InferRead Lung CT.AI
K240554Infervision Medical Technology Co., Ltd. · cleared 2025-05-16 · product code OEB · Radiology
Premarket evidence — what FDA accepted
source quote (p.6)
“InferRead Lung CT.AI uses the deep learning (DL) technology to perform nodule detection. It is a dedicated post-processing application that generates CADe marks as an overlay on original CT scans. The software can be installed in a healthcare facility or a cloud-based platform and is comprised of computer-assisted reading tools designed to aid radiologists in detecting, segmenting, measuring and localizing actionable pulmonary nodules that are 4mm or above during the review of chest CT examinations of asymptomatic populations, with enhanced capabilities for pulmonary nodule follow-up comparison and lung analysis. InferRead Lung CT.AI provides auxiliary information and is not intended to be used if the original CT series is not available.”
source quote (p.6)
“InferRead Lung CT.AI uses the deep learning (DL) technology to perform nodule detection.”
source quote (p.8)
“This feature displays the lobar location of each detected pulmonary nodule. It relies on the same nodule-detection algorithm used in predicate device K192880 and serves to enhance physicians' ability to accurately localize detected nodules.”
source quote (p.10)
“Cybersecurity and vulnerability analyses were conducted, and it has been determined that InferRead Lung CT.AI conforms to the cybersecurity requirements.”
Validation studies (3)
Retrospective clinical
n=98 cases
endpoints: overall nodule Match Rate; nodule match rates for 0-6 month interval; nodule match rates for 6-12 month interval; nodule match rates for 12-24 month interval
standards: IEC 62304:2006+A1:2015, ISO 14971:2019, AAMI TIR57:2016
Retrospective clinical
n=94 scans
endpoints: overall Lobe Localization Accuracy Rate
standards: IEC 62304:2006+A1:2015, ISO 14971:2019, AAMI TIR57:2016
Retrospective clinical
n=22 cases
endpoints: average Dice Coefficient
standards: IEC 62304:2006+A1:2015, ISO 14971:2019, AAMI TIR57:2016
Reported performance (2 observations)
source quote (p.9)
“InferRead Lung CT.AI achieved an overall Lobe Localization Accuracy Rate of 0.957 (95%CI: 0.929-0.986).”
source quote (p.9)
“The test results for pulmonary lobe segmentation showed that the average Dice Coefficient was 0.966 (95%CI: 0.962 to 0.969).”
Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.
Predicate network
Postmarket — what happened after clearance
Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.
Reimbursement — how devices like this got paid
Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).