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.

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.

Automatic Measurement of Vascular Calcifications in Patients with Aorto-Iliac Occlusive Disease to Predict the Risk of Re-intervention After Endovascular Repair.

BACKGROUND: There is currently a lack of consensus and tools to easily measure vascular calcification using computed tomography angiography (CTA). The aim of this study was to develop a fully automatic software to measure calcifications and to evaluate the interest as predictive factor in patients with aorto-iliac occlusive disease. METHODS: This study retrospectively included 171 patients who had endovascular repair of an aorto-iliac occlusive lesion at the University Hospital of Nice between January 2011 and December 2019. Calcifications volumes were measured from CTA using an automatic method consisting in three sequential steps: image pre-processing, lumen segmentation using expert system, and deep learning algorithms and segmentation of calcifications. Calcification volumes were measured in the infrarenal abdominal aorta and the iliac arterial segments, corresponding to the common and the external iliac arteries. RESULTS: Among 171 patients included with a mean age of 65 years, the revascularization was performed on the native external and internal iliac arteries in, respectively: 83 patients (48.5%), 107 (62.3%), and 7 (4.1%). The mean volumes of calcifications were 2,759 mm3 in the infrarenal abdominal aorta, 1,821 mm3 and 1,795 mm3 in the right and left iliac arteries, respectively. For a mean follow-up of 39 months, target lesion re-intervention was performed in 55 patients (32.2%). These patients had higher volume of calcifications in the right and left iliac arteries, compared with patients who did not have a re-intervention (2,274 mm3 vs. 1,606 mm3, P = 0.0319 and 2,278 vs. 1,567 mm3, P = 0.0213). CONCLUSIONS: The development of a fully automatic software would be useful to facilitate the measurement of vascular calcifications and possibly better inform the prognosis of patients.

Applications of Artificial Intelligence in Non-cardiac Vascular Diseases: A Bibliographic Analysis.

Research output related to artificial intelligence (AI) in vascular diseases has been poorly investigated. The aim of this study was to evaluate scientific publications on AI in non-cardiac vascular diseases. A systematic literature search was conducted using the PubMed database and a combination of keywords and focused on three main vascular diseases (carotid, aortic and peripheral artery diseases). Original articles written in English and published between January 1995 and December 2020 were included. Data extracted included the date of publication, the journal, the identity, number, affiliated country of authors, the topics of research, and the fields of AI. Among 171 articles included, the three most productive countries were USA, China, and United Kingdom. The fields developed within AI included: machine learning (n = 90; 45.0%), vision (n = 45; 22.5%), robotics (n = 42; 21.0%), expert system (n = 15; 7.5%), and natural language processing (n = 8; 4.0%). The applications were mainly new tools for: the treatment (n = 52; 29.1%), prognosis (n = 45; 25.1%), the diagnosis and classification of vascular diseases (n = 38; 21.2%), and imaging segmentation (n = 38; 21.2%). By identifying the main techniques and applications, this study also pointed to the current limitations and may help to better foresee future applications for clinical practice.

Automatic Measurement of Maximal Diameter of Abdominal Aortic Aneurysm on Computed Tomography Angiography Using Artificial Intelligence.

INTRODUCTION: The treatment of abdominal aortic aneurysm relies on surgical repair and the indication mainly depends on its size evaluated by the maximal diameter (Dmax). The aim of this study was to evaluate a new automatic method based on artificial intelligence to measure the Dmax on computed tomography angiography. METHODS: A fully automatic segmentation of the vascular system was performed using a hybrid method combining expert system with supervised deep learning. The aorta centreline was extracted from the segmented aorta and the aortic diameters were automatically calculated. Results were compared to manual segmentation performed by two human operators. RESULTS: The median absolute error between the two human operators was 1.2 mm (IQR 0.5-1.9). The automatic method using the deep learning algorithm demonstrated correlation with the human segmentation, with a median absolute error of 0.8 (0.5-4.2) mm and a coefficient correlation of 0.91 (P < 0.001). CONCLUSIONS: Although validation in larger cohorts is required, this method brings perspectives to develop new tools to standardize and automate the measurement of abdominal aortic aneurysm Dmax in order to help clinicians in the decision-making process.

Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning.

BACKGROUND: Computed tomography angiography (CTA) is one of the most commonly used imaging technique for the management of vascular diseases. Here, we aimed to develop a hybrid method combining a feature-based expert system with a supervised deep learning (DL) algorithm to enable a fully automatic segmentation of the abdominal vascular tree. METHODS: We proposed an algorithm based on the hybridization of a data-driven convolutional neural network and a knowledge-based model dedicated to vascular system segmentation. By using two distinct datasets of CTA from patients to evaluate independence to training dataset, the accuracy of the hybrid method for lumen and thrombus segmentation was evaluated compared to the feature-based expert system alone and to the ground truth provided by a human expert. RESULTS: The hybrid approach demonstrated a better accuracy for lumen segmentation compared to the expert system alone (volume similarity: 0.8128 vs. 0.7912, p = 0.0006 and Dice similarity coefficient: 0.8266 vs. 0.7942, p < 0.0001). The accuracy for thrombus segmentation was also enhanced using the hybrid approach (volume similarity: 0.9404 vs. 0.9185, p = 0.0027 and Dice similarity coefficient: 0.8918 vs. 0.8654, p < 0.0001). CONCLUSIONS: By enabling a robust and fully automatic segmentation, the method could be used to develop real-time decision support to help in the management of vascular diseases.

