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The SKIA-Head (SKIA-ST00) is a medical display system intended for 3D image visualization and image interaction in conjunction with traditional imaging and monitors. The virtual images are generated from 3D volumetric data previously acquired from CT sources and visualized to 3D patient images being displayed on a tablet PC using 3D cameras.

The device is intended to provide visual information and reference to be used by health care professionals for analysis of surgical options during pre-operative planning.

The device may also be used in a limited, early intraoperative context, specifically during phases in which the patient's skin surface remains consistent with the CT-acquired skin surface model. This generally corresponds to the period after anesthesia but before any manipulation that would deform or alter the patient's external skin surface. This represents a pre-operative to early intraoperative transition phase, during which the patient's posture is fixed and the skin surface remains sufficiently stable for skin-based registration.

The system is intended for visualization and reference purposes only and is not intended for real-time surgical navigation, instrument tracking, or stereotactic guidance.

This device is intended for prescription use only (Rx Only).

The SKIA-Head is an augmented reality-based standalone software device. The device reconstructs digital anatomical 3D models based on pre-procedural computed tomography (CT) images for users to review and fuses it with a real-time patient video image displayed on a tablet PC to generate augmented reality. The system is comprised of SKIA_Processor, SKIA_Application, and SKIA-Server. Users need compatible off-the-shelf hardware such as hospital server to operate SKIA-Server, user's PC to operate SKIA_Processor, 3D camera device to scan a patient and shoot a real time patient video, and iPad to proceed registration and display the AR output image.

The SKIA-Head provides users with review functions such as image position movement, zoom in/out, image reset, image rotation, and segmentation of the CT images. The AR anatomical model fused with the real time patient video can help users accurately recognize the actual location and size of lesions in the patient.

The SKIA-Head projects virtual representations of a) a spatially scanned patient image map using a 3D camera and b) reconstructed CT-based 3D anatomical model. Accordingly, the use of SKIA-Head involves the co-registration of these two virtual objects and real time patient video image displayed on a tablet PC for visual information using projection to real patients.

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LAIA XR is a software device for displaying digital medical images, especially for CT and MR images (DICOM format). It is intended to visualize 3D medical imaging of the patient for pre-operative planning outside the surgical room.

When accessing LAIA XR from a wired head-mounted display (HMD) or PC monitor, images viewed are for informational purposes only and not intended for diagnostic use. LAIA XR is indicated for use by qualified healthcare professionals including surgeons, radiologists, physicians, and technologists.

LAIA XR is a software device for displaying digital medical images, especially for CT and MR images (DICOM format). It is intended to visualize 3D medical imaging of the patient for pre-operative planning outside the surgical room.

When accessing LAIA XR from a wired head-mounted display (HMD) or PC monitor, images viewed are for informational purposes only and not intended for diagnostic use. LAIA XR is indicated for use by qualified healthcare professionals including surgeons, radiologists, physicians, and technologists.

The device consists of a software-based medical system, which runs on a standard computing platform (e.g., a laptop or desktop computer) with a display screen. Optionally, the system can be used in conjunction with virtual reality (VR) goggles to provide an immersive three-dimensional (3D) visualization environment; however, the use of VR hardware is not required for the software to perform its intended functions.

LAIA XR provides interactive visualization capabilities for three-dimensional medical images and includes functionalities and tools that support pre-operative planning activities, including the following:

• Advanced 3D visualization of anatomical structures,
• 2D and 3D multi-planar reconstruction views, with customizable transfer functions (color and opacity mappings) to highlight specific tissues or structures,
• Quantitative measurement tools, such as distance and angle measurements,
• Fiducial marker placement within defined regions of interest (ROIs),
• Capture and screenshots of 3D models for use in surgical preparation,
• Step-by-step surgical planning documentation, including written notes and screenshot capture.

These functionalities allow healthcare professionals to interact with and organize medical imaging data as part of pre-operative planning workflows.

The aim of the medical device software (MDSW) is to support healthcare personnel in the review and manipulation of CT and MR images. LAIA XR provides an interactive platform that allows users to view, manipulate, and organize medical image data for pre-operative planning purposes.

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LiveMedica Enterprise PACS is a system for distributing, viewing, processing and archiving medical images within and outside healthcare environments. The LiveMedica Enterprise PACS server receives image data in DICOM format via the hospital network. This provides universal connections to archives, modalities, and workstations. The supported modalities are listed in the DICOM Conformance Statement.

LiveMedica Enterprise PACS is to be used only by trained and instructed health care professionals. It can support physicians and/or their medical staff in providing their own diagnosis for medical cases. The final decision regarding diagnosis, however, resides with the doctor and/or their medical staff in their own area of responsibility.

Although the web and technologies allow the software to run on a variety of hardware platforms, for diagnostic purposes the user must make sure that the display hardware used for reading the images complies with state-of-the-art diagnostic requirements and currently valid laws.

Only DICOM for presentation images can be used on an FDA approved monitor for mammography for primary image diagnosis.

Only uncompressed or non-lossy compressed images must be used for primary image diagnosis in mammography.

LiveMedica Enterprise PACS is a Picture Archiving and Communication System designed to store, retrieve, manage and display DICOM-compliant medical images for diagnostic imaging workflows across hospitals, imaging centers and multi-site healthcare networks. It includes functionality for server-side image rendering, centralized and edge-based study management and routing. The system is to be used by trained healthcare professionals to perform advanced image manipulations, including zoom, pan, window/level adjustments, annotations, measurements and more. LiveMedica Enterprise PACS supports DICOM conformance, HL7/FHIR-based interoperability and integrates with HIS/RIS/EMR systems.

