FDA PRODUCT CODE · CLASS II
QDQ — Radiological Computer Assisted Detection/Diagnosis Software For Lesions Suspicious For Cancer
A radiological computer assisted detection and diagnostic software for suspected lesions is an image processing device intended to aid in the detection, localization, and characterization of lesions suspicious for cancer on acquired medical images (e.g., mammography , MR, CT, ultrasound, radiography). The device detects, identifies and characterizes lesions suspicious for cancer based on features or information extracted from the images, and may provide information about the presence, location, and characteristics of the lesion to the user. Primary diagnostic and patient management decisions are made by the clinical user.
What these counts are not
Clearance and recall counts are SPEQ’s aggregation of FDA’s own published records for the code. A recall count is a measure of history, not of a device’s safety, neither number is an FDA rating, and SPEQ is not affiliated with the FDA.
What this classification requires
Every statement below is FDA’s own classification record for this product code, quoted from the device classification field reference. The SPEQ reading beneath each one is SPEQ’s interpretation of what it means for the work, and is not FDA text.
510(K)
SPEQ READINGA premarket notification under 21 CFR Part 807 Subpart E is the route for this code. The submission argues substantial equivalence to a legally marketed predicate, so the engineering question is which predicate the design can actually be compared against, and the evidence question is what testing supports that comparison.
Class II (moderate to high risk): general controls and special controls
SPEQ READINGSpecial controls are the distinguishing feature: performance standards, guidance documents, patient registries or postmarket surveillance named for the device type. Find the special controls before designing the test plan — they usually name the tests.
Device is not eligible for The Voluntary Malfunction Summary Reporting Program
SPEQ READINGIndividual malfunction reporting under 21 CFR Part 803 applies. The complaint-handling system has to produce a decision per event, not per period.
Not exempt due to Good Manufacturing Practice (GMP)/Quality System
SPEQ READINGThe full Quality Management System Regulation (21 CFR 820, incorporating ISO 13485:2016) applies, and is what an FDA inspection of this device will be conducted against.
Device is not implantable
SPEQ READINGNo implant-specific expectations attach from the classification itself.
Device is not used for life sustaining purposes
SPEQ READINGNo life-sustaining designation attaches to this code.
Device is not a candidate for a third party review program
SPEQ READINGFDA reviews any premarket notification for this code directly.
Other product codes under 21 CFR 892.2090
One regulation usually governs several product codes. These share this code’s regulation, and therefore its classification basis.
21 CFR 892.2090 — Radiological computer-assisted detection and diagnosis software
The classification regulation for this product code, as published by the Office of the Federal Register. This is the regulation text itself — no SPEQ interpretation appears in it.
(a) Identification. A radiological computer-assisted detection and diagnostic software is an image processing device intended to aid in the detection, localization, and characterization of fracture, lesions, or other disease-specific findings on acquired medical images ( e.g., radiography, magnetic resonance, computed tomography). The device detects, identifies, and characterizes findings based on features or information extracted from images, and provides information about the presence, location, and characteristics of the findings to the user. The analysis is intended to inform the primary diagnostic and patient management decisions that are made by the clinical user. The device is not intended as a replacement for a complete clinician's review or their clinical judgment that takes into account other relevant information from the image or patient history.
(b) Classification. Class II (special controls). The special controls for this device are: (1) Design verification and validation must include: (i) A detailed description of the image analysis algorithm, including a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable.
(iii) Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures ( e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contains a sufficient number of cases from important cohorts ( e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.
(iv) Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures ( e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in paragraph (b)(1)(iii) of this section.
(v) Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures.
(2) Labeling must include the following: (i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the device instructions for use, including the intended reading protocol and how the user should interpret the device output.
(iii) A detailed description of the intended user, and any user training materials or programs that address appropriate reading protocols for the device, to ensure that the end user is fully aware of how to interpret and apply the device output.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level ( e.g., poor image quality or for certain subpopulations), as applicable.
(vii) A detailed summary of the performance testing, including test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment. [90 FR 24970, June 13, 2025]
Source: Electronic Code of Federal Regulations, Title 21. 21 CFR is a work of the United States Government and is in the public domain. SPEQ read this section on October 22, 2026. CFR is amended continuously, so verify against the source before acting on it: read 21 CFR 892.2090 on eCFR →
Recent clearances
Source: FDA device classification, 510(k)/PMA, and recall datasets. Recall entries count distinct FDA recall numbers, so one recall event affecting several products appears as several entries. Browse every product code SPEQ tracks →