Artificial intelligence in abdominal aortic aneurysm

How to improve epidemiological trustworthiness concerning abdominal aortic aneurysms

BACKGROUND

Research on degenerative abdominal aortic aneurysms (AAA) is hampered by complex pathophysiology, sub-optimal pre-clinical models, and lack of effective medical therapies. In addition, trustworthiness of existing epidemiological data is impaired by elements of ambiguity, inaccuracy, and inconsistency. Our aim is to foster debate concerning the trustworthiness of AAA epidemiological data and to discuss potential solutions.

METHODS

We searched the literature from the last five decades for relevant epidemiological data concerning AAA development, rupture, and repair. We then discussed the main issues burdening existing AAA epidemiological figures and proposed suggestions potentially beneficial to AAA diagnosis, prognostication, and management.

RESULTS

Recent data suggest a heterogeneous scenario concerning AAA epidemiology with rates markedly varying by country and study cohorts. Overall, AAA prevalence seems to be decreasing worldwide while mortality is apparently increasing regardless of recent improvements in aortic-repair techniques. Prevalence and mortality are decreasing in high-income countries, whereas low-income countries show an increase in both. However, several pieces of information are missing or outdated, thus systematic renewal is necessary. Current AAA definition and surgical criteria do not consider inter-individual variability of baseline aortic size, further decreasing their reliability.

CONCLUSIONS

Switching from flat aortic-size thresholds to relative aortic indices would improve epidemiological trustworthiness regarding AAAs. Aortometry standardization focusing on simplicity, univocity, and accuracy is crucial. A patient-tailored approach integrating clinical data, multi-adjusted indices, and imaging parameters is desirable. Several novel imaging modalities boast promising profiles for investigating the aortic wall. New contrast agents, computational analyses, and artificial intelligence-powered software could provide further improvements.

Volumetric analysis of acute uncomplicated type B aortic dissection using an automated deep learning aortic zone segmentation model

BACKGROUND

Machine learning techniques have shown excellent performance in three-dimensional medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) using Society for Vascular Surgery (SVS) and Society of Thoracic Surgeons (STS)-defined aortic zones. The purpose of this study was to establish a trained, automatic machine learning aortic zone segmentation model to facilitate performance of an aortic zone volumetric comparison between patients with auTBAD based on the rate of aortic growth.

METHODS

Patients with auTBAD and serial imaging were identified. For each patient, imaging characteristics from two computed tomography (CT) scans were analyzed: (1) the baseline CT angiography (CTA) at the index admission and (2) either the most recent surveillance CTA or the most recent CTA before an aortic intervention. Patients were stratified into two comparative groups based on aortic growth: rapid growth (diameter increase of ≥5 mm/year) and no or slow growth (diameter increase of <5 mm/year). Deidentified images were imported into an open source software package for medical image analysis and images were annotated based on SVS/STS criteria for aortic zones. Our model was trained using four-fold cross-validation. The segmentation output was used to calculate aortic zone volumes from each imaging study.

RESULTS

Of 59 patients identified for inclusion, rapid growth was observed in 33 patients (56%) and no or slow growth was observed in 26 patients (44%). There were no differences in baseline demographics, comorbidities, admission mean arterial pressure, number of discharge antihypertensives, or high-risk imaging characteristics between groups (P > .05 for all). Median duration between baseline and interval CT was 1.07 years (interquartile range [IQR], 0.38-2.57). Postdischarge aortic intervention was performed in 13 patients (22%) at a mean of 1.5 ± 1.2 years, with no difference between the groups (P > .05). Among all patients, the largest relative percent increases in zone volumes over time were found in zone 4 (13.9%; IQR, -6.82 to 35.1) and zone 5 (13.4%; IQR, -7.78 to 37.9). There were no differences in baseline zone volumes between groups (P > .05 for all). The average Dice coefficient, a performance measure of the model output, was 0.73. Performance was best in zone 5 (0.84) and zone 9 (0.91).

CONCLUSIONS

We describe an automatic deep learning segmentation model incorporating SVS-defined aortic zones. The open source, trained model demonstrates concordance to the manually segmented aortas with the strongest performance in zones 5 and 9, providing a framework for further clinical applications. In our limited sample, there were no differences in baseline aortic zone volumes between patients with rapid growth and patients with no or slow growth.

Applications of Artificial Intelligence in Acute Abdominal Imaging

Artificial intelligence (AI) is a rapidly growing field with significant implications for radiology. Acute abdominal pain is a common clinical presentation that can range from benign conditions to life-threatening emergencies. The critical nature of these situations renders emergent abdominal imaging an ideal candidate for AI applications. CT, radiographs, and ultrasound are the most common modalities for imaging evaluation of these patients. For each modality, numerous studies have assessed the performance of AI models for detecting common pathologies, such as appendicitis, bowel obstruction, and cholecystitis. The capabilities of these models range from simple classification to detailed severity assessment. This narrative review explores the evolution, trends, and challenges in AI applications for evaluating acute abdominal pathologies. We review implementations of AI for non-traumatic and traumatic abdominal pathologies, with discussion of potential clinical impact, challenges, and future directions for the technology.

Machine learning-based prediction of abdominal aortic aneurysms for individualized patient care

OBJECTIVE

The United States Preventative Services Task Force guidelines for screening for abdominal aortic aneurysms (AAA) are broad and exclude many at risk groups. We analyzed a large AAA screening database to examine the utility of a novel machine learning (ML) model for predicting individual risk of AAA.

