Artificial Intelligence Applications in Aortic Dissection Imaging

Deep learning-aided diagnosis of acute abdominal aortic dissection by ultrasound images

PURPOSE

Acute abdominal aortic dissection (AD) is a serious disease. Early detection based on ultrasound (US) can improve the prognosis of AD, especially in emergency settings. We explored the ability of deep learning (DL) to diagnose abdominal AD in US images, which may help the diagnosis of AD by novice radiologists or non-professionals.

METHODS

There were 374 US images from patients treated before June 30, 2022. The images were classified as AD-positive and AD-negative images. Among them, 90% of images were used as the training set, and 10% of images were used as the test set. A Densenet-169 model and a VGG-16 model were used in this study and compared with two human readers.

RESULTS

DL models demonstrated high sensitivity and AUC for diagnosing abdominal AD in US images, and DL models showed generally better performance than human readers.

CONCLUSION

Our findings demonstrated the efficacy of DL-aided diagnosis of abdominal AD in US images, which can be helpful in emergency settings.

In Vitro Studies on Hemodynamics of Type B Aortic Dissection: Accuracy and Reliability

Type B aortic dissection (TBAD) is associated with high mortality. Multiple in vitro models and computational fluid dynamics (CFD) simulations have been used to mimic the hemodynamic characteristics of TBAD to inform more effective therapeutic strategies. However, the results of these experiments are rarely used in clinical practice due to concerns about their accuracy and reliability. The development of 4-dimensional magnetic resonance imaging (4D-MRI) allows to verify the accuracy of the results of in vitro models and CFD simulations. This review provides an overview of the strengths, limitations, and accuracy of in vitro models, CFD simulations, and in vivo 4D flow MRI for the study of TBAD hemodynamics.Clinical Impact1. Hemodynamic of TBAD is important to improve the long-term outcome of TEVAR.2. This review provides an overview of the in-vitro for the hemodynamic study of TBAD.3. The accuracy and validity of in-vitro TBAD experiments should be further studied.

Applications of Artificial Intelligence in Acute Thoracic Imaging

The applications of artificial intelligence (AI) in radiology are rapidly advancing with AI algorithms being used in a wide range of disease pathologies and clinical settings. Acute thoracic pathologies including rib fractures, pneumothoraces, and acute PE are associated with significant morbidity and mortality and their identification is crucial for prompt treatment. AI models which increase diagnostic accuracy, improve radiologist efficiency and reduce time to diagnosis of acute abnormalities in the thorax have the potential to significantly improve patient outcomes. The purpose of this review is to summarize the current applications of AI in acute thoracic imaging, highlighting their strengths, limitations, and future research opportunities.

Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future

Background: Artificial intelligence (AI) has emerged as a transformative technology in healthcare, with its integration into cardiac surgery offering significant advancements in precision, efficiency, and patient outcomes. However, a comprehensive understanding of AI's applications, benefits, challenges, and future directions in cardiac surgery is needed to inform its safe and effective implementation. Methods: A systematic review was conducted following PRISMA guidelines. Literature searches were performed in PubMed, Scopus, Cochrane Library, Google Scholar, and Web of Science, covering publications from January 2000 to November 2024. Studies focusing on AI applications in cardiac surgery, including risk stratification, surgical planning, intraoperative guidance, and postoperative management, were included. Data extraction and quality assessment were conducted using standardized tools, and findings were synthesized narratively. Results: A total of 121 studies were included in this review. AI demonstrated superior predictive capabilities in risk stratification, with machine learning models outperforming traditional scoring systems in mortality and complication prediction. Robotic-assisted systems enhanced surgical precision and minimized trauma, while computer vision and augmented cognition improved intraoperative guidance. Postoperative AI applications showed potential in predicting complications, supporting patient monitoring, and reducing healthcare costs. However, challenges such as data quality, validation, ethical considerations, and integration into clinical workflows remain significant barriers to widespread adoption. Conclusions: AI has the potential to revolutionize cardiac surgery by enhancing decision making, surgical accuracy, and patient outcomes. Addressing limitations related to data quality, bias, validation, and regulatory frameworks is essential for its safe and effective implementation. Future research should focus on interdisciplinary collaboration, robust testing, and the development of ethical and transparent AI systems to ensure equitable and sustainable advancements in cardiac surgery.

