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The system is intended for use by Nuclear Medicine (NM) or Radiology practitioners and referring physicians for display, processing, archiving, printing, reporting and networking of NM data, including planar scans (Static, Whole Body, Dynamic, Multi-Gated) and tomographic scans (SPECT, Gated SPECT, dedicated PET or Camera-Based-PET) acquired by gamma cameras or PET scanners.

The system can run on dedicated workstation or in a server-client configuration.

The NM or PET data can be coupled with registered and/or fused CT or MR scans, and with physiological signals in order to depict, localize, and/or quantify the distribution of radionuclide tracers and anatomical structures in scanned body tissue for clinical diagnostic purposes.

DaTQUANT optional application enables visual evaluation and quantification of 131ioflupane (DaTscan™)) images. DaTQUANT Normal Database option enables quantification relative to normal population databases of 1231-ioflupane (DaTscan TM) images.

These applications may assist in detection of loss of functional dopaminergic neuron terminals in the striatum, which is correlated with Parkinson disease.

The Xeleris 3.1 is a Nuclear Medicine Workstation system intended for general nuclear medicine processing & review procedures for detection of radioisotope tracer uptake in the patient body, using a variety of processing modes supported by various clinical applications types and various features designed to enhance image quality. The components of the Xeleris 3.1 NM Workstation system are: operation console, monitor and peripherals. The Xeleris 3.1 is a modification of its predicate device Xeleris 3 while providing enhanced workflow to existing operations and enabling broader access to Xeleris applications in supporting PACS and GE AW Server and in offline client server configuration. Xeleris 3.1 also enables the use of normal data base comparison together with the quantification analysis of 123I-ioflupane brain NM images. Similar functionality for NM/PET brain image analysis also resides in the predicate devices K021656 and K123528.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Xeleris 3.1 Processing and Review Workstation, specifically focusing on the DaTQUANT application:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state formal acceptance criteria with specific numerical thresholds for the DaTQUANT application's accuracy. Instead, it describes a
The study for the DaTQUANT application compared "DaTQUANT analysis results to manual analysis results." The reported performance is that "DaTQUANT results were found to be as accurate as manual results."

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

• Sample Size: The document mentions that the data used for testing was "taken from brain phantoms injected symmetrically and asymmetrically." It does not specify the number of phantoms or the number of acquisitions/images used.
• Data Provenance: The data was derived from "brain phantoms injected symmetrically and asynchronously," simulating normal and abnormal uptakes, with "different contrast levels used to simulate different signal to noise ratio levels." This indicates a controlled, artificial data set (phantoms) rather than human clinical data. It is a retrospective analysis of phantom data. The country of origin for the phantom data is not specified.

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

The ground truth in this specific test was established by "manual analysis results," which inherently implies human expert involvement. However, the document does not specify the number of experts who performed the manual analysis, nor their specific qualifications (e.g., "radiologist with 10 years of experience").

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1). It simply states that the DaTQUANT results were compared to "manual analysis results," implying a direct comparison without detailing how discrepancies in manual analysis (if multiple experts were involved) would have been resolved.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned for the DaTQUANT application in this document. The testing described focuses on comparing the algorithm's output to manual analysis, not on how human readers' performance might improve with or without AI assistance.

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

Yes, a standalone performance test was done for the DaTQUANT application. The description, "Testing the accuracy of using the DaTQUANT application by comparing DaTQUANT analysis results to manual analysis results," indicates that the algorithm's output (DaTQUANT results) was directly evaluated against a ground truth (manual analysis) without an explicit human-in-the-loop interaction for the DaTQUANT itself during this specific accuracy test.

7. Type of Ground Truth Used

The ground truth used for the DaTQUANT accuracy testing was expert consensus / manual analysis results derived from phantom data.

8. Sample Size for the Training Set

The document does not specify the sample size or details regarding a training set for the DaTQUANT application. The description focuses solely on the accuracy testing using phantom data.

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

Since a training set is not mentioned, the method for establishing its ground truth is also not provided in this document.

