Key result
A convolutional neural network (CNN) model predicted the early rupture of acute type A aortic dissection with a sensitivity of 0.93, specificity of 0.90, and accuracy of 0.90.
Why the study?
Do artificial intelligence algorithms improve the risk prediction of early rupture in patients with acute type A aortic dissection compared to logistic regression?
Observational (n=200)
No
Do artificial intelligence algorithms improve the risk prediction of early rupture in patients with acute type A aortic dissection compared to logistic regression?
Artificial intelligence algorithms, particularly convolutional neural networks, demonstrate superior accuracy and sensitivity compared to traditional logistic regression in predicting the early rupture of acute type A aortic dissection.
Answers are grounded in this paper and cite their sources.
May aid ATAAD rupture risk stratification; leaves open prospective validation before clinical use.
Logistic regression (LR) and artificial intelligence algorithms were used to analyze the risk factors for the early rupture of acute type A aortic dissection (ATAAD). Data from electronic medical records of 200 patients diagnosed with ATAAD from the Department of Emergency of Guangdong Provincial People’s Hospital from April 2012 to March 2017 were collected. Logistic regression and artificial intelligence algorithms were used to establish prediction models, and the prediction effects of four models were analyzed. According to the LR models, we elucidated independent risk factors for ATAAD rupture, which included age > 63 years (odds ratio (OR) = 1.69), female sex (OR = 1.77), ventilator assisted ventilation (OR = 3.05), AST > 80 U/L (OR = 1.59), no distortion of the inner membrane (OR = 1.57), the diameter of the aortic sinus > 41 mm (OR = 0.92), maximum aortic diameter > 48 mm (OR = 1.32), the ratio of false lumen area to true lumen area > 2.12 (OR = 1.94), lactates > 1.9 mmol/L (OR = 2.28), and white blood cell > 14.2 × 109 /L (OR = 1.23). The highest sensitivity and accuracy were found with the convolutional neural network (CNN) model. Its sensitivity was 0.93, specificity was 0.90, and accuracy was 0.90. In this present study, we found that age, sex, select biomarkers, and select morphological parameters of the aorta are independent predictors for the rupture of ATAAD. In terms of predicting the risk of ATAAD, the performance of random forests and CNN is significantly better than LR, but the performance of the support vector machine (SVM) is worse than LR.
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Lin et al. (2022) conducted an observational in Acute type A aortic dissection (ATAAD) (n=200). Artificial intelligence algorithms (CNN, random forests, SVM) vs. Logistic regression was evaluated on Early rupture of acute type A aortic dissection (ATAAD). A convolutional neural network (CNN) model predicted the early rupture of acute type A aortic dissection with a sensitivity of 0.93, specificity of 0.90, and accuracy of 0.90.
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