Embracing Robotics in Microsurgery: Robotic-Assisted Deep Inferior Epigastric Perforator Flap Breast Reconstruction.

The integration of robotic-assisted surgery (RAS) has transformed various surgical disciplines, including more recently plastic surgery. While RAS has gained acceptance in multiple specialties, its integration in plastic surgery has been gradual, challenging traditional open methods. Robotic-assisted deep inferior epigastric perforator (DIEP) flap breast reconstruction is a technique aimed at overcoming drawbacks associated with the traditional open DIEP flap approach.

Contemporary Microsurgical Breast Reconstruction: Abdominally Based Flaps.

Learning Objectives: After studying this article, the participant should be able to: 1. Understand the types of abdominally based flaps, their anatomy, and their drawbacks. 2.

The Full Continuum of Robotic Breast Surgery: Robotic-assisted Mastectomy, Robotic DIEP Flap, and Robotic Supermicrosurgery.

In recent years, robotic surgery has rapidly expanded to improve surgical outcomes in a variety of surgical subspecialties. Although plastic surgery has taken longer to integrate robotic surgery into practice, the advantages of robotic-assisted surgery, including improved visualization and resolution, minimally invasive approaches, and the ability to surpass human precision and scale, have driven its more recent adoption into plastic surgery. Currently, procedures performed with robotic assistance that are considered part of the continuum of surgical treatment of breast cancer include robotic-assisted nipple-sparing mastectomy, reconstruction with the robotic latissimus dorsi flap or the deep inferior epigastric artery perforator flap, and robotic microsurgery for the flap anastomosis and/or the surgical treatment of lymphedema.

Inverse design of a nano-photonic wavelength demultiplexer with a deep neural network approach.

In this paper, we propose a pre-trained-combined neural network (PTCN) as a comprehensive solution to the inverse design of an integrated photonic circuit. By utilizing both the initially pre-trained inverse and forward model with a joint training process, our PTCN model shows remarkable tolerance to the quantity and quality of the training data. As a proof of concept demonstration, the inverse design of a wavelength demultiplexer is used to verify the effectiveness of the PTCN model.