Abstract
Objective
The authors sought to verify the use of a preoperative simulation software for the treatment of intracranial aneurysms using flow diverters (FDs) based on three-dimensional rotational angiography (3DRA) data.
Methods
Based on 3DRA data, the preoperative simulation software (UKNOW) was used to simulate the deployment of virtual FDs. The length and dimensions of virtual and real devices were compared. The deployment plan recommended by the UKNOW software was preliminarily used to complete implantations in the real world. During the experiment, experienced neurointerventional experts were responsible for supervising and judging information such as the length, dimension, and deployment location of the FDs.
Results
This study retrospectively analyzed the data of 29 patients who received FD treatment. There was no statistical difference between the length of the real device and the virtual device (p = 0.6). The dimensions of FDs recommended by the software were consistent with the dimensions used in 24 out of the 29 real cases. In four of the remaining five cases, neurointerventional experts found that the FD dimensions recommended by the software were superior to those were actually used. Thus, the accuracy rate for FD dimension recommendations by the UKNOW software was 96.6% (28/29). Procedures performed in five cases using deployment plans recommended by the UKNOW software all achieved good postoperative results; the deployment positions of the device were reasonable, and all devices showed good wall adherence.
Conclusions
UKNOW software could accurately simulate the length and deployment position of the real FDs and provide suitable device dimensions.
Keywords: Flow diverter, software, preoperative simulation, pipeline embolization device
Introduction
In recent years, flow diverters (FDs) have become the preferred method for treating intracranial aneurysms, especially large or giant aneurysms in the anterior circulation.1–5 Compared with traditional interventional treatments, the cure rate of FD in the treatment of complex aneurysms has significantly improved.6,7 In contrast to conventional intra-aneurysmal coil embolization, FDs work by altering the direction of blood flow from the aneurysm sac back to the parent vessel. In this way, the FD promotes thrombosis in the aneurysm sac and endothelial proliferation in the aneurysm neck, ultimately leading to aneurysm occlusion and parent vessel reconstruction.8,9
The pipeline embolization device (PED) is currently one of the most widely used FD in clinical practice,10,11 PED has a braided metal wire structure and can take many different dimensions. In the process of using PEDs to treat intracranial aneurysms, selecting the most suitable PED dimensions for different anatomical lesions is an important part of the treatment. Inappropriate selection of PED dimensions may lead to serious surgery-related complications such as device foreshortening, migration, prolapse into aneurysms, poor wall adherence, or thromboembolic events.12–16 Thus, selecting the correct PED dimensions is of great significance in improving the success rate of the operation and reducing the occurrence of postoperative complications. However, because PED is a kind of braided stent in structure, the actual diameter and length of a PED will change to some extent after deployment. This increases the difficulty of selecting appropriate PED dimensions, and a doctor must have relevant experience in order to correctly select PED dimensions.
UKNOW is a novel FD simulation software to solve this problem. It can help doctors choose appropriate FD dimensions through preoperative simulation, and can predict the specific position of the device after it is released in the blood vessel, so as to assist the doctor in optimal implantation of the FD. The purpose of this study is to verify the accuracy and usability of the software with a series of real cases.
Methods
Clinical data and imaging data of patients undergoing PED treatment at our hospital from August 2019 to December 2019 were retrospectively collected. Inclusion criteria were as follows: 1) Experienced neurointerventionists confirmed that the effect of the deployment of the FD was satisfactory. Experienced neurointerventionists was determined as neurointerventionists who had experience in 100 successful PED deployments. The effect of the treatment of the FD was satisfactory was determined as the device was fully opened and had a good wall adherence, and the distance from the distal and proximal positions of the device to the neck of the aneurysm is 5–8 mm. 2) Complete clinical data and imaging data with sufficient resolution were available; 3) 3D image data in DICOM format containing both aneurysm and intact parent artery segment. Exclusion criteria were: 1) Diagnosis of traumatic aneurysm or infectious aneurysm; 2) Absence of clinical data or presence of only low-quality imaging data. A total of 29 patients were enrolled in this study, all of whom were treated with single PED each. We collected baseline information, including the patients’ gender, age, aneurysm location, aneurysm maximum diameter, and whether to use additional coils or not. All patients involved in this study gave their informed consent. Institutional review board approval was not required for the study as it was a retrospective review.
