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The Samsung RAYPAX™ Archive will be used to store & retrieve digital medical images and information about the images. The typical users are trained medical professionals.

The Samsung LTA can be part of RAYPAX PACS or can be a separate device for other manufacturer's PACS. It is used to store & retrieve digital medical images and information about the images using the DICOM 3.0 communication standard. RAYPAX LTA differs from some other PACS systems by having an additional storage unit management for medical images. The RAYPAX database stores and manages examination and patient information while the RAYPAX storage devices store and manage the medical images. To gather medical image data, RAYPAX uses DICOM 3. Using DICOM 3.0, medical-image producing equipment requests to store medical-image data in the DICOM gateway, which acts as the gateway to the Short Term Storage (STS). The DICOM gateway, after receiving such a request, stores the medical images in the STS. All the medical images that come into the STS are stored in the Long Term Archive. If there are images that need to be highly accessible, the LTA Manager transfers it to the STS. The RAYPAX system administrator sets this transferring authority.

The provided text describes a 510(k) Pre-Market Notification for the Samsung RAYPAX™ Long Term Archive (LTA) System, an archiving system for digital medical images. It focuses on regulatory submission and substantial equivalence to a predicate device, rather than performance studies of an AI-powered diagnostic device.

Therefore, the requested information regarding acceptance criteria, device performance, sample sizes for test and training sets, ground truth establishment, expert qualifications, adjudication methods, MRMC studies, or standalone algorithm performance, cannot be extracted from this document.

The document states:
"The 510(k) Pre-Market Notification for the LTA device contains adequate information and data to enable FDA - CDRH to determine substantial equivalence to the predicate device."
And also, "The LTA device does not contact the patient, nor does it control any life sustaining devices. A physician, providing ample opportunity for competent human intervention interprets images and information being printed."

This indicates that the focus of this 510(k) submission is on the safety and effectiveness of the archiving system itself, and its substantial equivalence to an existing predicate device (Olicon Imaging Systems Archive K973463), rather than on the diagnostic performance of an AI algorithm interpreting medical images.

No information is available in the provided text to populate the requested table and details.

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The EchoLive™ medical image management device family is designed for use in all medical specialties including, but not limited to, radiology, cardiology, gynecology, ENT, neurology, pediatrics, podiatry, chiropractic, general surgery, oral surgery and dentistry. The device may be used in medical offices and health care facilities, to facilitate access to clinical images and information, both archived and dynamic, in distributed locations over intranets, LANs, Internet, direct or dial dial-up telephone lines. The system may transmit a wide range of data including, but not limited to, Sonographs (Ultrasound images), Computer Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET). In addition the device may communicate standard audio, video and digital transmissions and recordings, providing complete clinical imaging handling, providing direct capture, retrieval, storage, and direct transmission and printing of images, reports, and patient information.

The EchoLive™ medical image management device family is a PACS capable of digital real-time simulcast transmission of medical images. Each model varies in the particular features and capabilities it offers the medical professional.

The provided document is a 510(k) summary for a PACS device (EchoLive™). It does not contain information about acceptance criteria or a study proving the device meets specific performance metrics.

510(k) submissions typically focus on demonstrating substantial equivalence to a legally marketed predicate device rather than presenting new clinical performance studies with specific acceptance criteria as would be common for novel, high-risk devices or AI/CAD systems requiring performance validation.

Therefore, I cannot provide the requested information from this document.

Here's why and what information is missing:

• Acceptance Criteria and Reported Device Performance: This document states the device is a "Medical Image Management Device" and a "PACS." Its function is to transmit, store, and manage medical images. For such a device, "performance" would relate to its ability to handle various image formats, transmission speeds, storage capabilities, and display quality – none of which are quantifiable with specific acceptance criteria or results in this summary.
• Sample Size for Test Set and Data Provenance: Not applicable as no performance study is described.
• Number of Experts and Qualifications: Not applicable.
• Adjudication Method: Not applicable.
• MRMC Comparative Effectiveness Study: Not applicable. This is a PACS, not an AI/CAD system designed to assist human readers in diagnosis.
• Standalone Performance Study: No standalone performance study is mentioned as this is a PACS, not an AI algorithm intended for diagnostic interpretation.
• Type of Ground Truth Used: Not applicable.
• Sample Size for Training Set: Not applicable.
• How Ground Truth for Training Set Was Established: Not applicable.

