Intraoperative navigation system with a multi-modality fusion of 3D virtual model and laparoscopic real-time images in laparoscopic pancreatic surgery: a preclinical study

Du, Chengxu; Li, Jiaxuan; Zhang, Bin; Feng, Wenfeng; Zhang, Tengfei; Li, Dongrui

doi:10.1186/s12893-022-01585-0

“…Higher number of lymph node dissection was more signi cant for advanced gastric cancer and accurate staging [41]. With the progress of endoscopic instruments such as uorescence imaging laparoscopy and 3D laparoscopy, as well as the development and application of tracer materials such as carbon nanoparticles and indocyanine green, the e ciency of laparoscopic lymph node dissection is expected to be improved [42]. e rst postoperative exhaust time and postoperative hospital stay in the laparoscopic group were shorter than those in the open group.…”

Section: Discussionmentioning

confidence: 99%

Effects of Laparoscopic versus Open Surgery for Advanced Gastric Cancer after Neoadjuvant Chemotherapy: A Meta-Analysis

Huang

¹

,

Zhong

²

,

Jiang

³

2022

Journal of Healthcare Engineering

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Objective. To evaluate the efficacy of laparoscopy and laparotomy after neoadjuvant chemotherapy in the treatment of advanced gastric cancer by meta-analysis. Methods. Cochrane Library, Embase, and PubMed were searched by computer until December 1, 2021. Literature was screened according to inclusion and exclusion criteria, and relevant data were extracted for meta-analysis using RevMan 5.3. Results. A total of 1027 patients from 11 literature studies were included in this study, including 413 patients in the laparoscopic group and 614 patients in the open group. Meta-analysis showed that the laparoscopic group had less intraoperative bleeding (SMD = −1.11; 95% CI: −1.75–0.47; P = 0.0006 ), early postoperative exhaust (SMD = −0.45; 95% CI: −0.70–0.20; P = 0.0004 ), and shorter postoperative hospital stay (SMD = 0.97; 95% CI: 1.69∼0.26; P = 0.008 ), but had longer the operation time (SMD = 0.65; 95% CI: 0.52∼0.79; P < 0.00001 ). There was no significant difference in the number of lymph nodes dissected during operation (SMD = −0.45; 95% CI: −0.42–0.19; P = 0.45 ), the incidence of surgical complications 30 days after operation (OR = 0.78; 95% CI: 0.53∼1.13; P = 0.19 ), time of first defecation (MD = 0.00; 95% CI: −0.10∼0.10; P = 0.98 ), and time of first postoperative feeding (MD = −0.05; 95% CI: −0.22∼0.12; P = 0.54 ) between the two groups. For long-term prognosis, there was no significant difference in the 3-year overall survival rate after operation between the two groups (RR = 0.84; 95% CI: 0.63–1.12; P = 0.23 ). Conclusion. Compared with the open stomach cancer surgery, laparoscopic gastric cancer surgery has less intraoperative blood loss, shorter hospitalization time, and advantages such as early rehabilitation, postoperative complications rate, and long-term survival, which confirmed the validity and security of the laparoscopic surgery.

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“…In addition to recognition of anatomical structures, algorithms can identify surgical 'go and no-go zones' to indicate areas of high surgical risk and can propose preferred surgical dissection planes in oncological surgery, as demonstrated in recent publications [30,38]. Another approach to anatomy recognition is intraoperative use of preoperative imaging models [39][40][41][42]. Major challenges include tissue deformations and matching the imaging models with the intraoperative view.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy

Boer

¹

,

Jaspers

²

,

Jongh

³

et al. 2023

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Objective To develop a deep learning algorithm for anatomy recognition in thoracoscopic video frames from robot-assisted minimally invasive esophagectomy (RAMIE) procedures using deep learning. Background RAMIE is a complex operation with substantial perioperative morbidity and a considerable learning curve. Automatic anatomy recognition may improve surgical orientation and recognition of anatomical structures and might contribute to reducing morbidity or learning curves. Studies regarding anatomy recognition in complex surgical procedures are currently lacking. Methods Eighty-three videos of consecutive RAMIE procedures between 2018 and 2022 were retrospectively collected at University Medical Center Utrecht. A surgical PhD candidate and an expert surgeon annotated the azygos vein and vena cava, aorta, and right lung on 1050 thoracoscopic frames. 850 frames were used for training of a convolutional neural network (CNN) to segment the anatomical structures. The remaining 200 frames of the dataset were used for testing the CNN. The Dice and 95% Hausdorff distance (95HD) were calculated to assess algorithm accuracy. Results The median Dice of the algorithm was 0.79 (IQR = 0.20) for segmentation of the azygos vein and/or vena cava. A median Dice coefficient of 0.74 (IQR = 0.86) and 0.89 (IQR = 0.30) were obtained for segmentation of the aorta and lung, respectively. Inference time was 0.026 s (39 Hz). The prediction of the deep learning algorithm was compared with the expert surgeon annotations, showing an accuracy measured in median Dice of 0.70 (IQR = 0.19), 0.88 (IQR = 0.07), and 0.90 (0.10) for the vena cava and/or azygos vein, aorta, and lung, respectively. Conclusion This study shows that deep learning-based semantic segmentation has potential for anatomy recognition in RAMIE video frames. The inference time of the algorithm facilitated real-time anatomy recognition. Clinical applicability should be assessed in prospective clinical studies.

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“…Compared with 3D printing, 3DVR is readily available, less expensive, and less time-consuming. In addition, 3DVR can be integrated in holographic navigation surgery, in which the patient’s anatomy appears on the screen as a holographic 3D image, allowing the surgeon to then visualize the patient’s anatomy from multiple angles ( 28 , 29 ). 3DVR intraoperative navigation is a safe and effective adjunct to surgical procedures.…”

Section: Discussionmentioning

confidence: 99%

Accuracy and applicability of the novel pneumoperitoneum three-dimensional volume rendering technique in adhesive small bowel obstruction

Wei¹,

Chen²,

Zhang³

et al. 2023

Quant Imaging Med Surg

0

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Background: The accurate diagnosis of adhesive small bowel obstruction (ASBO) is challenging for surgeons. The aim of this study was to demonstrate that pneumoperitoneum 3-dimensional volume rendering (3DVR) can provide an accurate diagnosis and has applicability in ASBO.Methods: In this retrospective study, patients who underwent preoperative pneumoperitoneum 3DVR and surgery for ASBO between October 2021 and May 2022 were enrolled. The surgical findings were taken as the gold standard, and the kappa test was used to verify the consistency of the pneumoperitoneum 3DVR results and surgical findings.Results: A total of 22 patients with ASBO were included in this study, 27 sites of obstruction adhesions were found during surgery, and 5 patients had both parietal adhesions and interintestinal adhesions. Sixteen parietal adhesions (16/16) were found using pneumoperitoneum 3DVR (κ=1.00; P<0.001), and the diagnosis of parietal adhesions on pneumoperitoneum 3DVR was perfectly consistent with the surgical findings. Eight (8/11) interintestinal adhesions were found using pneumoperitoneum 3DVR (κ=0.727; P<0.001), and the diagnosis of interintestinal adhesions on pneumoperitoneum 3DVR was substantially consistent with the surgical findings. Conclusions:The novel pneumoperitoneum 3DVR is accurate and applicable in ASBO. It can help personalize the treatment of patients and can be useful in planning a more effective surgical approach.

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Intraoperative navigation system with a multi-modality fusion of 3D virtual model and laparoscopic real-time images in laparoscopic pancreatic surgery: a preclinical study

Cited by 10 publications

References 27 publications

Effects of Laparoscopic versus Open Surgery for Advanced Gastric Cancer after Neoadjuvant Chemotherapy: A Meta-Analysis

Effects of Laparoscopic versus Open Surgery for Advanced Gastric Cancer after Neoadjuvant Chemotherapy: A Meta-Analysis

Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy

Accuracy and applicability of the novel pneumoperitoneum three-dimensional volume rendering technique in adhesive small bowel obstruction

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