A Comparison of Accuracy of Image- versus Hardware-based Tracking Technologies in 3D Fusion in Aortic Endografting

The ARIA trial protocol: a randomised controlled trial to assess the clinical, technical, and cost-effectiveness of a cloud-based, ARtificially Intelligent image fusion system in comparison to standard treatment to guide endovascular Aortic aneurysm repair

BACKGROUND

Endovascular repair of aortic aneurysmal disease is established due to perceived advantages in patient survival, reduced postoperative complications, and shorter hospital lengths of stay. High spatial and contrast resolution 3D CT angiography images are used to plan the procedures and inform device selection and manufacture, but in standard care, the surgery is performed using image-guidance from 2D X-ray fluoroscopy with injection of nephrotoxic contrast material to visualise the blood vessels. This study aims to assess the benefit to patients, practitioners, and the health service of a novel image fusion medical device (Cydar EV), which allows this high-resolution 3D information to be available to operators at the time of surgery.

METHODS

The trial is a multi-centre, open label, two-armed randomised controlled clinical trial of 340 patient, randomised 1:1 to either standard treatment in endovascular aneurysm repair or treatment using Cydar EV, a CE-marked medical device comprising of cloud computing, augmented intelligence, and computer vision. The primary outcome is procedural time, with secondary outcomes of procedural efficiency, technical effectiveness, patient outcomes, and cost-effectiveness. Patients with a clinical diagnosis of AAA or TAAA suitable for endovascular repair and able to provide written informed consent will be invited to participate.

DISCUSSION

This trial is the first randomised controlled trial evaluating advanced image fusion technology in endovascular aortic surgery and is well placed to evaluate the effect of this technology on patient outcomes and cost to the NHS.

TRIAL REGISTRATION

ISRCTN13832085. Dec. 3, 2021.

Artificial intelligence in clinical workflow processes in vascular surgery and beyond

In the past decade, artificial intelligence (AI)-based applications have exploded in health care. In cardiovascular disease, and vascular surgery specifically, AI tools such as machine learning, natural language processing, and deep neural networks have been applied to automatically detect underdiagnosed diseases, such as peripheral artery disease, abdominal aortic aneurysms, and atherosclerotic cardiovascular disease. In addition to disease detection and risk stratification, AI has been used to identify guideline-concordant statin therapy use and reasons for nonuse, which has important implications for population-based cardiovascular disease health. Although many studies highlight the potential applications of AI, few address true clinical workflow implementation of available AI-based tools. Specific examples, such as determination of optimal statin treatment based on individual patient risk factors and enhancement of intraoperative fluoroscopy and ultrasound imaging, demonstrate the potential promise of AI integration into clinical workflow. Many challenges to AI implementation in health care remain, including data interoperability, model bias and generalizability, prospective evaluation, privacy and security, and regulation. Multidisciplinary and multi-institutional collaboration, as well as adopting a framework for integration, will be critical for the successful implementation of AI tools into clinical practice.

Automated image fusion during endovascular aneurysm repair: a feasibility and accuracy study

PURPOSE

Image fusion merges preoperative computed tomography angiography (CTA) with live fluoroscopy during endovascular procedures to function as an overlay 3D roadmap. However, in most current systems, the registration between imaging modalities is performed manually by vertebral column matching which can be subjective, inaccurate and time consuming depending on experience. Our objective was to evaluate feasibility and accuracy of image-based automated 2D-3D image fusion between preoperative CTA and intraoperative fluoroscopy based on vertebral column matching.

METHODS

A single-center study with offline procedure data was conducted in 10 consecutive patients which had endovascular aortic repair in which we evaluated unreleased automated fusion software provided by Philips (Best, the Netherlands). Fluoroscopy and digital subtraction angiography images were collected after the procedures and the vertebral column was fused fully automatically. Primary endpoints were feasibility and accuracy of bone alignment (mm). Secondary endpoint was vascular alignment (mm) between the lowest renal artery orifices. Clinical non-inferiority was defined at a mismatch of < 1 mm.