In summary, this 510(k) summary is for a Picture Archiving and Communication System (PACS), which is a device for managing and transmitting medical images. It relies on demonstrating substantial equivalence to existing PACS devices rather than presenting a clinical performance study with specific acceptance criteria as would be expected for a diagnostic AI device.

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The Sectra AB WISE II Image Management System device is intended for the management and displaying of x-ray images, other radiological objects and information. It can manage images from different modalities, single and multiple file servers, and interfaces to various Radiological Information Systems (RIS), image storage and printing devicesusing DICOM or similar interface standards.

WISE II is a system for managing digital radiological images. This includes image storage and searching for images in archives and retrieving image for re-consultation. WISE II generally provides functions to:

• Access information related to requests, examinations and images .
• Create, move, copy and delete folders and examination folders .
• Add images to and delete images from examinations .
• File examinations to the archives and retrieve examinations from the archives ●
• Retrieve examinations from DICOM conformant archives ●
• Manage images stored on multiple file servers
• Provide services (DICOM, WWW, etc) to clients via WISE gateways ●
• . Interface RIS for relational integrity
• . Send and retrieve images (teleradiology)

The provided text does not contain detailed information about specific acceptance criteria and a study proving a device meets these criteria in the way typically found in modern medical device submissions (e.g., an AI-powered diagnostic tool).

The document is a 510(k) summary for the "WISE II Image Management System," a device described as a "Digital Imaging System" for managing radiological images. The primary focus of this submission is to demonstrate substantial equivalence to a predicate device (WISE Image Management System, K971451), rather than proving specific performance metrics of an AI algorithm against a ground truth dataset.

Therefore, many of the requested details (like sample size for test sets, number of experts for ground truth, MRMC studies, standalone performance of an algorithm, and training set information) are not applicable or not available in this document.

Here's an attempt to answer the questions based only on the provided text, indicating where information is absent:

Acceptance Criteria and Study for Sectra WISE II Image Management System (K983447)

This 510(k) submission primarily focuses on demonstrating substantial equivalence to a predicate device (Sectra WISE Image Management System, K971451) for an image management system, not on proving the performance of an AI algorithm against specific clinical outcomes or established ground truth with quantified metrics. Therefore, many of the requested parameters related to AI performance studies are not present in this document.

1. A table of acceptance criteria and the reported device performance

The document does not specify quantitative acceptance criteria or reported performance metrics in the way one would for a diagnostic AI algorithm (e.g., sensitivity, specificity, AUC). Instead, the performance is described in terms of compliance with industry standards and safety regulations, and functional capabilities.

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

• Sample Size (Test Set): Not applicable / Not provided. This submission is for an image management system, not a diagnostic algorithm that would typically have a test set of medical images for performance evaluation.
• Data Provenance: Not applicable / Not provided.

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 / Not provided. The device manages images; it does not perform automated diagnoses requiring expert-established ground truth for a test set. The document notes that "Images and information being reviewed, processed, relayed, and or transmitted are interpreted by a physician or trained medical personnel, providing ample opportunity for competent human intervention."

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

• Not applicable / Not provided.

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. This is not an AI-assisted diagnostic device, but an image management system. Therefore, an MRMC study and related effect size are not applicable.

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

• No. This device's function is to manage and display images for human interpretation, not to provide standalone algorithmic diagnoses.

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

• Not applicable / Not provided. Ground truth is not a concept explicitly applied to the performance evaluation described for this type of device.