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The system is intended for use by Nuclear Medicine (NM) or Radiology practitioners and referring physicians for display, processing, archiving, printing, reporting and networking of NM data including planar scans (Static, Whole Body, Dynamic, Multi-Gated) and tomographic scans (SPECT, Gated SPECT, dedicated PET or Camera-Based-PET) acquired by gamma cameras or PET scanners. The system can run on dedicated workstation or in a server-client configuration. The NM or PET data can be coupled with registered and/or fused CT or MR scans and with physiological signals, in order to: depict, localize, and/or quantify the distribution of radionuclide tracers and anatomical structures in scanned body tissue for clinical diagnostic purposes.

Xeleris 3, as a modification of its predicate device - Xeleris 2 (K051673), is a Nuclear Medicine, PET, NM/CT, and PET/CT workstation. Xeleris 3, as a medical device, is computer workstation software used for the display, processing, archiving, printing, reporting and networking of NM, PET, NM/CT, and PET/CT studies. Xeleris 3 runs on Microsoft Windows XP based PC workstation (color monitor, keyboard, mouse, and CD-RW for archiving), an Ethernet network connection and system software. Optional DVD device is also available. Xeleris 3 also operates in client-server configurations. Xeleris 3 is classified as class II medical device, complies with voluntary Digital Imaging and Communication in Medicine (DICOM) standard.

The provided 510(k) summary does not contain specific acceptance criteria or a study that proves the device meets such criteria in the way typically expected for a new medical device claiming a specific performance metric. Instead, the document describes a modification of an existing device (Xeleris 3 as a modification of Xeleris 2) and asserts substantial equivalence to the predicate device.

Here's an analysis based on the provided text, addressing your points where information is available:

1. Table of Acceptance Criteria and Reported Device Performance

None provided in the document. The submission focuses on device modifications and substantial equivalence to a predicate device. Therefore, a table comparing specific performance metrics against pre-defined acceptance criteria is not included.

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

No specific test set or clinical data is cited for validating performance against acceptance criteria. The document states: "The XELERIS 3 PROCESSING AND REVIEW WORKSTATION did not require clinical studies to support substantial equivalence." This implies that clinical performance was not evaluated with a separate test set to establish new performance metrics. The validation activities mentioned are:

• Risk Analysis
• Requirements Reviews
• Design Reviews
• Testing on unit level (Module verification)
• Integration testing (System verification)
• Final acceptance testing (Validation)
• Performance testing (Verification)
• Safety testing (Verification)

These activities are focused on software development and verification rather than clinical performance evaluation against a patient dataset.

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

Not applicable. No ground truth establishment by experts for a clinical test set is mentioned, as clinical studies were not deemed necessary for substantial equivalence.

4. Adjudication method for the test set

Not applicable, as no clinical test set requiring adjudication is described.

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 such study was conducted or reported. This device is a "Nuclear Medicine Workstation" for display, processing, archiving, etc., and not an AI-assisted diagnostic tool that would typically undergo MRMC studies to assess reader improvement. The device employs "the same fundamental scientific technology as its predicate devices."

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

Not applicable. The device is a "workstation" intended for use by "Nuclear Medicine (NM) or Radiology practitioners," indicating a human-in-the-loop system. Its function is to provide tools for practitioners, not to perform diagnoses independently.

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

Not applicable, as no clinical performance study requiring ground truth is detailed.

8. The sample size for the training set

Not applicable. The document does not describe the development of an algorithm that would require a "training set" in the context of machine learning or AI. The device is a modification of existing software.

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

Not applicable.

Summary of Acceptance Criteria and Study Approach in the Provided Document:

The acceptance criteria for the XELERIS 3 PROCESSING AND REVIEW WORKSTATION, as implied by this 510(k) summary, are primarily centered around functional equivalence, safety, and effectiveness compared to its predicate device (XELERIS 2). The study that "proves the device meets the acceptance criteria" is, in essence, the documentation of software development, verification, and validation activities (Risk Analysis, Requirements Reviews, Design Reviews, various levels of testing), asserting that these activities ensure the modified device retains the same fundamental scientific technology and performs comparably to the predicate. The key phrase "The XELERIS 3 PROCESSING AND REVIEW WORKSTATION did not require clinical studies to support substantial equivalence" indicates that formal clinical performance studies with specific acceptance criteria and patient data were not conducted for this submission.

