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The equipment is intended to obtain position emission tomography (PET) images of parts of the fit in the patient aperture (e.g. head or breast) to detect abnormal pattern of distribution of a positron emitting radiopharmaceutical. This information can assist in diagnosis, therapeutic outcome assessment.

The SET-5002 PET scanner is designed to image the patient's breast or head which is placed in the aperture provided. The scanner can be easily switched from head to breast mode and back again with the push of a button on the control panel. When positrons are emitted from the radioactive drug administered to the patient, they annihilate electrons in body tissue. That process emits two annihilation photons (hereinafter "gamma rays") with an energy of 511 keV in the 180-degree direction. The PET System collects data by simultaneously counting both of these gamma rays using detectors arranged in a circular configuration. After collecting data for the specified scan time, an image is then reconstructed from that data based on the quantitative distribution of the radioactive drug. The following corrections are necessary for obtaining a quantitative distribution image: Normalization, Attenuation Correction, Scatter Correction, Random Correction, Decay Correction, Standard Time Correction, Count Losses Correction, and Cross Calibration. Operation of the system requires trained persons. The system is designed to be operated in a controlled medical environment such as a hospital or clinic. The unit is supplied with a computer which acquires and stores patient images. The scintillator type is Lutetium. The detectors are SiPM (Silicon photomultipliers).

The provided text describes the Shimadzu SET-5002 PET scanner and its premarket notification to the FDA. While it mentions performance testing and a clinical reader study, the document does not provide explicit acceptance criteria in a quantitative format, nor does it detail a comparative effectiveness study (MRMC) with human readers or a standalone AI performance study.

The information primarily focuses on demonstrating substantial equivalence to a predicate device (BBX-PET Scanner K210450) based on technological characteristics and general performance metrics, rather than specific acceptance criteria for a new AI/software component within the device.

However, based on the available information, I can infer and reconstruct some aspects relevant to performance validation as described:

Derived Acceptance Criteria and Reported Device Performance (Inferred):

Since no explicit quantitative acceptance criteria for image quality or diagnostic accuracy are listed, the "acceptance" is implied by the study's conclusion: "acceptable diagnostic results" and "supports the determination of substantial equivalence."

Study Details based on the provided text:

1. Sample Size and Data Provenance:

   • Test Set Sample Size: 18 images (12 brain images and 6 breast images).
   • Data Provenance: U.S. clinical site. The document states, "A clinical reader study using the SET-5002 was conducted at U.S. clinical site." The retrospective or prospective nature is not explicitly mentioned but typically clinical reader studies for regulatory submission are on retrospectively collected and de-identified data.

2. Number of Experts and Qualifications:

   • Number of Experts: One (singular "radiologist" and "reader attested").
   • Qualifications: "Board certified radiologist." Specific years of experience are not mentioned.

3. Adjudication Method:

   • Method: None specified in the document. The study was conducted with a single board-certified radiologist reviewing the images.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • Was it done?: No. The study described is a single-reader study rather than a multi-reader, multi-case comparative effectiveness study.
   • Effect Size: Not applicable, as no MRMC study was conducted comparing human readers with and without AI assistance. The study assessed "overall image quality... demonstrated acceptable diagnostic results" rather than an improvement metric for human readers.

5. Standalone (Algorithm Only) Performance Study:

   • Was it done?: No. The described "clinical reader study" involved a human radiologist reviewing images produced by the SET-5002. There is no mention of an AI algorithm within the SET-5002 that would have standalone performance measured independent of human interpretation. The SET-5002 is a PET imaging system, not an AI diagnostic software.

6. Type of Ground Truth Used:

   • Type: The ground truth for the reader study appears to be the assessment by the board-certified radiologist herself regarding "acceptable diagnostic results" and "PET imaging findings related to the progression of dementia, tumor detection, and the extent of spread." It's not stated that the images were confirmed against a definitive ground truth like pathology or long-term clinical outcomes. It implies the radiologist's assessment of image quality for diagnostic purposes served as the "ground truth" for the device's performance in this context.

