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PHAROS is a dedicated PET scanner intended to obtain Positron Emission Tomography (PET) images of parts of human body that fit in the patient aperture (brain, breast, arms and legs) to detect abnormal patterns of distribution of radioactivity after injection of a positron emitting radiopharmaceutical. This information can assist in diagnosis, therapeutic planning and therapeutic outcome assessment.

PHAROS is a specialized high-sensitivity and high-resolution PET system designed for imaging specific organs, such as the brain, breast, arms and legs.

Positron emission tomography (PET) captures images by detecting the distribution of internal radioactivity in human organs, utilizing radioactive pharmaceuticals. This technology reconstructs the body's internal biochemical and metabolic processes, producing high-resolution 3D visualizations. The method involves measuring a pair of simultaneous gamma rays, each with an energy of 511 keV, resulting from the annihilation of positrons. By labeling the positron emitter with a tracer and using a ring-shaped gamma ray detector, the spatial location of positron-emitting nuclides within the body is visualized.

PHAROS features four different scanning modes, each tailored for specific types of imaging:

1. Brain Scan Mode (Sitting Position):
   This mode is designed for brain imaging while the patient is seated.

2. Brain Scan Mode (Lying Position):
   This mode is designed for brain imaging while the patient lies down on a bed.

3. Breast Scan Mode:
   This mode is designed for breast imaging while the patient lies in a prone position.

4. Periphery Scan Mode:
   This mode is designed for imaging the periphery of the body, including the arms, hands, legs, and knees.

For both upper and lower extremity imaging, the height of detector head can be adjusted to ensure optimal patient comfort and accurate positioning. Aside from the physical height adjustment of the detector head, there is no difference in image acquisition method or image generation algorithm between upper and lower extremity scans.

Here's a summary of the acceptance criteria and study information for the PHAROS device, based on the provided FDA 510(k) clearance letter:

1. Table of Acceptance Criteria and Reported Device Performance

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The Siemens PET/CT systems are combined X-Ray Computed Tomography (CT) and Positron Emission Tomography (PET) scanners that provide registration and fusion of high resolution physiologic and anatomic information.

The CT component produces cross-sectional images of the body by computer reconstruction of X-Ray transmission data from either the same axial plane taken at different angles or spiral planes taken at different angles. The PET subsystem images and measures the distribution of PET radiopharmaceuticals in humans for the purpose of determining various metabolic (molecular) and physiologic functions within the human body and utilizes the CT for fast attenuation correction maps for PET studies and precise anatomical reference for the fused PET and CT images.

The system maintains independent functionality of the CT and PET devices, allowing for single modality CT and/or PET diagnostic imaging.

These systems are intended to be utilized by appropriately trained health care professionals to aid in detecting, localizing, diagnosing, staging and restaging of lesions, tumors, disease and organ function for the evaluation of diseases and disorders such as, but not limited to, cardiovascular disease, neurological disorders and cancer. The images produced by the system can also be used by the physician to aid in radiotherapy treatment planning and interventional radiology procedures.

This system can be used for low dose lung cancer screening in high risk populations.*

*As defined by professional medical societies. 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.

Biograph Trinion PET/CT systems are combined multi-slice X-Ray Computed Tomography and Positron Emission Tomography scanners. This system is designed for whole body oncology, neurology and cardiology examinations. Biograph Trinion PET/CT systems provide registration and fusion of high-resolution metabolic and anatomic information from the two major components of each system (PET and CT). Additional components of the system include a patient handling system and acquisition and processing workstations with associated software.

Biograph Trinion VK20 software is a command-based program used for patient management, data management, scan control, image reconstruction and image archival and evaluation. All images conform to DICOM imaging format requirements.

Biograph PET/CT systems, which are the subject of this application, are substantially equivalent to the commercially available Biograph Trinion VK10 family of PET/CT systems (K233677). Differences compared to the commercially available Biograph Trinion systems include:

• The commercially available SOMATOM go.All and go.Top systems with VB10 (K233650) software have been incorporated into the Biograph Trinion VK20 systems, including commercially available CT features.

• Additional PET axial field of view (FoV) systems allowing for more scalability.