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The GE Discovery NM/CT 670 system is a medical tool intended for use by appropriately trained healthcare professionals to aid in detecting, localizing, diagnosing of diseases and organ function of diseases, trauma, abnormalities, and disorders such as, but not limited to, cardiovascular disease, neurological disorders and cancer. The system out would also be used by the plysician for staging of tumors, planning, guiding, and monitoring theram on the CE Discovery NMCT 670 system, combining Nuclear Medicine (NM) and Computed Tomography (CT) systems, in inded to produce:

NM System: General Nuclear Medicine imaging procedures for detection of radioisotope tracer uptake in the patient body, using a variety of scanning modes supported by various acquisition types and imaging features designed to enhance image quality. The scanning modes include planar mode (Static, Multi-gated, Dynamic and Wolle body scanning) and tomographic mode (SPECT, Gated SPECT) The acquisition types include single and multi-isotope/multi peak frame/list mode single-photon imaging-enhancement features include assortment of collimators, gating by physiological signals, and real-time automatic body contractions

CT System: Cross sectional images of the body by computer reconstruction of X-Ray transmission data taken at different angles and planes, including Axial, Cine, Helical, Cardiac, and Gated acquisitions. These images may be obtained with or without contrast. The CT system is indicated for head, whole body, nordiac and vascular X-Ray Computed Tomography applications

NM + CT System: Combined, hybrid SPECT and CT protocols, for CT-based SPECT attenuation corrected imaging as well as functional and anatomical mapping imaging (localization, registration and fusion),

The GE Discovery NM/CT 670 system may include signal analysis and display equipment, patient and equipment supports, components and accessories. The system may include data and image processing to produce images in a variety of trans-axial and reformatted planes. The images can also be post processed to produce additional images, imaging planes, and analysis results. The system may be used fors pationts of all ages.

The Discovery NM/CT 670 system is a hybrid SPECT-CT system for performing nuclear cardiology medicine studies, CT studies or SPECT-CT hybrid studies wherein the SPECT and CT studies may be registered and displayed in a fused form on processing and review Workstation. The Discovery NM/CT 670 system is intended to allow healthcare facilities to carry out SPECT and CT studies using the same instrument Major parts of the hybrid system include the following parts : new NM Gantry with Dual detector heads , CT Gantry (same as BrightSpeed Elite (BSD16) gantry), new common patient table , Integrated operation Console including BSD16 operation console and NM operation console & BSD16 Power Distribution Unit .

The provided 510(k) summary for the Discovery NM/CT 670 system explicitly states that "The Discovery NM/CT 670 did not require clinical studies to support substantial equivalence."

Therefore, I cannot provide a table of acceptance criteria and reported device performance from a clinical study, nor specific details about sample sizes, expert ground truth, adjudication methods, MRMC studies, or standalone algorithm performance, as these were not conducted or reported for this submission.

Instead, the submission for the Discovery NM/CT 670 relies on demonstrating substantial equivalence to predicate devices (K022960- INFINIA and K082816- GE BRIGHTSPEED DELIGHT CT SCANNER SYSTEM) by showing that it employs the same fundamental scientific technology and complies with voluntary standards.

Here's a breakdown of the available information regarding how the device meets acceptance criteria, based on the provided text:

Acceptance Criteria and Device Performance (Based on Substantial Equivalence and Verification Activities)

Since no clinical studies were performed, the "acceptance criteria" discussed are related to manufacturing, design, and regulatory compliance rather than clinical performance metrics like sensitivity, specificity, or reader agreement. The "reported device performance" refers to the successful completion of these internal verification and validation activities.

