RSIP Vision

Every project starts from the clinical question, not from a pretrained model looking for a home. RSIP's AI algorithm development work builds detection, segmentation, classification, and quantification around the specific anatomy, modality, and workflow the client has. That matters when the target structure is small, low contrast, or moving.

The company covers CT, MRI, X-ray, ultrasound, OCT, microscopy, and point cloud data. Cardiology work spans CT, MRI, fluoroscopy, and ultrasound; ophthalmology work includes fundus stitching, eye tracking, and retinal layer segmentation on OCT. One vendor handling several modalities shortens the coordination work when a product combines them.

Medical video is the hardest input in the field. Cameras move, lighting shifts, tissue deforms, and smoke obscures the view for seconds at a time. RSIP has built surgical video analysis for bronchoscopy, cath lab, and laparoscopic use, including instrument tracking and tissue segmentation that hold up across cases.

A model that scores well in a notebook is not a product. RSIP takes research-grade code and turns it into software that runs inside a device: fixed runtime budget, defined memory ceiling, graceful failure, and an interface your engineering team can maintain. This step is where most academic collaborations stall, and it's the part RSIP sells explicitly.

The team writes algorithm documentation and validation evidence for FDA clearance and comparable pathways, often with clinical consultants who know what reviewers ask for. That reduces the usual loop of submissions returned for missing detail. The final clearance always belongs to the device maker.

Annotation is done in house by medical annotators working with clinical advisors, not outsourced to a general labeling shop. For imaging work this matters, since a boundary drawn by someone who has never read a scan is worse than no label at all. Data acquisition and curation are handled in the same engagement.