This study verifies the value of UKNOW software for clinical use in two ways. First, we verified the accuracy of the release length predicted by UKNOW when simulating a specific FD dimension, so as to provide the doctor with an accurate device position. Second, we compared the device dimensions recommended by UKNOW with the dimensions actually used for individual aneurysms. We then applied the treatment plan recommended by UKNOW to complete implantation of FDs for another five patients in the prospective way. The team of neurointerventional expert guided the implementation of the whole process of verification. The neurointerventional expert team consisted of two neurointerventionists with experience in 100 successful PED deployments each and one neurointerventionist with experience in 300 successful PED deployments.
Length verification
Length measurement of the real FD
Image data for all cases were processed as follows. First, the distal and proximal positions of the real FD were determined based on postoperative two-dimensional angiography images by members from the neurointerventional expert team. Second, a customized three-dimensional cerebrovascular model for each case was established based on the preoperative three-dimensional rotational angiography image. The threshold segmentation method was then used to segment the three-dimensional model and determine the centerline of the parent artery. Third, the three-dimensional blood vessel model was manually adjusted to the same angle as the two-dimensional image. The positions of the distal and proximal ends of the FD were compared between the two-dimensional image and the three-dimensional model. Finally, the software automatically measured the centerline distance between the distal and proximal end of the FD on the three-dimensional model. This distance was the length (measured length) of the real FD 17 (Figure 1).
Figure 1.
The process of measuring the length of the real flow diverter: The red arrows in the figure mark the distal and proximal positions of the real flow diverter.
Length measurement of the virtual FD
UKNOW uses an algorithm independently developed by the research team to simulate the release of a virtual FD. Given the distal end point of an FD, this algorithm predicts the proximal end point. It is based on analysis of FD foreshortening due to local vessel morphology. The algorithm is embedded in the UKNOW software, through which the simulation calculation is performed.
To accomplish this, the distal end of each virtual FD was set in UKNOW to be in the same position as the distal end of the FD in the real case. The dimensions of the FD used in the real case were entered, and the UKNOW program was run to predict the proximal position of the virtual FD after release. The distance of the centerline between the distal and proximal ends of the virtual FD were measured; and this distance was the length (simulated length) of the virtual FD (Figure 2).
Figure 2.
The process of measuring the length of the virtual flow diverter: The red arrow in the figure marks the distal position of the real flow diverter, and the red and blue circles indicate the distal and proximal positions of the virtual flow diverter, respectively.
Comparison of the length of the real FD and the virtual FD
The mean absolute error (MAE) and mean error (ME) were used to evaluate the error between the measured length and the simulated length of each FD. The MAE is defined as the average absolute value of the difference between the simulated length and the measured length. This metric does not take into account whether the simulated length is overestimated (larger) or underestimated (smaller) compared to the true length of the FD, only indicates the absolute difference between the simulated length and the measured length. The ME is defined as the average difference between the simulated length and the measured length, which does take into account whether the virtual FD size was an overestimation or underestimation. Paired sample t-tests were used to analyze the measured length and the simulated length, and the two groups of data were compared for statistical differences. All statistical analyses were performed in SPSS (Version 22.0; IBM, Armonk, New York), and p < 0.05 was considered statistically significant.
Dimension verification
Use UKNOW software to recommend the dimensions of virtual FD
The neurointerventional expert team marked the ideal deployment positions for each FD without knowing the parameters of the actual device used, and utilized the UKNOW software to recommend three alternative device dimensions. In the process of marking the ideal device deployment position, Jian Liu and Yisen Zhang marked the distal and proximal positions of the virtual FD independently. When the opinions of the two doctors were not consistent, the third doctor with more clinical experience (Xinjian Yang) decided the final deployment location of the device.