8. The sample size for the training set

• Not applicable / Not provided. This device is not an AI algorithm that undergoes a training phase with a labeled dataset.

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

• Not applicable / Not provided.

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The DICOM Archival Server (DAS)/DICOM IntraNET Service (DIS), software product, provides for the centralized management and storage of radiographic studies, replacing or augmenting the historical film-based processes.

The DICOM Archival Server (DAS) software product provides for the centralized management and storage of radiographic studies, replacing or augmenting the historical filmbased processes. The operating system utilizes Microsoft @ Windows NT @ Release 4.0 and later. This environment provides for all communication (i.e. TCP/IP), file storage (e.g. NTFS), security management, and display management processes. It also insulates the DAS from detail hardware management processes, allowing customers to make whatever choices in these areas best serve them. This environment supports a variety of back-up/restore processes which may be used at the customer's discretion. The Microsoft© SQL Server product is used as the primary management component for the archive contents and for responding to requests. whether they be in the form of SQL Server direct requests or via the DICOM Query/Retrieve process. This software-only product is fully DICOM-3.0 compliant for the following DICOM Service Classes: Verification (SCP), Storage (SCP and SCU), Query/Retrieve (SCP), Print (SCU), Patient Management (SCU), Study Management (SCU), Results Management (SCU). This product consists only of software and executes in a Windows© NT© environment on Intel© platforms. This product, when combined with appropriate server platform hardware and operating system, falls into the FDA proposed designation of a PACS system as Class II (Special Controls). The following attributes are noted: this product accepts, stores and transfers images; no image processing is done by this product; no compressed images are accepted for storage by this product; this product will supply compressed images as requested by client products, at the level of compression requested by those clients; no image viewing is provided by this product; the reliability of the product is dependent upon the hardware on which the software is installed, and is determined by the installation; disaster recovery attributes of an installation are dependent upon installationdetermined policies, and are totally under the control of the installation. The DAS product contains the following advanced features to facilitate rapid movement of images to desired targets within a PACS system: automatic image forwarding and automatic image printing. Automatic image forwarding enables a policy-managed forwarding process wherein each image received by the DAS will automatically be forwarded to one or more target workstations based upon the following image contents: modality station name, body part, referring physician, and modality type. Automatic image printing is an extension of the automatic forwarding process wherein the forwarding target is a DICOM Print Service Class Provider. The DICOM IntraNET Service (DIS) software product provides for centralized distribution of stored radiographic studies. DIS is an integrated client-server software system designed to allow secured access to radiographic images by licensed medical professionals. The software's server and client ends rely on off-the-shelf Windows NT software with compatible Intel hardware. The client end accesses images through a WEB Browser similar to Microsoft's Internet Explorer through a Query/Retrieve process. The DIS system becomes an extended Viewer attached to a secured internal network or an extended secured Intranet using TCP/IP communications protocol. Full resolution, lossless images are always available to the Client Browser. If lossy compression where used, similar to our equivalent products listed in item 10; Kodak, Base-Ten, or Autocyt, we would recommend that they be used for secondary viewing only and not for diagnostic interpretation. This DIS software is intended to provide the means for medical professionals to display data generated by medical scanning devices on a personal computer or workstation. Competent healthcare professionals would reasonably be expected to exercise judgment in use of this information.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Parameter Developments, DICOM Archival Server (DAS)/DICOM IntraNET Service (DIS):

It is important to note that the provided text is a 510(k) summary for a medical device (Picture Archiving and Communications System - PACS) and does not contain information about specific acceptance criteria or a dedicated performance study with a test set, ground truth, or expert readers in the way modern AI/ML device submissions typically do.

This document predates widespread use of AI/ML in medical imaging and focuses on demonstrating substantial equivalence to predicate devices based on functionalities, technical attributes, and safety/hazard analysis for a software-only product primarily concerned with image storage, retrieval, and management.

