Use of Artificial Intelligence With Deep Learning Approaches for the Follow-up of Infrarenal Endovascular Aortic Repair.

INTRODUCTION: Endoleaks represent one of the main complications after endovascular aortic repair (EVAR) and can lead to increased re-intervention rates and secondary rupture. Serial lifelong surveillance is required and traditionally involves cross-sectional imaging with manual axial measurements. Artificial intelligence (AI)-based imaging analysis has been developed and may provide a more precise and faster assessment. This study aims to evaluate the ability of an AI-based software to assess post-EVAR morphological changes over time, detect endoleaks, and associate them with EVAR-related adverse events. METHODS: Patients who underwent EVAR at a tertiary hospital from January 2017 to March 2020 with at least 2 follow-up computed tomography angiography (CTA) were analyzed using PRAEVAorta 2 (Nurea). The software was compared to the ground truth provided by human experts using Sensitivity (Se), Specificity (Sp), Negative Predictive Value (NPV), and Positive Predictive Value (PPV). Endovascular aortic repair-related adverse events were defined as aneurysm-related death, rupture, endoleak, limb occlusion, and EVAR-related re-interventions. RESULTS: Fifty-six patients were included with a median imaging follow-up of 27 months (interquartile range [IQR]: 20-40). There were no significant differences overtime in the evolution of maximum aneurysm diameters (55.62 mm [IQR: 52.33-59.25] vs 54.34 mm [IQR: 46.13-59.47]; p=0.2162) or volumes (130.4 cm3 [IQR: 113.8-171.7] vs 125.4 cm3 [IQR: 96.3-169.1]; p=0.1131) despite a -13.47% decrease in the volume of thrombus (p=0.0216). PRAEVAorta achieved a Se of 89.47% (95% confidence interval [CI]: 80.58 to 94.57), a Sp of 91.25% (95% CI: 83.02 to 95.70), a PPV of 90.67% (95% CI: 81.97 to 95.41), and an NPV of 90.12% (95% CI: 81.70 to 94.91) in detecting endoleaks. Endovascular aortic repair-related adverse events were associated with global volume modifications with an area under the curve (AUC) of 0.7806 vs 0.7277 for maximum diameter. The same trend was observed for endoleaks (AUC of 0.7086 vs 0.6711). CONCLUSIONS: The AI-based software PRAEVAorta enabled a detailed anatomic characterization of aortic remodeling post-EVAR and showed its potential interest for automatic detection of endoleaks during follow-up. The association of aortic aneurysmal volume with EVAR-related adverse events and endoleaks was more robust compared with maximum diameter. CLINICAL IMPACT: The integration of PRAEVAorta AI software into clinical practice promises a transformative shift in post-EVAR surveillance. By offering precise and rapid detection of endoleaks and comprehensive anatomic assessments, clinicians can expect enhanced diagnostic accuracy and streamlined patient management. This innovation reduces reliance on manual measurements, potentially reducing interpretation errors and shortening evaluation times. Ultimately, PRAEVAorta's capabilities hold the potential to optimize patient care, leading to more timely interventions and improved outcomes in endovascular aortic repair.

Serum S100B Level in the Management of Pediatric Minor Head Trauma: A Randomized Clinical Trial.