Record the device deployment positions and dimensions of real cases
Positions of the actual deployed FD were marked using the same method used for length verification, and the positions were also determined according to the positions of the real FD in the two-dimensional angiography image. The dimensions of the real FD were recorded in the clinical data.
Evaluation of the device dimensions recommended by the software
Device dimensions recommended by the software were compared with those used in real cases. If the device dimensions used in real cases matched any one of the device dimensions recommended by the software, the device dimensions recommended by the software were considered to be consistent with the dimensions used in real cases. Otherwise, the recommendations were considered inconsistent. Where inconsistencies occurred, the neurointerventional expert team evaluated whether the positions and dimensions of real FDs were reasonable and whether those recommended by the software were feasible, so as to evaluate the accuracy of the device dimensions recommended by the software (Figure 3).
Figure 3.
The process of dimension verification: The dimensions recommended by the software were consistent with the device dimension used in real case. The blue part in the figure is the aneurysm, and the yellow dots indicate the deployment position of the flow diverter.
Application of UKNOW software in clinical practice
In order to test the applicability of UKNOW in clinical practice, the research team used UKNOW to inform PED deployment in real cases. The researchers prospectively collected imaging and clinical data from five patients with intracranial aneurysms who planned to receive PED treatment in our hospital in June 2020. Inclusion criteria were as follows: 1) Complete clinical data and imaging data with sufficient resolution were available; 2) Three-dimensional image data met the requirements for processing by UKNOW. Researchers used UKNOW software to conduct preoperative simulation planning and formulate a deployment plan for the FD to be implanted. UKNOW showed the deployment position of the FD to be implanted in the form of three-dimensional images. At the same time, three available device dimensions were recommended, and treatments were performed with one of the recommended dimensions. The immediate results of the treatment were collected, including the deployment positions and the degree of wall apposition of the device.
Results
Baseline information
The characteristics and baseline information of the patients are shown in Table 1. The average age of the patients was 55.8 ± 7.9 years, of which 62.1% (18/29) were female. All patients used only one PED device during the operation, and nine patients (31.0%) used coils to assist embolization. Of all aneurysms, 75.9% were located in the internal carotid artery, including the lacerum segment (3.4%), clinoid segment (6.9%), ophthalmic artery segment (62.1%), and middle cerebral artery (3.4%). The rest were located in the vertebrobasilar artery (24.1%).
Table 1.
Baseline characteristics.
| Characteristics | |
|---|---|
| Sex (No.) (%) | |
| Male | 11(37.9%) |
| Female | 18(62.1%) |
| Patient age(mean) (SD, range) (year) | 55.8 (7.9, 40–70) |
| Location of aneurysms (No.) (%) | |
| Internal carotid artery | 22(75.9%) |
| Lacerum | 1(3.4%) |
| Clinoid | 2(6.9%) |
| Ophthalmic | 18(62.1%) |
| Middle cerebral artery | 1(3.4%) |
| Vertebral basilar artery | 7(24.1%) |
| Maximum aneurysm diameter (mean) (SD, range) (mm) | 6.3(2.4, 3.2–12.0) |
| Use of additional coils (No.) (%) | 9(31.0%) |
Note: Location of Internal carotid artery aneurysms is provided according to the classification proposed by Bouthillier et al. (1996).
Comparison of the length between real FD and virtual FD
The measured length, simulated length, MAE, and ME for each case are shown in Table 2. The mean and standard deviation of the measured length and the simulated length were 28.4 ± 6.7 mm and 28.5 ± 6.6 mm, respectively. The MAE of the measured length and the simulated length was 0.8 mm; that is, the average difference between the measured length and the simulated length was < 1 mm. The ME of the measured length and the simulated length was 0.1 mm, which means that the simulated length tended to be slightly larger (overestimated) compared to the measured length. A paired sample t-test was used to analyze the measured and simulated lengths of the devices, and no statistical difference was found (p = 0.6).