A Narrative Review of Biomarkers and Imaging in the Diagnosis of Acute Aortic Syndrome

Acute aortic syndrome (AAS) encompasses a range of life-threatening conditions, including classical dissection, intramural hematoma, and penetrating aortic ulcer. Each of these conditions presents distinct clinical characteristics and carries the potential to progress to rupture. Because AAS can be asymptomatic or present with diverse symptoms, its diagnosis requires clinical evaluation, risk scoring, and biomarkers such as D-dimer (DD), C-reactive protein (CRP), homocysteine, natriuretic peptides (BNP), and imaging modalities like computed tomography (CT), magnetic resonance imaging (MRI), and echocardiography. While this review primarily focuses on widely used and clinically accessible biomarkers and imaging techniques, it also discusses alternative biomarkers proposed for diagnostic use. Although CT remains the gold standard for diagnosis, biomarkers facilitate rapid risk stratification, complementing imaging techniques. Emerging technologies, such as metabolomics, are reshaping diagnostic algorithms. Despite advances in diagnostic methods, challenges such as misdiagnosis and missed diagnoses persist. Ongoing research into novel biomarkers and innovative imaging techniques holds promise for improving diagnostic accuracy and patient outcomes.

Streamlining Acute Abdominal Aortic Dissection Management-An AI-based CT Imaging Workflow

Life-threatening acute aortic dissection (AD) demands timely diagnosis for effective intervention. To streamline intrahospital workflows, automated detection of AD in abdominal computed tomography (CT) scans seems useful to assist humans. We aimed at creating a robust convolutional neural network (CNN)-based pipeline capable of real-time screening for signs of abdominal AD in CT. In this retrospective study, abdominal CT data from AD patients presenting with AD and from non-AD patients were collected (n 195, AD cases 94, mean age 65.9 years, female ratio 35.8%). A CNN-based algorithm was developed with the goal of enabling a robust, automated, and highly sensitive detection of abdominal AD. Two sets from internal (n = 32, AD cases 16) and external sources (n = 1189, AD cases 100) were procured for validation. The abdominal region was extracted, followed by the automatic isolation of the aorta region of interest (ROI) and highlighting of the membrane via edge extraction, followed by classification of the aortic ROI as dissected/healthy. A fivefold cross-validation was employed on the internal set, and an ensemble of the 5 trained models was used to predict the internal and external validation set. Evaluation metrics included receiver operating characteristic curve (AUC) and balanced accuracy. The AUC, balanced accuracy, and sensitivity scores of the internal dataset were 0.932 (CI 0.891-0.963), 0.860, and 0.885, respectively. For the internal validation dataset, the AUC, balanced accuracy, and sensitivity scores were 0.887 (CI 0.732-0.988), 0.781, and 0.875, respectively. Furthermore, for the external validation dataset, AUC, balanced accuracy, and sensitivity scores were 0.993 (CI 0.918-0.994), 0.933, and 1.000, respectively. The proposed automated pipeline could assist humans in expediting acute aortic dissection management when integrated into clinical workflows.

Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization

BACKGROUND AND OBJECTIVES

Acute aortic dissection (AD) is a life-threatening condition in which early detection can significantly improve patient outcomes and survival. This study evaluates the clinical benefits of integrating a deep learning (DL)-based application for the automated detection and prioritization of AD on chest CT angiographies (CTAs) with a focus on the reduction in the scan-to-assessment time (STAT) and interpretation time (IT).

MATERIALS AND METHODS

This retrospective Multi-Reader Multi-Case (MRMC) study compared AD detection with and without artificial intelligence (AI) assistance. The ground truth was established by two U.S. board-certified radiologists, while three additional expert radiologists served as readers. Each reader assessed the same CTAs in two phases: assessment unaided by AI assistance (pre-AI arm) and, after a 1-month washout period, assessment aided by device outputs (post-AI arm). STAT and IT metrics were compared between the two arms.

RESULTS

This study included 285 CTAs (95 per reader, per arm) with a mean patient age of 58.5 years ±14.7 (SD), of which 52% were male and 37% had a prevalence of AD. AI assistance significantly reduced the STAT for detecting 33 true positive AD cases from 15.84 min (95% CI: 13.37-18.31 min) without AI to 5.07 min (95% CI: 4.23-5.91 min) with AI, representing a 68% reduction (p < 0.01). The IT also reduced significantly from 21.22 s (95% CI: 19.87-22.58 s) without AI to 14.17 s (95% CI: 13.39-14.95 s) with AI (p < 0.05).