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View is a software application that displays, processes, and analyzes medical image data and associated clinical reports to aid in diagnosis for healthcare professionals. It streamlines standard and advanced medical imaging analysis by providing a complete suite of measurement tools intended to generate relevant findings automatically collected for export and save purposes.

Typical users of this system are authorized healthcare professionals.

Mammography images may only be interpreted using a monitor compliant with requirements of local regulations and must meet other technical specifications reviewed and accepted by the local regulatory agencies. Lossy compressed mammographic images and digitized film screen images should not be reviewed for primary image interpretations with use of the View.

View is a cloud-native software application designed to support healthcare professionals in the display, processing, and analysis of medical image data. It enhances diagnostic workflows by integrating intelligent tools, streamlined accessibility, and advanced visualization capabilities, including specialized support for breast imaging.

View brings together 2D imaging, basic 3D visualization and advance image analysis in a single, intuitive interface. This simplifies information access, improves workflow efficiency, and reduces the need for multiple applications.

Key features include:

• Smart Reading Protocol (SRP) which uses machine learning for creating and applying hanging protocols (HP)
• AI workflow to support both DICOM Secondary Capture Object & DICOM Structured Report for displaying AI findings and enabling rejection/modification of the AI findings.
• Displays 2D, 3D, and historical comparison exams in customizable layouts.
• Enables smooth transitions between 2D and 3D views, either manually or as part of hanging protocols.
• Advantage Workstation integration for deeper analysis through dedicated 3D applications.
• Offers a full suite of measurement, annotation and segmentation tools for DICOM images.
• Captures all measurements and annotations in a centralized findings panel.
• Enhanced access for DICOM images stored in the cloud server.
• Easy way to integrate with external systems using FHIRcast.
• Better user experience by having a native MIP/MPR/Smart Segmentation/Volume Rendering
• Seamless access to breast images through cloud with specific tools for Mammo images.

The provided FDA 510(k) clearance letter and summary for the "View" device (K253639) offer limited details regarding specific acceptance criteria and the studies conducted to prove device performance. The information is high-level and generalized.

Based on the available text, here's the breakdown of what can and cannot be extracted:

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

The document does not explicitly state quantitative acceptance criteria (e.g., minimum accuracy, sensitivity, specificity) or specific reported performance metrics for the device. It focuses on functional equivalence and verification/validation testing without presenting performance data.

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

The document states: "The Smart Reading Protocol function, which uses machine learning for creating and applying hanging protocols, was tested on various imaging modality datasets representative of the clinical scenarios where View is intended to be used."

• Sample Size: "various imaging modality datasets" – The exact sample size (number of images, cases, or patients) is not specified.
• Data Provenance: "representative of the clinical scenarios" – The country of origin and whether the data was retrospective or prospective are not specified.

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)

The document does not provide any information on the number or qualifications of experts used to establish ground truth for the test set.

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

The document does not specify any adjudication method used for the test set.

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

The document mentions a "comparison was performed between the predicate device (Universal Viewer) and the subject device (View) and showed that the devices are equivalent" for the Smart Reading Protocol function. However, this is a comparison between devices, not an MRMC study designed to assess human reader improvement with AI assistance. Therefore:

• A specific MRMC comparative effectiveness study involving human readers with and without AI assistance is not described.
• Consequently, an effect size for human reader improvement is not provided.

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

The document implies that the Smart Reading Protocol (SRP) function, which uses machine learning, was tested. The statement "A comparison was performed between the predicate device (Universal Viewer) and the subject device (View) and showed that the devices are equivalent" regarding SRP suggests an evaluation of the algorithm's output. However, whether this testing was strictly standalone performance (algorithm only) versus integrated system performance is not explicitly detailed. Given the context of a 510(k) summary for a "Medical Image Management And Processing System," the testing likely covers the integrated system.

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

The document does not specify the type of ground truth used for its testing.

8. The sample size for the training set

The document does not provide any information on the sample size used for the training set.

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

The document does not provide any information on how the ground truth for the training set was established.

Summary of available information regarding acceptance criteria and study data:

Unfortunately, the provided FDA 510(k) summary is very high-level and primarily focuses on justifying substantial equivalence based on technological characteristics and general verification/validation processes. It does not contain the detailed performance metrics, sample sizes, ground truth establishment methods, or reader study details typically found in more comprehensive clinical study reports. The approval is based on the device having "substantial equivalent technological characteristics" and being "as safe and as effective" as the predicate device.

The only specific functional testing mentioned is for the "Smart Reading Protocol" using machine learning, which was compared to the predicate device to show equivalence. However, the details of this comparison (e.g., performance metrics, specific acceptance criteria for equivalence, or study design) are not included.

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3DICOM MD Cloud is intended for use as a diagnostic and analysis tool for multi-modality medical images and their associated reports and information, enabling qualified healthcare professionals from hospitals, imaging centres, radiologists, and reading practices to view patient images, documents, and related data. 3DICOM MD Cloud enables qualified users to manipulate medical images, create markups, and perform measurements using a range of tools.

3DICOM MD Cloud is not intended for diagnostic use with mammography images. Usage for mammography is for reference and referral only. 3DICOM MD Cloud is not intended for diagnostic use on mobile devices.

3DICOM MD Cloud is a software as a medical device that provides diagnostic viewing and analysis of multi-modality DICOM images in a secure, web-based, cloud/server-hosted environment. Authorized users access studies from site-provisioned cloud storage sources and perform 2D multi-planar viewing (MPR) and 3D visualization, apply window/level and other standard adjustments, make 2D measurements and annotations, generate a DICOM-structured report summarizing tracked measurements, and export snapshots/measurement tables. The device does not generate diagnoses or provide automated clinical interpretation.

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