Three-Dimensional Printing to Guide Fenestrated/Branched TEVAR in Triple Aortic Arch Branch Reconstruction With a Curative Effect Analysis.

PURPOSE: To summarize experience with and the efficacy of fenestrated/branched thoracic endovascular repair (F/B-TEVAR) using physician-modified stent-grafts (PMSGs) under 3D printing guidance in triple aortic arch branch reconstruction. MATERIALS AND METHODS: From February 2018 to April 2022, 14 cases of aortic arch aneurysms and 30 cases of aortic arch dissection (22 acute aortic arch dissection and 8 long-term aortic arch dissection)were treated by F/B-TEVAR in our department, including 34 males and 10 females, with an average age of 59.84 ± 11.72 years. Three aortic arch branches were affected in all patients. A 3D-printed model was made according to computed tomography angiography images and used to guide the fabrication of PMSGs. All patients were followed up. RESULTS: A total of 132 branches were successfully reconstructed with no case of conversion to open surgery. The average operation time was 4.97 ± 1.40 hours, including a mean 44.05 ± 7.72 minutes for stent-graft customization, the mean postoperative hospitalization duration was 9.91 ± 4.47 days, the average intraoperative blood loss was 480.91 mL (100-2810 mL), and the mean postoperative intensive care unit monitoring duration was 1.02 days (0-5 days). No deaths occurred within 30 days of surgery. Postoperative neurological complications occurred in 1 case (2.3%), and retrograde type A dissection occurred in 1 case (2.3%). CONCLUSION: Compared with conventional surgery, triple aortic arch branch reconstruction under the guidance of 3D printing is a minimally invasive treatment method with the advantages of accurate positioning, rapid postoperative recovery, few complications, and reliable short- to mid-term effects. CLINICAL IMPACT: At present the PMSG usually depend on imaging data and software calculation. With the guidance of 3D printing technology, image data could be transformed into 3D model, which has improved the accuracy of the positioning of the fenestrations. The diameter reduction technique and the internal mini cuff technique have made a complement to the slimed-down fenestration selection process and the low rate of endoleak. As reproducible study, our results may provide reference for TEVAR in different cases.

3D parametric surface planar topological guide plate to create a PMSG for the emergency endovascular repair of an aortic arch aneurysm.

A 69-year-old male patient was admitted by 10 h severe chest pain. Computed tomography angiography showed a 7.3 cm aneurysm of the aortic arch. We used a three-dimensional parametric surface planar topological guide plate to prepare a guide plate in 40 min. The plate was used to localize the opening of the aortic arch branches on table to create a physician-modified stent graft (PMSG). The aneurysm was successfully repaired by the triple inner branched PMSG, with no endoleak and all the branched arteries patency in follow-up. This technique could not only make accurate fenestration but also meet the need for emergency surgery.

The Artificial Intelligence-Enabled Medical Imaging: Today and Its Future.

Medical imaging is now being reshaped by artificial intelligence (AI) and progressing rapidly toward future. In this article, we review the recent progress of AI-enabled medical imaging. Firstly, we briefly review the background about AI in its way of evolution. Then, we discuss the recent successes of AI in different medical imaging tasks, especially in image segmentation, registration, detection and recognition. Also, we illustrate several representative applications of AI-enabled medical imaging to show its advantage in real scenario, which includes lung nodule in chest CT, neuroimaging, mammography, and etc. Finally, we report the way of human-machine interaction. We believe that, in the future, AI will not only change the traditional way of medical imaging, but also improve the clinical routines of medical care and enable many aspects of the medical society.

XCSc: a novel approach to clustering with extended classifier system.

In this paper, we propose a novel approach to clustering noisy and complex data sets based on the eXtend Classifier Systems (XCS). The proposed approach, termed XCSc, has three main processes: (a) a learning process to evolve the rule population, (b) a rule compacting process to remove redundant rules after the learning process, and (c) a rule merging process to deal with the overlapping rules that commonly occur between the clusters. In the first process, we have modified the clustering mechanisms of the current available XCS and developed a new accelerate learning method to improve the quality of the evolved rule population. In the second process, an effective rule compacting algorithm is utilized. The rule merging process is based on our newly proposed agglomerative hierarchical rule merging algorithm, which comprises the following steps: (i) all the generated rules are modeled by a graph, with each rule representing a node; (ii) the vertices in the graph are merged to form a number of sub-graphs (i.e. rule clusters) under some pre-defined criteria, which generates the final rule set to represent the clusters; (iii) each data is re-checked and assigned to a cluster that it belongs to, guided by the final rule set. In our experiments, we compared the proposed XCSc with CHAMELEON, a benchmark algorithm well known for its excellent performance, on a number of challenging data sets. The results show that the proposed approach outperforms CHAMELEON in the successful rate, and also demonstrates good stability.