Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms.
Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms.
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DOI：
10.1038/s41467-021-22877-8
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发表时间：
2021-05-11
影响因子：
16.6
通讯作者：
Deo RC
中科院分区：
文献类型：
--
作者：
Goto S;Mahara K;Beussink-Nelson L;Ikura H;Katsumata Y;Endo J;Gaggin HK;Shah SJ;Itabashi Y;MacRae CA;Deo RC
心脏淀粉样变性（CA）等罕见疾病的患者很难识别，因为疾病表现与更常见的疾病相似。CA的批准治疗的部署受到这种疾病的延迟诊断的限制。人工智能（AI）可以检测罕见疾病。在这里，我们提出了一个使用心电图（ECG）或超声心动图作为输入的AI模型进行CA检测的管道。这些模型分别在3个和5个学术医疗中心（AMC）进行训练和验证，检测CA的C-统计量为0.85-0.91（ECG）和0.89-1.00（超声心动图）。在2个AMC上模拟部署表明，在52-71%的召回率下，ECG模型的阳性预测值（PPV）为3-4%。ECG预筛选可提高超声心动图模型的性能，召回率为67%，PPV为33%，PPV为74- 77%。总之，我们开发了一种自动化的策略来增强CA检测，这应该是可推广到其他罕见的心脏疾病。心脏淀粉样变性很难识别，因为它的患病率很低，而且症状与更普遍的疾病相似。在本文中，作者提出了一种多模态的人工智能管道，可以通过廉价且可获得的措施自动检测心脏淀粉样变性。
Patients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of this disease. Artificial intelligence (AI) could enable detection of rare diseases. Here we present a pipeline for CA detection using AI models with electrocardiograms (ECG) or echocardiograms as inputs. These models, trained and validated on 3 and 5 academic medical centers (AMC) respectively, detect CA with C-statistics of 0.85–0.91 for ECG and 0.89–1.00 for echocardiography. Simulating deployment on 2 AMCs indicated a positive predictive value (PPV) for the ECG model of 3–4% at 52–71% recall. Pre-screening with ECG enhance the echocardiography model performance at 67% recall from PPV of 33% to PPV of 74–77%. In conclusion, we developed an automated strategy to augment CA detection, which should be generalizable to other rare cardiac diseases. Cardiac amyloidosis is difficult to identify, given low prevalence and similarity of the symptoms to more prevalent disorders. Here the authors present a multi-modality, artificial intelligence-enabled pipeline, that enables automated detection of cardiac amyloidosis from inexpensive and accessible measures.
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影响因子：
37.8
作者：
Zhang J;Gajjala S;Agrawal P;Tison GH;Hallock LA;Beussink-Nelson L;Lassen MH;Fan E;Aras MA;Jordan C;Fleischmann KE;Melisko M;Qasim A;Shah SJ;Bajcsy R;Deo RC
通讯作者：
Deo RC
影响因子：
120.7
作者：
Berk, John L.;Suhr, Ole B.;Obici, Laura;Sekijima, Yoshiki;Zeldenrust, Steven R.;Yamashita, Taro;Heneghan, Michael A.;Gorevic, Peter D.;Litchy, William J.;Wiesman, Janice F.;Nordh, Erik;Corato, Manuel;Lozza, Alessandro;Cortese, Andrea;Robinson-Papp, Jessica;Colton, Theodore;Rybin, Denis V.;Bisbee, Alice B.;Ando, Yukio;Ikeda, Shu-ichi;Seldin, David C.;Merlini, Giampaolo;Skinner, Martha;Kelly, Jeffery W.;Dyck, Peter J.
通讯作者：
Dyck, Peter J.
影响因子：
5.7
作者：
Phelan, Dermot;Collier, Patrick;Thomas, James D.
通讯作者：
Thomas, James D.
影响因子：
39.3
作者：
Gonzalez-Lopez, Esther;Gallego-Delgado, Maria;Garcia-Pavia, Pablo
通讯作者：
Garcia-Pavia, Pablo
影响因子：
3.5
作者：
Sperry, BrettW.;Vranian, Michael N.;Hanna, Mazen
通讯作者：
Hanna, Mazen