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eRAD PACS is a software-only medical device used to receive medical images, scheduling information and clinical reports, organize and store them in an internal format, and make the information available across a network via web and customized user interfaces.

eRAD PACS includes software intended for use by qualified professionals for the presentation, review and comparison of diagnostic medical images.

eRAD PACS is for hospitals, imaging centers, reading practices, radiologists, technicians, physicians and other users who require and are granted access to patient image, exam and report information.

The eRAD PACS viewer displays images from DICOM compliant modalities and other devices including CT, computed and digital radiography, MRI, mammography, nuclear medicine, PET, secondary capture, ultrasound, x-ray angiography, x-ray fluoroscopy and visible light systems.

Lossy compressed images and digitized film images must not be used for primary diagnosis of mammography studies. When displaying mammography images for clinical interpretation, only monitors having regulatory clearance for mammography interpretation should be used.

eRAD PACS is a software-only medical image management and processing system, comprised of a central systems manager component, diagnostic viewing components, and an archiving component.

The system is used for patients who undergo an imaging procedure deemed necessary by the patient's physician.

The data flow is as follows:

• Patient and procedure information is optionally acquired by the central system manager to prepare for the acquisition of image objects.
• -Image objects are acquired from the image sources, such as imaging modalities, PACS, data archives, and other devices.
• After receiving the procedure information or the image objects, the central system manager searches for and retrieves relevant prior procedure data from the archiving component.
• When the central system manager registers the acquired image objects and the retrieved prior procedure data, a user can access the information from a workstation by selecting the item from the operator's worklist.
• The image data is transmitted to and rendered on the user's workstation using the diagnostic viewing components.
• -After reviewing the images in the diagnostic viewer, the user optionally creates a clinical report using a text editor or a commercially available speech recognition solution.
• -Once the central system manager registers a report, the report is available for access by the referring physician, or it can be exported into a third-party information system.
• At some configured point in time, the image data and the report information are delivered to the archiving component for backup and long-term storage.

The provided document, a 510(k) premarket notification for eRAD PACS, does not contain explicit acceptance criteria or a detailed study proving the device meets these criteria in the way a clinical performance study for an AI/ML medical device would.

The document primarily focuses on demonstrating substantial equivalence to a previously cleared predicate device (eRAD PACS, K120995). The crucial statement regarding testing is:

"Thorough non-clinical system verification and validation testing was conducted in accordance with applicable standards and internal design procedures to verify that the eRAD PACS software product meets user needs and its intended use. Testing demonstrated that the eRAD PACS software product is substantially equivalent to the predicate device."

This indicates that the "study" conducted was primarily non-clinical verification and validation (V&V) testing for a software-only medical image management and processing system (PACS), not a clinical performance study involving human readers or standalone algorithm performance against a clinical ground truth.

Therefore, for the specific questions asked, a direct answer cannot be fully provided from the given text as the nature of the submission (510(k) for a PACS update) does not require the same type of clinical performance data as, for example, an AI algorithm for disease detection.

However, I can deduce and infer information based on the context of a PACS 510(k) submission:

Analysis based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance:

• Acceptance Criteria: The document does not explicitly state quantitative acceptance criteria in terms of performance metrics (e.g., sensitivity, specificity, AUC) for the eRAD PACS. Instead, the acceptance criterion for this 510(k) submission is implicitly "substantial equivalence" to the predicate device for its intended use, as verified by non-clinical V&V testing. This means the device must function as intended, handle images correctly, and meet the relevant technical specifications and standards (e.g., DICOM compliance).
• Reported Device Performance: The document states: "Testing demonstrated that the eRAD PACS software product is substantially equivalent to the predicate device." This is the reported performance. Specific numerical metrics are not provided because the "performance" here refers to the system's ability to manage and display images functionally as a PACS, not to diagnostic accuracy.