Therefore, much of the requested information (acceptance criteria in terms of metrics like sensitivity/specificity, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, standalone performance with metrics, and ground truth types) is not present in this document.

However, I can extract the available relevant information and explain why other requested details are missing.

Analysis of Acceptance Criteria and Study Information

1. Table of Acceptance Criteria and Reported Device Performance

As mentioned, this document does not present explicit, quantitative acceptance criteria that would typically be seen for a diagnostic AI/ML device (e.g., minimum sensitivity, specificity, AUC). Instead, the "acceptance criteria" are implicitly met by demonstrating substantial equivalence to predicate devices through functional comparison and adherence to DICOM standards and safety considerations.

The "reported device performance" is descriptive of its capabilities rather than quantitative performance against a specific clinical task.

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

• Not Applicable / Not Provided. This document describes a PACS software system for storage and management, not a diagnostic algorithm that would be tested on a dataset of patient images for a specific clinical task. Therefore, there's no "test set" in the context of clinical performance metrics.

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

• Not Applicable / Not Provided. As there is no test set for clinical performance, there is no need for experts to establish ground truth.

4. Adjudication method for the test set

• Not Applicable / Not Provided. No test set for clinical performance.

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, not done. This device is a PACS system for image archival and distribution, not an AI-assisted diagnostic tool.

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

• No, not done. This device is a PACS system, not a diagnostic algorithm. Its "performance" is in its functional capabilities (storage, retrieval, DICOM compliance, etc.), which are demonstrated through technical verification and substantial equivalence to predicates, not through standalone clinical performance.

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

• Not Applicable / Not Provided. There's no diagnostic ground truth relevant to the functionality of this PACS system. Its primary "truth" is its adherence to DICOM standards and its ability to store and retrieve images reliably, which would be verified through technical implementation and testing, not clinical "ground truth."

8. The sample size for the training set

• Not Applicable / Not Provided. This is a software system for image management, not a machine learning model that requires a training set.

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

• Not Applicable / Not Provided. No training set.

Summary of the Document's Approach:

The 510(k) submission for the DICOM Archival Server (DAS)/DICOM IntraNet Service (DIS) primarily relies on:

• Functional Description: Clearly outlining what the software does (storage, retrieval, DICOM services).
• Safety Analysis: Identifying potential hazards (e.g., system component failure) and concluding a "Minor Level of Concern" for patient safety given its role as a PACS.
• DICOM Compliance: Stating full compliance with DICOM 3.0 service classes.
• Substantial Equivalence: Comparing its features and functionalities demonstrably against several legally marketed predicate PACS devices. This is the core "study" or justification for its market entry. The tables in Section 10 ("Substantial Equivalence") serve as the performance comparison in this context.

This approach is typical for medical devices of this vintage and type (PACS as infrastructure, not diagnostic AI), where the focus is on safety, interoperability, and proper functioning as a data management system rather than diagnostic accuracy.

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The Olicon Imaging Systems, NT Archive Systems stores and retrieves digital images together with information about the DICOM standard network protocol. communicates with other devices via the DICOM standard network protocol.

The NT ARCHIVE SYSTEM is a device for filing digital radiological images for storage and retrieval. The system design is layered with three storage technologies; magnetic, magneto-optical and DLT tape.

This document is a 510(k) summary for the Olicon Imaging Systems, Inc. NT Archive Systems. It describes a digital archive system for medical images.

Based on the provided text, the device described is a digital archive system for medical images, not a medical device that diagnoses or treats conditions. Therefore, concepts like "acceptance criteria" related to diagnostic accuracy, "device performance" in terms of clinical outcomes, "sample sizes" for test sets of patients, "ground truth" derived from expert consensus or pathology, or "multi-reader multi-case (MRMC) comparative effectiveness studies" are not applicable to this submission.

The document focuses on demonstrating substantial equivalence to a predicate device (Olicon Archive K922164) based on its technological characteristics, indications for use, and compliance with general regulatory standards.

