肺结节病放射学诊断的人工智能平台:胸部计算机断层扫描分析区分肺结节病与阴性肺癌筛查扫描的初步初步研究。

IF 4.6 2区 医学 Q1 RESPIRATORY SYSTEM
Lung Pub Date : 2023-12-01 Epub Date: 2023-11-14 DOI:10.1007/s00408-023-00655-1
Marc A Judson, Jianwei Qiu, Camille L Dumas, Jun Yang, Brion Sarachan, Jhimli Mitra
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引用次数: 0

摘要

目的:确定一种基于人工智能、深度学习(AI/DL)的胸部计算机断层扫描(CT)扫描分析方法在肺结节病与肺癌筛查阴性胸部CT扫描(肺成像报告和数据系统评分1分,肺- rads评分1分)中的可靠性。肺结节病的胸部CT扫描由有结节病经验的临床医生和胸部放射科医生评估结节病的临床和放射证据,并排除其他或伴发肺部疾病。基于AI/DL的方法使用了卷积神经网络(cnn)和视觉变压器(ViTs)相结合的集成网络架构。该方法应用于126例肺结节病和96例肺- rads评分1次CT扫描。AI/DL方法的训练和验证的分析方法使用了五重交叉验证技术,其中4/5的可用数据集用于训练诊断模型,并在剩余的1/5的数据集上进行测试,并在非重叠验证/测试数据上重复4次以上。概率值用于生成受试者工作特征(ROC)曲线,以评估模型的区分能力。结果:AI/DL方法对5组训练/验证集及整套CT扫描鉴别肺结节病与肺部rads评分1分胸部CT扫描的敏感性、特异性、阳性预测值和阴性预测值均在94%以上。相应ROC曲线的曲线下面积均大于97%。结论:该AL/DL模型有望通过最少的放射学数据来区分结节病和其他肺部疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An Artificial Intelligence Platform for the Radiologic Diagnosis of Pulmonary Sarcoidosis: An Initial Pilot Study of Chest Computed Tomography Analysis to Distinguish Pulmonary Sarcoidosis from a Negative Lung Cancer Screening Scan.

An Artificial Intelligence Platform for the Radiologic Diagnosis of Pulmonary Sarcoidosis: An Initial Pilot Study of Chest Computed Tomography Analysis to Distinguish Pulmonary Sarcoidosis from a Negative Lung Cancer Screening Scan.

Purpose: To determine the reliability of an artificial intelligence, deep learning (AI/DL)-based method of chest computer tomography (CT) scan analysis to distinguish pulmonary sarcoidosis from negative lung cancer screening chest CT scans (Lung Imaging Reporting and Data System score 1, Lung-RADS score 1).

Methods: Chest CT scans of pulmonary sarcoidosis were evaluated by a clinician experienced with sarcoidosis and a chest radiologist for clinical and radiologic evidence of sarcoidosis and exclusion of alternative or concomitant pulmonary diseases. The AI/DL based method used an ensemble network architecture combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The method was applied to 126 pulmonary sarcoidosis and 96 Lung-RADS score 1 CT scans. The analytic approach of training and validation of the AI/DL method used a fivefold cross-validation technique, where 4/5th of the available data set was used to train a diagnostic model and tested on the remaining 1/5th of the data set, and repeated 4 more times with non-overlapping validation/test data. The probability values were used to generate Receiver Operating Characteristic (ROC) curves to assess the model's discriminatory power.

Results: The sensitivity, specificity, positive and negative predictive value of the AI/DL method for the 5 folds of the training/validation sets and the entire set of CT scans were all over 94% to distinguish pulmonary sarcoidosis from LUNG-RADS score 1 chest CT scans. The area under the curve for the corresponding ROC curves were all over 97%.

Conclusion: This AL/DL model shows promise to distinguish sarcoidosis from alternative pulmonary conditions using minimal radiologic data.

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来源期刊
Lung
Lung 医学-呼吸系统
CiteScore
9.10
自引率
10.00%
发文量
95
审稿时长
6-12 weeks
期刊介绍: Lung publishes original articles, reviews and editorials on all aspects of the healthy and diseased lungs, of the airways, and of breathing. Epidemiological, clinical, pathophysiological, biochemical, and pharmacological studies fall within the scope of the journal. Case reports, short communications and technical notes can be accepted if they are of particular interest.
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