RESULTS

In total, 87 automated measurements and 40 manual measurements were performed on vertebrae T12-L5 in all 10 patients. Manual correction was needed in 3 of the 10 patients due to incomplete visibility of the vertebral edges in the fluoroscopy image. Median difference between automated fusion and manual fusion was 0.1 mm for bone alignment (p = 0.94). The vascular alignment was 4.9 mm (0.7-17.5 mm) for manual and 5.5 mm (1.0-14.0 mm) for automated fusion. This did not improve, due to the presence of stiff wires and stent graft.

CONCLUSION

Automated image fusion was feasible when all vertebral edges were visible. Accuracy was non-inferior to manual image fusion regarding bone alignment. Future developments should focus on intraoperative image-based correction of vascular alignment.

Use of surgical augmented intelligence maps can reduce radiation and improve safety in the endovascular treatment of complex aortic aneurysms

OBJECTIVE

The introduction of endovascular procedures has revolutionized the management of complex aortic aneurysms. Although repair has traditionally required longer operative times and increased radiation exposure compared with simple endovascular aneurysm repair, the recent introduction of three-dimensional technology has become an invaluable operative adjunct. Surgical augmented intelligence (AI) is a rapidly evolving tool initiated at our institution in June 2019. In our study, we sought to determine whether this technology improved patient and operator safety.

METHODS

A retrospective review of patients who had undergone endovascular repair of complex aortic aneurysms (pararenal, juxtarenal, or thoracoabdominal), type B dissection, or infrarenal (endoleak, coil placement, or renal angiography with or without intervention) at a tertiary care center from August 2015 to November 2021 was performed. Patients were stratified according to the findings from intelligent maps, which are patient-specific AI tools used in the operating room in conjunction with real-time fluoroscopic images. The primary outcomes included operative time, radiation exposure, fluoroscopy time, and contrast use. The secondary outcomes included 30-day postoperative complications and long-term follow-up. Linear regression models were used to evaluate the association between AI use and the main outcomes.

RESULTS

During the 6-year period, 116 patients were included in the present study, with no significant differences in the baseline characteristics. Of the 116 patients, 76 (65.5%) had undergone procedures using AI and 40 (34.5%) had undergone procedures without AI software. The intraoperative outcomes revealed a significant decrease in radiation exposure (AI group, 1955 mGy; vs non-AI group, 3755 mGy; P = .004), a significant decrease in the fluoroscopy time (AI group, 55.6 minutes; vs non-AI group, 86.9 minutes; P = .007), a decrease in the operative time (AI group, 255 minutes; vs non-AI group, 284 minutes; P = .294), and a significant decrease in contrast use (AI group, 123 mL; vs non-AI group, 199 mL; P < .0001). No differences were found in the 30-day and long-term outcomes.

CONCLUSIONS

The results from the present study have demonstrated that the use of AI technology combined with intraoperative imaging can significantly facilitate complex endovascular aneurysm repair by decreasing the operative time, radiation exposure, fluoroscopy time, and contrast use. Overall, evolving technology such as AI has improved radiation safety for both the patient and the entire operating room team.

Cloud-based fusion imaging improves operative metrics during fenestrated endovascular aneurysm repair

OBJECTIVE

Endovascular treatment of complex aortic pathology has been associated with increases in procedural-related metrics, including the operative time and radiation exposure. Three-dimensional fusion imaging technology has decreased the radiation dose and iodinated contrast use during endovascular aneurysm repair. The aim of the present study was to report our institutional experience with the use of a cloud-based fusion imaging platform during fenestrated endovascular aneurysm repair (FEVAR).

METHODS

A retrospective review of a prospectively maintained aortic database was performed to identify all patients who had undergone FEVAR with commercially available devices (Zenith Fenestrated; Cook Medical Inc, Bloomington, IN) between 2013 and 2020 and all endovascular aneurysm repairs performed using Cydar EV Intelligent Maps (Cydar Medical, Cambridge, UK). The Cydar EV cohort was reviewed further to select all FEVARs performed with overlay map guidance. The patient demographic, clinical, and procedure metrics were analyzed, with a comparative analysis of FEVAR performed without and with the Cydar EV imaging platform. Patients were excluded from comparative analysis if the data were incomplete in the dataset or they had a documented history of prior open or endovascular abdominal aortic aneurysm repair.