Importance: Minor head trauma (HT) is one of the most common causes of hospitalization in children. A diagnostic test could prevent unnecessary hospitalizations and cranial computed tomographic (CCT) scans. Objective: To evaluate the effectiveness of serum S100B values in reducing exposure to CCT scans and in-hospital observation in children with minor HT. Design, Setting, and Participants: This multicenter, unblinded, prospective, interventional randomized clinical trial used a stepped-wedge cluster design to compare S100B biomonitoring and control groups at 11 centers in France. Participants included children and adolescents 16 years or younger (hereinafter referred to as children) admitted to the emergency department with minor HT. The enrollment period was November 1, 2016, to October 31, 2021, with a follow-up period of 1 month for each patient. Data were analyzed from March 7 to May 29, 2023, based on the modified intention-to-treat and per protocol populations. Interventions: Children in the control group had CCT scans or were hospitalized according to current recommendations. In the S100B biomonitoring group, blood sampling took place within 3 hours after minor HT, and management depended on serum S100B protein levels. If the S100B level was within the reference range according to age, the children were discharged from the emergency department. Otherwise, children were treated as in the control group. Main Outcomes and Measures: Proportion of CCT scans performed (absence or presence of CCT scan for each patient) in the 48 hours following minor HT. Results: A total of 2078 children were included: 926 in the control group and 1152 in the S100B biomonitoring group (1235 [59.4%] boys; median age, 3.2 [IQR, 1.0-8.5] years). Cranial CT scans were performed in 299 children (32.3%) in the control group and 112 (9.7%) in the S100B biomonitoring group. This difference of 23% (95% CI, 19%-26%) was not statistically significant (P = .44) due to an intraclass correlation coefficient of 0.32. A statistically significant 50% reduction in hospitalizations (95% CI, 47%-53%) was observed in the S100B biomonitoring group (479 [41.6%] vs 849 [91.7%]; P < .001). Conclusions and Relevance: In this randomized clinical trial of effectiveness of the serum S100B level in the management of pediatric minor HT, S100B biomonitoring yielded a reduction in the number of CCT scans and in-hospital observation when measured in accordance with the conditions defined by a clinical decision algorithm. Trial Registration: ClinicalTrials.gov Identifier: NCT02819778.

Artificial intelligence-based predictive models in vascular diseases.

Cardiovascular disease represents a source of major health problems worldwide, and although medical and technical advances have been achieved, they are still associated with high morbidity and mortality rates. Personalized medicine would benefit from novel tools to better predict individual prognosis and outcomes after intervention. Artificial intelligence (AI) has brought new insights to cardiovascular medicine, especially with the use of machine learning techniques that allow the identification of hidden patterns and complex associations in health data without any a priori assumptions. This review provides an overview on the use of artificial intelligence-based prediction models in vascular diseases, specifically focusing on aortic aneurysm, lower extremity arterial disease, and carotid stenosis. Potential benefits include the development of precision medicine in patients with vascular diseases. In addition, the main challenges that remain to be overcome to integrate artificial intelligence-based predictive models in clinical practice are discussed.

Artificial intelligence in vascular surgical decision making.

Despite advances in prevention, detection, and treatment, cardiovascular disease is a leading cause of mortality and represents a major health problem worldwide. Artificial intelligence and machine learning have brought new insights to the management of vascular diseases by allowing analysis of huge and complex datasets and by offering new techniques to develop advanced imaging analysis. Artificial intelligence-based applications have the potential to improve prognostic evaluation and evidence-based decision making and contribute to vascular therapeutic decision making. In this scoping review, we provide an overview on how artificial intelligence could help in vascular surgical clinical decision making, highlighting potential benefits, current limitations, and future challenges.

Machine Learning and Omics Analysis in Aortic Aneurysm.

Aortic aneurysm is a life-threatening condition and mechanisms underlying its formation and progression are still incompletely understood. Omics approach has brought new insights to identify a broad spectrum of biomarkers and better understand cellular and molecular pathways involved. Omics generate a large amount of data and several studies have highlighted that artificial intelligence (AI) and techniques such as machine learning (ML)/deep learning (DL) can be of use in analyzing such complex datasets. However, only a few studies have so far reported the use of ML/DL for omics analysis in aortic aneurysms. The aim of this study is to summarize recent advances on the use of ML/DL for omics analysis to decipher aortic aneurysm pathophysiology and develop patient-tailored risk prediction models. In the light of current knowledge, we discuss current limits and highlight future directions in the field.

Comprehensive Review of Natural Language Processing (NLP) in Vascular Surgery.