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

• The document mentions "non-clinical system verification and validation testing." For a PACS system, this typically involves testing with a diverse set of synthetic and real-world DICOM images to ensure proper handling, display, and archiving across various modalities.
• Sample Size: Not specified. It would likely involve a large variety of DICOM images and system configurations to test different functionalities and edge cases.
• Data Provenance: Not specified. Given it's V&V for a PACS, the "data" would be medical images (DICOM files) from various modalities. It is likely a mix of internally generated test data, publicly available datasets, and potentially de-identified clinical data, but this is not stated. The provenance (country, retrospective/prospective) of these images is not detailed.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

• Not applicable in the context of this 510(k) submission. Ground truth established by experts is typically for validation of diagnostic accuracy (e.g., for an AI algorithm interpreting images), not for the functional performance of a PACS.
• For PACS V&V, the "ground truth" is that the system correctly displays the image, stores it accurately, and performs its functions as specified. This is verified by engineers and testers against technical specifications, not by clinical experts establishing diagnostic "ground truth."

4. Adjudication Method for the Test Set:

• Not applicable. Adjudication (e.g., 2+1, 3+1) is a method used in clinical studies to establish a rigorous "ground truth" for diagnostic tasks, usually when there is observer variability. This is not mentioned as part of the PACS V&V.

5. MRMC Comparative Effectiveness Study:

• No evidence. The document does not describe an MRMC study comparing human readers with and without AI assistance. This type of study is relevant for AI algorithms intended to aid diagnosis, not for a PACS system whose primary function is image management and display.

6. Standalone Performance (Algorithm Only without Human-in-the-Loop):

• Not applicable. The eRAD PACS is described as a "medical image management and processing system" and a "viewer" for qualified professionals. It is not an AI algorithm performing a diagnostic task independently. Its "performance" is in its ability to correctly manage and display images.

7. Type of Ground Truth Used:

• Technical Specifications and DICOM Standards: For a PACS system, the "ground truth" for verification and validation is primarily defined by its technical specifications (e.g., image fidelity, display characteristics, network communication standards like DICOM, storage integrity) and user requirements. It's about whether the system functions correctly as an imaging management system, not about establishing clinical diagnostic truth from patient outcomes or pathology.

8. Sample Size for the Training Set:

• Not applicable. The eRAD PACS described is not an AI/ML device that requires a "training set" in the machine learning sense. It's a software system built based on established programming principles and standards.

9. How the Ground Truth for the Training Set Was Established:

• Not applicable. As a non-AI/ML PACS, there is no "training set" or corresponding ground truth establishment process in the way an AI model would have.

Summary based on context:

The eRAD PACS 510(k) submission describes an update to an existing PACS system, specifically a "restructure of the internal components for deployment in a cloud environment." For such a device, the regulatory burden focuses on ensuring that the changes do not undermine the safety and effectiveness established by the predicate device. This is achieved through non-clinical verification and validation testing against technical specifications and performance requirements of an image management system. It is not a clinical study involving diagnostic accuracy metrics or human reader performance.

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eRAD PACS/ eRAD RIS/PACS is a PACS software product used to receive DICOM images, scheduling information and textual report, organize and store them in an internal format, and to make that information available across a network via web and customized user interfaces.

The eRAD PACS/ eRAD RIS/PACS viewer software is intended for use as a primary diagnostic and analysis tool for diagnostic images. eRAD PACS/ eRAD RIS/PACS is for hospitals, imaging centers, radiologists, reading practices and any user who requires and is granted access to patient image, demographic and report information.

The eRAD PACS/ eRAD RIS/PACS viewer displays images from CT, computed radiography, MRI, mammography, nuclear medicine, PET, secondary capture, ultrasound, x-ray angiography, x-ray fluoroscopy and visible light modalities.

