Eclipse II with Smart Noise Cancellation
K213307Carestream Health, Inc. · cleared 2022-01-14 · product code MQB · Radiology
Premarket evidence — what FDA accepted
source quote (p.3)
“The software performs digital enhancement of a radiographic image generated by an x-ray device. The software can be used to process adult and pediatric x-ray images. This excludes mammography applications. The Smart Noise Cancellation module consists of a Convolutional Neural Network (CNN) trained using clinical images with added simulated noise to represent reduced signal-to-noise acquisitions.”
source quote (p.4)
“The Smart Noise Cancellation module consists of a Convolutional Neural Network (CNN) trained using clinical images with added simulated noise to represent reduced signal-to-noise acquisitions. The Smart Noise Cancellation operation passes the acquired preprocessed image through a specially trained Convolutional Neural Network (CNN) based on a U-Net architecture to generate a 2D map of the estimated noise found in the image, identified in the document as a “Noise Field."”
source quote (p.4)
“The Smart Noise Cancellation module consists of a Convolutional Neural Network (CNN) trained using clinical images with added simulated noise to represent reduced signal-to-noise acquisitions. The Smart Noise Cancellation operation passes the acquired preprocessed image through a specially trained Convolutional Neural Network (CNN) based on a U-Net architecture to generate a 2D map of the estimated noise found in the image, identified in the document as a “Noise Field."”
Validation studies (1)
Reader study (MRMC)
sample size not stated
endpoints: 5-point visual difference preference scale (-2 to +2) tied to diagnostic confidence; 4-point RadLexscale
standards: ISO 14971, FDA “Guidance for the Submission of 510(k)s for Solid State X-ray Imaging Devices"
Reported performance (0 observations)
FDA source did not state a quantitative performance metric — non-reporting is itself the signal.
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).