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人工智能 CT 在特发性肺纤维化预后模型中的应用。

The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis.

机构信息

Department of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China.

Department of Pulmonary and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China.

出版信息

Ther Adv Respir Dis. 2024 Jan-Dec;18:17534666241282538. doi: 10.1177/17534666241282538.

DOI:10.1177/17534666241282538
PMID:39382448
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11489909/
Abstract

The review summarizes the applications of CT and AI algorithms for prognostic models in IPF and procedures of model construction. It reveals the current limitations and prospects of AI-aid models, and helps clinicians to recognize the AI algorithms and apply them to more clinical work.

摘要

该综述总结了 CT 和人工智能算法在特发性肺纤维化预后模型中的应用和模型构建步骤。它揭示了人工智能辅助模型目前的局限性和前景，帮助临床医生认识人工智能算法，并将其应用于更多的临床工作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e23/11489909/ed7f3beed85c/10.1177_17534666241282538-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e23/11489909/22b41dea8486/10.1177_17534666241282538-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e23/11489909/04294417905f/10.1177_17534666241282538-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e23/11489909/ed7f3beed85c/10.1177_17534666241282538-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e23/11489909/22b41dea8486/10.1177_17534666241282538-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e23/11489909/04294417905f/10.1177_17534666241282538-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e23/11489909/ed7f3beed85c/10.1177_17534666241282538-fig3.jpg

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本文引用的文献

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Medium-long term prognosis prediction for idiopathic pulmonary fibrosis patients based on quantitative analysis of fibrotic lung volume.基于纤维化肺容积的定量分析对特发性肺纤维化患者的中远期预后预测。
Respir Res. 2022 Dec 22;23(1):372. doi: 10.1186/s12931-022-02276-3.
2
Unsupervised machine learning identifies predictive progression markers of IPF.无监督机器学习可识别特发性肺纤维化的预测进展标志物。
Eur Radiol. 2023 Feb;33(2):925-935. doi: 10.1007/s00330-022-09101-x. Epub 2022 Sep 6.
3
Quantitative computed tomography predicts outcomes in idiopathic pulmonary fibrosis.
定量计算机断层扫描预测特发性肺纤维化的结局。
Respirology. 2022 Dec;27(12):1045-1053. doi: 10.1111/resp.14333. Epub 2022 Jul 25.
4
Diagnosis of Idiopathic Pulmonary Fibrosis in High-Resolution Computed Tomography Scans Using a Combination of Handcrafted Radiomics and Deep Learning.利用手工制作的放射组学和深度学习相结合的方法在高分辨率计算机断层扫描中诊断特发性肺纤维化
Front Med (Lausanne). 2022 Jun 23;9:915243. doi: 10.3389/fmed.2022.915243. eCollection 2022.
5
Idiopathic Pulmonary Fibrosis Mortality Risk Prediction Based on Artificial Intelligence: The CTPF Model.基于人工智能的特发性肺纤维化死亡风险预测：CTPF模型
Front Pharmacol. 2022 Apr 26;13:878764. doi: 10.3389/fphar.2022.878764. eCollection 2022.
6
Idiopathic Pulmonary Fibrosis (an Update) and Progressive Pulmonary Fibrosis in Adults: An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline.特发性肺纤维化（更新版）和成人进展性肺纤维化：美国胸科学会/欧洲呼吸学会/日本呼吸学会/拉丁美洲胸科学会临床实践指南。
Am J Respir Crit Care Med. 2022 May 1;205(9):e18-e47. doi: 10.1164/rccm.202202-0399ST.
7
U-Net-Based Medical Image Segmentation.基于 U-Net 的医学图像分割。
J Healthc Eng. 2022 Apr 15;2022:4189781. doi: 10.1155/2022/4189781. eCollection 2022.
8
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