CONCLUSIONS

The integration of a DL-based algorithm for AD detection on chest CTAs significantly reduces both the STAT and IT. By prioritizing urgent cases, the AI-assisted approach outperforms the standard First-In, First-Out (FIFO) workflow.

Pilot study exploring artificial intelligence for facial-image-based diagnosis of Marfan syndrome

Background

Marfan Syndrome (MFS), a genetic disorder impacting connective tissue, manifests in a wide array of phenotypes which can affect numerous bodily systems, especially the thoracic aorta. The syndrome often presents distinct facial features that potentially allow for diagnostic clinical recognition. Herein, we explore the potential of Artificial Intelligence (AI) in diagnosing Marfan syndrome from ordinary facial images, as assessed by overall accuracy, F1 score, and area under the ROC curve.

Methods

This study explores the utilization of Convolutional Neural Networks (CNN) for MFS identification through facial images, offering a novel, non-invasive, automated, and computerized diagnostic approach. The research examines the accuracy of Neural Networks in the diagnosis of Marfan Disease from ordinary on-line facial images. The model was trained on 80 % of 672 facial images (182 Marfan and 490 control). The other 20 % of images were used as the test set.

Results

Overall accuracy was 98.5 % (0 % false positive, 2 % false negative). F1 score was 97 % for Marfan facies and 99 % for non-Marfan facies. Area under the ROC curve was 100 %.

Conclusion

An Artificial Intelligence (AI) program was able to distinguish Marfan from non-Marfan facial images (from ordinary on-line photographs) with an extremely high degree of accuracy. Clinical usefulness of this program is anticipated. However, due to the limited and preliminary nature of this work, this should be viewed as only a pilot study.

Automatic Segmentation of Type A Aortic Dissection on Computed Tomography Images Using Deep Learning Approach

PURPOSE

Type A aortic dissection (TAAD) is a life-threatening aortic disease. The tear involves the ascending aorta and progresses into the separation of the layers of the aortic wall and the occurrence of a false lumen. Accurate segmentation of TAAD could provide assistance for disease assessment and guidance for clinical treatment.

METHODS

This study applied nnU-Net, a state-of-the-art biomedical segmentation network architecture, to segment contrast-enhanced CT images and quantify the morphological features for TAAD. CT datasets were acquired from 24 patients with TAAD. Manual segmentation and annotation of the CT images was used as the ground-truth. Two-dimensional (2D) nnU-Net and three-dimensional (3D) nnU-Net architectures with Dice- and cross entropy-based loss functions were utilized to segment the true lumen (TL), false lumen (FL), and intimal flap on the images. Four-fold cross validation was performed to evaluate the performance of the two nnU-Net architectures. Six metrics, including accuracy, precision, recall, Intersection of Union, Dice similarity coefficient (DSC), and Hausdorff distance, were calculated to evaluate the performance of the 2D and 3D nnU-Net algorithms in TAAD datasets. Aortic morphological features from both 2D and 3D nnU-Net algorithms were quantified based on the segmented results and compared.

RESULTS

Overall, 3D nnU-Net architectures had better performance in TAAD CT datasets, with TL and FL segmentation accuracy up to 99.9%. The DSCs of TLs and FLs based on the 3D nnU-Net were 88.42% and 87.10%. For the aortic TL and FL diameters, the FL area calculated from the segmentation results of the 3D nnU-Net architecture had smaller relative errors (3.89-6.80%), compared to the 2D nnU-Net architecture (relative errors: 4.35-9.48%).

CONCLUSIONS

The nnU-Net architectures may serve as a basis for automatic segmentation and quantification of TAAD, which could aid in rapid diagnosis, surgical planning, and subsequent biomechanical simulation of the aorta.

Aorta Segmentation in 3D CT Images by Combining Image Processing and Machine Learning Techniques

PURPOSE

Aorta segmentation is extremely useful in clinical practice, allowing the diagnosis of numerous pathologies, such as dissections, aneurysms and occlusive disease. In such cases, image segmentation is prerequisite for applying diagnostic algorithms, which in turn allow the prediction of possible complications and enable risk assessment, which is crucial in saving lives. The aim of this paper is to present a novel fully automatic 3D segmentation method, which combines basic image processing techniques and more advanced machine learning algorithms, for detecting and modelling the aorta in 3D CT imaging data.

METHODS

An initial intensity threshold-based segmentation procedure is followed by a classification-based segmentation approach, based on a Markov Random Field network. The result of the proposed two-stage segmentation process is modelled and visualized.