Table 2.
Result of length verification.
| Case | Measured length (mm) | Simulated length (mm) | Absolute error (mm) | Relative error (%) |
|---|---|---|---|---|
| 1 | 31.5 | 32.8 | 1.3 | 4.2 |
| 2 | 42.5 | 43.8 | 1.3 | 3.2 |
| 3 | 28.3 | 28.6 | 0.3 | 1.2 |
| 4 | 33.2 | 34.1 | 0.9 | 2.6 |
| 5 | 23.3 | 22.3 | −1.0 | −4.4 |
| 6 | 26.8 | 26.5 | −0.3 | −1.3 |
| 7 | 32.2 | 31.8 | −0.3 | −1.1 |
| 8 | 23.0 | 23.7 | 0.7 | 3.0 |
| 9 | 30.6 | 31.3 | 0.7 | 2.3 |
| 10 | 21.4 | 20.7 | −0.7 | −3.3 |
| 11 | 35.4 | 34.4 | −1.0 | −2.8 |
| 12 | 25.4 | 25.0 | −0.3 | −1.3 |
| 13 | 25.2 | 26.2 | 1.0 | 4.0 |
| 14 | 33.0 | 32.7 | −0.3 | −1.0 |
| 15 | 18.0 | 17.4 | −0.6 | −3.4 |
| 16 | 23.6 | 25.3 | 1.7 | 7.0 |
| 17 | 21.4 | 21.0 | −0.3 | −1.6 |
| 18 | 36.6 | 36.3 | −0.3 | −0.9 |
| 19 | 21.2 | 20.3 | −0.9 | −4.5 |
| 20 | 32.4 | 32.1 | −0.3 | −1.0 |
| 21 | 29.2 | 29.6 | 0.3 | 1.1 |
| 22 | 28.7 | 29.0 | 0.3 | 1.1 |
| 23 | 21.3 | 22.4 | 1.0 | 4.9 |
| 24 | 21.4 | 21.8 | 0.3 | 1.5 |
| 25 | 27.2 | 26.2 | −1.0 | −3.6 |
| 26 | 27.9 | 28.3 | 0.3 | 1.2 |
| 27 | 28.3 | 30.1 | 1.8 | 6.3 |
| 28 | 48.2 | 45.7 | −2.5 | −5.1 |
| 29 | 27.5 | 27.9 | 0.3 | 1.2 |
Comparison of the dimensions between the real FD and the virtual FD
The dimensions recommended by the software were consistent with the device dimensions used in 24 of the 29 real cases (Table 3). In these cases, the neurointerventional team judged that the deployment positions of the real FD were reasonable and that the device dimensions recommended by the software were accurate. There were five cases in which the recommended dimensions were inconsistent with the devices actually used. In these cases, the neurointerventional team determined that the deployment positions of the real FDs were too long at the proximal end in four cases (Figure 4); the device dimensions chosen by the doctor were inappropriate, and furthermore, the device dimensions recommended by the software were more reasonable. The deployment position of the last real FD (case19) of the five cases was determined to be reasonable (Figure 5). The actual device dimension used was PED-400-16, whereas the three dimensions recommended by the software were PED-375-20, PED-375-18, and PED-400-18. We posit that the dimensions recommended by the software still have value as a reference. In summary, the accuracy rate for FD dimensions recommended by UKNOW was 96.6% (28/29).
Table 3.