METHODS

We created a ML model to predict the presence of AAAs (>3 cm) from the database of a national nonprofit screening organization (AAAneurysm Outreach). Participants self-reported demographics and comorbidities. The model is a two-layered feed-forward shallow network. The ML model then generated AAA probability based on patient characteristics. We evaluated graphs to determine significant factors, and then compared those graphs with a traditional logistic regression model.

RESULTS

We analyzed a cohort of 10,033 patients with an AAA prevalence of 2.74%. Consistent with logistic regression analysis, the ML model identified the following predictors of AAA: Caucasian race, male gender, advancing age, and recent or past smoker with recent smoker having a more profound affect (P < .05). Interestingly, the ML model showed body mass index (BMI) was associated with likelihood of AAAs, especially for younger females. The ML model also identified a higher than predicted risk of AAA in several groups, including female nonsmokers with cardiac disease, female diabetics, those with a family history of AAA, and those with hypertension or hyperlipidemia at older ages. An elevated BMI conveyed a higher than expected risk in male smokers and all females. The ML model also identified a complex relationship of both diabetes mellitus and hyperlipidemia with gender. Family history of AAA was a more important risk factor in the ML model for both men and women too.

CONCLUSIONS

We successfully developed an ML model based on an AAA screening database that unveils a complex relationship between AAA prevalence and many risk factors, including BMI. The model also highlights the need to expand AAA screening efforts in women. Using ML models in the clinical setting has the potential to deliver precise, individualized screening recommendations.

Identification and Evaluation of Blood-Based Biomarkers for Abdominal Aortic Aneurysm

INTRODUCTION

Blood-based biomarkers for abdominal aortic aneurysm (AAA) have been studied individually; however, we considered a panel of proteins to investigate AAA prognosis and its potential to improve predictive accuracy.

MATERIALS AND METHODS

Using a prospectively recruited cohort of patients with/without AAA (n = 452), we conducted a prognostic study to develop a model that accurately predicts AAA outcomes using clinical features and circulating biomarker levels. Serum concentrations of 9 biomarkers were measured at baseline, and the cohort was followed for 2 years. The primary outcome was major adverse aortic event (MAAE; composite of rapid AAA expansion [>0.5 cm/6 months or >1 cm/12 months], AAA intervention, or AAA rupture). Using 10-fold cross-validation, we trained a random forest model to predict 2 year MAAE using (1) clinical characteristics, (2) biomarkers, and (3) clinical characteristics and biomarkers.

RESULTS

Two-year MAAE occurred in 114 (25%) patients. Two proteins were significantly elevated in patients with AAA compared with those without AAA (angiopoietin-2 and aggrecan), composing the protein panel. For predicting 2 year MAAE, our random forest model achieved area under the receiver operating characteristic curve (AUROC) 0.74 using clinical features alone, and the addition of the 2-protein panel improved performance to AUROC 0.86.

CONCLUSIONS

Using a combination of clinical/biomarker data, we developed a model that accurately predicts 2 year MAAE.

Machine learning algorithms for the prognostication of abdominal aortic aneurysm progression: a systematic review

INTRODUCTION

Abdominal aortic aneurysm (AAA), often characterized by an abdominal aortic diameter over 3.0 cm, is managed through screening, surveillance, and surgical intervention. AAA growth can be heterogeneous and rupture carries a high mortality rate, with size and certain risk factors influencing rupture risk. Research is ongoing to accurately predict individual AAA growth rates for personalized management. Machine learning, a subset of artificial intelligence, has shown promise in various medical fields, including endoleak detection post-EVAR. However, its application for predicting AAA growth remains insufficiently explored, thus necessitating further investigation. Subsequently, this paper aims to summarize the current status of machine learning in predicting AAA growth.

EVIDENCE ACQUISITION

A systematic database search of Embase, MEDLINE, Cochrane, PubMed and Google Scholar from inception till December 2022 was conducted of original articles that discussed the use of machine learning in predicting AAA growth using the aforementioned databases.

EVIDENCE SYNTHESIS

Overall, 2742 articles were extracted, of which seven retrospective studies involving 410 patients were included using a predetermined criteria. Six out of seven studies applied a supervised learning approach for their machine learning (ML) models, with considerable diversity observed within specific ML models. The majority of the studies concluded that machine learning models perform better in predicting AAA growth in comparison to reference models. All studies focused on predicting AAA growth over specified durations. Maximal luminal diameter was the most frequently used indicator, with alternative predictors being AAA volume, ILT (intraluminal thrombus) and flow-medicated diameter (FMD).

CONCLUSIONS

The nascent field of applying machine learning (ML) for Abdominal Aortic Aneurysm (AAA) expansion prediction exhibits potential to enhance predictive accuracy across diverse parameters. Future studies must emphasize evidencing clinical utility in a healthcare system context, thereby ensuring patient outcome improvement. This will necessitate addressing key ethical implications in establishing prospective studies related to this topic and collaboration among pivotal stakeholders within the AI field.

Development and validation of a machine-learning prediction model to improve abdominal aortic aneurysm screening

OBJECTIVE

Despite recommendations by the United States Preventive Services Task Force and the Society for Vascular Surgery, adoption of screening for abdominal aortic aneurysms (AAAs) remains low. One challenge is the low prevalence of AAAs in the unscreened population, and therefore a low detection rate for AAA screenings. We sought to use machine learning to identify factors associated with the presence of AAAs and create a model to identify individuals at highest risk for AAAs, with the aim of increasing the detection rate of AAA screenings.

