(22 days)
NOVA View® Automated Fluorescence Microscope is an automated system consisting of a fluorescence microscope and software that acquires, analyzes, stores and displays digital images of stained indirect immunofluorescent slides. It is intended as an aid in the detection and classification of certain antibodies by indirect immunofluorescence technology. The device can only be used with cleared or approved in vitro diagnostic assays that are indicated for use with the device. A trained operator must confirm results generated with the device.
NOVA View is an automated fluorescence microscope that acquires, analyses, stores and displays digital images of stained indirect immunofluorescent slides.
The NOVA View AUTOLoader is an optional hardware accessory that performs the automated transfer of slide carriers to and from NOVA View, thereby providing a continuous load capability without human interaction.
AUTOLoader hardware components consist of a NOVA View alignment base, 3-position stack base, 3 slide carrier stacks (labelled as "Pending", "Completed", "and "Error"), telescoping arm with rotary gripper, and a 2D barcode scanner station. The AUTOLoader can be connected to up to two NOVA View devices.
Below is a summary of the acceptance criteria and study information for the NOVA View® Automated Fluorescence Microscope with AUTOLoader, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The provided document describes the NOVA View® Automated Fluorescence Microscope with AUTOLoader as an aid in the detection and classification of certain antibodies. It explicitly states that "A trained operator must confirm results generated with the device." This implies that the device is not intended as a standalone diagnostic tool, but rather as one that assists a human expert.
Given this context and the fact that this is a Special 510(k) submission for the addition of an AUTOLoader, the primary focus of the performance evaluation appears to be on demonstrating that the addition of the AUTOLoader does not negatively impact the existing functionality of the NOVA View, and that the AUTOLoader itself performs its mechanical tasks reliably and accurately.
The document does not provide specific numerical acceptance criteria or performance metrics (e.g., sensitivity, specificity, accuracy) for the diagnostic performance of the device (i.e., its ability to correctly detect and classify antibodies). Instead, it focuses on demonstrating that the system functionality is maintained with the addition of the AUTOLoader.
Acceptance Criteria for AUTOLoader Functionality (Inferred):
| Acceptance Criteria Category | Reported Device Performance |
|---|---|
| Software Functionality | New AUTOLoader module interfaces correctly with NOVA View software (version 2.1.4). Regression testing performed to confirm no change in NOVA View functionality. |
| Automated Slide Handling (Loading) | AUTOLoader successfully picks up slide carriers from "Pending" stack and places them on NOVA View stage. |
| Automated Barcode Scanning | AUTOLoader captures images of barcodes on slides. |
| Automated Slide Handling (Unloading) | AUTOLoader successfully picks up slide carriers after scanning and transports them to "Completed" or "Error" stack. |
| Continuous Load Capability | AUTOLoader provides continuous load capability without human interaction for multiple carriers. |
| Integration with NOVA View | AUTOLoader connects to NOVA View and operates as an integrated system. |
Note on Diagnostic Performance: The document explicitly states "Analytical performance characteristics n/A" and "Clinical performance n/a." This indicates that this specific submission for the AUTOLoader addition did not involve new analytical or clinical performance studies related to the diagnostic accuracy of antibody detection. The regulatory submission leverages the established performance of the predicate device (NOVA View without AUTOLoader) for its core diagnostic function.
2. Sample Size Used for the Test Set and Data Provenance
The document does not specify a distinct "test set" in the traditional sense for evaluating diagnostic performance. The studies described are focused on the functionality of the AUTOLoader and the integration of new software.
- Software Testing: "All new functions were tested during software verification, and regression testing has been performed to demonstrate that NOVA View functionality has not changed." The sample size (number of test cases, scenarios, etc.) for this software testing is not provided.
- AUTOLoader Mechanical Testing: "This procedure [automated handling of slide carriers] is automatically repeated with the rest of the carriers that are in the Pending stack." While the number of carriers per stack (up to 12) is mentioned, the total number of carriers or repeated cycles used for testing the AUTOLoader's mechanical reliability is not specified.
- Data Provenance: Not explicitly stated as the primary focus is on system functionality and software verification rather than a clinical dataset.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
Not applicable in the context of the studies described. The "ground truth" here pertains to the correct functioning of the AUTOLoader and software, which is evaluated through verification and validation processes rather than expert clinical consensus on diagnostic outcomes. The device's diagnostic "results" still require confirmation by a "trained operator."
4. Adjudication Method for the Test Set
Not applicable for the described functionality and software verification testing.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, If So, What Was the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance
No MRMC comparative effectiveness study is mentioned. The device is intended as an "aid" for a "trained operator" who "must confirm results." This inherently suggests a human-in-the-loop system, but a formal study comparing human performance with and without the device's assistance is not part of this specific submission.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
No standalone performance study is explicitly described. The "Indications for Use" clearly state that "A trained operator must confirm results generated with the device," indicating it's not a standalone diagnostic device.
