ProtoSAM-3D: Interactive semantic segmentation in volumetric medical imaging via a Segment Anything Model and mask-level prototypes.

Semantic segmentation of volumetric medical images is essential for accurate delineation of anatomic structures and pathology, enabling quantitative analysis in precision medicine applications. While volumetric segmentation has been extensively studied, most existing methods require full supervision and struggle to generalize to new classes at inference time, particularly for irregular, ill-defined targets such as tumors, where fine-grained, high-salience segmentation is required. Consequently, conventional semantic segmentation methods cannot easily offer zero/few-shot generalization to segment objects of interest beyond their closed training set.

Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities.

Background: Emergency/trauma radiology artificial intelligence (AI) is maturing along all stages of technology readiness, with research and development (R&D) ranging from data curation and algorithm development to post-market monitoring and retraining.

Purpose: To develop an expert consensus document on best research practices and methodological priorities for emergency/trauma radiology AI.

Methods: A Delphi consensus exercise was conducted by the ASER AI/ML expert panel between 2022-2024.

Interpretable Severity Scoring of Pelvic Trauma Through Automated Fracture Detection and Bayesian Inference.

Pelvic ring disruptions result from blunt injury mechanisms and are potentially lethal mainly due to associated injuries and massive pelvic hemorrhage. The severity of pelvic fractures in trauma victims is frequently assessed by grading the fracture according to the Tile AO/OTA classification in whole-body Computed Tomography (CT) scans. Due to the high volume of whole-body CT scans generated in trauma centers, the overall information content of a single whole-body CT scan and low manual CT reading speed, an automatic approach to Tile classification would provide substantial value, e.