METHODS

A machine-learning model was trained using longitudinal medical records containing lab results, medications, and other data from our institutional database. A retrospective cohort study was performed identifying current or past smoking in patients aged 65 to 75 years and stratifying the patients by sex and smoking status as well as determining which patients had a confirmed diagnosis of AAA. The model was then adjusted to maximize fairness between sexes without significantly reducing precision and validated using six-fold cross validation.

RESULTS

Validation of the algorithm on the single-center institutional data utilized 18,660 selected patients over 2 years and identified 314 AAAs. There were 41 factors identified in the medical record included in the machine-learning algorithm, with several factors never having been previously identified to be associated with AAAs. With an estimated 100 screening ultrasounds completed monthly, detection of AAAs is increased with a lift of 200% using the algorithm as compared with screening based on guidelines. The increased detection of AAAs in the model-selected individuals is statistically significant across all cutoff points.

CONCLUSIONS

By utilizing a machine-learning model, we created a novel algorithm to detect patients who are at high risk for AAAs. By selecting individuals at greatest risk for targeted screening, this algorithm resulted in a 200% lift in the detection of AAAs when compared with standard screening guidelines. Using machine learning, we also identified several new factors associated with the presence of AAAs. This automated process has been integrated into our current workflows to improve screening rates and yield of high-risk individuals for AAAs.

The sac evolution imaging follow-up after endovascular aortic repair: An international expert opinion-based Delphi consensus study

OBJECTIVE

Management of follow-up protocols after endovascular aortic repair (EVAR) varies significantly between centers and is not standardized according to sac regression. By designing an international expert-based Delphi consensus, the study aimed to create recommendations on follow-up after EVAR according to sac evolution.

METHODS

Eight facilitators created appropriate statements regarding the study topic that were voted, using a 4-point Likert scale, by a selected panel of international experts using a three-round modified Delphi consensus process. Based on the experts' responses, only those statements reaching a grade A (full agreement ≥75%) or B (overall agreement ≥80% and full disagreement <5%) were included in the final document.

RESULTS

One-hundred and seventy-four participants were included in the final analysis, and each voted the initial 29 statements related to the definition of sac regression (Q1-Q9), EVAR follow-up (Q10-Q14), and the assessment and role of sac regression during follow-up (Q15-Q29). At the end of the process, 2 statements (6.9%) were rejected, 9 statements (31%) received a grade B consensus strength, and 18 (62.1%) reached a grade A consensus strength. Of 27 final statements, 15 (55.6%) were classified as grade I, whereas 12 (44.4%) were classified as grade II. Experts agreed that sac regression should be considered an important indicator of EVAR success and always be assessed during follow-up after EVAR.

CONCLUSIONS

Based on the elevated strength and high consistency of this international expert-based Delphi consensus, most of the statements might guide the current clinical management of follow-up after EVAR according to the sac regression. Future studies are needed to clarify debated issues.

Humans use tools: From handcrafted tools to artificial intelligence

Human evolution is instrument based. Humans created tools >2 million years ago to aid them in hunting, gathering, and defense, allowing them to build shelters and farms and transport goods and people over great distances. Written records preserved our knowledge and experiences for future generations. Instruments have greatly influenced surgery. Knives and needles were used by ancient surgeons, whereas lasers, endoscopes, and robotics are used today. Artificial intelligence (AI) is the future of surgical instruments, increasing precision through self-evaluation, but development remains in the early stages. Vascular surgery research and practice has used AI-powered systems that can track patient progress and identify vascular disease risk using deep learning and pattern recognition, as well as improved radiological interpretation of vascular imaging and medicine. Using insights and data-driven recommendations, AI-powered decision support systems could help surgeons in enhancing patient outcomes by providing guidance to navigate complex anatomy and identify anomalies. Robots can assist surgeons in performing risky, complex operations with optimal outcomes. Human expertise and AI will revolutionize surgery, enhancing its safety, precision, and efficacy. Surgical applications of AI raise numerous questions and debates. Data must be representative of all populations, data management must protect the privacy of patients and physicians, and the AI decision-making process must be clarified to produce validated models that can be used ethically. Vascular surgeons' judgment and experience should not be automated. Instead, AI should contribute to the efficiency and effectiveness of vascular surgeons. Human clinicians must interpret AI-generated data, use clinical judgment, and build empathy, compassion, and shared decision-making to sustain doctor-patient relationships. From simple tools to complex modern technologies, the history of tools reveals human creativity. Our environment has been altered by technology, ensuring our survival and growth. AI is still a half-told tale that will inspire and amaze us for years to come.

Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment

Cardiovascular CT is being widely used in the diagnosis of cardiovascular disease due to the rapid technological advancements in CT scanning techniques. These advancements include the development of multi-slice CT, from early generation to the latest models, which has the capability of acquiring images with high spatial and temporal resolution. The recent emergence of photon-counting CT has further enhanced CT performance in clinical applications, providing improved spatial and contrast resolution. CT-derived fractional flow reserve is superior to standard CT-based anatomical assessment for the detection of lesion-specific myocardial ischemia. CT-derived 3D-printed patient-specific models are also superior to standard CT, offering advantages in terms of educational value, surgical planning, and the simulation of cardiovascular disease treatment, as well as enhancing doctor-patient communication. Three-dimensional visualization tools including virtual reality, augmented reality, and mixed reality are further advancing the clinical value of cardiovascular CT in cardiovascular disease. With the widespread use of artificial intelligence, machine learning, and deep learning in cardiovascular disease, the diagnostic performance of cardiovascular CT has significantly improved, with promising results being presented in terms of both disease diagnosis and prediction. This review article provides an overview of the applications of cardiovascular CT, covering its performance from the perspective of its diagnostic value based on traditional lumen assessment to the identification of vulnerable lesions for the prediction of disease outcomes with the use of these advanced technologies. The limitations and future prospects of these technologies are also discussed.

