Automated Measurements of Body Composition in Abdominal CT Scans Using Artificial Intelligence Can Predict Mortality in Patients With Cirrhosis.
Automated Measurements of Body Composition in Abdominal CT Scans Using Artificial Intelligence Can Predict Mortality in Patients With Cirrhosis.
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DOI：
10.1002/hep4.1768
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发表时间：
2021-11
影响因子：
5.1
通讯作者：
Su GL
中科院分区：
文献类型：
--
作者：
Zou WY;Enchakalody BE;Zhang P;Shah N;Saini SD;Wang NC;Wang SC;Su GL
从已有的电子病历(计算机断层扫描[CT]扫描)中得出的身体成分测量可能具有重大价值，但临床实施需要测量的自动化。我们试图使用人工智能来开发一种自动方法来测量身体成分，并在临床队列中测试该算法以预测死亡率。我们使用谷歌的DeepLabv3+在一组去识别的CT扫描(n=12,067)上构建了一个深度学习算法。为了测试算法的准确性和临床有效性，我们使用了一组独特的前瞻性跟踪观察的肝硬变患者(n=2238)，这些患者进行了CT扫描。为了评估模型的性能，我们使用混淆矩阵，计算出平均精度为0.977±0.02(训练集和测试集分别为0.975±0.018)。为了评估空间重叠，我们测量了联合上的平均交集和平均边界轮廓分数，发现手动方法和自动方法之间存在很好的重叠，平均分数分别为0.954±0.030、0.987±0.009和0.948±0.039(训练集和测试集分别为0.983±0.013)。使用这些自动测量，我们发现身体成分特征可以预测肝硬变患者的死亡率。在多变量分析中，身体成分指标的加入显著改善了仅有终末期肝病模型的肝硬变患者的死亡率预测(P<0.001)。结论：利用人工智能技术可以实现人体成分测量的自动化，对于其他临床适应症的附带CT检查具有重要的价值。这是概念的证明，这种方法可以允许更广泛的实施到临床领域。
Body composition measures derived from already available electronic medical records (computed tomography [CT] scans) can have significant value, but automation of measurements is needed for clinical implementation. We sought to use artificial intelligence to develop an automated method to measure body composition and test the algorithm on a clinical cohort to predict mortality. We constructed a deep learning algorithm using Google’s DeepLabv3+ on a cohort of de‐identified CT scans (n = 12,067). To test for the accuracy and clinical usefulness of the algorithm, we used a unique cohort of prospectively followed patients with cirrhosis (n = 238) who had CT scans performed. To assess model performance, we used the confusion matrix and calculated the mean accuracy of 0.977 ± 0.02 (0.975 ± 0.018 for the training and test sets, respectively). To assess for spatial overlap, we measured the mean intersection over union and mean boundary contour scores and found excellent overlap between the manual and automated methods with mean scores of 0.954 ± 0.030, 0.987 ± 0.009, and 0.948 ± 0.039 (0.983 ± 0.013 for the training and test set, respectively). Using these automated measurements, we found that body composition features were predictive of mortality in patients with cirrhosis. On multivariate analysis, the addition of body composition measures significantly improved prediction of mortality for patients with cirrhosis over Model for End‐Stage Liver Disease alone (P < 0.001). Conclusion: The measurement of body composition can be automated using artificial intelligence and add significant value for incidental CTs performed for other clinical indications. This is proof of concept that this methodology could allow for wider implementation into the clinical arena.
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影响因子：
2
作者：
Pencina, Michael J.;D'Agostino, Ralph B., Sr.;Steyerberg, Ewout W.
通讯作者：
Steyerberg, Ewout W.
影响因子：
9.8
作者：
Buzzetti, Elena;Pinzani, Massimo;Tsochatzis, Emmanuel A.
通讯作者：
Tsochatzis, Emmanuel A.
影响因子：
9
作者：
Englesbe, Michael J.;Lee, Jay S.;Wang, Stewart C.
通讯作者：
Wang, Stewart C.
DOI：
10.1097/meg.0000000000000274
发表时间：
2015-03-01
影响因子：
2.1
作者：
Giusto, Michela;Lattanzi, Barbara;Merli, Manuela
通讯作者：
Merli, Manuela
影响因子：
8.5
作者：
Schmid, Matthias;Wright, Marvin N.;Ziegler, Andreas
通讯作者：
Ziegler, Andreas