RESULTS

During the 7-year study period, 191 FEVARs had been performed. The Cydar EV imaging platform was implemented in 2018 and used in 124 complex endovascular aneurysm repairs, including 69 consecutive FEVARs. A complete dataset was available for 137 FEVARs. With exclusion to select for de novo FEVAR, a comparative analysis was performed of 53 FEVAR without and 63 with Cydar EV imaging guidance. The cohorts were similar in patient demographics, medical comorbidities, and aortic aneurysm characteristics. No significant difference was noted between the two groups for major adverse postoperative events, length of stay, or length of intensive care unit stay. The use of Cydar EV resulted in nonsignificant decreases in the mean fluoroscopy time (69.3 ± 28 minutes vs 66.2 ± 33 minutes; P = .598) and operative time (204.4 ± 64 minutes vs 186 ± 105 minutes; P = .278). A statistically significant decrease was found in the iodinated contrast volume (105 ± 44 mL vs 83 ± 32 mL; P = .005), patient radiation exposure using the dose area product (1,049,841 mGy/cm² vs 630,990 mGy/cm²; P < .001) and cumulative air kerma levels (4518 mGy vs 3084 mGy; P = .02) for patients undergoing FEVAR with Cydar EV guidance.

CONCLUSIONS

At our aortic center, we have observed a trend toward shorter operative times and significant reductions in both iodinated contrast use and radiation exposure during FEVAR using the Cydar EV intelligent maps. Intelligent map guidance improved the efficiency of complex endovascular aneurysm repair, providing a safer intervention for both patient and practitioner.

Augmented Reality in Vascular and Endovascular Surgery: Scoping Review

Background

Technological advances have transformed vascular intervention in recent decades. In particular, improvements in imaging and data processing have allowed for the development of increasingly complex endovascular and hybrid interventions. Augmented reality (AR) is a subject of growing interest in surgery, with the potential to improve clinicians’ understanding of 3D anatomy and aid in the processing of real-time information. This study hopes to elucidate the potential impact of AR technology in the rapidly evolving fields of vascular and endovascular surgery.

Objective

The aim of this review is to summarize the fundamental concepts of AR technologies and conduct a scoping review of the impact of AR and mixed reality in vascular and endovascular surgery.

Methods

A systematic search of MEDLINE, Scopus, and Embase was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. All studies written in English from inception until January 8, 2021, were included in the search. Combinations of the following keywords were used in the systematic search string: (“augmented reality” OR “hololens” OR “image overlay” OR “daqri” OR “magic leap” OR “immersive reality” OR “extended reality” OR “mixed reality” OR “head mounted display”) AND (“vascular surgery” OR “endovascular”). Studies were selected through a blinded process between 2 investigators (JE and AS) and assessed using data quality tools.

Results

AR technologies have had a number of applications in vascular and endovascular surgery. Most studies (22/32, 69%) used 3D imaging of computed tomography angiogram–derived images of vascular anatomy to augment clinicians’ anatomical understanding during procedures. A wide range of AR technologies were used, with heads up fusion imaging and AR head-mounted displays being the most commonly applied clinically. AR applications included guiding open, robotic, and endovascular surgery while minimizing dissection, improving procedural times, and reducing radiation and contrast exposure.

Conclusions

AR has shown promising developments in the field of vascular and endovascular surgery, with potential benefits to surgeons and patients alike. These include reductions in patient risk and operating times as well as in contrast and radiation exposure for radiological interventions. Further technological advances are required to overcome current limitations, including processing capacity and vascular deformation by instrumentation.

Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes

Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss and excess rates of cardiovascular morbidity and death. Machine learning algorithms and artificially intelligent systems have shown great promise in application to many areas in health care, such as accurately detecting disease, predicting patient outcomes, and automating image interpretation. Although the application of these technologies to peripheral artery disease are in their infancy, their promises are tremendous. In this review, we provide an introduction to important concepts in the fields of machine learning and artificial intelligence, detail the current state of how these technologies have been applied to peripheral artery disease, and discuss potential areas for future care enhancement with advanced analytics.

