(27 days)
The uMI Panvivo is a PET/CT system designed for providing anatomical and functional images. The PET provides the distribution of specific radiopharmaceuticals. CT provides diagnostic tomographic anatomical information as well as photon attenuation information for the scanned region. PET and CT scans can be performed separately. The system is intended for assessing metabolic (molecular) and physiologic functions in various parts of the body. When used with radiopharmaceuticals approved by the regulatory authority in the country of use, the uMI Panvivo system generates images depicting the distribution of these radiopharmaceuticals. The images produced by the uMI Panvivo are intended for analysis and interpretation by qualified medical professionals. They can serve as an aid in detection, localization, evaluation, diagnosis, staging, re-staging, monitoring, and/or follow-up of abnormalities, lesions, tumors, inflammation, infection, organ function, disorders, and/or diseases, in several clinical areas such as oncology, infection and inflammation, neurology. The images produced by the system can also be used by the physician to aid in radiotherapy treatment planning and interventional radiology procedures.
The CT system can be used for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer. The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical societv. *
- Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.
The proposed device uMI Panvivo combines a 235/295 mm axial field of view (FOV) PET and 160-slice CT system to provide high quality functional and anatomical images, fast PET/CT imaging and better patient experience. The system includes PET system. CT system, patient table, power distribution unit, control and reconstruction system (host, monitor, and reconstruction computer, system software, reconstruction software), vital signal module and other accessories.
The uMI Panvivo was previously cleared by the FDA via K241596. The modification in this submission involves the addition of a new model. The previous uMI Panvivo(K241596) is designed with scalable PET rings and uMI Panvivo S is scaling to 80 PET rings compare to the uMI Panvivo 100 PET rings.
This document does not contain the detailed acceptance criteria and study information that would typically be found in a FDA Summary of Safety and Effectiveness Data (SSED) report or a more comprehensive clinical study report. The provided text is a 510(k) Summary, which primarily focuses on demonstrating substantial equivalence to a predicate device rather than providing extensive details on novel performance studies for acceptance criteria.
However, based on the limited information available in the "Performance Verification" section on page 8, I can infer some points related to image quality and the type of evaluation performed.
Here's a breakdown of what can and cannot be extracted from the provided text according to your requested categories:
1. A table of acceptance criteria and the reported device performance
The document states:
"A Sample clinical images were reviewed by U.S. board-certified radiologist. It was shown that the proposed device can generate images as intended and the image quality is sufficient for diagnostic use."
This implies that the acceptance criteria for image quality were met, as determined by a qualified professional. However, the specific quantitative acceptance criteria (e.g., minimum spatial resolution, signal-to-noise ratio, contrast-to-noise ratio, lesion detection sensitivity/specificity targets) and the reported device performance against these specific criteria are not detailed in this summary.
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
The document mentions "Sample clinical images." The exact sample size of images or cases used in this review is not specified. The data provenance (country of origin, retrospective/prospective nature) is also not mentioned.
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)
The document states that images were "reviewed by U.S. board-certified radiologist."
- Number of experts: Singular ("radiologist") suggests one radiologist, but it could also implicitly mean "radiologists" as a group of experts. The exact number is unclear.
- Qualifications: "U.S. board-certified radiologist" is a qualification. Specific experience (e.g., "10 years of experience") is not provided.
- Role in ground truth: Based on the text, the radiologist(s) reviewed images to confirm "image quality is sufficient for diagnostic use." This implies they evaluated the image quality itself, rather than strictly establishing a ground truth for a diagnostic task (e.g., confirming presence/absence of a lesion against a gold standard).
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
Since the number of experts is unclear or potentially singular, and the nature of the review was for "image quality is sufficient for diagnostic use," an explicit adjudication method like 2+1 or 3+1 is not mentioned and likely not applied in the traditional sense for a diagnostic ground truth.
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
The document does not describe an MRMC comparative effectiveness study, nor does it mention AI assistance. The device is described as a PET/CT system, and the performance verification mentions evaluation of image quality by a radiologist. This is not an MRMC study comparing human readers with and without AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This section describes a PET/CT imaging system, not an AI algorithm. Therefore, the concept of "standalone (algorithm only)" performance does not directly apply to the described device in this context. The study described is a human evaluation of the device's output (images).
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The verification states that images were reviewed by a radiologist to determine if "image quality is sufficient for diagnostic use." This implies a subjective expert evaluation of image quality rather than a definitive "ground truth" established by pathology, clinical outcomes, or expert consensus for a diagnostic task. The ground truth here is essentially the radiologist's assessment of image diagnostic sufficiency.
8. The sample size for the training set
The document does not describe any machine learning or AI components that would require a "training set." It focuses on verification of a hardware imaging system. Therefore, a sample size for a training set is not applicable and not mentioned.
9. How the ground truth for the training set was established
As there is no mention of a training set, this information is not applicable and not provided.
Summary of what is available and what is missing:
The provided 510(k) Summary focuses on demonstrating substantial equivalence of the uMI Panvivo with a new model (uMI Panvivo S) to its predicate device (uMI Panvivo K241596). The "Performance Verification" section mentions a review of sample clinical images by a U.S. board-certified radiologist to confirm that the device generates images as intended and that the image quality is sufficient for diagnostic use. This is a very high-level statement and lacks the quantitative details typically associated with detailed acceptance criteria and study results. The document does not provide specifics on:
- Quantitative acceptance criteria for image quality or diagnostic performance.
- Specific device performance metrics against these criteria.
- The exact sample size of images/cases.
- The data provenance (country, retrospective/prospective).
- The precise number of experts or their detailed experience.
- Any formal adjudication method for ground truth.
- MRMC studies for AI assistance or standalone algorithm performance.
- Details on how "ground truth" was established beyond general expert review of image sufficiency.
- Training set information, as it's not an AI/ML device per se in this context.
§ 892.1200 Emission computed tomography system.
(a)
Identification. An emission computed tomography system is a device intended to detect the location and distribution of gamma ray- and positron-emitting radionuclides in the body and produce cross-sectional images through computer reconstruction of the data. This generic type of device may include signal analysis and display equipment, patient and equipment supports, radionuclide anatomical markers, component parts, and accessories.(b)
Classification. Class II.