21 CFR 892.2052 — Post-Ablation Tissue Response Prediction Software
Radiology (RA) · Subpart B—Diagnostic Devices · § 892.2052
Identification
Post-ablation tissue response prediction software is an image processing software device intended to aid physicians with adjunctive information in their clinical assessment of the ablation zone following a tissue ablation procedure. This device uses information extracted from medical images along with other clinical data to predict the ablation zone post treatment. BioTraceIO Lite is indicated for use in patients undergoing radiofrequency (RF) or microwave (MW) liver ablation procedures to provide physicians with adjunctive information in their clinical assessment of the ablation zone created by liver tissue ablation, as part of their overall post-procedure clinical assessment.
Classification Rationale
Class II (special controls). The device is classified as Class II because the probable benefits outweigh the probable risks, and the risks can be mitigated by the use of general controls and the identified special controls.
Product Codes
| Product Code | Device Name | Class | Devices | Attributes |
|---|---|---|---|---|
| QZL | Post-Ablation Tissue Response Prediction Software | 2 | 2 | SaMD |
Special Controls
QZL — Post-Ablation Tissue Response Prediction Software
In combination with the general controls of the FD&C Act, the post-ablation tissue response prediction software is subject to the following special controls: - (1) Clinical performance testing must demonstrate the device performs as intended under anticipated conditions of use and evaluate the following: - Ability to identify and visualize the ablation zone seen on images post treatment; (i) and - Accuracy in predicting the ablation zone post treatment. (ii) - (2) Software verification and validation must demonstrate device and algorithm functionality as informed by hazard analysis. Software documentation must include a detailed description of algorithm inputs and outputs, and any limitations of the algorithm. - (3) Labeling must include: - (i) A detailed description of the user workflow; and - A detailed summary of the clinical performance testing, including test methods, (ii) dataset characteristics, imaging modality/equipment, anatomical region, patient population specifying type and pathology of target tissue, target locations and sizes, and results.
De Novo Order DEN230020
