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The GE MyoSPECT and MyoSPECT ES systems are a medical tool intended for use by appropriately trained healtheare professionals to aid in detecting, localizing and diagnosing of cardiac diseases and heart function of diseases, abnormalities, and disorders. The systems output can also be used by the physician for planning, guiding, and monitoring.

MyoSPECT and MyoSPECT ES perform Nuclear Medicine (NM) imaging procedures for the detection of radioisotope tracer uptake in the patient body, using tomographic scanning of single or multi-isotopes with either single or multi energy peaks. The tomographic scanning is supported by various acquisition types.

The GE MyoSPECT and MyoSPECT ES systems include signal analysis and display equipment, patient and equipment supports, components and accessories. The systems may include data and image processing to produce reconstructed trans-axial images. The images can also be post processed to obtain additional images, imaging planes, analysis results and uptake quantitation.

The systems may be used for patients of all ages.

The GE MyoSPECT and MyoSPECT ES are Single Photon Emission Computed Tomography (SPECT) systems intended for nuclear cardiology imaging. MyoSPECT and MyoSPECT ES include a nuclear medicine imaging system using a CZT-based multi-detector array. MyoSPECT ES are identical systems that are differentiated by the number of detectors within the multi-detector array. MyoSPECT is offered with 19 detectors as opposed to 9 detectors for MyoSPECT ES is upgradable to MyoSPECT. The MyoSPECT and MyoSPECT ES systems do not introduce any new Intended Use.

The multi-detector array on MyoSPECT ES has a multi-pinhole collimator providing each detector with a pinhole collimator that is focused on a volume in space, all together forming the Quality Field of View (Q.FOV) in which the patient heart is positioned for a cardiac scan.

MyoSPECT and MyoSPECT ES add an option for Extended Field of View (EFOV) processing that enables more flexible positioning. The predicate device patient positioning workflow is also enhanced to provide users with visual means of the Q.FOV and EFOV boundaries, and recommendations on the use of Q.FOV or EFOV. The enhanced workflow is named "Smart Positioning" workflow. MyoSPECT's and MyoSPECT ES's patient table is the same as the one used on the NM 830 reference device with slight adaptations.

The provided text describes the GE MyoSPECT and MyoSPECT ES systems, which are SPECT imaging systems for cardiac diseases. This document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing extensive details of a standalone clinical study with predefined acceptance criteria for performance metrics.

However, based on the provided text, here's an attempt to extract and infer the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria for device performance. Instead, it relies on demonstrating that the device's performance, as evaluated by experienced Nuclear Medicine (NM) physicians in a reader study, is "acceptable" and supports "substantial equivalence" to the predicate device. The clinical study's primary output for reporting appears to be qualitative assessments.

Inferred Acceptance Criteria based on the spirit of the 510(k) submission:

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

• Test Set Sample Size: 28 studies for MyoSPECT and 32 studies for MyoSPECT ES.
• Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It only mentions "clinically representative sample."

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

• Number of Experts: Three (3) experienced NM physicians.
• Qualifications of Experts: Described as "experienced NM physicians." Specific years of experience or board certifications are not provided.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1). It only states that the three experienced NM physicians scored the images and attested to acceptable diagnostic results. It's unclear if there was a consensus process or if individual physician assessments were aggregated.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the effect size of how much human readers improve with AI vs without AI assistance

There is no indication of an MRMC comparative effectiveness study involving AI assistance or human reader improvement with AI. The study described is a clinical reader study to evaluate image quality and diagnostic acceptability of MyoSPECT/MyoSPECT ES images. The device itself is a SPECT imaging system, and the document does not mention any integrated AI components for image interpretation or reader assistance.

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

Based on the information provided, a standalone algorithm performance study (without human-in-the-loop) was not performed, nor is it applicable to this submission. The device is an imaging system, not an AI algorithm intended for standalone diagnostic interpretation. Performance testing focused on the system's ability to produce diagnostically acceptable images for human readers.

7. The Type of Ground Truth Used

The ground truth for the clinical reader study relied on the expert consensus (or individual expert assessment, as the adjudication method is unclear) of the three experienced NM physicians regarding the "acceptable diagnostic results" of the images. It does not mention pathology or outcomes data as independent ground truth.

8. The Sample Size for the Training Set

The document does not mention a training set sample size. This is expected as the submission describes a hardware device (SPECT system) rather than an AI/ML algorithm that would typically require a training set. The clinical reader study serves as a validation of the system's output for human interpretation.

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

As there is no mention of a training set, there is no information on how its ground truth was established. This applies since the device is a SPECT imaging system, not an AI algorithm with a training phase.

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