Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification.

Deep learning (DL) models for medical image classification frequently struggle to generalize to data from outside institutions. Additional clinical data are also rarely collected to comprehensively assess and understand model performance amongst subgroups. Following the development of a single-center model to identify the lung sliding artifact on lung ultrasound (LUS), we pursued a validation strategy using external LUS data.

Anatomic evaluation of Pancoast tumors using three-dimensional models for surgical strategy development.

Objective: Pancoast tumor resection planning requires precise interpretation of 2-dimensional images. We hypothesized that patient-specific 3-dimensional reconstructions, providing intuitive views of anatomy, would enable superior anatomic assessment.

Methods: Cross-sectional images from 9 patients with representative Pancoast tumors, selected from an institutional database, were randomly assigned to presentation as 2-dimensional images, 3-dimensional virtual reconstruction, or 3-dimensional physical reconstruction.

Augmented Reality and Intraoperative Navigation in Sinonasal Malignancies: A Preclinical Study.

Objective: To report the first use of a novel projected augmented reality (AR) system in open sinonasal tumor resections in preclinical models and to compare the AR approach with an advanced intraoperative navigation (IN) system.

Methods: Four tumor models were created. Five head and neck surgeons participated in the study performing virtual osteotomies.