Artificial intelligence (AI) medical imaging company Oxipit is previewing the Dynamics feature for its ChestEye CAD suite at RSNA 2019 in Chicago. The feature facilitates comparisons of previous and current radiographic images.
Using AI, Dynamics allows ChestEye to compare the sizes of nodules and explain progression of a condition, enabling treatment decisions based on the evidence for progression. With Dynamics, reports can be automatically generated that will document changes in imaging longitudinally. In its initial rollout, Dynamics will support longitudinal comparison for pneumothorax, consolidation, mass, nodule, pleural effusion, pulmonary edema, and lung congestion findings on x-rays.
The feature is also tied to ChestEye Queue, a triage application for patient management that accounts for the progress of a given patient's condition.
Other offerings from Oxipit highlighted at RSNA 2019 include ChestEye Quality, an automated clinical audit service designed for retrospective x-ray report analysis, and ChestEye Negative, which generates preliminary reports for chest x-ray images that show no abnormality, freeing up time for radiologists to focus on images with pathology.
Whereas current quality assurance applications choose a small, random sample of reports for secondary analysis, ChestEye Quality allows for a double check of all retrospective patient cases and, by operating in real-time, permits detection of discrepancies that could affect treatment of a patient.
ChestEye Negative may be of particular benefit in clinical settings that have a low segment of abnormal studies, namely, large-scale health screening projects and out-patient clinics, according to Oxipit.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
