Search Results

The KODAK DIGITAL SCIENCE (KDS) Medical Image and Information Library is a DICOM conformant archival product designed for use within a Picture Archiving and Communication System (PACS). The system utilizes a prefetching Archiving and Sommercess to previous imagery.

The system consists of three main components: 1) the host CPU (a Sun SPARCstation 5, SPARCstation 20, or UltraSparc) with pre-installed proprietary software, 2) the magnetic cache, a Sun SPARC storage Redundant Array of Inexpensive Disks (RAID) subsystem, and 3) a KODAK DIGITAL SCIENCE ADL 150 writeable CD jukebox or a Digital Linear Tape (DLT) library. The RAID subsystem, combined with either the CD jukebox or DLT library, are managed by a hierarchical storage library that presents these two subsystems to the system software running on the host CPU as a single, very large filesystem. The power supply provides power to operate the unit from a line voltage for 100, 120, 220, or 240 VAC, 50/60 Hz.

This document is a 510(k) summary for a PACS Storage Device, specifically the KODAK DIGITAL SCIENCE™ (KDS) Medical Image and Information Library (MIL). It primarily focuses on the device's technological specifications and its substantial equivalence to a predicate device, rather than clinical performance or studies involving human expertise. Therefore, many of the requested categories are not applicable or cannot be answered from the provided text.

Here's an breakdown based on the information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implied by the comparison to the predicate device and the FDA's "substantial equivalence" determination. The performance is described in terms of technical specifications.

Study Information:

The provided document describes a 510(k) premarket notification process, which determines "substantial equivalence" to a predicate device rather than conducting a new clinical study to establish performance against acceptance criteria in the same way a device with clinical efficacy claims would. The "study" here is essentially the comparison of features and technical specifications to the predicate device.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Not applicable. This is a technical comparison of device features, not a clinical study involving a test set of data or patient cases.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. No ground truth based on expert review or clinical data was established for a test set.

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

Not applicable. No test set requiring expert adjudication was used.

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 storage and archival system, not an AI-powered diagnostic or assistive tool for human readers.

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

Not applicable. This device is a PACS storage and archival system; it does not contain a standalone algorithm for diagnostic or analytical performance that would be evaluated in this manner.

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

Not applicable. The "truth" in this context is the technical compliance with DICOM standards and the stated technical specifications, and the functional equivalence to the predicate device. This is assessed by comparing technical documentation and features.

8. The sample size for the training set

Not applicable. This device does not use machine learning or AI that would require a training set.

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

Not applicable. As there is no training set, there is no ground truth for it.

Ask a specific question about this device

The KODAK DIGITAL SCIENCE (KDS) Medical Image and Information Library is a DICOM conformant archival product designed for use within a Picture Archiving and Communication System (PACS). The system utilizes a prefetching algorithm to facilitate fast access to previous imagery.

The system consists of three main components: 1) the host CPU (a Sun SPARCstation 5 or 20) with pre-installed propriatary software, 2) the magnetic BI ARCORACIP S OF 201 orage Array RAID subsystem), and 3) a KODAK DIGITAL SCIENCE ADL 150 writeable CD jukebox. The RAID subsystem and the CD jukebox are managed by a hierarchical storage library that presents these two subsystems to the system software running on the host CPU as a single, very large filesystem. The power supply Provides power to operate the unit from a line voltage for 100, 120, 220, or 240 VAC, 50/60 Hz.

This 510(k) notification describes a medical image and information library system, not a device that would typically have performance-based acceptance criteria in the context of diagnostic accuracy or equivalent studies. The device is for image storage, distribution, and archival within a Picture Archiving and Communication System (PACS). As such, the "performance" here relates to its technical specifications and functionality as an archival system, rather than clinical efficacy as a diagnostic tool.

Therefore, the requested information elements (1-9) such as reported device performance, sample sizes for test sets, expert involvement, MRMC studies, standalone performance, and ground truth establishment are not applicable in the traditional sense for this type of device submission.

The "acceptance criteria" for a device like the KODAK DIGITAL SCIENCE Medical Image and Information Library would primarily revolve around its technical specifications, compliance with standards (like DICOM), and functional equivalence to its predicate device. The "study" proving it meets these criteria would be the technical documentation and comparison provided in the 510(k) submission itself.

However, I can extract information related to its technical specifications and comparison to the predicate device, which serves as its form of "performance" and "acceptance criteria" in this context.

Here's an interpretation based on the provided text, modified to fit the context of an archival imaging system:

1. Table of Acceptance Criteria (Technical Specifications) and Reported Device Performance

Note regarding "Acceptance Criteria": For this type of device, the "acceptance criteria" are largely based on demonstrating substantial equivalence to a predicate device and meeting technical specifications and industry standards (e.g., DICOM conformance). The provided "predicate device" column serves as the benchmark against which the new device is compared.

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

• Not Applicable. This device is an image storage and retrieval system. It does not perform diagnostic functions that require a "test set" of patient data in the sense of clinical evaluation for accuracy. The "testing" would involve functional tests, performance benchmarks (e.g., retrieval speed, storage capacity verification), and DICOM conformance tests, not clinical data sets for diagnostic performance.

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

• Not Applicable. See point 2. No ground truth establishment related to diagnostic accuracy is relevant for an image archival system.

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

• Not Applicable. See point 2.

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 an archival system, not an AI-assisted diagnostic tool.

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

• Not Applicable. This device is an infrastructure component (archival system), not an algorithm that performs a standalone task requiring performance evaluation against human interpretation. Its standalone "performance" would relate to its technical specifications for storage, retrieval, and DICOM conformance.

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

• Not Applicable. See point 2. The "ground truth" for this device would be its adherence to technical specifications, proper functioning of its storage and retrieval mechanisms, and compliance with DICOM standards, all verified through engineering and functional testing.

8. The sample size for the training set:

• Not Applicable. This device is a hardware/software system for image archiving, not a machine learning model that requires a "training set."

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

• Not Applicable. See point 8.

Ask a specific question about this device