Oxipit will deploy its latest AI quality assurance tool for CT pulmonary embolism (PE) at the Leiden University Medical Center (LUMC) in the Netherlands, expanding cooperation between the two entities.
This latest deployment was facilitated through the radiology imaging product by Sectra and its integration capabilities. The medical center is already using the full suite of Oxipit AI products, including quality assurance tools for other modalities.
Oxipit said that CT PE Quality helps reduce the number of missed findings in CT chest angiography studies. The application is currently used in research capacity at LUMC.
CT PE Quality follows an "AI as a second reader" approach. After a radiologist report is submitted, CT PE Quality checks the report against its own findings. If potential missed findings are identified, the study is flagged for a secondary radiologist review and an automated notification is then sent to the reporting radiologist to check the study.
![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)