• Additional patient communication and comfort features.

• PET respiratory gating with an external gating device has been implemented.

The Biograph Trinion models may also use the names Biograph Mission, Biograph Wonder, Biograph Ambition and Biograph Devotion for marketing purposes.

The provided FDA 510(k) clearance letter for the Biograph Trinion PET/CT system primarily focuses on demonstrating substantial equivalence to a predicate device and adherence to recognized performance standards. It indicates that "all performance testing met the predetermined acceptance values," but does not provide specific numerical acceptance criteria or reported device performance for an AI/algorithm component, nor does it detail a study proving the device meets AI-specific acceptance criteria. The context suggests the "performance testing" refers to general PET/CT system performance, not AI-driven diagnostic assistance.

Therefore, many of the requested details, particularly those related to a standalone AI algorithm's performance, human-in-the-loop studies, dataset characteristics (sample size, provenance), and ground truth establishment methods for an AI component, are not available in the provided text.

Based on the information available in the document, here's what can be extracted and inferred, with explicit notes where information is missing or not applicable in the context of an AI study.

Acceptance Criteria and Reported Device Performance

The document states that "all performance testing met the predetermined acceptance values." However, it does not specify what those acceptance values were or the precise reported performance metrics beyond this general statement. The tests conducted were primarily related to the physical performance of the PET/CT system as per NEMA NU 2:2024 and NEMA XR 25:2019 standards, not specifically an AI component for diagnostic aid.

Table of Acceptance Criteria and Reported Device Performance (Based on available information for the PET/CT system):

Study Details (Focusing on AI-related aspects where applicable, and general system testing otherwise)

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

   • For System Performance (NEMA tests): The document does not specify a "test set" in terms of patient data. NEMA tests typically involve phantom studies rather than patient data. Thus, sample size and data provenance are not applicable in the traditional sense for these tests.
   • For AI Component: The document does not provide any information on a test set (patient cases, images) or data provenance (e.g., country of origin, retrospective/prospective) for validating an AI component for diagnostic assistance. The descriptions are entirely about the physical PET/CT system.

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

   • For System Performance: Ground truth for NEMA tests is established by physical measurements and calibration standards, not human experts.
   • For AI Component: This information is not provided in the document as there's no mention of an AI-driven diagnostic aid requiring expert-established ground truth.

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

   • For System Performance: Not applicable.
   • For AI Component: This information is not provided in the document.

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:

   • The document does not indicate that an MRMC study was performed for an AI component. The focus is on the substantial equivalence of the PET/CT hardware and software to a predicate device, and compliance with performance standards for the imaging system itself.

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

   • The document does not detail any standalone algorithm performance testing. The performance testing described is for the integrated PET/CT system's physical and functional characteristics.

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

   • For System Performance: Ground truth for NEMA tests involves physical phantoms and established measurement protocols.
   • For AI Component: This information is not provided in the document.

7. The sample size for the training set:

   • This information is not provided in the document, as there is no mention of an AI model that undergoes a separate training process requiring a distinct training set.

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

   • This information is not provided in the document, as there is no mention of an AI model's training set.

Summary of Device and Performance Information from Document:

The provided 510(k) clearance letter for the Biograph Trinion is for a PET/CT imaging system, not an AI-based diagnostic software. The "performance testing" described in the document pertains to the physical and functional aspects of the PET/CT scanner (e.g., spatial resolution, sensitivity, image quality) as measured against industry standards (NEMA NU 2:2024). The clearance is based on proving substantial equivalence to a predicate device and adherence to these well-established performance standards for imaging hardware.

Therefore, the detailed questions regarding AI acceptance criteria, AI test set characteristics, human-in-the-loop studies, and AI ground truth establishment are not addressed in this document because the device being cleared is the imaging system itself, not an AI software component for image analysis or diagnostic support. The document implies that the system can be used for certain clinical applications (like lung cancer screening), but it doesn't describe an automated AI system within the device that requires separate clinical validation with reader studies or large patient datasets.

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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, cardiology, neurology, infection and inflammation. 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 society.

