A new AI technique named xvr enables patient‑specific use of X‑ray imaging for surgical navigation, potentially improving the safety and precision of minimally invasive procedures. The method is applicable in fields such as orthopedics and neurosurgery, where accurate guidance is critical.
Radiology AI is evolving beyond report generation. CARE-X explores a unified approach that combines flexible reasoning, calibrated predictions, and measurement-based tools for chest X-ray interpretation.
arXiv:2411. 15122v2 Announce Type: replace-cross Abstract: AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays.
Accurate execution of preoperative plans in corrective femoral osteotomies remains challenging. Current techniques are limited by variable accuracy, invasiveness, and radiation exposure, with free-hand methods and patient-specific instrumentation (PSI) often requiring >30 and >6 fluoroscopic images, respectively.
arXiv:2608. 07606v1 Announce Type: cross Abstract: Despite advances in 3D ultrasound, most percutaneous cardiac interventions still rely on 2D visualization, limiting depth perception and spatial understanding.
arXiv:2502. 21187v4 Announce Type: replace Abstract: AI-based lung cancer screening is constrained by scarce, annotated CT data, particularly for rare nodule presentations.
arXiv:2607. 11949v1 Announce Type: cross Abstract: We present a clinically deployed end-to-end auto-contouring system for cervical cancer radiotherapy planning, anchored by the Boundary-Aware Transformer with Region-Aware Mamba (BAT-RM), a hybrid architecture that integrates Sobel-gated boundary attention, a linear-time, multi-directional Mamba module for long-range context, and a boundary-skeleton-guided fusion gate.
arXiv:2603.26509v2 Announce Type: replace Abstract: Computed tomography (CT) provides rich 3D anatomical detail but is often constrained by high radiation exposure, substantial costs, and limited ava...
The study introduces a gaze-informed transformer framework that classifies radiologist expertise during thoracic CT interpretation by integrating eye‑tracking data into volumetric feature learning. Using a DINOv2 backbone, the model incorporates a learnable log‑space bias in self‑attention and gaze‑weighted pooling of patch embeddings. Trained on 182 CT reading sessions from five radiologists, it achieved an ROC‑AUC of 0.91 and an F1 score of 0.86, outperforming adapted baseline methods.
arXiv:2608. 11288v1 Announce Type: cross Abstract: Assessing catheter and tube placement on chest X-rays is safety-critical yet tedious and error-prone.
arXiv:2607. 17789v1 Announce Type: cross Abstract: Early and timely screening of laryngeal cancer is crucial for improving clinical outcomes.
The paper introduces LiftXR, a geometry‑guided framework that first reconstructs a 3D anatomical layout from bi‑planar X‑ray images and then uses this layout to guide CT volume reconstruction. An anatomical parser refines the layout by analyzing the reconstructed CT, enabling region‑specific intensity calibration. Experiments on two public datasets show LiftXR surpasses recent X‑ray‑to‑CT methods and improves downstream segmentation performance.