Result of dimension verification.
| Case | Virtual FD dimensions | Real FD dimensions |
|---|---|---|
| 1 | PED-350-35, PED-375-30, PED-375-25 | PED-375-25 |
| 2* | PED-350-16, PED-350-18, PED-375-16 | PED-475-20 |
| 3 | PED-475-30, PED-475-25, PED-450-25 | PED-450-25 |
| 4 | PED-400-30, PED-400-25, PED-425-25 | PED-400-30 |
| 5 | PED-450-25, PED-450-20, PED-475-20 | PED-450-20 |
| 6 | PED-375-25, PED-375-20, PED-400-25 | PED-375-20 |
| 7 | PED-350-25, PED-325-20, PED-325-25 | PED-350-25 |
| 8 | PED-325-35, PED-350-30, PED-325-30 | PED-325-30 |
| 9* | PED-375-16, PED-375-18, PED-400-16 | PED-425-20 |
| 10 | PED-425-30, PED-425-20, PED-400-30 | PED-425-20 |
| 11 | PED-350-30, PED-325-35, PED-350-35 | PED-350-30 |
| 12 | PED-450-20, PED-475-18, PED-450-25 | PED-475-18 |
| 13 | PED-425-20, PED-400-20, PED-425-25 | PED-425-20 |
| 14 | PED-400-20, PED-425-20, PED-400-25 | PED-425-20 |
| 15 | PED-400-18, PED-400-16, PED-425-16 | PED-400-16 |
| 16 | PED-375-25, PED-375-20, PED-400-18 | PED-400-18 |
| 17 | PED-350-20, PED-375-20, PED-375-25 | PED-375-20 |
| 18 | PED-450-20, PED-425-30, PED-450-25 | PED-425-30 |
| 19* | PED-375-20, PED-375-18, PED-400-18 | PED-400-16 |
| 20* | PED-425-20, PED-425-18, PED-450-18 | PED-425-25 |
| 21 | PED-500-30, PED-500-25, PED-475-30 | PED-500-25 |
| 22* | PED-400-20, PED-400-18, PED-425-18 | PED-450-16 |
| 23 | PED-400-20, PED-400-18, PED-425-18 | PED-400-18 |
| 24 | PED-400-20, PED-400-25, PED-425-20 | PED-425-20 |
| 25 | PED-375-20, PED-375-25, PED-400-20 | PED-375-20 |
| 26 | PED-375-20, PED-375-25, PED-400-20 | PED-400-20 |
| 27 | PED-375-25, PED-350-30, PED-375-25 | PED-350-30 |
| 28 | PED-300-30, PED-325-30, PED-325-25 | PED-300-30 |
| 29 | PED-450-30, PED-450-35, PED-475-30 | PED-450-30 |
Note: * The cases that the recommended dimensions were inconsistent with the devices actually used.
Figure 4.
The 4 cases that the proximal position of the real FD was too long. The red arrow in the second row indicates the location of the real FD.
Figure 5.
The dimensions recommended by the software were inconsistent with the device dimensions used in real cases.
Preliminary application of UKNOW software in clinical practice
In all five cases, Interventional procedures were performed in accordance with the treatment plan recommended by the UKNOW software. The outcomes were satisfactory. Immediate angiography showed that the FD in each case opened well and had good wall adherence, the deployment positions of the device were reasonable, the parent artery was unobstructed, and definite stagnation of the contrast inside the aneurysm was achieved (Figure 6 & Table 4).
Figure 6.
Interventional surgery performed in accordance with the treatment plan recommended by the UKNOW software.
Table 4.
Follow-up results of 5 cases deployed under the guidance of UKNOW software.
| Case No. | Location | Size of PED | Clinical mRS | Radiographic Outcomes | Follow up (months) |
|---|---|---|---|---|---|
| 1 | L-ICA | PED-350-25 | 0 | CO | 12 |
| 2 | R-ICA | PED-400-25 | 0 | CO | 10 |
| 3 | L-ICA | PED-450-25 | 0 | CO | 12 |
| 4 | R-VA | PED-400-35 | 0 | Near CO | 6 |
| 5 | R-ICA | PED-300-20 | 0 | CO | 8 |
Note: L, left; R, right; ICA, internal carotid artery; VA, vertebral artery; CO, complete occlusion.
