Enhancing Airway Assessment with a Secure Hybrid Network-Blockchain System for CT & CBCT Image Evaluation

Uppalapati Vamsi Krishna, Srinivasa Rao G, Lavanya Addepalli, Bhavsingh M, V. Sd, Lloret Mauri Jaime
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引用次数: 0

Abstract

Our investigation explored the intricacies of airway evaluation through Cone-Beam Computed Tomography (CBCT) and Computed Tomography (CT) images. By employing innovative data augmentation strategies, we expanded our dataset significantly, enabling a more comprehensive analysis of airway characteristics. The utility of these techniques was evident in their ability to yield a diverse array of synthetic images, each representing different airway scenarios with high fidelity. A notable outcome of our study was the effective categorization of the initial image as "Class II" under the Mallampati Classification system. The augmented images further enhanced our understanding by exhibiting a spectrum of airway parameters. Moreover, our approach included training a Recurrent Neural Network (RNN) model on a dataset of CT images. This model, fortified with pseudo-labels created via K-means clustering, showcased its proficiency by accurately predicting airway assessment categories in various test scenarios. These results underscore the model's potential as a tool for swift and precise airway evaluation in clinical settings, marking a significant advancement in medical imaging technologies.
利用用于 CT 和 CBCT 图像评估的安全混合网络-区块链系统加强气道评估
我们的研究通过锥形束计算机断层扫描(CBCT)和计算机断层扫描(CT)图像探索了气道评估的复杂性。通过采用创新的数据增强策略,我们极大地扩展了数据集,从而能够对气道特征进行更全面的分析。这些技术的实用性体现在它们能够生成各种合成图像,每种图像都能高保真地代表不同的气道情况。我们研究的一个显著成果是根据 Mallampati 分类系统将初始图像有效地归类为 "II 类"。增强图像通过展示气道参数谱进一步增强了我们的理解。此外,我们的方法还包括在 CT 图像数据集上训练一个循环神经网络(RNN)模型。通过 K-means 聚类创建的伪标签强化了该模型,该模型在各种测试场景中准确预测了气道评估类别,从而展示了其能力。这些结果凸显了该模型作为临床环境中快速、精确气道评估工具的潜力,标志着医学成像技术的重大进步。
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