• CT part: Identical to predicate device K082816 (16 slices, BSD16), with a new equivalent patient table (meets NM attenuation requirements or less) and minor software modifications.
• NM part: Improved modification of predicate device K022960- Infinia (new NM Gantry with Dual detector heads). |

Additional Information Not Applicable or Not Provided in the Document:

1. Sample size used for the test set and the data provenance: Not applicable as no clinical test set was used.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable as no clinical test set with expert ground truth was used.
3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable as no clinical test set with expert adjudication was used.
4. 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. The device is not an AI-assisted diagnostic tool as understood in the context of MRMC studies for AI, but a hybrid imaging system. No human reader studies with or without AI assistance were conducted or reported.
5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable. The device is a medical imaging system, not a standalone algorithm. Its "performance" is assessed through engineering verification and validation against its design specifications and equivalence to predicate devices, not through standalone diagnostic accuracy studies.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For the internal verification and validation activities, the "ground truth" would be the engineering specifications and established performance benchmarks of the predicate devices. No clinical outcomes, pathology, or expert consensus ground truth was established from clinical studies.
7. The sample size for the training set: Not applicable as no machine learning algorithm requiring a training set is discussed or implied to be part of the substantial equivalence determination process for this device's regulatory review. The device is a hardware imaging system with associated software.
8. How the ground truth for the training set was established: Not applicable for the same reason as above.

In summary, the K093514 submission specifically states that clinical studies were not required to support substantial equivalence. The device's acceptance relied on demonstrating technical equivalence to predicate devices and adherence to internal quality assurance measures and voluntary standards.

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The intended use of the GE Discovery NM/CT 570c system is primarily to perform combined cardiac SPECT and CT diagnostic imaging applications, including CT-based SPECT attenuation correction and functional-anatomical mapping (registration and fusion).

The GE Discovery NM/CT 570c system intended uses include performing nuclear cardiac imaging procedures for detection and imaging of racer uptake in the patient body for clinical diagnostic purposes as well as performing general Head & Body Computed Tomography (CT) applications

The GE Discovery NM/CT 570c system is a back-to-back combination of the Ventri 1.1 SPECT scanner (K080124) and the LightSpeed 7.1 CT scanner (K061817), sharing a common LightSpeed 7.1 patient table. In addition to providing CT and SPECT standalone capabilities, it uses the CT images to correct for non-uniform attenuation of the SPECT images and to facilitate localization of the emission activity in the patient anatomy.

The provided document describes the GE Discovery NM/CT 570c system, a combination of SPECT and CT imaging systems. However, the available text does not contain detailed acceptance criteria, specific study results in terms of numerical performance metrics, or information regarding sample sizes, data provenance, expert qualifications, or adjudication methods for studies typically associated with AI/algorithm performance claims.

The submission is for a device system (hardware and associated software), not specifically an AI-driven diagnostic device as per modern understanding. Therefore, many of the requested categories like "multi-reader multi-case comparative effectiveness study" or "standalone algorithm performance" are not applicable in this context.

Here's an attempt to fill in the table and provide information based only on the provided text, with many fields necessarily marked as "Not Applicable" or "Not Provided."

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not provided. The document mentions "Data acquired with uniform phantom," indicating a phantom study was conducted. No patient sample size for a test set is mentioned.
• Data Provenance: The study was conducted with a "uniform phantom." No country of origin for data or retrospective/prospective nature is specified, although phantom studies are typically controlled laboratory experiments.

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

• Number of Experts: Not provided.
• Qualifications of Experts: Not provided. The evaluation appears to be based on physical phantom data and direct measurement/observation of image characteristics (uniformity, localization) rather than expert interpretation of patient images for ground truth.

4. Adjudication method for the test set

• Adjudication Method: Not applicable/Not provided. The evaluation described involves a "uniform phantom" and is focused on the physical performance of the system (attenuation correction, localization) rather than a diagnostic performance study requiring expert adjudication of clinical cases.