Objective: The use of Natural Language Processing (NLP) has attracted increased interest in healthcare with various potential applications including identification and extraction of health information, development of chatbots and virtual assistants. The aim of this comprehensive literature review was to provide an overview of NLP applications in vascular surgery, identify current limitations, and discuss future perspectives in the field. Data sources: The MEDLINE database was searched on April 2023. Review methods: The database was searched using a combination of keywords to identify studies reporting the use of NLP and chatbots in three main vascular diseases. Keywords used included Natural Language Processing, chatbot, chatGPT, aortic disease, carotid, peripheral artery disease, vascular, and vascular surgery. Results: Given the heterogeneity of study design, techniques, and aims, a comprehensive literature review was performed to provide an overview of NLP applications in vascular surgery. By enabling identification and extraction of information on patients with vascular diseases, such technology could help to analyse data from healthcare information systems to provide feedback on current practice and help in optimising patient care. In addition, chatbots and NLP driven techniques have the potential to be used as virtual assistants for both health professionals and patients. Conclusion: While Artificial Intelligence and NLP technology could be used to enhance care for patients with vascular diseases, many challenges remain including the need to define guidelines and clear consensus on how to evaluate and validate these innovations before their implementation into clinical practice.

Natural Language Processing for Literature Search in Vascular Surgery: A Pilot Study Testing an Artificial Intelligence Based Application.

Introduction: The use of natural language processing (NLP) for a literature search has been poorly investigated in vascular surgery so far. The aim of this pilot study was to test the applicability of an artificial intelligence (AI) based mobile application for literature searching in a topic related to vascular surgery. Technique: A focused scientific question was defined to evaluate the performance of the AI application for a literature search and compare the results with the ground truth provided via a traditional literature search performed by human experts. Using pre-defined keywords, the literature search was performed automatically by the AI application through different steps, including quality assessment based on evaluation of the information available and quality filters using indicators of level of evidence, selection of publications based on relevancy filters using NLP, summarisation, and visualisation of the publications via the mobile app. A traditional literature search performed by human experts required 10 hours to check 154 original articles, among which 26 (16.9%) were truly related to the question, 63 (40.9%) related to the field but not to the specific question, and 65 (42.2%) were unrelated. The AI based search was performed in less than one hour, and, compared with traditional search, the method identified 17 original articles (48.6%) truly related to the question (p < .010), 18 (51.4%) related to the field but not to the specific question (p = .26), and no unrelated publications (p < .001). Fifteen truly related articles (88.2%) were identified jointly by the two methods. No significant difference was observed regarding the median number of citations, year of publications, and impact factor of journals. Discussion: The AI based method enabled a targeted, focused, and time saving literature search, although the selection of publications was not completely exhaustive. These results suggest that such an AI driven application is a complementary tool to help researchers and clinicians for continuous education and dissemination of knowledge.

Automatic Detection of Visceral Arterial Aneurysms on Computed Tomography Angiography Using Artificial Intelligence Based Segmentation of the Vascular System.

Introduction: Visceral arterial aneurysms (VAAs) are life threatening. Due to the paucity of symptoms and rarity of the disease, VAAs are underdiagnosed and underestimated. Artificial intelligence (AI) offers new insights into segmentation of the vascular system, and opportunities to better detect VAAs. This pilot study aimed to develop an AI based method to automatically detect VAAs from computed tomography angiography (CTA). Methods: A hybrid method combining a feature based expert system with a supervised deep learning algorithm (convolutional neural network) was used to enable fully automatic segmentation of the abdominal vascular tree. Centrelines were built and reference diameters of each visceral artery were calculated. An abnormal dilatation (VAAs) was defined as a substantial increase in diameter at the pixel of interest compared with the mean diameter of the reference portion. The automatic software provided 3D rendered images with a flag on the identified VAA areas. The performance of the method was tested in a dataset of 33 CTA scans and compared with the ground truth provided by two human experts. Results: Forty-three VAAs were identified by human experts (32 in the coeliac trunk branches, eight in the superior mesenteric artery, one in the left renal, and two in the right renal arteries). The automatic system accurately detected 40 of the 43 VAAs, with a sensitivity of 0.93 and a positive predictive value of 0.51. The mean number of flag areas per CTA was 3.5 ± 1.5 and they could be reviewed and checked by a human expert in less than 30 seconds per CTA. Conclusion: Although the specificity needs to be improved, this study demonstrates the potential of an AI based automatic method to develop new tools to improve screening and detection of VAAs by automatically attracting clinicians' attention to suspicious dilatations of the visceral arteries.