Applying Artificial Intelligence to Predict Complications After Endovascular Aneurysm Repair

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.

Using machine learning to predict outcomes following open abdominal aortic aneurysm repair

OBJECTIVE

Prediction of outcomes following open abdominal aortic aneurysm (AAA) repair remains challenging with a lack of widely used tools to guide perioperative management. We developed machine learning (ML) algorithms that predict outcomes following open AAA repair.

METHODS

The Vascular Quality Initiative (VQI) database was used to identify patients who underwent elective open AAA repair between 2003 and 2023. Input features included 52 preoperative demographic/clinical variables. All available preoperative variables from VQI were used to maximize predictive performance. The primary outcome was in-hospital major adverse cardiovascular event (MACE; composite of myocardial infarction, stroke, or death). Secondary outcomes were individual components of the primary outcome, other in-hospital complications, and 1-year mortality and any reintervention. We split our data into training (70%) and test (30%) sets. Using 10-fold cross-validation, six ML models were trained using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score. The top 10 predictive features in our final model were determined based on variable importance scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median area deprivation index, proximal clamp site, prior aortic surgery, and concomitant procedures.

RESULTS

Overall, 12,027 patients were included. The primary outcome of in-hospital MACE occurred in 630 patients (5.2%). Compared with patients without a primary outcome, those who developed in-hospital MACE were older with more comorbidities, demonstrated poorer functional status, had more complex aneurysms, and were more likely to require concomitant procedures. Our best performing prediction model for in-hospital MACE was XGBoost, achieving an AUROC of 0.93 (95% confidence interval, 0.92-0.94). Comparatively, logistic regression had an AUROC of 0.71 (95% confidence interval, 0.70-0.73). For secondary outcomes, XGBoost achieved AUROCs between 0.84 and 0.94. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.05. These findings highlight the excellent predictive performance of the XGBoost model. The top three predictive features in our algorithm for in-hospital MACE following open AAA repair were: (1) coronary artery disease; (2) American Society of Anesthesiologists classification; and (3) proximal clamp site. Model performance remained robust on all subgroup analyses.

CONCLUSIONS

Open AAA repair outcomes can be accurately predicted using preoperative data with our ML models, which perform better than logistic regression. Our automated algorithms can help guide risk-mitigation strategies for patients being considered for open AAA repair to improve outcomes.

Machine learning to predict outcomes following endovascular abdominal aortic aneurysm repair

BACKGROUND

Endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) carries important perioperative risks; however, there are no widely used outcome prediction tools. The aim of this study was to apply machine learning (ML) to develop automated algorithms that predict 1-year mortality following EVAR.

METHODS

The Vascular Quality Initiative database was used to identify patients who underwent elective EVAR for infrarenal AAA between 2003 and 2023. Input features included 47 preoperative demographic/clinical variables. The primary outcome was 1-year all-cause mortality. Data were split into training (70 per cent) and test (30 per cent) sets. Using 10-fold cross-validation, 6 ML models were trained using preoperative features with logistic regression as the baseline comparator. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score.

RESULTS

Some 63 655 patients were included. One-year mortality occurred in 3122 (4.9 per cent) patients. The best performing prediction model for 1-year mortality was XGBoost, achieving an AUROC (95 per cent c.i.) of 0.96 (0.95-0.97). Comparatively, logistic regression had an AUROC (95 per cent c.i.) of 0.69 (0.68-0.71). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.04. The top 3 predictive features in the algorithm were 1) unfit for open AAA repair, 2) functional status, and 3) preoperative dialysis.

CONCLUSIONS

In this data set, machine learning was able to predict 1-year mortality following EVAR using preoperative data and outperformed standard logistic regression models.

Artificial Intelligence-Assisted Sac Diameter Assessment for Complex Endovascular Aortic Repair

PURPOSE

Artificial intelligence (AI) using an automated, deep learning-based method, Augmented Radiology for Vascular Aneurysm (ARVA), has been verified as a viable aide in aneurysm morphology assessment. The aim of this study was to evaluate the accuracy of ARVA when analyzing preoperative and postoperative computed tomography angiography (CTA) in patients managed with fenestrated endovascular repair (FEVAR) for complex aortic aneurysms (cAAs).

MATERIALS AND METHODS

Preoperative and postoperative CTAs from 50 patients (n=100 CTAs) who underwent FEVAR for cAAs were extracted from the picture archiving and communication system (PACS) of a single aortic center equipped with ARVA. All studies underwent automated AI aneurysm morphology assessment by ARVA. Appropriate identification of the outer wall of the aorta was verified by manual review of the AI-generated overlays for each patient. Maximum outer-wall aortic diameters were measured by 2 clinicians using multiplanar reconstruction (MPR) and curved planar reformatting (CPR), and among studies where the aortic wall was appropriately identified by ARVA, they were compared with ARVA automated measurements.