7. Training Set Sample Size:

   • Sample Size: Not applicable. The SET-5002 is described as an "Emission Computed Tomography System" (PET scanner), not an AI/machine learning software that requires a training set. The performance validation for such a device typically involves demonstrating image quality and physical performance metrics (resolution, sensitivity, etc.) and confirming suitability for diagnostic use by experts.

8. How Ground Truth for Training Set was Established:

   • Method: Not applicable, as there is no mention of a training set for an AI/ML algorithm within the device.

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The Trinias is an angiographic X-ray system, which is used for diagnostic imaging and interventional procedures. The Trinias is intended to be used for cardiac angiography, neurovascular angiography, abdominal angiography, peripheral angiography, rotational angiography, multi-purpose angiography and whole body radiographic/fluoroscopic procedures.

This notification is for a modification is the addition of the new MH-700 (ceiling mounted C-Arm). This is the functional equivalent of previous C-Arm MH-400. All options declared under K203535 remain available. The Lateral Angiographic C-arm Support MH-700 allows examination of a patient by fluoroscopy or radiography from different angles with the patient kept in a horizontal position in combination with a Frontal C-arm Support MH-600, an X-ray high voltage unit, X-ray tube unit, X-ray image recording unit (FPD), digital angiography system, catheterization table, etc. The range of available digital receptor panels remains unchanged from our predicate K203535. For installation, ceiling strength is important because the total weight of MH-700 exceeds 1200 kg. The MH-700 features faster movement speeds as compared to the previous model, the MH-400.

The provided document is a 510(k) Premarket Notification for a medical device called "Trinias." It focuses on a modification to the device (addition of a new C-Arm model MH-700) and its substantial equivalence to a predicate device (Trinias K203535).

Based on the information provided, no clinical study was conducted to establish acceptance criteria for device performance related to diagnostic accuracy or interventional outcomes with human-in-the-loop or standalone AI performance. This document primarily focuses on demonstrating substantial equivalence through non-clinical testing, compliance with standards, and comparison of technical specifications.

Therefore, many of the specific points requested regarding acceptance criteria and clinical study details cannot be answered from the provided text.

Here's a breakdown of what can be extracted and what cannot:

1. Table of acceptance criteria and the reported device performance:

The document doesn't present "acceptance criteria" in the context of diagnostic performance metrics (e.g., sensitivity, specificity, accuracy) or interventional success rates. Instead, the "performance" described pertains to technical specifications and compliance with safety and electrical standards.

2. Sample size used for the test set and the data provenance: Not applicable. No clinical test set. The testing was non-clinical (bench and standards testing).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. No clinical test set.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable. No clinical 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: Not applicable. No MRMC study was done, nor does the device appear to include AI for interpretation or improvement of human reader performance.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable. This device is an X-ray system, not an AI algorithm for standalone performance.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable to a clinical performance study. For non-clinical testing, "ground truth" would relate to engineering specifications and performance against established standards, confirmed by third-party laboratories.

8. The sample size for the training set: Not applicable. This document does not describe training of an AI algorithm based on a dataset.

9. How the ground truth for the training set was established: Not applicable.

In summary: The provided 510(k) summary focuses entirely on non-clinical aspects to demonstrate substantial equivalence of a modified X-ray system to its predicate. It explicitly states: "Summary of clinical testing: Not applicable. Clinical testing was not deemed to show substantial equivalence. We relied on non-clinical testing and compliance with standards." Therefore, questions related to clinical study design, performance metrics (like sensitivity, specificity), ground truth, expert readers, or AI-related evaluations cannot be answered from this document.

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The Trinias is an angiographic X-ray system, which is used for diagnostic imaging and interventional procedures. The Trinias is intended to be used for cardiac angiography, neurovascular angiography, abdominal angiography, peripheral angiography, rotational angiography, multi-purpose angiography and whole body radiographic/fluoroscopic procedures.