Nationwide Study in France To Predict One Year Major Bleeding and Validate the OAC3-PAD Score in Patients Undergoing Revascularisation for Lower Extremity Arterial Disease.

OBJECTIVE: Antithrombotic strategies are currently recommended for the treatment of lower extremity artery disease (LEAD) but specific scores to assess the risk of bleeding in these patients are scarce. To fill the gap, the OAC3-PAD bleeding score was recently developed and validated in German cohorts. The aim of this study was to determine whether this score performs appropriately in another real world nationwide cohort. METHODS: This 10 year retrospective, multicentre study based on French national electronic health data included patients who underwent revascularisation for LEAD between January 2013 and June 2022. The OAC3-PAD score was calculated and from this, the population was classified into four groups: low, low to moderate, moderate to high and high risk. A binary logistic regression model was applied, with major bleeding occurring at one year (defined using the International Classification of Diseases ICD-10) as the dependent variable. The performance of the OAC3-PAD bleeding score was investigated using a receiver operating characteristic curve. RESULTS: Among 161 205 patients hospitalised for LEAD treatment in French institutions, the one year incidence of major bleeding was 13 672 patients (8.5%). The distribution of the population according to the OAC3-PAD bleeding score was: 88 835 patients (55.1%), 34 369 (21.3%), 27 914 (17.3%), and 10 087 (6.3%) in the low, low to moderate, moderate to high, and high risk groups, respectively; with an incidence of one year major bleeding of 5.0%, 9.8%, 13.2%, and 21.3%. The OAC3-PAD model achieved an AUC of 0.650 to predict one year major bleeding following LEAD repair (95% CI 0.645 - 0.655), with a sensitivity of 0.67 and a specificity of 0.57. CONCLUSION: This nationwide analysis confirmed the accuracy of the OAC3-PAD model to predict one year major bleeding and served as external validation. Although further studies are required, it adds evidence and perspectives to further generalise its use to guide the management of patients with LEAD.

Applications of artificial intelligence for patients with peripheral artery disease.

OBJECTIVE: Applications of artificial intelligence (AI) have been reported in several cardiovascular diseases but its interest in patients with peripheral artery disease (PAD) has been so far less reported. The aim of this review was to summarize current knowledge on applications of AI in patients with PAD, to discuss current limits, and highlight perspectives in the field. METHODS: We performed a narrative review based on studies reporting applications of AI in patients with PAD. The MEDLINE database was independently searched by two authors using a combination of keywords to identify studies published between January 1995 and December 2021. Three main fields of AI were investigated including natural language processing (NLP), computer vision and machine learning (ML). RESULTS: NLP and ML brought new tools to improve the screening, the diagnosis and classification of the severity of PAD. ML was also used to develop predictive models to better assess the prognosis of patients and develop real-time prediction models to support clinical decision-making. Studies related to computer vision mainly aimed at creating automatic detection and characterization of arterial lesions based on Doppler ultrasound examination or computed tomography angiography. Such tools could help to improve screening programs, enhance diagnosis, facilitate presurgical planning, and improve clinical workflow. CONCLUSIONS: AI offers various applications to support and likely improve the management of patients with PAD. Further research efforts are needed to validate such applications and investigate their accuracy and safety in large multinational cohorts before their implementation in daily clinical practice.