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

• MRMC Study: No, not applicable. This submission predates widespread AI in medical imaging devices and does not describe any human-in-the-loop diagnostic assistance features or related studies. The device is a scanner system, not an AI diagnostic tool.
• Effect size: Not applicable.

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

• Standalone Performance: Not applicable as per the modern definition of an "algorithm only" device. The "data acquired with uniform phantom" is essentially a technical validation of the system's performance (attenuation correction, localization) in a controlled setting, which could be considered a form of standalone performance for the system as a whole, but not an AI algorithm.

7. The type of ground truth used

• Ground Truth Type: For the phantom study, the "ground truth" implicitly refers to the known physical properties and uniformity of the phantom. For clinical applications, substantial equivalence relies on the established performance of the predicate devices. There is no mention of expert consensus, pathology, or outcomes data being used for the performance evaluation detailed in the summary.

8. The sample size for the training set

• Sample Size for Training Set: Not provided. This being a conventional imaging system rather than an AI/machine learning device, the concept of a "training set" for an algorithm, as typically defined, is not directly applicable.

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

• Ground Truth for Training Set: Not applicable. As above, there is no mention of an AI algorithm training set. The device functions based on established physics and engineering principles for SPECT and CT imaging, with software for processing and fusion.

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The intended use of the Ventri 1.1 system is to perform nuclear imaging procedures for detection and imaging of radioisotope tracer uptake in the patient body for clinical diagnostic purposes. Ventri 1.1 is primarily intended for cardiac applications.

The Ventri 1.1 is a high-performance Single Photon Emission Computed Tomography system, mainly for nuclear cardiology imaging.

The GE Ventri 1.1 system is a Single Photon Emission Computed Tomography (SPECT) system primarily intended for cardiac imaging. The provided document is a 510(k) summary for the device, detailing modifications from a predicate device (Ventri K051855) and establishing substantial equivalence.

Here's an analysis of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list "acceptance criteria" in a quantitative manner (e.g., target specificity or sensitivity values) nor does it provide detailed quantitative "reported device performance." Instead, it states that the system was tested to comply with safety and performance standards.

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

The document mentions "clinical images" were used in the substantiation, but does not specify the sample size for this test set or the data provenance (e.g., country of origin, retrospective or prospective nature).

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

The document does not provide information on the number of experts used, their qualifications, or how ground truth was established for the clinical images mentioned.

4. Adjudication Method for the Test Set

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

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it discuss human reader improvement with or without AI assistance. The device described appears to be an imaging system, not an AI-driven diagnostic-aid software.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The document does not describe a standalone algorithm performance study. The "SW validation tests" and "Bench data" are mentioned, but details on standalone performance metrics are not provided. Given this is an imaging system, not a purely algorithmic diagnostic tool, a "standalone" study in the sense of an AI algorithm might not be directly applicable without further context.

7. Type of Ground Truth Used

The document states that "clinical images" were used to demonstrate substantial equivalence, but it does not specify the type of ground truth used (e.g., expert consensus, pathology, outcomes data).

8. Sample Size for the Training Set

The document does not specify a training set sample size. This device is an imaging system, and while it involves software ("SW validation tests"), it's not explicitly presented as an "AI algorithm" that undergoes a distinct training phase in the context of machine learning. Thus, the concept of a "training set" as typically understood for AI might not apply directly here.

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

Since no training set is explicitly mentioned or detailed in the context of an AI algorithm, this information is not applicable and not provided.

Summary of Missing Information:

The provided 510(k) summary focuses on demonstrating the substantial equivalence of the Ventri 1.1 system to its predicate device based on compliance with industry safety and performance standards, and a general statement about "SW validation tests, Bench data and clinical images." However, it lacks detailed information regarding:

• Quantitative acceptance criteria for clinical performance.
• Specific quantitative performance metrics from clinical studies.
• Sample sizes, provenance, and ground truth methodologies for any clinical data used.
• Involvement of expert readers or adjudication processes.
• Any studies related to AI or algorithmic standalone performance, or human-AI collaboration.

