Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT

Hata, Akinori; Yanagawa, Masahiro; Yamagata, Kazuki; Suzuki, Yuki; Shoji, K.; Kawata, Atsushi; Doi, Shuhei; Yoshida, Yuki; Miyata, Tomo; Tsubamoto, Mitsuko; Kikuchi, Noriko; Tomiyama, Noriyuki

doi:10.1007/s00330-020-07213-w

Cited by 35 publications

(21 citation statements)

References 17 publications

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“…It was found that the proportion of increased lung texture, stripe shadow, ground glass shadow, atelectasis, and pleural effusion in the observation group was substantially lower than that in the control group after nursing, and the difference was great (P < 0.05). is was similar to the findings of Hata et al [22], indicating that specific nursing intervention can more effectively promote the recovery of children with MP compared with routine nursing. In addition, there was no considerable difference between the observation group and the control group in the proportion of small patchy shadow, large patchy consolidation shadow, and bronchiole dilation (P > 0.05), which was different from the research of Branco et al [23].…”

Section: Discussionsupporting

confidence: 90%

Computed Tomography Image Texture under Feature Extraction Algorithm in the Diagnosis of Effect of Specific Nursing Intervention on Mycoplasma Pneumonia in Children

Jiang

Hua

et al. 2021

Journal of Healthcare Engineering

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To evaluate the effect of specific nursing intervention in children with mycoplasma pneumonia (MP), a feature extraction algorithm based on gray level co-occurrence matrix (GLCM) was proposed and combined with computed tomography (CT) image texture features. Then, 98 children with MP were rolled into the observation group with 49 cases (specific nursing) and the control group with 49 cases (routine nursing). CT images based on feature extraction algorithm of optimized GLCM were used to examine the children before and after nursing intervention, and the recovery of the two groups of children was discussed. The results showed that the proportion of lung texture increase, rope shadow, ground glass shadow, atelectasis, and pleural effusion in the observation group (24.11%, 3.86%, 8.53%, 15.03%, and 3.74%) was significantly lower than that in the control group (28.53%, 10.23%, 13.34%, 21.15%, and 8.13%) after nursing ( P < 0.05 ). There were no significant differences in the proportion of small patchy shadows, large patchy consolidation shadows, and bronchiectasis between the observation group and the control group ( P > 0.05 ). In the course of nursing intervention, in the observation group, the disappearance time of cough, normal temperature, disappearance time of lung rales, and absorption time of lung shadow (2.15 ± 0.86 days, 4.81 ± 1.14 days, 3.64 ± 0.55 days, and 5.96 ± 0.62 days) were significantly shorter than those in the control group (2.87 ± 0.95 days, 3.95 ± 1.06 days, 4.51 ± 1.02 days, and 8.14 ± 1.35 days) ( P < 0.05 ). After nursing intervention, the proportion of satisfaction and total satisfaction in the experimental group (67.08% and 28.66%) was significantly higher than that in the control group (40.21% and 47.39%), while the proportion of dissatisfaction (4.26%) was significantly lower than that in the control group (12.4%) ( P < 0.05 ). To sum up, specific nursing intervention was more beneficial to improve the progress of characterization recovery and the overall recovery effect of children with MP relative to conventional nursing. CT image based on feature extraction algorithm of optimized GLCM was of good adoption value in the diagnosis and treatment of MP in children.

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Section: Discussionsupporting

confidence: 90%

Computed Tomography Image Texture under Feature Extraction Algorithm in the Diagnosis of Effect of Specific Nursing Intervention on Mycoplasma Pneumonia in Children

Jiang

Hua

et al. 2021

Journal of Healthcare Engineering

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“…Notably, the specificity of the deep-integrated model was improved to 92.3% in the internal testing cohort, which was slightly lower than that of the radiologists and apparently higher than the corresponding results from a prior study of 85.5% ( 12 ). However, in the external testing cohort, the specificity performance decreased to 55.4%.…”

Section: Discussioncontrasting

confidence: 65%

“…Studies have reported that the DL technology contributes greatly to expanding the amount of information accessible in CT images beyond human recognizability limitations. Recently, Hata et al (12) designed the 2D DL algorithm for the detection of AD on non-contrast CT and reached an AUC of 0.940 on the internal testing set. The results of the 2D model showed comparable diagnostic performance to radiologists, which was consistent with the FIGURE 4 | The µ and σ parameters of the deep-integrated model.…”