Identification of Parameters Influencing the Vascular Structure Displacement in Fusion Imaging during Endovascular Aneurysm Repair

PURPOSE

To quantify the displacement of the vascular structures after insertion of stiff devices during endovascular aneurysm repair (EVAR) of abdominal aortic aneurysm and to identify potential parameters influencing this displacement.

MATERIALS AND METHODS

A total of 50 patients from a single center undergoing EVAR were prospectively enrolled between January 2016 and December 2017. Fusion imaging was employed using the EndoNaut (Therenva, Rennnes, France) station through a 3-dimensional (3D)/2-dimensional (2D) technology synchronizing the 3D computed tomography scan to the live intraoperative fluoroscopy. The accuracy of the fusion roadmap was evaluated before deployment by conventional digital subtraction angiogram on a single plane (with different C-arm incidences).

RESULTS

The mean displacement error of the ostium of the lowest renal artery was 4.1 ± 2.4 mm (range, 0-11.7 mm), with a left/right displacement of 1.6 ± 1.7 mm (range, 0-6.9 mm) and a craniocaudal displacement of 3.5 ± 2.4 mm (range, 0-11.3 mm). The correction required for the ostium of the lower renal artery was mostly cranial and to the left. Multiple linear regression analysis revealed only the sharpest angle between the aneurysm neck and sac as the factor influencing the accuracy of fusion imaging. All other parameters did not show any correlation.

CONCLUSIONS

This study identified the sources of fusion error after insertion of rigid material during EVAR. As the sharpest angulation between aneurysm neck and sac increases, the overall accuracy of the fusion might be affected.

Two-dimensional-three-dimensional registration for fusion imaging is noninferior to three-dimensional- three-dimensional registration in infrarenal endovascular aneurysm repair

BACKGROUND

Fusion imaging is a tool for intraoperative three-dimensional (3D) guidance in endovascular aneurysm repair (EVAR). In many aortic centers, the registration for location is based on an intraoperative 3D dataset acquired by means of cone-beam computed tomography (3D-3D registration). Another registration method is based on two two-dimensional (2D) images (lateral and posteroanterior) acquired with the use of intraoperative fluoroscopy for registration with a computed tomographic angiogram (2D-3D registration). The aim of the present study was to compare 2D-3D registration with 3D-3D registration regarding noninferiority in accuracy and to describe radiation exposure and ease of use of both modalities.

METHODS

From December 2014 to September 2015, 50 sequentially enrolled patients received EVAR with the use of fusion imaging using 2D-3D registration. No adjustments were made until the first angiography with inserted stent graft. The deviation of fusion imaging to the actual position of the lower renal artery compared with digital subtraction angiography was measured. A historic cohort of 101 patients treated with EVAR and fusion imaging with 3D-3D registration (3D-3D cohort) served as the control group for this study.

RESULTS

Craniocaudal deviation did not differ significantly (4.6 ± 4.4 mm in the 2D-3D cohort vs 3.6 ± 3.9 mm in the 3D-3D cohort; P = .17). The difference of the means was 1.05 mm with a 95% confidence interval of -2.45 to 0.34 and a P value for the noninferiority test of .0249, indicating that 2D-3D registration was noninferior in terms of a margin of δ = 2.5 mm. 2D-3D registration was significantly faster with significantly less additional radiation necessary: 0.45 ± 0.28 vs 45.7 ± 9.1 Gy·cm² in the 3D-3D cohort (P < .001); 2.3 ± 1.3 vs 5.3 ± 4.3 minutes in the 3D-3D cohort (P < .001).

CONCLUSIONS

Fusion imaging during EVAR with the use of 2D-3D registration is feasible in routine EVAR. Our findings of two consecutive cohorts with the same clinical, hardware, and software setup used for the procedures underscore that the accuracy of 2D-3D registration is noninferior to that of a 3D-3D registration workflow, with advantages in terms of radiation exposure, intraoperative time demand, and ease of use.

Endovascular robotics

The current state and the future direction.