Applications of Head-Mounted Displays and Smart Glasses in Vascular Surgery.

OBJECTIVES: Advances in virtual, augmented and mixed reality have led to the development of wearable technologies including head mounted displays (HMD) and smart glasses. While there is a growing interest on their potential applications in health, only a few studies have addressed so far their use in vascular surgery. The aim of this review was to summarize the fundamental notions associated with these technologies and to discuss potential applications and current limits for their use in vascular surgery. METHODS: A comprehensive literature review was performed to introduce the fundamental concepts and provide an overview of applications of HMD and smart glasses in surgery. RESULTS: HMD and smart glasses demonstrated a potential interest for the education of surgeons including anatomical teaching, surgical training, teaching and telementoring. Applications for pre-surgical planning have been developed in general and cardiac surgery and could be transposed for a use in vascular surgery. The use of wearable technologies in the operating room has also been investigated in both general and cardiovascular surgery and demonstrated its potential interest for image-guided surgery and data collection. CONCLUSION: Studies performed so far represent a proof of concept of the interest of HMD and smart glasses in vascular surgery for education of surgeons and for surgical practice. Although these technologies exhibited encouraging results for applications in vascular surgery, technical improvements and further clinical research in large series are required before hoping using them in daily clinical practice.

Artificial intelligence in abdominal aortic aneurysm.

OBJECTIVE: Abdominal aortic aneurysm (AAA) is a life-threatening disease, and the only curative treatment relies on open or endovascular repair. The decision to treat relies on the evaluation of the risk of AAA growth and rupture, which can be difficult to assess in practice. Artificial intelligence (AI) has revealed new insights into the management of cardiovascular diseases, but its application in AAA has so far been poorly described. The aim of this review was to summarize the current knowledge on the potential applications of AI in patients with AAA. METHODS: A comprehensive literature review was performed. The MEDLINE database was searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search strategy used a combination of keywords and included studies using AI in patients with AAA published between May 2019 and January 2000. Two authors independently screened titles and abstracts and performed data extraction. The search of published literature identified 34 studies with distinct methodologies, aims, and study designs. RESULTS: AI was used in patients with AAA to improve image segmentation and for quantitative analysis and characterization of AAA morphology, geometry, and fluid dynamics. AI allowed computation of large data sets to identify patterns that may be predictive of AAA growth and rupture. Several predictive and prognostic programs were also developed to assess patients' postoperative outcomes, including mortality and complications after endovascular aneurysm repair. CONCLUSIONS: AI represents a useful tool in the interpretation and analysis of AAA imaging by enabling automatic quantitative measurements and morphologic characterization. It could be used to help surgeons in preoperative planning. AI-driven data management may lead to the development of computational programs for the prediction of AAA evolution and risk of rupture as well as postoperative outcomes. AI could also be used to better evaluate the indications and types of surgical treatment and to plan the postoperative follow-up. AI represents an attractive tool for decision-making and may facilitate development of personalized therapeutic approaches for patients with AAA.

Fundamentals in Artificial Intelligence for Vascular Surgeons.

Artificial intelligence (AI) corresponds to a broad discipline that aims to design systems, which display properties of human intelligence. While it has led to many advances and applications in daily life, its introduction in medicine is still in its infancy. AI has created interesting perspectives for medical research and clinical practice but has been sometimes associated with hype leading to a misunderstanding of its real capabilities. Here, we aim to introduce the fundamental notions of AI and to bring an overview of its potential applications for medical and surgical practice. In the limelight of current knowledge, limits and challenges to face as well as future directions are discussed.

A fully automated pipeline for mining abdominal aortic aneurysm using image segmentation.

Imaging software have become critical tools in the diagnosis and the treatment of abdominal aortic aneurysms (AAA). The aim of this study was to develop a fully automated software system to enable a fast and robust detection of the vascular system and the AAA. The software was designed from a dataset of injected CT-scans images obtained from 40 patients with AAA. Pre-processing steps were performed to reduce the noise of the images using image filters. The border propagation based method was used to localize the aortic lumen. An online error detection was implemented to correct errors due to the propagation in anatomic structures with similar pixel value located close to the aorta. A morphological snake was used to segment 2D or 3D regions. The software allowed an automatic detection of the aortic lumen and the AAA characteristics including the presence of thrombus and calcifications. 2D and 3D reconstructions visualization were available to ease evaluation of both algorithm precision and AAA properties. By enabling a fast and automated detailed analysis of the anatomic characteristics of the AAA, this software could be useful in clinical practice and research and be applied in a large dataset of patients.