RESULTS

Identification of the outer wall of the aorta was accurate in 89% of CTA studies. Among these, diameter measurements by ARVA were comparable to clinician measurements by MPR or CPR, with a median absolute difference of 2.4 mm on the preoperative CTAs and 1.6 mm on the postoperative CTAs. Of note, no significant difference was detected between clinician measurements using MPR or CPR on preoperative and postoperative scans (range 0.5-0.9 mm).

CONCLUSION

For patients with cAAs managed with FEVAR, ARVA provides accurate preoperative and postoperative assessment of aortic diameter in 89% of studies. This technology may provide an opportunity to automate cAA morphology assessment in most cases where time-intensive, manual clinician measurements are currently required.

CLINICAL IMPACT

In this retrospective analysis of preoperative and postoperative imaging from 50 patients managed with FEVAR, AI provided accurate aortic diameter measurements in 89% of the CTAs reviewed, despite the complexity of the aortic anatomies, and in post-operative CTAs despite metal artifact from stent grafts, markers and embolization materials. Outliers with imprecise automated aortic overlays were easily identified by scrolling through the axial AI-generated segmentation MPR cuts of the entire aorta.This study supports the notion that such emerging AI technologies can improve efficiency of routine clinician workflows while maintaining excellent measurement accuracy when analyzing complex aortic anatomies by CTA.

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.

Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography

The aim of our study is to validate a totally automated deep learning (DL)-based segmentation pipeline to screen abdominal aortic aneurysms (AAA) in computed tomography angiography (CTA) scans. We retrospectively evaluated 73 thoraco-abdominal CTAs (48 AAA and 25 control CTA) by means of a DL-based segmentation pipeline built on a 2.5D convolutional neural network (CNN) architecture to segment lumen and thrombus of the aorta. The maximum aortic diameter of the abdominal tract was compared using a threshold value (30 mm). Blinded manual measurements from a radiologist were done in order to create a true comparison. The screening pipeline was tested on 48 patients with aneurysm and 25 without aneurysm. The average diameter manually measured was 51.1 ± 14.4 mm for patients with aneurysms and 21.7 ± 3.6 mm for patients without aneurysms. The pipeline correctly classified 47 AAA out of 48 and 24 control patients out of 25 with 97% accuracy, 98% sensitivity, and 96% specificity. The automated pipeline of aneurysm measurements in the abdominal tract reported a median error with regard to the maximum abdominal diameter measurement of 1.3 mm. Our approach allowed for the maximum diameter of 51.2 ± 14.3 mm in patients with aneurysm and 22.0 ± 4.0 mm in patients without an aneurysm. The DL-based screening for AAA is a feasible and accurate method, calling for further validation using a larger pool of diagnostic images towards its clinical use.

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.

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.

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.

Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence

Artificial intelligence (AI)-based technologies have garnered interest across a range of disciplines in the past several years, with an even more recent interest in various health care fields, including Vascular Surgery. AI offers a unique ability to analyze health data more quickly and efficiently than could be done by humans alone and can be used for clinical applications such as diagnosis, risk stratification, and follow-up, as well as patient-used applications to improve both patient and provider experiences, mitigate health care disparities, and individualize treatment. As with all novel technologies, AI is not without its risks and carries with it unique ethical considerations that will need to be addressed before its broad integration into health care systems. AI has the potential to revolutionize the way care is provided to patients, including those requiring vascular care.

Bias in artificial intelligence in vascular surgery

Application of artificial intelligence (AI) has revolutionized the utilization of big data, especially in patient care. The potential of deep learning models to learn without a priori assumption, or without prior learning, to connect seemingly unrelated information mixes excitement alongside hesitation to fully understand AI's limitations. Bias, ranging from data collection and input to algorithm development to finally human review of algorithm output affects AI's application to clinical patient presents unique challenges that differ significantly from biases in traditional analyses. Algorithm fairness, a new field of research within AI, aims to mitigate bias by evaluating the data at the preprocessing stage, optimizing during algorithm development, and evaluating algorithm output at the postprocessing stage. As the field continues to develop, being cognizant of the inherent biases and limitations related to black box decision making, biased data sets agnostic to patient-level disparities, wide variation of present methodologies, and lack of common reporting standards will require ongoing research to provide transparency to AI and its applications.

Machine learning and image analysis in vascular surgery

Deep learning, a subset of machine learning within artificial intelligence, has been successful in medical image analysis in vascular surgery. Unlike traditional computer-based segmentation methods that manually extract features from input images, deep learning methods learn image features and classify data without making prior assumptions. Convolutional neural networks, the main type of deep learning for computer vision processing, are neural networks with multilevel architecture and weighted connections between nodes that can "auto-learn" through repeated exposure to training data without manual input or supervision. These networks have numerous applications in vascular surgery imaging analysis, particularly in disease classification, object identification, semantic segmentation, and instance segmentation. The purpose of this review article was to review the relevant concepts of machine learning image analysis and its application to the field of vascular surgery.

Pulsatile Changes of the Aortic Diameter May Be Irrelevant Regarding Endograft Sizing in Patients With Aortic Disease

PURPOSE

Endovascular aortic repair has become the preferred elective treatment of infrarenal aortic aneurysms. Aortic pulsatility may pose problems regarding endograft sizing. The aims of this study are to determine the aortic pulsatility in patients with aortic disease and to evaluate the effect of pulsatility on the growth of aneurysms.

