Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data.
Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data.
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DOI：
10.1038/s41467-023-37720-5
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发表时间：
2023-04-20
影响因子：
16.6
通讯作者：
Yang, Yang
中科院分区：
文献类型：
--
作者：
Mei, Xueyan;Liu, Zelong;Singh, Ayushi;Lange, Marcia;Boddu, Priyanka;Gong, Jingqi Q. X.;Lee, Justine;DeMarco, Cody;Cao, Chendi;Platt, Samantha;Sivakumar, Ganesh;Gross, Benjamin;Huang, Mingqian;Masseaux, Joy;Dua, Sakshi;Bernheim, Adam;Chung, Michael C.;Deyer, Timothy;Jacobi, Adam;Padilla, Maria;Fayad, Zahi A. A.;Yang, Yang
为了准确诊断间质性肺病（ILD），影像学、病理学和临床表现的一致性至关重要。ILD的管理还需要通过计算机断层扫描（CT）研究和肺功能检查进行全面随访，以评估疾病进展、严重程度和治疗反应。然而，ILD亚型的准确分类可能具有挑战性，特别是对于那些不习惯定期阅读胸部CT的患者。由于疾病的复杂性、变异性和不规则的访视间隔，基于纵向数据预测患者生存率的动态模型具有挑战性。在这里，我们利用RadImageNet预训练模型来诊断五种类型的ILD与多模态数据和一个Transformer模型来确定患者的3年生存率。当临床病史和相关CT扫描可用时，拟议的深度学习系统可以帮助临床医生诊断和分类ILD患者，重要的是，动态预测疾病进展和预后。间质性肺病亚型的准确诊断和患者生存率的预测仍然具有挑战性。在这里，作者开发了人工智能算法，将联合收割机患者的临床病史和纵向CT图像结合起来，帮助临床医生诊断和分类亚型，并动态预测疾病进展和预后。
For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of ILD subtypes can be challenging, especially for those not accustomed to reading chest CTs regularly. Dynamic models to predict patient survival rates based on longitudinal data are challenging to create due to disease complexity, variation, and irregular visit intervals. Here, we utilize RadImageNet pretrained models to diagnose five types of ILD with multimodal data and a transformer model to determine a patient’s 3-year survival rate. When clinical history and associated CT scans are available, the proposed deep learning system can help clinicians diagnose and classify ILD patients and, importantly, dynamically predict disease progression and prognosis. Accurate diagnosis of interstitial lung disease subtypes and prediction of patient survival rates remains challenging. Here, the authors develop AI algorithms to combine patient’s clinical history and longitudinal CT images to help clinicians diagnose and classify subtypes and dynamically predict disease progression and prognosis.
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影响因子：
11.2
作者：
van Griethuysen JJM;Fedorov A;Parmar C;Hosny A;Aucoin N;Narayan V;Beets-Tan RGH;Fillion-Robin JC;Pieper S;Aerts HJWL
通讯作者：
Aerts HJWL
影响因子：
5.9
作者：
Trusculescu AA;Manolescu D;Tudorache E;Oancea C
通讯作者：
Oancea C
DOI：
10.1164/rccm.2009-040gl
发表时间：
2011-03-15
影响因子：
24.7
作者：
Raghu, Ganesh;Collard, Harold R.;Schuenemann, Holger J.
通讯作者：
Schuenemann, Holger J.
影响因子：
19.7
作者：
Choe, Jooae;Hwang, Hye Jeon;Kim, Byeongsoo
通讯作者：
Kim, Byeongsoo
影响因子：
4.7
作者：
Zhang X;Zhang Y;Zhang G;Qiu X;Tan W;Yin X;Liao L
通讯作者：
Liao L