Lossy compressed mammography images and digitized film screen mammography images must not be reviewed for primary image interpretations. Mammography images may only be interpreted using an FDA approved monitor that offers at least 5 mega-pixel resolution and meets other technical specifications reviewed and accepted by FDA.

eRAD PACS/ eRAD RIS/PACS Software Product is a PACS system, comprised of acquisition components, a central systems manager component, diagnostic viewing components, and an archiving component. The data flow is such that patient and procedure information is optionally delivered to the central system manager, followed by the acquisition of the image objects directly from the image sources or by one of the acquisition components. After receiving the procedure information or after receiving image objects, the central system manager searches for and retrieves relevant prior procedure data from the archiving a component. When the central system manager registers the acquired image objects and the retrieved prior procedure data, a user can access the information by selecting the item from the operator worklist. The image data is transmitted to and rendered on the user's workstation using the diagnostic viewing components. After using the workstation to view the images, the user optionally dictates a report into the system, after which, a user can play back the diction and transcribe it to text. Once eRAD PACS's central system manager registers a report, the report is available for access by the referring physician, or it can be exported into an information system. At some configured point in time, the image data and the report information is delivered to the archiving component for backup and long-term storage.

The provided 510(k) submission for K120995 (eRAD PACS/eRAD RIS/PACS Software) does not contain acceptance criteria or a detailed study proving the device meets specific performance criteria in the way a clinical performance study for an AI/CADe device would.

This submission is for a Picture Archiving and Communications System (PACS), which is a foundational medical imaging infrastructure. The "testing" referred to is about demonstrating that the modified software (making it available in a software-only option) is substantially equivalent to its predicate devices and functions as intended, not about its diagnostic performance against specific clinical endpoints.

Therefore, many of the requested elements for a clinical performance study are not applicable to this documentation.

Here's a breakdown based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Not applicable in the context of a clinical performance study. The 510(k) focuses on substantial equivalence for a PACS system, not on specific diagnostic performance metrics like sensitivity or specificity. The "performance" assessment is about system functionality and equivalence to the predicate device.

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

Not applicable. No clinical "test set" in the context of diagnostic performance (e.g., a set of patient images with confirmed diagnoses) is described. The non-clinical testing mentioned refers to system verification and validation against design requirements.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. Ground truth, as it pertains to diagnostic accuracy, is not established or discussed in this submission.

4. Adjudication Method for the Test Set

Not applicable. No clinical test set or ground truth adjudication process is described.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. A MRMC study was not done, as this submission is not about the diagnostic performance of an AI/CADe tool requiring human-in-the-loop assessment.

6. Standalone (Algorithm Only) Performance Study

No. A standalone performance study for an algorithm's diagnostic capabilities was not performed. The device is a PACS system, intended for displaying and managing images for human interpretation, not an automated diagnostic algorithm.

7. Type of Ground Truth Used

Not applicable. No ground truth for diagnostic purposes is mentioned. The testing focused on functional verification and validation of the PACS system.

8. Sample Size for the Training Set

Not applicable. The device is a PACS system, not an AI or machine learning algorithm that requires a training set in the diagnostic performance sense.

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

Not applicable. As there is no training set for an AI/ML algorithm, this question is not relevant to the provided documentation.

Summary of what the submission does say about "testing":

• Discussion of Non-Clinical Testing Performed: "Thorough non-clinical system verification and validation testing was conducted in accordance with applicable international standards and internal design requirement to verify that the eRAD PACS/ eRAD RIS/PACS Software Product meet user needs and indications for use. Testing demonstrated that the eRAD PACS/ eRAD RIS/PACS Software Product were substantial equivalent to their predicate devices." (Page 2)
• Conclusions: "The information provided in this premarket notification submission has shown that the eRAD PACS/ eRAD RIS/PACS Software Product is substantially equivalent to the predicate devices and are safe and effective for its intended use." (Page 3)

In essence, this 510(k) focuses on demonstrating that the eRAD PACS is functionally equivalent to its predicate, safe, and effective for its intended use as a PACS system, not on providing evidence of diagnostic accuracy comparable to an AI/CADe device.

