Comparison of minimally invasive cardiac surgery incisions: Periareolar approach in female patients

A randomized controlled trial (PAMI trial) on our new trend periareolar minimally invasive (PAMI) technique versus inframammary approach for minimally invasive cardiac surgery

Background

A minimally invasive approach has become standard for mitral valve surgery. The periareolar approach has grown in popularity regarding the cosmesis for patients. We have adopted a new modification to the periareolar approach: the periareolar minimally invasive (PAMI) technique. The objectives of the current study are to test the hypothesis that the PAMI approach is more feasible and safer than the inframammary approach in addition to identify risk factors and assess outcomes of both periareolar and inframammary approach.

Methods

A randomized controlled trial of 3 months compared the PAMI technique to the inframammary approach for minimally invasive cardiac surgery.

Results

A total of 102 patients were enrolled and randomized into two groups: 53 received minimally invasive cardiac surgery through the periareolar approach, and 49 were the control group using the inframammary approach.

Using intention-to-treat analysis, the periareolar approach was superior to the inframammary approach in surgical site infection (two cases in comparison to 8 with P = 0.004), and the number needed to treat for effectiveness was 8.

No early deaths occurred, 97 cases (95.1%) needed no reoperation, and 5 cases (4.9%) were reopened for bleeding. The primary endpoints compared were the duration of procedure, duration of mechanical ventilation, amount of bleeding, ICU stay, and hospital stay. We found no statistically significant differences between the groups.

At 3 months, the secondary endpoints evaluated were the rate of surgical site infection, respiratory complications, groin complications, pericardial effusion, breast hematoma, and cosmoses using a Likert scale. We found no statistically significant difference between the groups, except for surgical site infection (P = 0.004) and cosmesis (P< 0.001).

Conclusions

The results of this PAMI trial are suggestive that the PAMI technique is most probably applicable for the right side of the heart, such as in atrial septal defect closure, tricuspid disease, and mitral valve surgery.

Trial registration

PAMI Trial NCT04726488 Registered January 27, 2021

Minimally invasive tricuspid valve surgery

Tricuspid valve disease carries a very unfavorable prognosis when medically treated. Despite that, surgical intervention is still underperformed for tricuspid valve disease due to the reported high morbidity and mortality from a sternotomy approach. This had led to a shift towards maximizing medical therapy for right ventricular failure and, as a result, a more significant delay in surgical referrals with surgical risks when patients are finally referred. Tricuspid valve patients usually have other co-morbidities resulting from their systemic venous congestion and low flow cardiac output. Minimally invasive tricuspid valve surgery provides less tissue injury and, as a result, less trauma during surgery. This provides a hope for both patients and treating doctors to be more open for providing this procedure with less complications. Isolated minimally invasive tricuspid valve surgery is still not performed as widely as expected. This can be partly due to the adverse outcomes historically labelled to tricuspid valve surgery or by the long journey of learning the surgical team would need to commit to with a minimal access approach. In this article we will review the perioperative pathway, and outcomes of isolated minimally invasive tricuspid valve surgery in the available English literature.

Catheter segmentation in X-ray fluoroscopy using synthetic data and transfer learning with light U-nets

Background and objectivesAutomated segmentation and tracking of surgical instruments and catheters under X-ray fluoroscopy hold the potential for enhanced image guidance in catheter-based endovascular procedures. This article presents a novel method for real-time segmentation of catheters and guidewires in 2d X-ray images. We employ Convolutional Neural Networks (CNNs) and propose a transfer learning approach, using synthetic fluoroscopic images, to develop a lightweight version of the U-Net architecture. Our strategy, requiring a small amount of manually annotated data, streamlines the training process and results in a U-Net model, which achieves comparable performance to the state-of-the-art segmentation, with a decreased number of trainable parameters. MethodsThe proposed transfer learning approach exploits high-fidelity synthetic images generated from real fluroscopic backgrounds. We implement a two-stage process, initial end-to-end training and fine-tuning, to develop two versions of our model, using synthetic and phantom fluoroscopic images independently. A small number of manually annotated in-vivo images is employed to fine-tune the deepest 7 layers of the U-Net architecture, producing a network specialized for pixel-wise catheter/guidewire segmentation. The network takes as input a single grayscale image and outputs the segmentation result as a binary mask against the background. ResultsEvaluation is carried out with images from in-vivo fluoroscopic video sequences from six endovascular procedures, with different surgical setups. We validate the effectiveness of developing the U-Net models using synthetic data, in tests where fine-tuning and testing in-vivo takes place both by dividing data from all procedures into independent fine-tuning/testing subsets as well as by using different in-vivo sequences. Accurate catheter/guidewire segmentation (average Dice coefficient of  ~ 0.55,  ~ 0.26 and  ~ 0.17) is obtained with both U-Net models. Compared to the state-of-the-art CNN models, the proposed U-Net achieves comparable performance ( ± 5% average Dice coefficients) in terms of segmentation accuracy, while yielding a 84% reduction of the testing time. This adds flexibility for real-time operation and makes our network adaptable to increased input resolution. ConclusionsThis work presents a new approach in the development of CNN models for pixel-wise segmentation of surgical catheters in X-ray fluoroscopy, exploiting synthetic images and transfer learning. Our methodology reduces the need for manually annotating large volumes of data for training. This represents an important advantage, given that manual pixel-wise annotations is a key bottleneck in developing CNN segmentation models. Combined with a simplified U-Net model, our work yields significant advantages compared to current state-of-the-art solutions.