MATERIALS AND METHODS

In this retrospective study, analyses of computed tomography angiography (CTA) images of 31 patients under conservative treatment for small abdominal aortic aneurysms were performed. Reconstructions of the raw electrocardiography (ECG) gated dataset at 30% and 90% of the R-R cycle were used. After lumen segmentation, total aortic cross-sectional area was measured in diastole and systole in the following zones: Z0, Z3, Z5, Z6, Z8, and Z9. Effective diameters (EDs) were calculated from the systolic (ED_sys) and diastolic (ED_dia) cross-sectional areas to determine absolute (ED_sys - ED_dia, mm) and relative pulsatility [(ED_sys - ED_dia) / ED_dia, %]. Diameter of the aneurysms was measured on baseline images and the last preoperative follow-up study of each patient.

RESULTS

A total of 806 measurements were completed, 24 pulsatility and 2 growth measurements per patient. The mean pulsatility values at each point were as follows: Z0: 0.7±0.8 mm, Z3: 1.0±0.6 mm, Z5: 1.0±0.6 mm, Z6: 0.8±0.7 mm, Z8: 0.7±1.0 mm, Z9: 0.9±0.9 mm. Follow-up time was 5.5±2.2 years during which a growth of 13.42±9.09 mm (2.54±1.55 mm yearly) was observed. No correlation was found between pulsatility values and growth rate of the aneurysms.

CONCLUSION

The pulsatility of the aorta is in a submillimetric range for the vast majority of patients with aortic disease, thus probably not relevant regarding endograft sizing. Pulsatility of the ascending aorta is smaller than that of the descending segment, making an additional oversize of a Z0 implantation questionable.

CLINICAL IMPACT

Endovascular aortic repair reqiures precise preoperative planning. Pulsatile changes of the aortic diameter may pose issues regarding endograft sizing. In our retrospective single-centre study, aortic pulsatility of patients with AAA was measured on ECG gated CTA images. Pulsatility values reached a maximum at the descending aorta, however absolute pulsatility values did not exceed 1 mm at any point along the aorta. Therefore, significance of aortic pulsatility regarding the sizing of EVAR prostheses is questionable. Correlation between pulsatility and AAA growth was not found.

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.

Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors

Artificial intelligence (AI), also known as machine intelligence, is widely utilized in the medical field, promoting medical advances. Malignant tumors are the critical focus of medical research and improvement of clinical diagnosis and treatment. Mediastinal malignancy is an important tumor that attracts increasing attention today due to the difficulties in treatment. Combined with artificial intelligence, challenges from drug discovery to survival improvement are constantly being overcome. This article reviews the progress of the use of AI in the diagnosis, treatment, and prognostic prospects of mediastinal malignant tumors based on current literature findings.

Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of surgical pathology in the abdominopelvic cavity: a systematic review

OBJECTIVES

There is emerging use of artificial intelligence (AI) models to aid diagnostic imaging. This review examined and critically appraised the application of AI models to identify surgical pathology from radiological images of the abdominopelvic cavity, to identify current limitations and inform future research.

DESIGN

Systematic review.

DATA SOURCES

Systematic database searches (Medline, EMBASE, Cochrane Central Register of Controlled Trials) were performed. Date limitations (January 2012 to July 2021) were applied.

ELIGIBILITY CRITERIA

Primary research studies were considered for eligibility using the PIRT (participants, index test(s), reference standard and target condition) framework. Only publications in the English language were eligible for inclusion in the review.

DATA EXTRACTION AND SYNTHESIS

Study characteristics, descriptions of AI models and outcomes assessing diagnostic performance were extracted by independent reviewers. A narrative synthesis was performed in accordance with the Synthesis Without Meta-analysis guidelines. Risk of bias was assessed (Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2)).

RESULTS

Fifteen retrospective studies were included. Studies were diverse in surgical specialty, the intention of the AI applications and the models used. AI training and test sets comprised a median of 130 (range: 5-2440) and 37 (range: 10-1045) patients, respectively. Diagnostic performance of models varied (range: 70%-95% sensitivity, 53%-98% specificity). Only four studies compared the AI model with human performance. Reporting of studies was unstandardised and often lacking in detail. Most studies (n=14) were judged as having overall high risk of bias with concerns regarding applicability.

CONCLUSIONS

AI application in this field is diverse. Adherence to reporting guidelines is warranted. With finite healthcare resources, future endeavours may benefit from targeting areas where radiological expertise is in high demand to provide greater efficiency in clinical care. Translation to clinical practice and adoption of a multidisciplinary approach should be of high priority.

PROSPERO REGISTRATION NUMBER

CRD42021237249.

Limb graft occlusion after endovascular aneurysm repair with the Cook Zenith Alpha abdominal graft

OBJECTIVE

Prior reports of the low profile Zenith Alpha abdominal graft (Cook Medical Inc, Bloomington, IN) have shown impaired limb graft patency to be the primary causes of reintervention. Special notices from the manufacturer have indicated certain instructions for use (IFU) violations as the main reasons for these complications. In the present study, we assessed the incidence of limb graft occlusion (LGO) and analyzed the effects of the detailed anatomic risk factors for LGO highlighted in the IFU and previously reported studies.

METHODS

A retrospective study was performed of 241 patients treated with the low profile Zenith Alpha at a single institution from October 1, 2015 to September 30, 2018. All computed tomography angiograms were analyzed using three-dimensional software. Data were extracted from the electronic medical records until the end of the study period (December 31, 2020). The cumulative incidence of LGO and LGO-related reinterventions were assessed. A regression analysis was performed to evaluate the possible risk factors associated with the development of LGO at specified time points. These included aortic and iliac diameters, graft component oversizing, iliac tortuosity and calcification, overlap of graft components, proximal alignment of ipsilateral and contralateral legs, and sealing zone in the external iliac artery. Reader agreement of iliac calcification and tortuosity was assessed in patients with LGO.

