Evaluation of Interstitial Lung Diseases with Deep Learning Method of Two Major Computed Tomography Patterns.

IF 1.1 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Current Medical Imaging Reviews Pub Date : 2024-01-01 DOI:10.2174/0115734056279295231229095436

Hüseyin Alper Kiziloğlu, Emrah Çevik, Kenan Zengin

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引用次数: 0

Abstract

Background: Interstitial lung diseases (ILD) encompass various disorders characterized by inflammation and/or fibrosis in the lung interstitium. These conditions produce distinct patterns in High-Resolution Computed Tomography (HRCT).

Objective: We employ a deep learning method to diagnose the most commonly encountered patterns in ILD differentially.

Materials and methods: Patients were categorized into usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), and normal lung parenchyma groups. VGG16 and VGG19 deep learning architectures were utilized. 85% of each pattern was used as training data for the artificial intelligence model. The models were then tasked with diagnosing the patterns in the test dataset without human intervention. Accuracy rates were calculated for both models.

Results: 1 The success of the VGG16 model in the test phase was 95.02% accuracy. 2 Using the same data, 98.05% accuracy results were obtained in the test phase of the VGG19 model.

Conclusion: Deep Learning models showed high accuracy in distinguishing the two most common patterns of ILD.

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用深度学习法评估肺间质疾病的两种主要计算机断层扫描模式

背景：间质性肺疾病（ILD）包括以肺间质炎症和/或纤维化为特征的各种疾病。这些疾病会在高分辨率计算机断层扫描（HRCT）中产生不同的模式：我们采用一种深度学习方法，对 ILD 中最常遇到的模式进行差异化诊断：将患者分为普通间质性肺炎（UIP）、非特异性间质性肺炎（NSIP）和正常肺实质组。采用 VGG16 和 VGG19 深度学习架构。每个模式的 85% 用作人工智能模型的训练数据。然后，模型在没有人工干预的情况下负责诊断测试数据集中的模式。两个模型的准确率都得到了计算：结果：VGG16 模型在测试阶段的准确率为 95.02%。使用相同的数据，VGG19 模型在测试阶段获得了 98.05% 的准确率：深度学习模型在区分两种最常见的 ILD 模式方面表现出了很高的准确性。

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

Current Medical Imaging Reviews 医学-核医学

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.