Discussion
How to correctly select the appropriate FD dimension remains a problem for doctors, especially in the low-volume center. This study aimed to verify the accuracy and usability of a novel FD simulation software to assist doctors in choosing the correct dimensions of FDs and to better complete FD implantations. Through comparison with 29 real cases, it was shown that the UKNOW software can accurately simulate the deployment of FDs and could accurately predict the specific position of the device after deployment. The preliminary usage of the software in five cases showed a positive help in providing doctors with a reference value for FD deployment.
A meta-analysis showed that the overall complication rate for the treatment of intracranial aneurysms with PED was 16.0%. 18 Among those, complications related to device migration or poor device deployment position accounted for 5.8%. 18 When using an FD to treat an intracranial aneurysm, an oversized device may become elongated in the blood vessel, causing the actual deployment position of the device to be very different from the expected position. As a result, the device's pores may become larger 19 and the metal coverage may become insufficient, 20 impairing the flow diversion effect and lowering the chances of successfully causing aneurysm thrombosis. An undersized device may cause an endoleak-like phenomenon due to insufficient coverage of the aneurysm neck. More seriously, it may cause prolapse of the device, 21 and if salvage surgery is not performed in time, the aneurysm may rupture or the patient may die. Therefore, correct selection of the FD dimensions is critical in the treatment of intracranial aneurysms. A bench test of the PED by Boston Scientific showed that the device can foreshorten by up to 50%, 19 causing difficulties for doctors in choosing the appropriate device dimensions. At present, the selection of PED dimensions in clinical work mainly relies on morphological measurement of lesions and the experience of doctors. In the cases of complex lesions or less experienced doctors, the risk of perioperative complications in patients treated with the device is increased. 18 Therefore, tools that could provide doctors with reliable help in choosing the correct FD dimension should greatly improve the accuracy of a doctor's device dimension selection, thus reducing the occurrence of complications resulting from incorrectly sized devices.
In the process of developing auxiliary tools, how to accurately simulate the movement trajectory, the diameter and length after deployment, the wall adherence, and other related parameters of the FD have become the focus of researchers. In response to this key issue, a variety of different methods have been proposed. Kellermann et al. 22 predicted the foreshortening of PEDs in 18 clinical cases based on the BDF algorithm, confirming that the algorithm can accurately predict the length of the device after deployment. It should be noted that in order to facilitate the comparison of the length of the virtual FD and the real FD, the study puts forward the hypothesis that the centerline of the parent artery before and after the operation is the same. However, compared with the vessels without interventional treatment before the operation, the shape and size of the vessels after the operation are in fact changed. A study by Bouillot et al. confirmed the influence of vascular morphology changes on the deployment of virtual devices. 23 Based on a large number of geometric assumptions, they proposed a virtual device deployment method 24 to predict the local characteristics and related parameters of the FD during the release process. However, the accuracy of this model in predicting the length of the FD was only 10%. This is because the shape and size of the parent artery change after the FD is implanted; this can be a result of the radial force of the device, vasodilator drugs, additional manipulation for the improvement of wall apposition during device implantation (such as balloon expansion), or many other factors. Ospel et al. 25 described the potential of virtual device technology in the selection of FD dimensions. They compared the nominal length (the length of the device specified by the manufacturer) of the presumed optimal device selected manually and by the software and found that the length of the device suggested by the software was significantly shorter than the length of the manually selected device. Their explanation was that the software can more accurately predict the length of the device and the landing zones after deployment, and thus confidently chooses a shorter device length. However, although the use of the minimum device length required for a lesion area can avoid some unpredictable branch vessel coverage, the true consequences of the reduction in device length still need to be confirmed by further studies.26,27
