IQ-UIP

The IQ-UIP is a computer-aided radiology software designed to passively identify radiological findings suggestive of Usual Interstitial Pneumonia (UIP) in non-contrast adult chest CT scans. It utilizes an artificial intelligence algorithm trained and validated on anonymized multi-center CT data to generate informational reports, assisting specialists in detecting fibrotic lung disease patterns.

Imbio, Inc.'s radiology software, IQ-UIP, has been determined to be substantially equivalent to a legally marketed device for its stated indications for use. This classification allows the Class II device, regulated as radiology software for referral of findings related to fibrotic lung disease, to be marketed. The manufacturer is responsible for adhering to ongoing regulatory requirements, including annual registration, device listing, good manufacturing practices, proper labeling, and adherence to specific quality system controls. The IQ-UIP is a computer-aided software designed to passively notify specialists at interstitial lung disease centers of radiological findings suggestive of Usual Interstitial Pneumonia (UIP) in non-contrast adult chest CT scans. It uses an artificial intelligence algorithm to analyze images and generate informational summary reports on a dedicated worklist, separate from the standard imaging interpretation process. The device does not alter original medical images and is not intended for diagnostic purposes. Its development utilized anonymized, multi-center CT scan data, and its performance was validated through extensive testing, confirming its safety and effectiveness compared to a predicate device.

AI Regulatory Concerns

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Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. What specific features of CT scans does the AI algorithm prioritize or analyze to identify findings suggestive of UIP?; How does the performance of IQ-UIP compare to human expert interpretation in terms of sensitivity and specificity for detecting UIP-like patterns?; Given that the device is not for diagnostic purposes, how should clinicians integrate its "informational summary reports" into their diagnostic workflow without over-reliance?; What data sets were used for training and validation, and what was their demographic and disease severity distribution?; Are there plans to expand the capabilities of IQ-UIP to other interstitial lung diseases or to integrate it more directly into standard PACS systems?