This level of detail is typically not required for a 510(k) submission when the device is an updated version of a previously cleared predicate and primarily focuses on hardware/software modifications and compliance with established standards, rather than introducing novel diagnostic algorithms requiring extensive clinical validation against a defined ground truth.

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The intended use of the VENTRi system is to perform nuclear imaging procedures for detection and imaging of radioisotope tracer uptake in the patient body for clinical diagnostic purposes. VENTRi is primarily intended for cardiac applications but also supports non-cardiac procedures of the patient's head, chest and body extremities.

The VENTRi is a high-performance dual-head Single Photon Emission Computed Tomography system dedicated mainly for nuclear cardiology imaging.

I am sorry, but the provided text does not contain detailed acceptance criteria or a comprehensive study report that proves the device meets specific performance criteria.

The document is a 510(k) summary for a medical device (VENTRi, a Single Photon Emission Computed Tomography system). It primarily focuses on demonstrating substantial equivalence to predicate devices rather than providing a detailed performance study with explicit acceptance criteria.

Here's what I can extract and what's missing based on your request:

Missing Information:

The document does not contain any of the following requested information:

• A specific table of acceptance criteria and reported device performance.
• Sample size used for a test set or its provenance.
• Number of experts, their qualifications, or the adjudication method for ground truth establishment.
• Information on Multi-Reader Multi-Case (MRMC) comparative effectiveness studies or effect sizes.
• Details about standalone (algorithm-only) performance.
• The type of ground truth used (e.g., pathology, outcomes data).
• Sample size for the training set.
• How ground truth for the training set was established.

What can be inferred/extracted:

The "Summary of Studies" section (page 2, {1}) states:
"Bench and images data show that the VENTRi images are similar to the images of The predicate devices."

This broadly suggests that the implicit acceptance criterion was that the VENTRi system's image quality and performance should be "similar" or equivalent to the legally marketed predicate devices. However, no specific metrics, quantitative comparisons, or a detailed study methodology are provided to elaborate on this "similarity."

The document focuses on the description of changes or modifications to the predicate devices and asserts the substantial equivalence of the VENTRi system based on these changes and general "bench and images data." It does not present a performance study with the level of detail requested for acceptance criteria and proof of meeting those criteria.

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The intended use of the "Infinia LightSpeed" system is to perform:

• General Nuclear Medicine imaging procedures for detection of radioisotope . tracer uptake in the patient body, using a variety of scanning modes supported by various acquisition types and optional imaging features designed to enhance image quality in oncology, cardiology, neurology and other clinical diagnostic imaging applications.
• General Head & Body computed tomography (CT) applications. ◆
• Combined, hybrid SPECT & CT protocols, for CT-based SPECT attenuation . correction imaging as well as functional and anatomical mapping imaging (registration and fusion).

The "Infinia LightSpeed" system is a combination of the Infinia NM Scanner (K022960 & K991841) and the "LightSpeed" 5 CT Scanner (K030420). In addition to providing CT and NM standalone capabilities, it uses the CT images to correct for non-uniform attenuation of the NM images and to facilitate localization of the emission activity in the patient anatomy.

This document describes the Infinia LightSpeed system, a combination of a Nuclear Medicine (NM) scanner and a Computed Tomography (CT) scanner, primarily for performing SPECT/CT attenuation correction and functional/anatomical mapping.

Here's an analysis of the provided text regarding acceptance criteria and the study:

Acceptance Criteria and Reported Device Performance

Note: The provided document is a 510(k) summary, which typically focuses on demonstrating substantial equivalence to a predicate device rather than presenting extensive new clinical study data with detailed acceptance criteria and standalone performance metrics. The performance criteria listed are general regulatory and industry standards, and the "reported device performance" is a high-level summary of phantom study results.

Study Information

The provided document refers to a "Summary of Studies" that relies on phantom data. It is a submission for substantial equivalence based on modifications to existing devices, rather than a de novo submission requiring extensive new clinical trials.