This notification is for a modified device. The modifications are: Updated user interfaces (wireless mouse, keyboard) A new model of catheterization table A new type of digital system console Additional x-ray tube choices Add alternate choices for the same sizes of digital flat panel detectors An additional size of available flat panel detector (12" x 16") An additional type of control cabinet.

This document is a 510(k) summary for the Shimadzu Trinias angiographic X-ray system. It describes modifications to an existing device and demonstrates substantial equivalence to a predicate device (K123508).

Based on the provided document, the device in question is a medical imaging system (angiographic X-ray system), not an AI/ML-based device. Therefore, the typical acceptance criteria and study designs associated with AI/ML systems (e.g., performance metrics like sensitivity/specificity, multi-reader multi-case studies, ground truth establishment by experts, training/test set provenance) are not applicable here.

The regulatory approval for this device is based on showing substantial equivalence to a previously cleared predicate device, rather than proving performance against specific AI/ML acceptance criteria. The modifications are hardware and software updates to the existing X-ray system.

Here's an analysis of the provided information in the context of device approval, highlighting why AI/ML-specific criteria are not met or relevant:

1. Table of Acceptance Criteria and Reported Device Performance

Not Applicable (for AI/ML performance).

Since this is not an AI/ML device, there are no acceptance criteria related to typical AI/ML performance metrics (e.g., accuracy, sensitivity, specificity, AUC).

The "acceptance criteria" for this submission are compliance with various safety and performance standards for X-ray systems. The reported "performance" is that the modified device meets these standards and is comparable to the predicate.

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

Not Applicable (for AI/ML test set data).

There is no "test set" in the sense of a clinical image dataset used to evaluate an AI algorithm's diagnostic performance. The testing performed was non-clinical bench and standards testing. This involves engineering tests, electrical safety tests, radiation safety compliance tests, and software validation tests.

The data provenance refers to the origin of the device's design, manufacturing, and testing; it does not refer to clinical image data. The manufacturer is Shimadzu Corporation, based in Kyoto, Japan.

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

Not Applicable.

No "ground truth" derived from expert interpretation of medical images was established for this submission, as it's not an AI/ML diagnostic aid. The device's performance is validated against engineering specifications, safety standards, and functional requirements.

4. Adjudication Method for the Test Set

Not Applicable.

Since there's no expert interpretation of a test set, there is no adjudication method.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No. A MRMC study was not done.

MRMC studies are typically performed for AI/ML diagnostic devices to assess how human reader performance (e.g., radiologists) improves with AI assistance compared to without it. This submission is for an X-ray imaging system itself, not an AI-assisted diagnostic tool.

Therefore, there is no effect size of how human readers improve with AI vs. without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

No.

This concept applies to AI/ML algorithms that can produce an output independently. The Trinias is an imaging system; its "performance" is its ability to acquire images, comply with safety standards, and function as intended.

7. The Type of Ground Truth Used

Compliance with regulated standards and functional specifications.

The "ground truth" for this device's approval lies in its adherence to international safety standards (e.g., IEC 60601 series, IEC 62304 for software) and U.S. performance standards (21 CFR 1020.30, .31, .32), as well as verification of its mechanical and electrical functions. This is demonstrated through "bench and standards testing" and "proper system operation is fully verified upon installation."

8. The Sample Size for the Training Set

Not Applicable.

This refers to training data for AI/ML models. The Trinias is a hardware and software system. While its internal software components undergo development and testing, there isn't a "training set" in the AI/ML sense.

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

Not Applicable.

As there is no AI/ML training set, there is no ground truth established for it. Software validation (IEC 62304) and adherence to design specifications guide the software development, but this is distinct from training an AI model.

In summary: The provided document is a 510(k) submission for an updated medical imaging hardware system (X-ray). Its approval focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance and safety testing, and compliance with established regulatory standards. It does not involve AI/ML technology or associated clinical performance studies with diagnostic accuracy endpoints.

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