A comparison of acellular dermal matrices (ADM) efficacy and complication profile in women undergoing implant-based breast reconstruction: a systematic review and network meta-analysis.

Introduction: Breast cancer is the leading cause of cancer amongst women in the United Kingdom, with implant-based reconstruction (IBR) using Acellular Dermal Matrices (ADM) gaining popularity for post-mastectomy procedures. This study compares outcomes of different ADMs that are commonly used in women undergoing IBR, this was short and long-term complications.

Methods: A systematic search of MEDLINE, Embase, CENTRAL, and CDSR databases was performed according to the PRISMA guidelines, focusing on women undergoing IBR with FlexHD, AlloDerm, Bovine, or Porcine ADMs.

Sustainability in Reconstructive Breast Surgery: An Eco-audit of the Deep Inferior Epigastric Perforator Flap Pathway.

Background: The deep inferior epigastric perforator (DIEP) flap provides an effective and popular means for autologous breast reconstruction. However, with the complexity of the pathway, the environmental impact of the pathway has yet to be evaluated.

Methods: A retrospective analysis of 42 unilateral DIEPs at a single reconstructive center was performed.

Does the use of Acellular Dermal Matrices (ADM) in women undergoing pre-pectoral implant-based breast reconstruction increase operative success versus non-use of ADM in the same setting? A systematic review.

Background: Breast cancer is the most common malignancy among women in the UK. Reconstruction - of which implant-based breast reconstruction (IBBR) is the most common - forms a core part of surgical management of breast cancer. More recently, pre-pectoral IBBR has become common as technology and operative techniques have evolved.

A systematic review of generative adversarial networks (GANs) in plastic surgery.

Introduction: Generative adversarial networks (GANs) are a form of deep learning architecture based on the zero-sum game theory, which uses real data to generate realistic fake data. GANs use two opposing neural networks working: a generator and a discriminator. They represent a powerful tool for generating realistic synthetic patient data sets and can potentially revolutionize research.