SIS System
K230977Surgical Information Sciences, Inc. · cleared 2023-05-02 · product code QIH · Radiology
Premarket evidence — what FDA accepted
source quote (p.4)
“The SIS System is a software only device based on machine learning and image processing.”
source quote (p.5)
“The SIS System provides a patient-specific, 3D anatomical model of specific brain structures based on the patient's own clinical MR image using pre-trained deep learning neural network models.”
source quote (p.5)
“The SIS System provides a patient-specific, 3D anatomical model of specific brain structures based on the patient's own clinical MR image using pre-trained deep learning neural network models. As discussed in more detail below, the method incorporates ultra-high resolution 7T (7 Tesla) Magnetic Resonance images to determine ground truth for the training data set to train the deep learning models. These pre-trained deep learning neural network models are then applied to a patient's clinical image to predict the shape and position of the patient's specific brain structures of interest.”
Validation studies (1)
Bench
sample size not stated
endpoints: validate that the software functions as specified and performs similarly to the predicate device; validate visualization of the STN and GPi/GPe structures; ensure that 3D transformation remains accurate; validate the lead segmentation; validate electrode orientation functionality for the new lead models; validation of the head pose for the standardized head position view
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
- re_clearance
The FDA AI/ML device list shows a newer 510(k) K241083 (decision 2024-06-14) from Surgical Information Sciences, Inc. for a matching device line ("SIS System") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K241083
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).