Shape completion in the dark: completing vertebrae morphology from 3D ultrasound.

Purpose: Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D information. While performing US-based diagnosis or investigation, medical professionals therefore create a mental map of the 3D anatomy. In this work, we aim to replicate this process and enhance the visual representation of anatomical structures.

From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery.

Purpose: Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.

Real-time integration between Microsoft HoloLens 2 and 3D Slicer with demonstration in pedicle screw placement planning.

Purpose: Up to date, there has been a lack of software infrastructure to connect 3D Slicer to any augmented reality (AR) device. This work describes a novel connection approach using Microsoft HoloLens 2 and OpenIGTLink, with a demonstration in pedicle screw placement planning.

Methods: We developed an AR application in Unity that is wirelessly rendered onto Microsoft HoloLens 2 using Holographic Remoting.

The use of 3D digital anatomy model improves the communication with patients presenting with prostate disease: The first experience in Senegal.

Objectives: We hypothesized that the use of an interactive 3D digital anatomy model can improve the quality of communication with patients about prostate disease.

Methods: A 3D digital anatomy model of the prostate was created from an MRI scan, according to McNeal's zonal anatomy classification. During urological consultation, the physician presented the digital model on a computer and used it to explain the disease and available management options.

Use of Machine Learning to Assess Cataract Surgery Skill Level With Tool Detection.

Purpose: To develop a method for objective analysis of the reproducible steps in routine cataract surgery.

Design: Prospective study; machine learning.

Participants: Deidentified faculty and trainee surgical videos.