A study of aortic dissection screening method based on multiple machine learning models

Abstract

Background:The main purpose of the study was to develop an early screening method for aortic dissection (AD) based on machine learning. Due to the rarity of AD and the complexity of symptoms, many doctors have no clinical experience with it. Many patients are not suspected of having AD, which lead to a high rate of misdiagnosis. Here, we report the preliminary study and feasibility of rapid and accurate screening method of AD with machine learning methods. Methods: The dataset analyzed was composed by examination data provided by the Xiangya Hospital Central South University of China which include a total of 60,000 samples, including aortic patients and non-aortic ones. Each sample has 76 features which is consist of routine examinations and other easily accessible information. Since the proportion of people who are affected is usually imbalanced compared to non-diseased people, multiple machine learning models were used, include AdaBoost, SmoteBagging, EasyEnsemble and CalibratedAdaMEC. They used different methods such as ensemble learning, undersampling, oversampling, and cost-sensitivity to solve data imbalance problems. Results: AdaBoost performed poorly with an average recall of 16.1% and a specificity of 99.8%.SmoteBagging achieved a statistically significant better performance for this problem with an average recall of 78.1% and a specificity of 79.2%. EasyEnsemble reached the values of 77.8% and 79.3% for recall and specificity respectively. CalibratedAdaMEC's recall and specificity are 75.8% and 76%.Conclusions: It was found that the screening performance of the models evaluated in this paper had a misdiagnosis rate lower than 25% except AdaBoost. The data used in these methods are only routine inspection data. This means that machine learning methods can help us build a fast, cheap, worthwhile and effective early screening approach for AD.

An early aortic dissection screening model and applied research based on ensemble learning

“…The results show that the misdiagnosis rate of the XGBF algorithm is lower than that of the other four algorithms. At the same time, the screening results of XGBF were also better than the best results obtained by using Smotebagging in the literature (19), and also better than the clinical misdiagnosis rate (29,30). In particular, the improved algorithm XGBF made the missed diagnosis rate less than 20%, which is less than the missed diagnosis rate of 21.9% (19), 35.5% (29) and 39.69% (30).…”

“…The sample size was small. Liu et al (19) analyzed the performance of several machine learning models in AD screening, among which the SmoteBagging was the best, and the sensitivity reached 78.1%. Wu et al (20) used the Random Forest model to investigate the risk of in-hospital rupture in type A AD.…”

A Combined Deep Learning System for Automatic Detection of “Bovine” Aortic Arch on Computed Tomography Scans

“…In some studies, e.g., [34], deep learning has been applied on CT thoracic images for different purposes, including aorta segmentation, aortic disease detection, and risk stratification, demonstrating excellent correlations and adequate agreements compared with manual measurements for most segmented structures. A detailed analysis of these studies is out of the purpose of this work and can be found, for example, in [16][17][18]46]. To the best of our knowledge, no AI application has yet been implemented for the automatic CILCA detection and classification of CT scans acquired for different clinical indications.…”

“…During the last years, artificial intelligence (AI) has gained a crucial role in medical image analysis, especially in the field of radiology, where it allows the automation of tasks that would be too cumbersome or not easily feasible in routine practice, thus offering a powerful aid to radiologists and, finally, to clinicians and patients [15]. In particular, concerning the assessment of aorta by CT thoracic studies, AI applications include, among others, automatic aortic diameter measurements to assess the progression of aortic aneurysms [16], screening for aortic dissection [17], and classification of aortic dissection [18]. Considering the low rate of aortic arch variant described on CT scans in clinical practice (only 5-8% [9]), suggesting a relevant under-reporting, and the high proportion of CILCA among these variants, the availability of a fully automatic system for CILCA detection on CT thorax studies, not needing any human time-consuming post-processing or segmentation, could help in stratifying the risk of aortic disease and the reporting of incidental findings in patients undergoing chest CT. To the best of our knowledge, no AI application has yet been implemented for the automatic CILCA detection and classification of CT scans acquired for different clinical indications.…”

Temporal Trends and Outcomes of Elective Thoracic Aortic Repair and Acute Aortic Syndromes in Bicuspid Aortic Valves: Insights from a National Database

“…Beside traditional clinical predictors, the use of machine learning models has been proposed in risk-stratifying patients with aortic aneurysms and predicting risk of AAS. Future studies are warranted to develop machine learning models for predicting adverse outcomes among patients with BAV-related aortopathy [ 32 , 33 ].…”