Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT.
Nicola FinkBasel YacoubU Joseph SchoepfEmese ZsarnoczayDaniel PinosMilan Vecsey-NagySaikiran RapakaPuneet SharmaJim O'DohertyJens RickeAkos Varga-SzemesTilman EmrichPublished in: Diagnostics (Basel, Switzerland) (2024)
This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced and contrast-enhanced electrocardiogram-gated chest CT were evaluated. All the DNN measurements were compared to manual assessment, overall and between the following subgroups: (1) ascending (AA) vs. descending aorta (DA); (2) non-obese vs. obese; (3) without vs. with aortic repair; (4) without vs. with aortic dissection. Furthermore, the presence of aortic dissection was determined (yes/no decision). The automated and manual diameters differed significantly ( p < 0.05) but showed excellent correlation and agreement (r = 0.89; ICC = 0.94). The automated and manual values were similar in the AA group but significantly different in the DA group ( p < 0.05), similar in obese but significantly different in non-obese patients ( p < 0.05) and similar in patients without aortic repair or dissection but significantly different in cases with such pathological conditions ( p < 0.05). However, in all the subgroups, the automated diameters showed strong correlation and agreement with the manual values (r > 0.84; ICC > 0.9). The accuracy, sensitivity and specificity of DNN-based aortic dissection detection were 92.1%, 88.1% and 95.7%, respectively. This DNN-based algorithm enabled accurate quantification of the largest aortic diameter and detection of aortic dissection in a heterogenous patient population with various aortic pathologies. This has the potential to enhance radiologists' efficiency in clinical practice.
Keyphrases
- aortic dissection
- contrast enhanced
- computed tomography
- machine learning
- deep learning
- artificial intelligence
- obese patients
- dual energy
- magnetic resonance imaging
- image quality
- bariatric surgery
- neural network
- diffusion weighted
- magnetic resonance
- positron emission tomography
- end stage renal disease
- weight loss
- adipose tissue
- metabolic syndrome
- ejection fraction
- high throughput
- type diabetes
- clinical practice
- loop mediated isothermal amplification
- label free
- real time pcr
- high resolution
- mass spectrometry
- gastric bypass
- pulmonary artery