Fluoroscopy Video
Buy and sell fluoroscopy video data. Real-time X-ray video — swallowing studies, catheter placement, GI series — dynamic imaging AI needs moving pictures.
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Find Me This Data →Overview
What Is Fluoroscopy Video Data?
Fluoroscopy video data captures real-time X-ray imaging of dynamic medical procedures, recording the motion of instruments and anatomy during interventions. These videos are essential for training AI models to detect, segment, and track medical devices like catheters and guidewires, as well as to analyze procedural steps such as swallowing studies, catheter placement, and gastrointestinal series. The data typically includes frame-by-frame annotations identifying instrument positions, collisions with vessel walls, and anatomical landmarks. Fluoroscopy videos are computationally valuable because they encode temporal motion—heartbeats, breathing, and instrument navigation—that static images cannot capture, making them crucial for developing robust computer vision systems in interventional cardiology, endovascular surgery, and gastroenterology.
Market Data
500×500 pixels
Standard Video Resolution
Source: arXiv
24 frames per second (FPS)
Typical Frame Rate
Source: arXiv
400 videos, 16 frames per video (512×512 pixels)
Cardiac Fluoroscopy Video Dataset Size
Source: AAAI
Typically 2-3 pixels wide
Guidewire Thickness in Pixels
Source: AAAI
Who Uses This Data
What AI models do with it.do with it.
Catheter and Guidewire Segmentation
AI models trained to detect, segment, and track catheters and guidewires in real-time during endovascular interventions, with annotation of collision events when instruments contact vessel walls.
Swallowing Study Analysis
Videofluoroscopic swallowing study (VFSS) systems using video data to automatically detect pharyngeal phases and identify aspiration events in dysphagia patients.
Intervention Action Recognition
Deep learning models that classify and predict procedural actions in cardiac catheterization and endovascular surgery, validated by experienced surgeons.
Data Augmentation and Synthesis
Video diffusion models trained on fluoroscopy videos to generate synthetic training data, reducing manual annotation burden while preserving medical imaging realism.
What Can You Earn?
What it's worth.worth.
Unannotated Fluoroscopy Video
Varies
Raw video footage without frame-level annotations; lower compensation than annotated data.
Single-Task Annotated Video
Varies
Video with bounding box or segmentation masks for one task (e.g., catheter collision or guidewire delineation only).
Multi-Task Annotated Video
Varies
Video with professional-verified annotations covering multiple elements (instrument segmentation, action labels, collision detection, anatomical landmarks).
Certified Clinical Dataset
Varies
Large-scale, hospital-sourced fluoroscopy video collections with certified professional annotation and manual verification by surgeons.
What Buyers Expect
What makes it valuable.valuable.
Frame-Level Annotation Accuracy
All bounding boxes, segmentation masks, and action labels must be manually checked and modified by experienced endovascular surgeons or certified clinical professionals to ensure ground-truth quality.
Consistent Encoding Standards
Videos must be encoded uniformly (typically 24 FPS minimum, 500×500 or 512×512 resolution) to ensure coherence across frame sequences and compatibility with deep learning pipelines.
Complete Temporal Coverage
Annotations must cover all relevant frames in a sequence, including procedural phases (e.g., pharyngeal phase in swallowing studies) and critical events (instrument collisions, anatomical landmarks).
Clinical Provenance Documentation
Data sourced from hospitals or clinical settings should include imaging parameters, patient anonymization certification, and documentation of imaging angles and equipment used.
Companies Active Here
Who's buying.buying.
Developing real-time automatic interventional X-ray collimation systems using deep learning on fluoroscopy video data to optimize imaging during catheterization and endovascular procedures.
Training and validating AI models for guidewire segmentation, catheter tracking, and swallowing study automation using annotated fluoroscopy videos from hospital interventional suites.
Building computer vision systems for minimally invasive surgery guidance, using fluoroscopy video datasets to develop catheter and guidewire detection algorithms.
FAQ
Common questions.questions.
What makes fluoroscopy video different from static X-ray images?
Fluoroscopy video captures motion over time at 24+ FPS, encoding temporal dynamics like instrument navigation, heartbeats, and breathing. This temporal information is essential for training AI models to predict future instrument positions and detect dynamic events like vessel collisions, which static images cannot provide.
What annotation tasks are most valuable for fluoroscopy videos?
Multi-task annotations are highest value: bounding boxes for catheter collision detection, segmentation masks for instrument delineation, action labels for procedural phases, and anatomical landmark identification. Videos verified by experienced surgeons or certified professionals command premium pricing.
Do I need hospital affiliation to sell fluoroscopy video data?
Clinical provenance significantly increases value. Data sourced from hospitals, interventional suites, or certified medical centers with proper anonymization and imaging documentation is preferred by AI developers. However, synthetic or augmented fluoroscopy videos generated from existing datasets are also in demand for data augmentation.
What are the privacy and regulatory requirements?
All patient-derived fluoroscopy videos must comply with HIPAA and medical imaging standards. Proper de-identification, consent documentation, and institutional review board approval are essential. Synthetic or phantom-based fluoroscopy videos have fewer regulatory constraints.
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