Here's a breakdown of the requested information based on the provided text, indicating where the information is not applicable (N/A) for this type of device:

1. Table of acceptance criteria and the reported device performance

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

• N/A. This device does not involve a "test set" in the sense of clinical data or patient images for performance evaluation of a diagnostic algorithm. It's an archiving system. The "test" would be functional testing of the software and hardware for storage and retrieval, which is not detailed in this summary.

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)

• N/A. Ground truth for clinical interpretation is not applicable as this is an archiving system, not an interpretive device.

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

• N/A. Adjudication methods are not applicable as there is no "test set" for clinical evaluation.

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

• N/A. MRMC studies are not applicable as this is an archiving system, not a diagnostic AI system intended to assist human readers.

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

• N/A. The concept of "standalone performance" for an algorithm for clinical interpretation is not applicable here. The device itself is "standalone" in its primary function of archiving, but its performance is measured by its functional capabilities (storage, retrieval, DICOM compatibility) rather than diagnostic accuracy. The document states: "Images and information being stored and retrieved are interpreted by a physician, providing ample opportunity for competent human intervention," indicating the device's role is to support, not replace, human interpretation.

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

• N/A. No clinical ground truth is applicable. The "ground truth" for this device would be whether it accurately stores and retrieves the digital images as intended.

8. The sample size for the training set

• N/A. There is no "training set" in the context of machine learning or AI as this device is an archive system.

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

• N/A. Not applicable, as there is no training set mentioned or implied for this type of device.

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ID.Store is a software device intended to direct the lossless archival storage and retrieval, including the use of pre-fetching and auto-routing capabilities, of digital medical images within a Picture Archiving and Communications System (PACS).

ID.Store may be used for the permanent storage and subsequent retrieval of any medical imaging data presented in DICOM or other supported format, within a Picture Archiving and Communications System (PACS).

The ID.Store device is a software package composed of several application modules which work in tandem using a distributed storage architecture to control the archival storage and retrieval of digitized medical images. The ID.gate (gateway), ID.store (archiving), and ID.dbs (image database manager) modules communicate with each other using the dedicated administrative communication protocol ImageTalk. Permanent archival storage media supported include CD-R, WORM, MOD/WORM, DVD, etc. Recommended hardware are standard general purpose equipment, e.g. Sun Sparc or UltraSparc CPU's, and RAID devices for temporary data storage.

The provided text does not contain detailed information about acceptance criteria or a specific study proving the device meets them. The document is primarily a 510(k) summary for the "ID.Store" device, focusing on its intended use, description, comparison to a predicate device, and regulatory approval.

Specifically, the text is a 510(k) summary for the "ID.Store" (PACS Component Software) and letters from the FDA regarding its clearance. It describes the device's function as software for archival storage and retrieval of digital medical images within a PACS, and states its substantial equivalence to a predicate device (Kodak Medical Image and Information Library (MIIL)).

Therefore, I cannot provide the requested information, which typically involves performance metrics, study design, and ground truth establishment. This type of information is usually found in detailed performance studies or validation reports, which are not part of this 510(k) summary.

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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.

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The TDK Medical Grade CD-R is a storage medium used for picture archiving and exchange of recordable information. The TDK Medical Grade CD-R is a multisession disc with 650MB capacity, which is used in conjunction with cardio review stations and mini review stations that accept this type of media. TDK Medical Grade CD-R media provides the following capabilities; acquisition, exchange, display, review and archiving.

The TDK Medical Grade CD-R is a storage medium used for picture archiving and exchange of recordable information. The TDK Medical Grade CD-R is a multisession disc with 650MB capacity, which is used in conjunction with cardio review stations and mini review stations that accept this type of media. TDK Medical Grade CD-R media provides the following capabilities; acquisition, exchange, display, review and archiving. The TDK Medical Grade CD-R conforms to ISO 9002, Orange Book, Part II.

