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Chest Feminization and Masculinization Surgery: Part Two of the Plastic Surgeon's Perspective of Gender-Affirming Surgery.
Plast Aesthet Nurs (Phila)
December 2024
Eva S. Hale, MS, is an MD/MBA candidate at the University of Miami Miller School of Medicine, Miami, FL.
Transgender individuals commonly feel significant distress and discomfort, termed gender dysphoria, as a result of the discrepancy between their gender assigned at birth and their gender identity. A major source of gender dysphoria stems from distinct anatomical differences between the male and female chest. Gender-affirming mastectomy of transmasculine patients and breast augmentation for chest feminization of transfeminine patients, also referred to as top surgery, are often the first surgical interventions and most commonly pursued physical modifications for the treatment of gender dysphoria among this patient population.
View Article and Find Full Text PDFToward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy.
J Imaging
December 2024
Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany.
In recent years, synthetic Computed Tomography (CT) images generated from Magnetic Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be comparable to real CT images in terms of dose computation for radiotherapy simulation. However, until now, there has been no independent strategy to assess the quality of each synthetic image in the absence of ground truth. In this work, we propose a Deep Learning (DL)-based framework to predict the accuracy of synthetic CT in terms of Mean Absolute Error (MAE) without the need for a ground truth (GT).
View Article and Find Full Text PDFA prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy.
Lancet Digit Health
January 2025
Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA. Electronic address:
Background: Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based spine-targeting quality assurance system (DL-SpiQA) for detecting treatment at the wrong anatomic level. DL-SpiQA was evaluated based on retrospective testing of spine radiation therapy treatments and prospective clinical deployment.
View Article and Find Full Text PDFAR-assisted surgery: Precision placement of patient specific hip implants based on 3D printed PSIs.
Healthc Technol Lett
December 2024
Departamento de Bioingeniería Universidad Carlos III de Madrid Leganés Spain.
Patient-specific implant placement in the case of pelvic tumour resection is usually a complex procedure, where the planned optimal position of the prosthesis may differ from the final location. This discrepancy arises from incorrect or differently executed bone resection and improper final positioning of the prosthesis. In order to overcome such mismatch, a navigation solution is presented based on an augmented reality application for HoloLens 2 to assist the entire procedure.
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