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eRAD PACS is a PACS and teleradiology system used to receive DICOM images, scheduling information and textual reports, organize and store them in an internal format, and to make that information available across a network via web and customized user interfaces. eRAD PACS is for hospitals, imaging centers, radiologist reading practices and any user who requires and is granted access to patient image, demographic and report information.

eRAD PACS is a PACS system, comprised of acquisition components (GatewayServer and SendServer), a central system manager component (SmartServer), a diagnostic workstation component (Workstation and Viewer), (and an archiving component (ArchiveServer). The data flow is such that patient and on and is optionally delivered to the central system manager, followed by the acquisition of the image objects directly from the image sources or by one of the acquisition components. After receiving the procedure information or after receiving image objects, the central system manager information of and retrieves relevant procedure data from the archive component. When the central system manager registers the acquired image objects and the retrieved prior procedure data, a user can access the information by selecting the item from the operator worklist. The image data is transmitted to by oreading and the user's workstation using the diagnostic workstation components. After using the workstation to view the images, the user optionally dictates a report into the system, after which, a user can play back the dictation and transcribe it to text. Once eRAD PACS's central system manager registers a and the report is available for access by the referring physician, or it can be exported into an information system. At some configured point in time, the image data and the report information is delivered to the archiving component for backup and long-term storage.

eRAD PACS is a PACS and teleradiology system used to receive DICOM images, scheduling information and textual reports, organize and store them in internal format, and to make that information available across a network via web and customized user interfaces.

This document describes the eRAD PACS (Picture Archiving and Communications System), a medical device intended for managing and displaying medical images and related information. However, the provided text does not contain information about specific acceptance criteria for device performance, nor does it describe any study (clinical or otherwise) that proves the device meets such criteria.

The document focuses on:

• Company Identification and Submission Details: Basic administrative information.
• Device Name and Substantial Equivalence: Classifies the device as a PACS soft-copy reading and acquisition system and asserts its substantial equivalence to other legally marketed PACS devices (Stentor's iSite, Toshiba's TICS, Ultravisual's Vortex, Dynamic Imaging's INTEGRADWeb MPR/MIP).
• Device Description and Intended Use: Explains the components and workflow of eRAD PACS (acquisition, central system manager, diagnostic workstation, archiving) and its purpose in hospitals, imaging centers, and radiology practices.
• Software Development: States that the software is designed, developed, tested, and validated according to written procedures.
• Safety and Effectiveness: Claims that the device has a "minor" level of concern and raises no new safety or effectiveness issues compared to predicate devices.
• FDA Communication: A letter from the FDA confirming the device's substantial equivalence and permitting its marketing.

Therefore, based solely on the provided text, I cannot provide the requested information regarding acceptance criteria or the study proving their fulfillment. The document does not describe:

1. A table of acceptance criteria and reported device performance: No specific performance metrics (e.g., image quality, processing speed, diagnostic accuracy) or their targets are mentioned.
2. Sample size for the test set and data provenance: No test sets, patients, or images used for evaluation are described.
3. Number of experts and their qualifications for ground truth: No expert involvement in establishing ground truth is mentioned.
4. Adjudication method for the test set: No adjudication process is described.
5. MRMC comparative effectiveness study: No study involving human readers with or without AI assistance is mentioned.
6. Standalone performance study: No study evaluating the algorithm's performance independent of human readers is described.
7. Type of ground truth used: No mention of expert consensus, pathology, or outcomes data being used as ground truth.
8. Sample size for the training set: No training set or its size is mentioned.
9. How ground truth for the training set was established: Not applicable, as no training set is mentioned.

The document discusses "testing" and "validation" as part of software development, but it does not detail the nature of these tests, their specific objectives, the data used, or the performance metrics achieved. It primarily focuses on the device's functional equivalence to existing PACS systems and adherence to general safety and software development practices.

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