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The NDOHD system is intended for viewing, acquiring, recording, 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 NDOнd High Definition Imaging System (NDOнD) previously acquired FDA clearance in 2013 as a Class II picture archiving and communication system that provides capabilities related to the acquisition, transfer, display, storage, and digital processing of images and videos (K131873). Altaravision replaced off the shelf hardware components due to the hardware becoming obsolete. Altaravision also updated the NDOHD software to enable use of the new hardware, improve the software user interface and conform to Apple's latest coding best practices.

The provided text is a 510(k) Summary for the Altaravision, Inc. NDŌHD High Definition Imaging System (NDOHD). This submission is for a modification to an already cleared device (K131873), primarily due to hardware obsolescence and software updates. It is a Picture Archiving Communications System (PACS) device.

Key takeaway: This is a 510(k) submission for an updated version of a previously cleared PACS device. The core of the submission is to demonstrate substantial equivalence to the predicate device, not to prove clinical efficacy through a new clinical trial against defined acceptance criteria for a novel AI algorithm.

Therefore, many of the typical questions regarding acceptance criteria for AI/ML devices and their associated studies (e.g., sample size for test sets, number of experts, adjudication methods, MRMC studies, standalone performance, ground truth for training) are not directly applicable or explicitly detailed in this type of submission, as the device is not an AI/ML diagnostic or assistive tool in the way that those questions imply. The testing described is primarily for software verification, system validation, and electrical safety, ensuring the new hardware and software function as intended and are as safe and effective as the predicate device.

However, I will extract relevant information where possible and explicitly state when a requested piece of information is not present or applicable.

Acceptance Criteria and Study to Prove Device Meets Criteria

Given that this is a 510(k) for an updated PACS system, the "acceptance criteria" are primarily related to demonstrating substantial equivalence to the predicate device (NDŌHD High Definition Imaging System, K131873) in terms of intended use, indications for use, technological characteristics, and safety and effectiveness. The "study" is the collection of verification and validation testing performed.

1. Table of acceptance criteria and the reported device performance:

Since this is a substantial equivalence submission for a PACS device update, the "acceptance criteria" are not presented as numerical performance targets (e.g., sensitivity, specificity) for a diagnostic task. Instead, they are implied through direct comparison to the predicate device across various characteristics and verification/validation testing. The performance is essentially reported as "equivalent" or "identical" to the predicate, with differences not affecting intended use, safety, or effectiveness.

2. Sample sized used for the test set and the data provenance:

• Sample Size for Test Set: This information is not provided in the summary. For a PACS system, validation testing would involve testing functionalities (acquisition, storage, display, retrieval, etc.) with various typical image/video files, but specific "test set" sizes for clinical evaluation (like for an algorithm) are not explicitly mentioned.
• Data Provenance: Not applicable in the context of this submission, as it's not evaluating clinical data for diagnoses made by the system. The system handles endoscopic and fluoroscopic procedure videos and images.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• This information is not applicable as the device is a PACS system for managing images, not an AI diagnostic tool requiring expert-established ground truth for performance evaluation of a clinical task.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

• This information is not applicable for the reasons stated above.

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, an MRMC comparative effectiveness study was not performed. This device is a PACS system, not an AI assistant intended to improve human reader performance.

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

• A "standalone" performance evaluation in the context of a diagnostic algorithm is not applicable. The device's performance relates to its ability to acquire, store, display, and retrieve images/videos correctly and safely, which was evaluated through verification and validation (V&V) testing, not through standalone diagnostic accuracy.

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

• "Ground truth" in the diagnostic sense (e.g., pathology for cancer detection) is not applicable to this PACS system. The V&V testing would establish that functionalities (e.g., image integrity, compression, display fidelity, storage/retrieval accuracy) meet their specifications, rather than clinical ground truth.

8. The sample size for the training set:

• This device is not an AI/ML algorithm that is "trained" on a dataset in the typical sense. Therefore, a "training set" sample size is not applicable. The software was updated and developed following coding best practices, and subjected to verification and validation.

9. How the ground truth for the training set was established:

• As the device is not an AI/ML algorithm with a "training set," this question is not applicable.

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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.

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