Search Results

The NDOHD system is intended for viewing, acquiring, archiving and retrieving video and still images of endoscopic and fluoroscopic procedures. The professionals or practitioners using this system would be medical doctors or clinicians such as speech pathologists. The device is a prescription device. The NDOHD system is not intended to be used in an environment that requires sterilization.

The NDOHD High Definition Imaging System (NDOHD) was initially commercialized in 2011 as a photographic accessory for endoscopes (FEM), Class I Exempt device. Altaravision has expanded the capabilities of the NDOHD system to include a computer and a camera, included a lossy image compression mechanism using standard irreversible compression technique, H.264, added a time code on the display of the image, created camera controls and added profiles for multiple camera settings and user preferences. Therefore, Altaravision has created a picture archiving and communication system that provides capabilities related to the acceptance, transfer, display, storage and digital processing of images and videos.

Here's an analysis of the provided 510(k) summary regarding the Altaravision NDOHD High Definition Imaging System, structured according to your requested points:

This device is a Picture Archiving and Communications System (PACS) and underwent a 510(k) submission, classifying it as a Class II medical device. The 510(k) submission primarily focuses on demonstrating substantial equivalence to a predicate device rather than conducting a separate clinical study to prove novel performance against specific acceptance criteria.

Therefore, there is no explicit table of acceptance criteria and reported device performance in the same manner one would expect for a diagnostic AI device or a device requiring new efficacy claims. The "acceptance criteria" for a 510(k) of this nature are implicitly met by demonstrating substantial equivalence through technical and functional comparisons, and adherence to relevant standards.

1. Table of Acceptance Criteria and Reported Device Performance

As noted, this 510(k) is for a PACS device demonstrating substantial equivalence, not a new AI diagnostic device with specific performance metrics like sensitivity or specificity. Thus, a traditional table of "acceptance criteria" and "reported device performance" in terms of clinical accuracy is not provided in the document.

Instead, the "acceptance criteria" are implied by conformance to international and FDA standards, and the "reported performance" is essentially the device's functional capabilities compared to a predicate device.

2. Sample Size Used for the Test Set and the Data Provenance

This document does not describe a clinical study with a "test set" of patient data in the typical sense (e.g., a set of medical images for diagnostic performance evaluation). The testing described relates to software validation and electrical safety, which are engineering and quality assurance activities, not clinical performance studies using patient data.

• Sample Size for Test Set: Not applicable/not specified for clinical performance. The testing involved functional and safety assessments of the device itself.
• Data Provenance: Not applicable, as no external patient data test set was used for performance claims.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

Not applicable. No ground truth establishment by experts for a test set of patient data is mentioned because this is a PACS device whose 510(k) focused on substantial equivalence through functional and safety testing, not diagnostic performance.

4. Adjudication Method (e.g., 2+1, 3+1, none) for the Test Set

Not applicable. No patient data test set requiring expert adjudication was described.

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

Not applicable. This device is a PACS for viewing, acquiring, archiving, and retrieving images. It is not an AI-assisted diagnostic tool, and therefore, an MRMC study comparing human reader performance with and without AI assistance was not conducted or mentioned.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Not applicable. The NDOHD system is a human-in-the-loop system (a PACS) for medical professionals. It does not contain a standalone AI algorithm for diagnostic interpretation in the way one might evaluate AI performance for, say, lesion detection.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

Not applicable. For the software validation and electrical safety tests, the "ground truth" would be the specifications, requirements, and industry standards themselves, rather than clinical ground truth from patient data.

8. The Sample Size for the Training Set

Not applicable. This document is for a medical device (PACS) that does not describe an AI algorithm explicitly trained on a dataset for clinical performance.

9. How the Ground Truth for the Training Set was Established

Not applicable, as no AI training set is described.

Ask a specific question about this device