This is a 510(k) clearance letter and Indications for Use statement for a medical grade CD-R, not a study proving device performance against acceptance criteria for an AI/ML medical device. Therefore, the requested information (acceptance criteria, study details, sample sizes, expert qualifications, adjudication, MRMC, standalone performance, ground truth, training set details) is not applicable to this document.

The document states that the TDK Medical Grade CD-R is "substantially equivalent" to devices marketed prior to May 28, 1976. This is a regulatory finding primarily based on the device's intended use and technological characteristics being comparable to a legally marketed predicate device, rather than a performance study as would be conducted for a novel AI/ML algorithm.

The "Indications for Use" section describes the device's purpose: "a storage medium used for picture archiving and exchange of recordable information... used in conjunction with cardio review stations and mini review stations that accept this type of media. TDK Medical Grade CD-R media provides the following capabilities; acquisition, exchange, display, review and archiving." It also notes conformance to "ISO 9002, Orange Book, Part II." These are specifications and standards the product meets, not performance metrics derived from a clinical study.

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The SECTRA AB, WISE Image Management System device is intended for the management and displaying of x-ray images, other radiological objects, and information. It can manage images from different modalities, single and multiple file servers, and interfaces to various Radiological Information Systems (RIS), image storage and printing devices using DICOM or similar interface standards.

WISE will be used for management of radiological images.

This document is a 510(k) summary for the SECTRA-Imtec WISE Image Management System, submitted in 1997. It explicitly states that the submission aims to demonstrate "substantial equivalence" of WISE to a predicate device, SECTRA-Imtec ImageServer 2000 (K963395).

The submission does not contain specific acceptance criteria, performance metrics, or study details such as sample sizes, ground truth establishment, or human reader studies typically associated with demonstrating clinical efficacy or diagnostic accuracy. Instead, it focuses on regulatory compliance and equivalence to a previously cleared device.

Therefore, many of the requested details cannot be extracted from the provided text.

Here is an attempt to address the points based on the available information, noting where information is absent:

1. A table of acceptance criteria and the reported device performance

   This information is not provided in the given text. The filing is for demonstrating substantial equivalence, not for presenting performance against specific statistical acceptance criteria for diagnostic accuracy (e.g., sensitivity, specificity, AUC).

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

   This information is not provided. The document does not describe a test set or clinical study of this nature.

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)

   This information is not provided. There is no mention of a test set or ground truth establishment process.

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

   This information is not provided.

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

   This information is not provided. The device is an "Image Management System," suggesting its primary function is handling and displaying images, not providing diagnostic AI assistance. Therefore, an MRMC study comparing human readers with and without AI assistance would not be relevant or expected for this type of device.

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

   This information is not provided. The WISE system is described as an "Image Management System" for displaying and managing images for "trained medical professionals," implying a human-in-the-loop system for image review and interpretation. It is not presented as an AI algorithm providing standalone diagnostic outputs.

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

   This information is not provided.

8. The sample size for the training set

   This information is not provided. The document does not describe any machine learning or AI models requiring a training set.

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

   This information is not provided.

Summary of available information related to acceptance criteria and study:

The 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (SECTRA-Imtec ImageServer 2000, K963395) rather than providing specific performance metrics from a clinical study. The basis for substantial equivalence is listed as:

• Compliance with Federal Performance Standards (21 CFR, part 1000).
• Manufacture in accordance with voluntary standards.
• Comprehensive user guides ensuring safe and effective use.
• Results of a hazard analysis.

The FDA's response letter (JUN 26 1997) confirms that they reviewed the 510(k) and "decemined the device is substantially equivalent... to devices marketed in interstate commerce prior to May 28, 1976." This indicates that the device met the regulatory bar for substantial equivalence at the time, which did not necessarily require the same type of clinical performance studies expected for newer AI/ML diagnostic devices.

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The AccessPoint™ software is intended to translate different medical image formats into a single format suitable for display on a standard Personal Computer.