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

   • Sample Size: Not specified. The study involved "Phantom data," implying a controlled test using physical phantoms, not human subjects.
   • Data Provenance: Not specified, but likely obtained in a controlled laboratory setting (prospective data from phantom experiments).

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

   • Not applicable. The study utilized phantom data and objective measurements for uniformity and localization, which do not typically require expert consensus for ground truth establishment in the same way clinical image interpretation does.

3. Adjudication method for the test set:

   • Not applicable. As the study used phantom data and objective measurements, there was no adjudication of expert interpretations.

4. 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. The document describes a technical and performance evaluation using phantom data, not a multi-reader, multi-case clinical study involving human readers or AI.

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

   • The "study" described is a standalone performance evaluation of the SPECT/CT system's capabilities using phantom data, focusing on image quality (uniformity) and localization. It evaluates the system's inherent ability to produce corrected and localized images.

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

   • The ground truth for the phantom study would be known physical properties and distributions within the phantom, along with objective measures of uniformity and spatial accuracy.

7. The sample size for the training set:

   • Not applicable. The document describes a medical device system, not an AI algorithm that would typically have a "training set." The system's algorithms for attenuation correction and image reconstruction are based on established physics and engineering principles, not machine learning from a large training dataset as understood in AI/ML contexts.

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

   • Not applicable, for the same reasons as point 7.

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The intended use of the MyoLIGHT system is to perform Nuclear imaging procedures for detection and imaging of single photon radioisotope tracer uptake in the patient body for clinical diagnostic purposes.

The MyoLIGHT system is a dedicated Gamma Camera for nuclear cardiology, highperformance dual-head nuclear medicine imaging system. It is based on the following legally marketed device: CARDIOSPECT D90 - K021823. Modifications include reduced gantry size, adapted patient table, detectors moving in an enclosed space on a fixed radius and set at 90 degrees, integrated patient table, integrated acquisition station and operator console, and built-in cardiac gating circuitry.

This document is a 510(k) summary for the GE Medical Systems F.I. Haifa MyoLIGHT device, a dedicated Gamma Camera for nuclear cardiology.

1. Acceptance Criteria and Reported Device Performance:

The document states that the MyoLIGHT device is a modification of a legally marketed predicate device, CARDIOSPECT D90 (K021823). The primary acceptance criterion for this 510(k) submission is to demonstrate substantial equivalence to the predicate device in terms of safety and effectiveness.

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

The document mentions "Bench and images data" but does not specify the sample size for the test set or the data provenance (e.g., country of origin, retrospective or prospective).

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

The document does not provide information on the number of experts used to establish ground truth or their qualifications.

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

The document does not specify an adjudication method.

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:

A multi-reader multi-case (MRMC) comparative effectiveness study was not performed, nor is it applicable as this is not an AI-assisted device in the context of the provided document. The study focuses on demonstrating equivalence of the MyoLIGHT system to a predicate device.

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

A standalone performance evaluation (algorithm only) was not explicitly detailed as such. The "Bench and images data" likely refers to technical performance assessment of the system, not an algorithm's performance independent of human interaction for interpretation.

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

The document does not specify the type of ground truth used. The statement "Bench and images data show that the MyoLIGHT images are similar to the CARDIOSPECT D90 (K021823) images" suggests a comparison against the imaging characteristics or output of the predicate device, rather than a clinical ground truth like pathology.

8. The sample size for the training set:

There is no mention of a training set as this device is not described as involving a learning algorithm. The study focuses on comparing a modified device to a predicate.

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

Not applicable, as there is no training set mentioned.

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The Photon Energy Recovery (PER) Option is intended for image quality and quantification improvement by scatter reduction in planar and tomographic nuclear medicine images. This option enables Gated SPECT acquisition in multi-energy window format which allows normal GSPECT processing along with PER scatter correction on the summed data. It can be used for acquisitions of single or multi-peak isotope as well as simultaneous multi-isotope images.