7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)
For the specific performance studies described in this submission (AUTOLoader addition):
- Functional 'Ground Truth': The expected operational behavior of the AUTOLoader (e.g., correctly picking up, barcode scanning, placing carriers) and the established, unchanged functionality of the NOVA View software. This is assessed through engineering and software verification standards rather than clinical ground truth types.
8. The Sample Size for the Training Set
Not applicable. The document does not describe any machine learning or AI components that would require a "training set" in the conventional sense for diagnostic algorithm development. The software update is for controlling the AUTOLoader and preserving existing NOVA View functionality.
9. How the Ground Truth for the Training Set Was Established
Not applicable, as no training set for AI/ML was mentioned.
§ 866.4750 Automated indirect immunofluorescence microscope and software-assisted system.
(a)
Identification. An automated indirect immunofluorescence microscope and software assisted system is a device that acquires, analyzes, stores, and displays digital images of indirect immunofluorescent slides. It is intended to be used as an aid in the determination of antibody status in clinical samples. The device may include a fluorescence microscope with light source, a motorized microscope stage, dedicated instrument controls, a camera, a computer, a sample processor, or other hardware components. The device may use fluorescent signal acquisition and processing software, data storage and transferring mechanisms, or assay specific algorithms to suggest results. A trained operator must confirm results generated with the device.(b)
Classification. Class II (special controls). The special controls for this device are:(1) The labeling for the device must reference legally marketed assays intended for use with the device.
(2) Premarket notification submissions must include the following information:
(i) A detailed description of the device that includes:
(A) A detailed description of instrumentation and equipment, and illustrations or photographs of non-standard equipment or methods, if applicable;
(B) Detailed documentation of the software, including, but not limited to, stand-alone software applications and hardware-based devices that incorporate software, if applicable;
(C) A detailed description of appropriate internal and external quality controls that are recommended or provided. The description must identify those control elements that are incorporated into the recommended testing procedures;
(D) Detailed description and specifications for sample preparation, processing, and storage, if applicable;
(E) Methodology and protocols for detecting fluorescence and visualizing results; and
(F) Detailed specification of the criteria for test results interpretation and reporting.
(ii) Data demonstrating the performance characteristics of the device, which must include:
(A) A comparison study of the results obtained with the conventional manual method (
i.e., reference standard), the device, and the reading of the digital image without aid of the software, using the same set of patient samples for each. The study must use a legally marketed assay intended for use with the device. Patient samples must be from the assay-specific intended use population and differential diagnosis population. Samples must also cover the assay measuring range, if applicable;(B) Device clinical performance established by comparing device results at multiple U.S. sites to the clinical diagnostic standard used in the United States, using patient samples from the assay-specific intended use population and the differential diagnosis population. For all samples, the diagnostic clinical criteria and the demographic information must be collected and provided. Clinical validation must be based on the determination of clinical sensitivity and clinical specificity using the test results (
e.g., antibody status based on fluorescence to include pattern and titer, if applicable) compared to the clinical diagnosis of the subject from whom the clinical sample was obtained. The data must be summarized in tabular format comparing the result generated by automated, manual, and digital only interpretation to the disease status;(C) Device precision/reproducibility data generated from within-run, between-run, between-day, between-lot, between-operator, between-instruments, between-site, and total precision for multiple nonconsecutive days (as applicable) using multiple operators, multiple instruments and at multiple sites. A well-characterized panel of patient samples or pools from the associated assay specific intended use population must be used;
(D) Device linearity data generated from patient samples covering the assay measuring range, if applicable;
(E) Device analytical sensitivity data, including limit of blank, limit of detection, and limit of quantitation, if applicable;
(F) Device assay specific cutoff, if applicable;
(G) Device analytical specificity data, including interference by endogenous and exogenous substances, if applicable;
(H) Device instrument carryover data, if applicable;
(I) Device stability data including real-time stability under various storage times and temperatures, if applicable; and
(J) Information on traceability to a reference material and description of value assignment of calibrators and controls, if applicable.
(iii) Identification of risk mitigation elements used by the device, including description of all additional procedures, methods, and practices, incorporated into the directions for use that mitigate risks associated with testing.
(3) Your 21 CFR 809.10 compliant labeling must include:
(i) A warning statement that reads “The device is for use by a trained operator in a clinical laboratory setting”;
(ii) A warning statement that reads “All software-aided results must be confirmed by the trained operator”;
(iii) A warning statement that reads “This device is only for use with reagents that are indicated for use with the device”; and
(iv) A description of the protocol and performance studies performed in accordance with paragraph (b)(2)(ii) of this section and a summary of the results, if applicable.