Artificial Intelligence Tracking of Otologic Instruments in Mastoidectomy Videos.

Objective: Develop an artificial intelligence (AI) model to track otologic instruments in mastoidectomy videos.

Study Design: Retrospective case series.

Setting: Tertiary care center.

Use of Ultra-Short Echo Time MRI to Improve Temporal Bone Imaging.

Objective: The short T2 nature of cortical bone causes it to appear similar to air on MR, forcing clinicians to rely on computed tomography imaging, with its attendant ionizing radiation exposure, to define temporal bone structures. Through the use of novel MR sequences with ultra-short echo times (UTE), short T2 structures are now able to be visualized, allowing for improved understanding of anatomical relationships.

Methods: Eight patients (50% female) undergoing MR imaging of the skull base for diagnostic purposes (62.

From microscope to head-mounted display: integrating hand tracking into microsurgical augmented reality.

Purpose: The operating microscope plays a central role in middle and inner ear procedures that involve working within tightly confined spaces under limited exposure. Augmented reality (AR) may improve surgical guidance by combining preoperative computed tomography (CT) imaging that can provide precise anatomical information, with intraoperative microscope video feed. With current technology, the operator must manually interact with the AR interface using a computer.

Single-cell transcriptomic atlas reveals increased regeneration in diseased human inner ear balance organs.

Mammalian inner ear hair cell loss leads to permanent hearing and balance dysfunction. In contrast to the cochlea, vestibular hair cells of the murine utricle have some regenerative capacity. Whether human utricular hair cells regenerate in vivo remains unknown.

Deep Learning Method for Rapid Simultaneous Multistructure Temporal Bone Segmentation.

Objective: To develop and validate a deep learning algorithm for the automated segmentation of key temporal bone structures from clinical computed tomography (CT) data sets.

Study Design: Cross-sectional study.

Setting: A total of 325 CT scans from a clinical database.