Section: Discussionmentioning

confidence: 99%

“…He et al proposed residual networks (ResNets) ( 10 ) that won first place on the ImageNet Large Scale Visual Recognition Challenge ( 11 ), which outperformed human accuracy in image classification. Hata et al ( 12 ) designed a DL algorithm for the detection of AD on non-contrast CT; however, their method was limited to two-dimensional (2D) models with image data from a single center.…”

Section: Introductionmentioning

confidence: 99%

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Advanced Warning of Aortic Dissection on Non-Contrast CT: The Combination of Deep Learning and Morphological Characteristics

Mao²,

Wang³

et al. 2022

Front. Cardiovasc. Med.

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Background: The identification of aortic dissection (AD) at baseline plays a crucial role in clinical practice. Non-contrast CT scans are widely available, convenient, and easy to perform. However, the detection of AD on non-contrast CT scans by radiologists currently lacks sensitivity and is suboptimal.Methods: A total of 452 patients who underwent aortic CT angiography (CTA) were enrolled retrospectively from two medical centers in China to form the internal cohort (341 patients, 139 patients with AD, 202 patients with non-AD) and the external testing cohort (111 patients, 46 patients with AD, 65 patients with non-AD). The internal cohort was divided into the training cohort (n = 238), validation cohort (n = 35), and internal testing cohort (n = 68). Morphological characteristics were extracted from the aortic segmentation. A deep-integrated model based on the Gaussian Naive Bayes algorithm was built to differentiate AD from non-AD, using the combination of the three-dimensional (3D) deep-learning model score and morphological characteristics. The areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to evaluate the model performance. The proposed model was also compared with the subjective assessment of radiologists.Results: After the combination of all the morphological characteristics, our proposed deep-integrated model significantly outperformed the 3D deep-learning model (AUC: 0.948 vs. 0.803 in the internal testing cohort and 0.969 vs. 0.814 in the external testing cohort, both p < 0.05). The accuracy, sensitivity, and specificity of our model reached 0.897, 0.862, and 0.923 in the internal testing cohort and 0.730, 0.978, and 0.554 in the external testing cohort, respectively. The accuracy for AD detection showed no significant difference between our model and the radiologists (p > 0.05).Conclusion: The proposed model presented good performance for AD detection on non-contrast CT scans; thus, early diagnosis and prompt treatment would be available.

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“…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 ].…”

Section: Discussionmentioning

confidence: 99%

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

Elbadawi

Mahmoud

et al. 2021

Cardiol Ther

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Introduction: There is paucity of data on the outcomes of hospitalization for bicuspid aortic valve (BAV)-related aortopathies. Methods: We queried the NIS database (2012)(2013)(2014)(2015)(2016) for hospitalizations for elective thoracic aortic repair or acute aortic syndrome (AAS) among those with BAV versus trileaflet aortic valve (TAV).Results: Our analysis yielded 38,010 hospitalizations for elective aortic repair, of whom 34.4% had BAV, as well as 81,875 hospitalizations for thoracic AAS, of whom 1.1% had BAV. Hospitalizations for BAV were younger and had fewer comorbidities compared with their TAV counterparts. The number of hospitalizations for BAV during the observational period was unchanged. After propensity matching, elective aortic repair for BAV was associated with lower mortality (0.5% versus 1.7%, odds ratio = 0.28; 95% CI 1.5-0.50, p \ 0.001), use of mechanical circulatory support, acute stroke, and shorter length of hospital stay compared with TAV. After propensity matching, AAS among those with BAV had a greater incidence of bleeding

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Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT

Cited by 35 publications

References 17 publications

Computed Tomography Image Texture under Feature Extraction Algorithm in the Diagnosis of Effect of Specific Nursing Intervention on Mycoplasma Pneumonia in Children

Computed Tomography Image Texture under Feature Extraction Algorithm in the Diagnosis of Effect of Specific Nursing Intervention on Mycoplasma Pneumonia in Children

Advanced Warning of Aortic Dissection on Non-Contrast CT: The Combination of Deep Learning and Morphological Characteristics

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

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