In this study, there was no statistical difference between the simulated length and the measured length of PEDs (28.5 ± 6.6 mm vs. 28.4 ± 6.7 mm). A ME value of 0.1 mm shows that the UKNOW software slightly overestimated the correct length of the device when simulating deployment. The MAE for all FDs in this study was 0.8 mm, meaning that the absolute value of the difference between simulated length and measured lengths did not exceed 1 mm. The deployment of an FD is generally an imprecise process that is affected by many factors, including the accuracy of two-dimensional or three-dimensional imaging, the technical proficiency of the doctor and other unpredictable factors. Considering these influencing factors, a deviation of no more than 1 mm is acceptable for accuracy and feasibility in clinical practice. In the dimension verification, the accuracy rate of FD dimensions recommended by the software was 96.6%, which is higher than the results seen in previous similar studies.22,23,25 There were five cases in which the recommended dimensions were inconsistent with the device dimensions that were actually used. However, the neurointerventional expert team judged that the proximal position of the real device was too long in four cases, and the unsatisfactory deployment location of the real FD due to the poor choice of device dimension may be the most important factor. The advantages of software recommendations are demonstrated in these four cases. In the last of the five cases in which the recommended dimensions were inconsistent with the real dimensions, the neurointerventional expert team judged that there was no issue with the deployment position of the real FD. The dimension of the real FD is PED-400-16, and the dimensions recommended by the software are PED-375-20, PED-375-18, and PED-400-18, of which PED-400-18 has the same diameter as the device dimension of real FD, the length difference is only 2 mm. The other two recommended device dimensions are between 18-20 mm in length and 3.75 mm in diameter. When the device is deployed in the blood vessel, since the diameter of the recommended device is slightly smaller than the diameter of the blood vessel (3.75 < 4.00), its length will be foreshortening occurs to a certain extent. Therefore, we have reason to believe that the recommended device model has reference value.
In order to verify the usability of the software more intuitively, the research team used treatment plans recommended by the UKNOW software to perform five interventional operations using PED to treat intracranial aneurysms. The immediate angiography after the operation showed that the deployment position of the FDs were reasonable and the device had good wall adherence in each case. This result intuitively confirmed the accuracy of the UKNOW software's predictions for FD deployment and its usability in the real world.
The algorithm embedded in the UKNOW software is an improvement of the BDF algorithm. 28 When providing a customized treatment plan for patients with intracranial aneurysm, the number of device dimensions available for selection is reasonably expanded. It solves the key problem of low accuracy when using a virtual device to predict the behavior of a real FD. At the same time, it provides more choices for doctors who use the software, and can effectively avoid unpredictable factors such as incomplete device dimensions in the actual application process. It is worth mentioning that in this study, in order to compare the virtual FD with the real FD, the distal position of the virtual FD was set to be consistent with the real FD during the process of simulation. However, in the actual application of UKNOW software, the distal position of the device can be adjusted as needed, which provides the possibility for users to try different deployment schemes. It is useful to doctors to have more reasonable and customized preoperative planning programs for patients with different conditions.
Limitations
Our study has the following limitations: First, the sample size here was small, and large-scale prospective studies are still needed to further verify our results. Second, this study only validates PEDs and lacks verification of other FDs. Third, our software still has some shortcomings that need to be improved, such as how to accurately simulate mechanical operations (such as compaction of devices) during the manual deployment of FDs. In the future, we will continue to improve the relevant sections of the software, increase the calculation parameters of different dimensions in the process of simulating the FD, and further optimize the accuracy of the simulation by improving the real-time binding with the clinical imaging system, reducing the time spent on simulation calculations, and improving the usability of the software.
Conclusions
UKNOW software could accurately simulate the length and deployment position of the real FDs and provide suitable device dimensions.
Footnotes
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Beijing Municipal Administration of Hospitals Incubating Program (grant number: PX2022022), the National Natural Science Foundation of China ( grant numbers: 82072036, 81771304), “13·5” Science and Technology Research Planning Project of Jilin Education Department ( grant number: No.JJKH20190079KJ).
ORCID iDs: Yixuan Wang https://orcid.ogr/0000-0003-2367-625X
Miao Li https://orcid.org/0000-0001-6672-8692
Jian Liu https://orcid.org/0000-0002-5454-2847
Xinjian Yang https://orcid.org/0000-0001-7306-0125
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