RESULTS

A total of 33 limbs (7%) in 27 patients (11%) had become occluded. The cumulative incidence of LGO was 7% (95% confidence interval [CI], 5%-9%) per limb up to 3 years postoperatively. The previously described risk factors for LGO were studied using regression analysis; however, no positive association with LGO was identified. Heavily calcified common iliac arteries (CIAs) and external iliac arteries were protective against LGO compared with noncalcified vessels up to 3 years postoperatively (decreased risk, 17% [95% CI, -27% to -7%]; P = .001; and 15% [95% CI, -26 to -5]; P = .005, respectively). The reader agreement of iliac calcification and tortuosity showed substantial agreement (CIA intrareader kappa = 0.75; CIA interreader kappa = 0.62) and almost perfect agreement (intrareader kappa = 0.85; interreader kappa = 0.84), respectively.

CONCLUSIONS

The cumulative incidence of LGO after endovascular aneurysm repair with the Zenith Alpha graft was 7% per limb up to 3 years postoperatively. None of the analyzed risk factors suggested by the IFUs or current literature were positively associated with LGO.

The neglected association between central obesity markers and abdominal aortic aneurysm presence: A systematic review and meta-analysis

Purpose

To review the association between central obesity and abdominal aortic aneurysm (AAA).

Materials and methods

The PubMed, Web of Sciences, Embase, The China national knowledge infrastructure (CNKI), and Cochrane Library were searched up to April 30, 2022. Researches includes investigation of the relationship between central obesity markers and AAA. Included studies must use recognized measures of central obesity, i.e., waist circumference (WC) and waist-to-hip ratio (WHR), or use imaging techniques to calculate abdominal fat distribution, such as computed tomography (CT) imaging.

Results

Eleven clinical researches were identified of which eight discussed the association between physical examination and AAA, and three studies mainly focused on abdominal fat volume (AFV). Seven researches concluded that there was a positive correlation between markers of central obesity and AAA. Three studies found no significant link between markers of central obesity and AAA. One of the remaining studies reported different results for each sex. Three studies pooled in a meta-analysis identified correlation between central obesity and AAA presence (RR = 1.29; 95% confidence interval, 1.14-1.46).

Conclusion

Central obesity plays a role in the risk of AAA. Standardized central obesity markers may be predictors of AAA. However, there was no association between abdominal fat volume and AAA. Additional relevant evidence and specific mechanisms warrant further study.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?IDCRD42022332519, identifier CRD42022332519.

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.

Artificial intelligence in atherosclerotic disease: Applications and trends

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.

Patient-Specific Numerical Simulations of Endovascular Procedures in Complex Aortic Pathologies: Review and Clinical Perspectives

The endovascular technique is used in the first line treatment in many complex aortic pathologies. Its clinical outcome is mostly determined by the appropriate selection of a stent-graft for a specific patient and the operator's experience. New tools are still needed to assist practitioners with decision making before and during procedures. For this purpose, numerical simulation enables the digital reproduction of an endovascular intervention with various degrees of accuracy. In this review, we introduce the basic principles and discuss the current literature regarding the use of numerical simulation for endovascular management of complex aortic diseases. Further, we give the future direction of everyday clinical applications, showing that numerical simulation is about to revolutionize how we plan and carry out endovascular interventions.

Perceptions of Canadian vascular surgeons toward artificial intelligence and machine learning

Background

Artificial intelligence (AI) and machine learning (ML) are rapidly advancing fields with increasing utility in health care. We conducted a survey to determine the perceptions of Canadian vascular surgeons toward AI/ML.

Methods

An online questionnaire was distributed to 162 members of the Canadian Society for Vascular Surgery. Self-reported knowledge, attitudes, and perceptions with respect to potential applications, limitations, and facilitators of AI/ML were assessed.

Results

Overall, 50 of the 162 Canadian vascular surgeons (31%) responded to the survey. Most respondents were aged 30 to 59 years (72%), male (80%), and White (67%) and practiced in academic settings (72%). One half of the participants reported that their knowledge of AI/ML was poor or very poor. Most were excited or very excited about AI/ML (66%) and were interested or very interested in learning more about the field (83.7%). The respondents believed that AI/ML would be useful or very useful for diagnosis (62%), prognosis (72%), patient selection (56%), image analysis (64%), intraoperative guidance (52%), research (88%), and education (80%). The limitations that the participants were most concerned about were errors leading to patient harm (42%), bias based on patient demographics (42%), and lack of clinician knowledge and skills in AI/ML (40%). Most were not concerned or were mildly concerned about job replacement (86%). The factors that were most important to encouraging clinicians to use AI/ML models were improvements in efficiency (88%), accurate predictions (84%), and ease of use (84%). The comments from respondents focused on the pressing need for the implementation of AI/ML in vascular surgery owing to the potential to improve care delivery.

Conclusions

Canadian vascular surgeons have positive views on AI/ML and believe this technology can be applied to multiple aspects of the specialty to improve patient care, research, and education. Current self-reported knowledge is poor, although interest was expressed in learning more about the field. The facilitators and barriers to the effective use of AI/ML identified in the present study can guide future development of these tools in vascular surgery.

Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

•

An overview of systematic reviews on the application of AI including 129 studies.

•

AI use is prominent in Universal Health Coverage, featuring image analysis in neoplasms.

•

Half of the reviews did not evaluate validation procedures nor reporting guidelines.

•

Risk of bias was only included un a third of the reviews.

•

There is not sufficient evidence to transfer AI to actual healthcare delivery.

Background

Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people’s health. It is necessary to assess the current status on the application of AI towards the improvement of people’s health in the domains defined by WHO’s Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges.

Objective

To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people’s health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality.

Methods

A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO’s PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist.

Results

The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews.

Conclusion

Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.

Prediction of abdominal aortic aneurysm growth by artificial intelligence taking into account clinical, biologic, morphologic, and biomechanical variables

OBJECTIVES

To develop a prediction model that could risk stratify abdominal aortic aneurysms (AAAs) into high and low growth rate groups, using machine learning algorithms based on variables from different pathophysiological fields.

METHODS

A cohort of 40 patients with small AAAs (maximum diameter 32-53 mm) who had at least an initial and a follow-up CT scan (median follow-up 12 months, range 3-36 months) were included. 29 input variables from clinical, biological, morphometric, and biomechanical pathophysiological aspects extracted for predictive modeling. Collected data were used to build two supervised machine learning models. A gradient boosting (XGboost) and a support vector machines (SVM) algorithm were trained with 60% and tested with 40% of the data to predict which AAA would achieve a growth rate higher than the median of our study cohort. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used for the evaluation of the developed algorithms.

RESULTS

XGboost achieved the highest AUC in predicting high compared to low AAA growth rate with an AUC of 81.2% (95% CI from 61.1 to 100%). SVM achieved the second highest performance with an AUC of 68.8% (95% CI from 46.5 to 91%). Based on the best performing algorithm, variable importance was estimated. Diameter-diameter ratio (maximum diameter/neck diameter), Tortuosity from Renal arteries to aortic bifurcation, and maximum thickness of the intraluminal thrombus were found to be the most important factors for model predictions. Other factors were also found to play a significant but less important role.

CONCLUSIONS

A prediction model that can risk stratify AAAs into high and low growth rate groups could be developed by analyzing several factors implicated in the multifactorial pathophysiology of this disease, with the use of machine learning algorithms. Future studies including larger patient cohorts and implementing additional risk markers may aid in the establishment of such methodology during AAA rupture risk estimation.

An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology

Imaging in the emergent setting carries high stakes. With increased demand for dedicated on-site service, emergency radiologists face increasingly large image volumes that require rapid turnaround times. However, novel artificial intelligence (AI) algorithms may assist trauma and emergency radiologists with efficient and accurate medical image analysis, providing an opportunity to augment human decision making, including outcome prediction and treatment planning. While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide quantitative characterization of disease severity based on morphologic image details, such as geometry and fluid flow. Taken together, the benefits provided by implementing AI in radiology have the potential to improve workflow efficiency, engender faster turnaround results for complex cases, and reduce heavy workloads. Although analysis of AI applications within abdominopelvic imaging has primarily focused on oncologic detection, localization, and treatment response, several promising algorithms have been developed for use in the emergency setting. This article aims to establish a general understanding of the AI algorithms used in emergent image-based tasks and to discuss the challenges associated with the implementation of AI into the clinical workflow.

Fully automatic volume segmentation using deep learning approaches to assess aneurysmal sac evolution after infra-renal endovascular aortic repair

OBJECTIVE

Endovascular aortic repair (EVAR) surveillance relies on serial measurements of maximal diameter despite significant inter- and intra-observer variability. Volumetric measurements are more sensitive but general use is hampered by the time required for their implementation. An innovative fully automated software (PRAEVAorta® from Nurea), using artificial intelligence (AI), previously demonstrated fast and robust detection of infra-renal abdominal aortic aneurysm's (AAA) characteristics on pre-operative imaging. This study aimed to assess the robustness of these data on post-EVAR computed tomography (CT) scans.

METHODS

Comparison was made between fully automatic and semi-automatic segmentation manually corrected by a senior surgeon on a dataset of 48 patients (48 early post-EVAR CT scans with 6466 slices, and a total of 101 follow-up CT scans with 13708 slices).

RESULTS

The analyses confirmed an excellent correlation of post-EVAR volumes and surfaces, as well as, proximal neck and maximum aneurysm diameters measured with the fully automatic and manually corrected segmentation methods (Pearson's coefficient correlation >.99, p<.0001). Comparison between the fully automatic and manually corrected segmentation method revealed a mean Dice Similarity Coefficient of 0.950±0.015, Jaccard index of 0.906±0.028, Sensitivity of 0.929±0.028, Specificity of 0.965±0.016, Volumetric Similarity (VS) of 0.973±0.018 and mean Hausdorff Distance/slice of 8.7±10.8mm. The mean VS reached 0.873±0.100 for the lumen and 0.903±0.091 for the thrombus. The segmentation time was 9 times faster with the fully automatic method (2.5 vs 22 min/patient with the manually corrected method; p<.0001). Preliminary analysis also demonstrated that a diameter increase of 2mm can actually represent >5% volume increase.

CONCLUSION

PRAEVAorta® enables a fast, reproducible, and fully automated analysis of post-EVAR AAA sac and neck characteristics, with comparison between different time points. It could become a crucial adjunct for EVAR follow-up through early detection of sac evolution, which may reduce the risk of secondary rupture.