RESULTS

The proposed methodology was applied to 16 3D CT data sets and the extracted aortic segments were reconstructed as 3D models. The performance of segmentation was evaluated both qualitatively and quantitatively against other commonly used segmentation techniques, in terms of the accuracy achieved, compared to the actual aorta, which was defined manually by experts.

CONCLUSION

The proposed methodology achieved superior segmentation performance, compared to all compared segmentation techniques, in terms of the accuracy of the extracted 3D aortic model. Therefore, the proposed segmentation scheme could be used in clinical practice, such as in treatment planning and assessment, as it can speed up the evaluation of the medical imaging data, which is commonly a lengthy and tedious process.

Unraveling the Enigma of Aortic Dissection: From Genetics to Innovative Therapies

Aortic dissection (AD) presents a critical medical emergency characterized by a tear in the aortic wall, necessitating prompt recognition and management to mitigate catastrophic complications. Despite advancements in medical technology and therapeutic interventions, AD remains a formidable challenge, often resulting in severe morbidity and mortality. This narrative review provides a comprehensive overview of AD, encompassing its clinical presentation, diagnostic modalities, and management strategies, while also exploring emerging trends and innovations in its management. Genetic predispositions significantly influence AD pathogenesis, with over 30 contributory genes identified, emphasizing the importance of genetic screening and counseling. Classification systems such as Stanford and DeBakey, alongside their revised counterparts, aid in categorizing AD and guiding treatment decisions. Advancements in diagnostic imaging, including transesophageal echocardiography and computed tomography angiography, have enhanced diagnostic precision, augmented by artificial intelligence and machine learning algorithms. Pharmacological innovations focus on optimizing medical therapy, while surgical and endovascular approaches offer minimally invasive treatment options. Hybrid procedures and aortic valve-sparing techniques broaden treatment avenues, while bioresorbable stent grafts hold promise for tissue regeneration. Collaborative efforts and ongoing research are essential to address remaining challenges and improve outcomes in managing AD. This review contributes to the understanding of AD's complexity and facilitates informed decision-making in clinical practice, underscoring the imperative for continued innovation and research in AD management.

Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection

BACKGROUND

Guidelines recommend that aortic dimension measurements in aortic dissection should include the aortic wall. This study aimed to evaluate two-dimensional (2D)- and three-dimensional (3D)-based deep learning approaches for extraction of outer aortic surface in computed tomography angiography (CTA) scans of Stanford type B aortic dissection (TBAD) patients and assess the speed of different whole aorta (WA) segmentation approaches.

METHODS

A total of 240 patients diagnosed with TBAD between January 2007 and December 2019 were retrospectively reviewed for this study; 206 CTA scans from 206 patients with acute, subacute, or chronic TBAD acquired with various scanners in multiple different hospital units were included. Ground truth (GT) WAs for 80 scans were segmented by a radiologist using an open-source software. The remaining 126 GT WAs were generated via semi-automatic segmentation process in which an ensemble of 3D convolutional neural networks (CNNs) aided the radiologist. Using 136 scans for training, 30 for validation, and 40 for testing, 2D and 3D CNNs were trained to automatically segment WA. Main evaluation metrics for outer surface extraction and segmentation accuracy were normalized surface Dice (NSD) and Dice coefficient score (DCS), respectively.

RESULTS

2D CNN outperformed 3D CNN in NSD score (0.92 versus 0.90, p = 0.009), and both CNNs had equal DCS (0.96 versus 0.96, p = 0.110). Manual and semi-automatic segmentation times of one CTA scan were approximately 1 and 0.5 h, respectively.

CONCLUSIONS

Both CNNs segmented WA with high DCS, but based on NSD, better accuracy may be required before clinical application. CNN-based semi-automatic segmentation methods can expedite the generation of GTs.

RELEVANCE STATEMENT

Deep learning can speeds up the creation of ground truth segmentations. CNNs can extract the outer aortic surface in patients with type B aortic dissection.

KEY POINTS

• 2D and 3D convolutional neural networks (CNNs) can extract the outer aortic surface accurately. • Equal Dice coefficient score (0.96) was reached with 2D and 3D CNNs. • Deep learning can expedite the creation of ground truth segmentations.

Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications

Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists. In thoracic imaging, multiple applications are being evaluated for chest radiography and computed tomography and include applications for lung nodule evaluation and cancer imaging, quantifying diffuse lung disorders, and cardiac imaging, to name a few. This review aims to provide an overview of current clinical AI models, focusing on the most common clinical applications of AI in cardiothoracic imaging.