Generally speaking, AccessPoint is a software product designed to provide the translation of different medical image formats into a form compatible to be displayed on a standard PC. Connection between sites including hospitals, out reach clinics, physician home and office are provided through the use of e-mail. The images and reports (patient studies) may be transported via e-mail in a secure manor (encryption of data) following standards used by the Internet and local LAN (Intranet), transport systems. A description of the various elements follow:
The AccessPoint™ software conforms to DICOM V3 service classes in the role of Service Class Reader.

Please note that the provided text is a "Safety and Effectiveness Summary" for a 510(k) submission, which typically focuses on demonstrating substantial equivalence to a predicate device rather than providing detailed acceptance criteria and a study report as would be found in a full efficacy study. As such, some of the requested information (like specific acceptance criteria values, sample sizes for test sets, number/qualifications of experts, adjudication methods, MRMC study details, and detailed ground truth methodology for training sets) is not explicitly stated or derivable from the provided document.

However, I can extract the available information and highlight what is missing.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly define a table of "acceptance criteria" with quantitative targets that the AccessPoint™ device must meet (e.g., a specific accuracy percentage, sensitivity, or specificity). Instead, it describes the device's functional capabilities and compares them to a predicate device to establish substantial equivalence.

The "performance" described is largely functional equivalence and compatibility rather than clinical performance metrics. The table provided in the document is a "Substantial Equivalence Table" comparing features.

Substantial Equivalence Table (from document)

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

• Sample Size for Test Set: Not explicitly mentioned in the document.
• Data Provenance: Not explicitly mentioned. The device is software designed to work with various existing imaging systems (Biosound/Esaote, Hewlett Packard, TomTec, MicroSonics, DICOM US, NM), implying it would be used with data potentially originating from different sources and countries where these systems are used. The document does not specify if a specific dataset (retrospective or prospective) was used for testing.

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

This information is not provided in the document. A "Safety and Effectiveness Summary" for a 510(k) usually focuses on technical equivalence and functional performance rather than clinical ground truth validation with experts.

4. Adjudication Method for the Test Set

This information is not provided in the document.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

• MRMC Study: No, there is no mention of a Multi Reader Multi Case (MRMC) comparative effectiveness study, nor an effect size for human readers improving with or without AI assistance. This type of study is not typically part of a 510(k) submission focused on image viewing and transport software. The device itself is described as "Medical Image Reading and Transport Software," not an AI diagnostic aid for interpretation.

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

• Standalone Performance: The document describes the software's standalone functionalities (e.g., its ability to translate image formats, display images, handle compression). It is inherently a "standalone" software product in that it performs its functions without direct human intervention in the processing of the image data, but its purpose is to present data to a human for interpretation. It is not an AI algorithm that makes diagnostic claims independently. The "performance" assessment is functional and comparative to existing predicate devices based on features.

7. The Type of Ground Truth Used

• Given the nature of the device (image translation and display software) and the context of a 510(k) summary focused on substantial equivalence to a predicate device, clinical "ground truth" (like pathology or outcome data) in the diagnostic sense is not applicable or mentioned. The "ground truth" for this device's performance would be whether it correctly translates and displays the various image formats as intended, and whether its features are comparable to the predicate device. This would likely be assessed through technical verification and validation, not clinical ground truth.

8. The Sample Size for the Training Set

• This information is not provided. The AccessPoint™ device is described as "Medical Image Reading and Transport Software" and is not an AI/ML model that is "trained" on a dataset in the conventional sense of machine learning for diagnostic inference. It is software that adheres to standards and handles various image formats.

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

• As explained in point 8, the concept of a "training set" and "ground truth" for a training set in the context of an AI/ML model does not directly apply to this type of medical image viewing and transport software as described in the document. The software is developed based on programming standards (e.g., DICOM V3, Windows operating systems, internet protocols) and compatibility with existing commercial ultrasound image formats. Its "correctness" is established through engineering and software validation against these specifications, not via a human-labeled training set for diagnostic purposes.

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