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The ergo Imaging System is intended to image the distribution of radionuclides in the body by means of a photon radiation detector. In so doing, the system produces images depicting the anatomical distribution of radioisotopes within the human body for interpretation by authorized medical personnel. The ergo Imaging System is used by trained medical personnel to perform nuclear medicine studies.

It is indicated for lymphatic scintigraphy and parathyroid scintigraphy, It can be used intraoperatively when protected by sterile drapes. It is also indicated to aid in the evaluation of lesions in the breast and other small body parts. When used for breast imaging, it is indicated to serve as an adjunct to mammography or other primary breast imaging modalities.

The ergo Imaging System incorporates Digirad's Solid State RIM detector design with 3mm pixels for general purpose planar imaging, cleared under K100838. Sterile drapes are specified for intraoperative use. The ergo Imaging System, in conjunction with the optional Breast Imaging Accessory (BIA), enables the user to perform scintimammography and extremity imaging with stabilization.

The provided text is a 510(k) summary for the Digirad ergo Imaging System, which is a gamma camera. The document primarily focuses on demonstrating substantial equivalence to a predicate device and expanding indications for use.

Based on the provided text, the device itself is a gamma camera, not an AI/ML-based device. The "Testing" section (H) explicitly states: "Verification and Validation tests were conducted to demonstrate the ergo Imaging System functions per specification. These tests include Electromagnetic Compatibility, Electrical Safety, and gamma camera performance testing including NEMA standard NU 1-2007 with phantoms."

This indicates that the acceptance criteria and performance evaluation are related to the physical performance of the gamma camera, not to algorithmic performance on image interpretation. Therefore, the requested information elements related to AI/ML device testing (such as ground truth establishment with experts, MRMC studies, standalone algorithm performance, training/test set sample sizes for algorithms, etc.) are not applicable to this submission as described.

The acceptance criteria are likely standard NEMA performance metrics for gamma cameras. While the document broadly states "Testing results demonstrate that the ergo Imaging System continues to meet the specifications," it does not list specific numerical acceptance criteria or performance metrics in a table format within this 510(k) summary.

Therefore, many of the requested items cannot be extracted from this specific document.

Here's an attempt to answer the quantifiable parts based on the provided text, while noting the limitations:

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

• Acceptance Criteria: Not explicitly listed as numerical targets in the summary. Implied to be compliance with NEMA NU 1-2007 standards for gamma camera performance.
• Reported Device Performance: Not explicitly listed as numerical results in the summary. The summary states: "Testing results demonstrate that the ergo Imaging System continues to meet the specifications and is substantially equivalent to the predicate devices, based on comparisons of intended use and technology, and overall system performance."

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: Not applicable in the context of an AI/ML test set. The testing described involves physical phantoms and engineering tests, not patient data sets.
• Data Provenance: Not applicable.

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. Ground truth was established by physical phantoms and engineering measurements according to NEMA standards for gamma camera performance.

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

• Not applicable.

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/ML device, and no MRMC study is mentioned.

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

• Not applicable. This is not an AI/ML diagnostic algorithm.

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

• Physical phantoms and engineering specifications/measurements (NEMA standard NU 1-2007).

8. The sample size for the training set

• Not applicable. This is not an AI/ML device; there is no training set mentioned.

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

• Not applicable.

In summary, the provided document describes a traditional medical device (gamma camera) and its regulatory submission, which relies on engineering performance standards (like NEMA) rather than clinical studies involving AI/ML performance on patient data sets with human experts.

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The ergo Imaging System is intended to image the distribution of radionuclides in the body by means of a photon radiation detector. In so doing, the system produces images depicting the anatomical distribution of radioisotopes within the human body for interpretation by authorized medical personnel.

The proposed changes involve modifications to the 2020tc Imaging System to increase size of the detector head field of view (FOV) from 8"x 8" to 12"x 15". Modifications include mechanical and electrical design changes to support the large field-of-view (LFOV) detector head. The modified device (ergo Imaging System) incorporates Digirad's solid-state RIM detector design, modified with 3mm size pixels required for general purpose planar imaging. The 2020tc Imager detectors currently utilize the same 3mm pixel size.

The detectors used in the modified and predicate device utilize a pixelated, multi-crystal CsI scintillator detector with each pixel optically coupled to a low noise photodiode array. The charge detected from each gamma ray is amplified and processed using an amplifier circuit. The RIM detector design technology is currently used in the Digirad Cardius XPO imager systems with a 6mm pixel size for Cardiac SPECT imaging. The RIM detector has been modified to incorporate a 3mm size pixel required for general planar imaging. The RIM detector design includes electrical and mechanical configurations allowing for field replacement of detector modules and improved system performance (better energy resolution). The updated design of the RIM detector head assembly allows some of the current sub-systems to be moved into the detector head assembly (air dryer) and simplification of others (cooling and power distribution systems).

The modified device uses the 2020tc Imaging System SeeQuanta Acquisition software, with minor modifications required for use with the 3mm pixel size RIM detector modules.

The 2020tc Imager was initially marketed as the Digirad Notebook Imager (K961104), then re-branded as the 2020tc Imaging System (K982855) when it was used in conjunction, with the SPECTour Rotating Chair to obtain SPECT images in patients who are seated in an upright position. The modified device (ergo Imaging System) is a general purpose Nuclear Medicine Imaging device used for planar imaging, the same as the Notebook Imager/20201c Imager when imaging without the rotating chair.

The provided text describes a 510(k) premarket notification for the "ergo Imaging System," a scintillation (gamma) camera. The submission focuses on demonstrating substantial equivalence to predicate devices, rather than a standalone clinical study proving specific acceptance criteria in terms of diagnostic performance metrics like sensitivity or specificity.

Here's an analysis of the provided information based on your questions:

1. Table of acceptance criteria and the reported device performance

The document does not specify quantitative acceptance criteria for diagnostic performance (e.g., sensitivity, specificity, accuracy) that are typically reported for AI/algorithm-based devices. Instead, it focuses on demonstrating functional equivalence and meeting design specifications compared to predicate devices. The acceptance criteria essentially revolve around the system performing as per its specifications, which are similar to the predicate device's functional specifications, and not raising new safety or effectiveness concerns.

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

The document does not explicitly mention a "test set" in the context of diagnostic performance (e.g., a set of patient images for evaluation). The testing described is primarily internal verification and validation of the system's technical specifications and phantom images. Therefore, details like data provenance or sample size for a diagnostic test set are not provided.

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

Not applicable. The document describes technical testing and phantom images, not a clinical study involving experts establishing ground truth for diagnostic accuracy.

4. Adjudication method for the test set

Not applicable, as no diagnostic test set 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 MRMC study is mentioned. This device is a gamma camera, a hardware imaging system, not an AI-assisted diagnostic software.

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

This is a hardware device; the concept of "standalone performance" for an algorithm doesn't directly apply in the same way it would for AI-driven software. The "system meets design specifications" and "equivalent efficacy to predicate devices" with phantom images represent its standalone performance relative to its intended function.

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

The document refers to "phantom images" and "Digirad internal testing." This implies the "ground truth" for the testing was based on known properties of the phantoms and the expected technical performance of the imaging system. There is no mention of clinical ground truth like pathology or outcomes data.

8. The sample size for the training set

Not applicable. This document describes a hardware device submission, not a machine learning algorithm that requires a training set.

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

Not applicable, as no training set is described.

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