The Photon Energy Recovery (PER) option is a software application for reducing the Compton scatter contribution in nuclear images. This enables scatter correction of single as well as multipeak isotope imaging. In addition it allows for the correction of cross-talk down scatter in simultaneous multiisotope imaging. The method is based on a spectral deconvolution analysis using iterative recurrent linear regressions on nuclear spectra. These spectra are broken down into multiple energy windows. This submission provides an extension on the PER features of the currently legally marketed device, GE Vision Nuclear Medicine Workstation - K012568.

The provided text describes a 510(k) submission for the GE Photon Energy Recovery (PER) Option, a software application designed to reduce Compton scatter in nuclear images for improved image quality and quantification. However, the document does not contain a detailed study proving the device meets specific acceptance criteria. Instead, it makes a general statement about substantial equivalence to a predicate device.

Here's an analysis based on the information available:

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

The document does not explicitly state acceptance criteria in the format of a table or defined metrics. It broadly states that the device is intended for "image quality and quantification improvement by scatter reduction." The "reported device performance" is summarized as:

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

The document does not provide details on the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective). It simply states, "Bench and clinical data show that the PER corrected simultaneous dual-isotope images are similar to the conventional single isotope images." This is a very high-level summary and lacks specifics about the study design.

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

This information is not provided in the document.

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, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

There is no mention of an MRMC comparative effectiveness study or any assessment of human reader improvement with or without AI assistance. The PER Option is described as a software application for image processing, not a tool for direct human-in-the-loop decision support that would typically be evaluated with an MRMC study in this context.

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

While not explicitly called a "standalone study," the statement "Bench and clinical data show that the PER corrected simultaneous dual-isotope images are similar to the conventional single isotope images" implies an assessment of the algorithm's performance in generating images. However, the specifics of this assessment (metrics, methodology) are not detailed.

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

The document does not specify the type of ground truth used. The comparison is made against "conventional single isotope images," implying that these uncorrected images might serve as a baseline or reference, but not necessarily a "ground truth" in the sense of a definitive diagnosis or outcome.

8. The sample size for the training set

The document does not provide any information about a training set since the PER Option is likely based on spectral deconvolution analysis, which might not involve machine learning training in the same way as some other AI algorithms. If there was a training phase for any parameters, it's not described.

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

As no training set is described, information on how its ground truth was established is also absent.

In summary:

The provided 510(k) summary focuses on demonstrating substantial equivalence to a predicate device and broadly describes the device's intended function. It lacks the detailed study information typically found in submissions for novel AI/ML devices that require rigorous performance evaluation against specific acceptance criteria. The claim of "similarity to conventional single isotope images" serves as the primary evidence of effectiveness, but the methodology, sample sizes, and detailed results of this comparison are not provided.

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The intended use of the Quasar system is to perform general Nuclear Medicine imaging procedures for detection of radioisotope tracer uptake in the patient body, using a variety of scanning modes supported by various acquisition types and optional imaging features designed to enhance image quality in Oncolgy, Cardiology, Neurology and other clinical diagnostic imaging applications.

The scanning modes include planar (Static, Multi-gated, Dynamic, Whole body scanning) and tomographic (SPECT . Gated SPECT . Whole body SPECT , Camera based PET - also known as Coincidence Detection). Acquisition types include single and multiisotope/multi-peak frame/list mode single-photon and positron imaging. Optional imaging-enhancement features include assortment of collimators, gating by physiological signals, real-time automatic body contouring, and CT-based attenuation corrcction and functional anatomic mapping.

Not Found

The provided text is a 510(k) summary for the GE Quasar System, a nuclear medicine imaging system. It details the device's name, submitter, classification, and intended use. However, it does not contain any information regarding acceptance criteria, device performance studies, or details about ground truth establishment, sample sizes, or expert qualifications.

Therefore, I cannot provide the requested information based on the given document.

The document primarily focuses on establishing substantial equivalence to legally marketed predicate devices for the purpose of FDA clearance. It does not include the detailed performance study results that would typically contain acceptance criteria and performance metrics.

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