaydih
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引用次数: 0

摘要

背景:蝶窦是经蝶窦手术的重要入口,但其通气的变化可能使手术安全性复杂化。深度学习可以用来识别这些解剖变异。方法:我们建立了一个卷积神经网络(CNN)模型,用于自动预测计算机断层扫描(CT)中蝶窦充气模式。该模型在中矢状位CT图像上进行了验证。两名放射科医生将所有CT图像标记为四种气化模式:鼻甲(I型)、鞍前(II型)、鞍前(III型)和鞍后(IV型)。然后,我们扩大了训练集,以解决数据的有限大小和不平衡性质。结果:初始数据集包括249张CT图像,分为训练数据集(n = 174)和测试数据集(n = 75)。训练数据集扩充到378张图像。增强后,模型的总体诊断准确率由76.71%提高到84%,曲线下面积(AUC)为0.84,具有很好的诊断效果。亚组分析显示,IV型患者预后良好,最高AUC为0.93,灵敏度为100%,f1评分为0.94。该模型对I型也表现稳健,准确率为97.33%,特异性高(99%)。这些指标突出了该模型在可靠的临床应用中的潜力。结论:本文提出的CNN模型在识别各种蝶窦通气模式方面具有很好的诊断准确性,尤其在IV型方面表现出色,IV型因其手术并发症风险较高,对内镜鼻窦手术至关重要。通过协助放射科医生和外科医生,该模型提高了经蝶窦手术的安全性,突出了其在临床环境中的价值、新颖性和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning for Automated Prediction of Sphenoid Sinus Pneumatization in Computed Tomography.

Background: The sphenoid sinus is an important access point for trans-sphenoidal surgeries, but variations in its pneumatization may complicate surgical safety. Deep learning can be used to identify these anatomical variations.

Methods: We developed a convolutional neural network (CNN) model for the automated prediction of sphenoid sinus pneumatization patterns in computed tomography (CT) scans. This model was tested on mid-sagittal CT images. Two radiologists labeled all CT images into four pneumatization patterns: Conchal (type I), presellar (type II), sellar (type III), and postsellar (type IV). We then augmented the training set to address the limited size and imbalanced nature of the data.

Results: The initial dataset included 249 CT images, divided into training (n = 174) and test (n = 75) datasets. The training dataset was augmented to 378 images. Following augmentation, the overall diagnostic accuracy of the model improved from 76.71% to 84%, with an area under the curve (AUC) of 0.84, indicating very good diagnostic performance. Subgroup analysis showed excellent results for type IV, with the highest AUC of 0.93, perfect sensitivity (100%), and an F1-score of 0.94. The model also performed robustly for type I, achieving an accuracy of 97.33% and high specificity (99%). These metrics highlight the model's potential for reliable clinical application.

Conclusion: The proposed CNN model demonstrates very good diagnostic accuracy in identifying various sphenoid sinus pneumatization patterns, particularly excelling in type IV, which is crucial for endoscopic sinus surgery due to its higher risk of surgical complications. By assisting radiologists and surgeons, this model enhances the safety of transsphenoidal surgery, highlighting its value, novelty, and applicability in clinical settings.

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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
246
审稿时长
1 months
期刊介绍: Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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