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UniWeb™ is a device that receives digital images and data from various sources (including but not limited to digital mammography, MR scanners, ultrasound systems, CT Scanners, R/F Units, computed & direct radiographic devices, secondary capture devices, scanners, imaging gateways or other imaging sources). Images and data can be stored, communicated, processed and displayed within the system and or across a computer network at distributed locations.

UniWeb™ will be used to display FFDM (Full Field Digital Mammography) postprocessed images in DICOM for presentation only formats.

UniWeb™ will NOT utilize digitized film images, only images from FFDM modalities.

UniWeb™ will be utilized for interpretation of mammography images in a primary diagnostic setting.

UniWeb™ will be utilized only on FDA approved monitors used for mammography.

Typical users of this device are trained professionals, including but not limited to physicians, nurses and technicians.

This product will only use lossless compression or no compression when displaying mammography images.

For image diagnosis, this device can only be used by trained professionals.

UniWeb™ is a device that receives digital images and data from various sources (including but not limited to digital mammography, MR scanners, ultrasound systems, CT Scanners, R/F Units, computed & direct radiographic devices, secondary capture devices, scanners, imaging gateways or other imaging sources). Images and data can be stored, communicated, processed and displayed within the system and or across a computer network at distributed locations.

Here's an analysis of the provided text regarding the acceptance criteria and study for the UniWeb™ device:

This document is a marketing clearance letter for a Picture Archiving and Communications System (PACS) named UniWeb™, version 5.0. It is not a detailed study report or clinical trial summary. Therefore, much of the requested information regarding detailed acceptance criteria and a study proving performance against them is not present in this specific document.

The document primarily focuses on the device's substantial equivalence to a legally marketed predicate device, its intended use, and regulatory compliance. It does not contain primary data from performance studies.

However, based on the "Indications for Use Statement," we can infer some operational acceptance criteria for the device itself and constraints on its use, rather than strict performance metrics.

Acceptance Criteria and Device Performance (Inferred from "Indications for Use")

Missing Information and Explanation:

The provided document (an FDA 510(k) clearance letter) does not contain the following information:

1. Sample size used for the test set and the data provenance: This document does not describe a specific test set or clinical study data. The 510(k) pathway often relies on demonstrating substantial equivalence to a predicate device, which may involve engineering benchmarks rather than extensive clinical efficacy trials.
2. Number of experts used to establish the ground truth for the test set and their qualifications: Not applicable, as detailed ground truth establishment for a specific test set is not described.
3. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable, as a test set and ground truth adjudication are not described.
4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and its effect size: Not described. PACS systems typically demonstrate equivalence for image display and handling rather than comparative diagnostic performance studies like those for AI algorithms.
5. If a standalone performance (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable. This device is a PACS for display and management of images. It is not an AI algorithm performing diagnosis independently.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable, as detailed ground truth is not described.
7. The sample size for the training set: Not applicable. This PACS is not an AI/ML device that requires a training set in the conventional sense. Its "training" would be more akin to software development and testing based on DICOM standards and image processing algorithms.
8. How the ground truth for the training set was established: Not applicable, as it's not an AI/ML device with a training set.

Summary regarding the "study that proves the device meets the acceptance criteria":

The provided text does not describe a specific study that proves the UniWeb™ device meets detailed performance acceptance criteria in the manner one would expect for a diagnostic AI algorithm. Instead, the FDA's 510(k) clearance process determined that the device is "substantially equivalent" to legally marketed predicate devices for its stated "Indications for Use." This equivalence typically relies on demonstrating that the device performs as intended and introduces no new safety or effectiveness concerns compared to existing devices. The "Indications for Use" statement implicitly defines the scope within which the device is deemed safe and effective based on this equivalence.

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UniSight™ system with UniGate™ is a device that receives digital images and data from various sources (including but not limited to CT scanners, MR scanners, ultrasound systems, R/F Units, computed & direct radiographic devices, secondary capture devices, scanners, imaging gateways or other imaging sources). Images and data can be stored, communicated, processed and displayed within the system and or across computer network at distributed locations. Typical users of this system are trained professionals, including but not limited to physicians, nurses and technicians.

Not Found

The provided FDA document (K023394) is a 510(k) clearance letter for the "EBM UniSight™ System with UniGate™". This document is a regulatory approval letter and does not contain any information regarding acceptance criteria or a study proving the device meets acceptance criteria.

The document states that the device receives digital images and data from various sources, and images and data can be stored, communicated, processed, and displayed. The nature of the device (Picture Archiving and Communications System - PACS) suggests that its performance would typically be evaluated based on:

• Image fidelity and integrity: Ensuring images are not corrupted or degraded during transmission, storage, and display.
• Data integrity: Ensuring patient data and image metadata are accurately preserved.
• Communication speed and reliability: How quickly and consistently images and data can be moved within the system and across networks.
• Display accuracy: Whether the displayed images accurately represent the original acquired images (e.g., resolution, contrast, brightness).
• System uptime and reliability.
• Compliance with imaging standards (e.g., DICOM).

However, the provided text does not include any of the requested information, such as:

• A table of acceptance criteria and reported device performance.
• Sample sizes for test sets or training sets.
• Data provenance (country of origin, retrospective/prospective).
• Number or qualifications of experts.
• Adjudication methods.
• Results of MRMC comparative effectiveness studies.
• Standalone performance studies.
• Types of ground truth used or how ground truth was established for either test or training sets.

Therefore,Based on the information provided in the given document, I cannot answer the question. The document is an FDA 510(k) clearance letter for a Picture Archiving and Communications System (PACS) and does not detail the acceptance criteria or a study proving the device meets those criteria.

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