Prediction, pattern recognition and modelling of complications post-endovascular infra renal aneurysm repair by artificial intelligence

Kordzadeh, Ali; Hanif, M.; Ramirez, Manfred; Railton, Nicholas; Prionidis, Ioannis; Browne, Thomas R.

doi:10.1177/1708538120949658

Cited by 17 publications

(20 citation statements)

References 29 publications

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“…Our model features end-to-end training which is a methodology that uses images as the only input without any human or software directed measurements or calculations, in which features were automatically learned and selected from the downloaded data. Such a training pipeline has been widely adopted in 10 other areas and successfully implemented in other applications of AI. 14 The positive results attained with our model in this study suggests that it can be useful in other areas of vascular surgery as well.…”

Section: Discussionmentioning

confidence: 99%

Applying Artificial Intelligence to Predict Complications After Endovascular Aneurysm Repair

Long

Cremat

Serpa

et al. 2023

Vasc Endovascular Surg

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Objective: Complications after Endovascular Aneurysm Repair (EVAR) can be fatal. Patient follow-up for surveillance imaging is becoming more challenging as fewer patients are seen, particularly after the first year. The aim of this study was to develop an artificial intelligence model to predict the complication probability of individual patients to better identify those needing more intensive post-operative surveillance. Methods: Pre-operative CTA 3D reconstruction images of AAA from 273 patients who underwent EVAR from 2011-2020 were collected. Of these, 48 patients had post-operative complications including endoleak, AAA rupture, graft limb occlusion, renal artery occlusion, and neck dilation. A deep convolutional neural network model (VascAI©) was developed which utilized pre-operative 3D CT images to predict risk of complications after EVAR. The model was built with TensorFlow software and run on the Google Colab Platform. An initial training subset of 40 randomly selected patients with complications and 189 without were used to train the AI model while the remaining 8 positive and 36 negative cases tested its performance and prediction accuracy. Data down-sampling was used to alleviate data imbalance and data augmentation methodology to further boost model performance. Results: Successful training was completed on the 229 cases in the training set and then applied to predict the complication probability of each individual in the held-out performance testing cases. The model provided a complication sensitivity of 100% and identified all the patients who later developed complications after EVAR. Of 36 patients without complications, 16 (44%) were falsely predicted to develop complications. The results therefore demonstrated excellent sensitivity for identifying patients who would benefit from more stringent surveillance and decrease the frequency of surveillance in 56% of patients unlike to develop complications. Conclusion: AI models can be developed to predict the risk of post-operative complications with high accuracy. Compared to existing methods, the model developed in this study did not require any expert-annotated data but only the AAA CTA images as inputs. This model can play an assistive role in identifying patients at high risk for post-EVAR complications and the need for greater compliance in surveillance.

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Section: Discussionmentioning

confidence: 99%

Applying Artificial Intelligence to Predict Complications After Endovascular Aneurysm Repair

Long

Cremat

Serpa

et al. 2023

Vasc Endovascular Surg

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“…Our work has demonstrated that radiomics datasets can capture the level of complexity of the phenomenon. AI has been also used to predict reintervention and mortality after EVAR without providing data on the effects of T2EL (17) and artificial neural networks were able to accurately predict the appearance of endoleak or occlusion (18). Along the same lines, Ding et al (19) utilized texture analysis, a limited version of radiomics, to predict sac expansion after EVAR.…”

Section: Discussionmentioning

confidence: 99%

Radiomics and machine learning to predict aggressive type 2 endoleaks after endovascular aneurysm repair: a proof of concept

et al. 2021

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Background Persistent type 2 endoleaks (T2EL) require lifelong surveillance to avoid potentially life-threatening complications. Purpose To evaluate the performance of radiomic features (RF) derived from computed tomography angiography (CTA), for differentiating aggressive from benign T2ELs after endovascular aneurysm repair (EVAR). Material and Methods A prospective study was performed on patients who underwent EVAR from January 2018 to January 2020. Analysis was performed in patients who were diagnosed with T2EL based on the CTA of the first postoperative month and were followed at six months and one year. Patients were divided into two groups according to the change of aneurysm sac dimensions. Segmentation of T2ELs was performed and RF were extracted. Feature selection for subsequent machine-learning analysis was evaluated by means of artificial intelligence. Two support vector machines (SVM) classifiers were developed to predict the aneurysm sac dimension changes at one year, utilizing RF from T2EL at one- and six-month CTA scans, respectively. Results Among the 944 initial RF of T2EL, 58 and 51 robust RF from the one- and six-month CTA scans, respectively, were used for the machine-learning model development. The SVM classifier trained on one-month signatures was able to predict sac expansion at one year with an area under curve (AUC) of 89.3%, presenting 78.6% specificity and 100% sensitivity. Similarly, the SVM classifier developed with six-month radiomics data showed an AUC of 95.5%, specificity of 90.9%, and sensitivity of 100%. Conclusion Machine-learning algorithms utilizing CTA-derived RF may predict aggressive T2ELs leading to aneurysm sac expansion after EVAR.

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“…Korzadeh et al applied AI to predict not only the presence, but also the severity of EL (I-III). The model was fed with 26 non-imaging clinical attributes (e.g., biometrics, demographics, blood values) recorded preoperatively and achieved an overall accuracy of more than 86% (125). The authors notice that the model may well be further enhanced with imaging data, highlighting the adaptability of AI in different and diverse datasets and its high potential to provide even more powerful tools for the clinical management of AAA in the future.…”

Section: Abdominal Aortic Aneurysmmentioning

confidence: 99%

Artificial intelligence in atherosclerotic disease: Applications and trends

Kampaktsis

Emfietzoglou²,

Shehhi³

et al. 2023

Front. Cardiovasc. Med.

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Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.

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Prediction, pattern recognition and modelling of complications post-endovascular infra renal aneurysm repair by artificial intelligence

Cited by 17 publications

References 29 publications

Applying Artificial Intelligence to Predict Complications After Endovascular Aneurysm Repair

Applying Artificial Intelligence to Predict Complications After Endovascular Aneurysm Repair

Radiomics and machine learning to predict aggressive type 2 endoleaks after endovascular aneurysm repair: a proof of concept

Artificial intelligence in atherosclerotic disease: Applications and trends

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