Browse hierarchy: [Radiology (RA)](/submissions/RA) → [Subpart B — Diagnostic Devices](/submissions/RA/subpart-b%E2%80%94diagnostic-devices) → [21 CFR 892.2055](/submissions/RA/subpart-b%E2%80%94diagnostic-devices/892.2055) → QVD — Radiological Machine Learning Based Quantitative Imaging Software With Change Control Plan

# QVD · Radiological Machine Learning Based Quantitative Imaging Software With Change Control Plan

_Radiology · 21 CFR 892.2055 · Class 2_

**Canonical URL:** https://fda.innolitics.com/submissions/RA/subpart-b%E2%80%94diagnostic-devices/QVD

## Overview

- **Product Code:** QVD
- **Device Name:** Radiological Machine Learning Based Quantitative Imaging Software With Change Control Plan
- **Regulation:** [21 CFR 892.2055](/submissions/RA/subpart-b%E2%80%94diagnostic-devices/892.2055)
- **Device Class:** 2
- **Review Panel:** [Radiology](/submissions/RA)

## Identification

Radiological machine learning-based quantitative imaging software with predetermined change control plan. It is a software-only device that employs machine learning algorithms on radiological images to provide quantitative imaging outputs, including functions to support view selection, segmentation, and landmarking. The Caption Interpretation Automated Ejection Fraction software is used to process previously acquired transthoracic cardiac ultrasound images, to store images, and to manipulate and make measurements on images using an ultrasound device, personal computer, or a compatible DICOM-compliant PACS system in order to provide automated estimation of left ventricular ejection fraction in adult patients.

## Classification Rationale

Class II (special controls). The device is classified as Class II because the probable benefits of the device, including the capability to improve performance through iterative modifications via a predetermined change control plan (PCCP), outweigh the risks, and the risks can be mitigated by the use of general controls and the identified special controls.

## Special Controls

In combination with the general controls of the FD&C Act, the radiological machine learningbased quantitative imaging software with predetermined change control plan is subject to the following special controls:

- Design verification and validation must include: (1)
	- A detailed description of the image postprocessing algorithms, including a detailed (i) description of the algorithm inputs and outputs, each major component or block, and algorithm limitations.
	- Detailed description of training data including detailed annotation methods and (ii) important cohorts (e.g., subsets defined by patient demographics, clinically relevant confounders, and subsets defined by image acquisition characteristics).
	- (iii) Performance testing protocols and results that demonstrate that the underlying algorithms function as intended. The performance assessment must be based on objective performance measures (e.g., error metrics, Bland-Altman plots, dice similarity coefficient (DSC), Hausdorff distance, sensitivity, specificity, predictive value). The test dataset must be independent from data used in training/development and contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant diseases, 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) Software verification, validation, and hazard analysis.
- (2) As part of the design verification and validation activities, you must document the planned device modifications of the quantitative imaging software, and the associated methodology for the development, verification, and validation of modifications made consistent with the performance requirements in the plan.
- As part of the risk management activities, you must identify and assess the risks of the (3) planned modification(s) and identify corresponding risk mitigations.
- (4) Labeling must include:
- A detailed description of the patient population for which the device was validated; (i)
- (ii) A description of the intended user and expertise needed for safe use of the device:
- A detailed description of the device inputs and outputs; (iii)
- A detailed description of compatible imaging hardware and imaging protocols; (iv)
- A detailed summary of the current performance of the device and a summary of the (v) performance testing conducted to support safe and effective use of the device including test methods, dataset characteristics (including demographics), testing environment, results (with confidence intervals), and a summary of sub-analyses on case distributions stratified by relevant confounders:
- (vi) A description of 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) Labeling related to the predetermined change control plan (PCCP), including:
	- (A) A statement that the device has a PCCP:
	- (B) A description of modification(s) implemented for quantitative imaging and supporting algorithms, including a summary of current performance, associated inputs, validation requirements, and related evidence: and
	- A version history, a description of how device modification(s) will be (C) implemented, and a description of how users will be informed of device modification(s) made in accordance with the PCCP.

## Recent Cleared Devices (1 of 1)

| Record | Device Name | Applicant | Decision Date | Decision |
| --- | --- | --- | --- | --- |
| [DEN220063](https://fda.innolitics.com/submissions/RA/subpart-b%E2%80%94diagnostic-devices/QVD/DEN220063.md) | Caption Interpretation Automated Ejection Fraction Software | Caption Health, Inc. | Feb 24, 2023 | DENG |

## Top Applicants

- Caption Health, Inc. — 1 clearance

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**Source:** [https://fda.innolitics.com/submissions/RA/subpart-b%E2%80%94diagnostic-devices/QVD](https://fda.innolitics.com/submissions/RA/subpart-b%E2%80%94diagnostic-devices/QVD)

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