The proposed device uMI Panvivo combines a 295/235 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 has been previously cleared by FDA via K243538. The main modifications performed on the uMI Panvivo (K243538) in this submission are due to the addition of Deep MAC(also named AI MAC), Digital Gating(also named Self-gating), OncoFocus(also named uExcel Focus and RMC), NeuroFocus(also named HMC), DeepRecon.PET (also named as HYPER DLR or DLR), uExcel DPR (also named HYPER DPR or HYPER AiR)and uKinetics. Details about the modifications are listed as below:

• Deep MAC, Deep Learning-based Metal Artifact Correction (also named AI MAC) is an image reconstruction algorithm that combines physical beam hardening correction and deep learning technology. It is intended to correct the artifact caused by metal implants and external metal objects.

• Digital Gating (also named Self-gating, cleared via K232712) can automatically extract a respiratory motion signal from the list-mode data during acquisition which called data-driven (DD) method. The respiratory motion signal was calculated by tracking the location of center-of-distribution(COD) in body cavity mask. By using the respiratory motion signal, system can perform gate reconstruction without respiratory capture device.

• OncoFocus (also named uExcel Focus and RMC, cleared via K232712) is an AI-based algorithm to reduce respiratory motion artifacts in PET/CT images and at the same time reduce the PET/CT misalignment.

• NeuroFocus (also named HMC) is head motion correction solution, which employs a statistics-based head motion correction method that correct motion artifacts automatically using the centroid-of-distribution (COD) without manual parameter tuning to generate motion free images.

• DeepRecon.PET (also named as HYPER DLR or DLR, cleared via K193210) uses a deep learning technique to produce better SNR (signal-to-noise-ratio) image in post-processing procedure.

• uExcel DPR (also named HYPER DPR or HYPER AiR, cleared via K232712) is a deep learning-based PET reconstruction algorithm designed to enhance the SNR of reconstructed images. High-SNR images improve clinical diagnostic efficacy, particularly under low-count acquisition conditions (e.g., low-dose radiotracer administration or fast scanning protocols).

• uKinetics(cleared via K232712) is a kinetic modeling toolkit for indirect dynamic image parametric analysis and direct parametric analysis of multipass dynamic data. Image-derived input function (IDIF) can be extracted from anatomical CT images and dynamic PET images. Both IDIF and populated based input function (PBIF) can be used as input function of Patlak model to generate kinetic images which reveal biodistribution map of the metabolized molecule using indirect and direct methods.

The provided FDA 510(k) clearance letter describes the uMI Panvivo PET/CT System and mentions several new software functionalities (Deep MAC, Digital Gating, OncoFocus, NeuroFocus, DeepRecon.PET, uExcel DPR, and uKinetics). The document includes performance data for four of these functionalities: DeepRecon.PET, uExcel DPR, OncoFocus, and DeepMAC.

The following analysis focuses on the acceptance criteria and study details for these four AI-based image processing/reconstruction algorithms as detailed in the document. The document presents these as "performance verification" studies.

Overview of Acceptance Criteria and Device Performance (for DeepRecon.PET, uExcel DPR, OncoFocus, DeepMAC)

The document details the evaluation of four specific software functionalities: DeepRecon.PET, uExcel DPR, OncoFocus, and DeepMAC. Each of these has its own set of acceptance criteria and reported performance results, detailed below.

1. Table of Acceptance Criteria and Reported Device Performance

Coefficient of Variation (COV) calculated using uniform cylindrical phantom data on images reconstructed with both uExcel DPR and OSEM methods. | The averaged CR, BV, and CNR of the uExcel DPR images should be superior to those of the OSEM images.

uExcel DPR requires fewer counts to achieve a matched COV compared to OSEM. | Pass.

• NEMA IQ Phantom Analysis: an average noise reduction of 81% and an average SNR enhancement of 391% were observed.
• Uniform cylindrical Analysis: 1/10 of the counts can obtain the matching noise level. |
  | | Qualitative evaluation | uExcel DPR images reconstructed at lower counts qualitatively compared with full-count OSEM images. | uExcel DPR reconstructions with reduced count levels demonstrate comparable or superior image quality relative to higher-count OSEM reconstructions. | Pass.
• 1.72.5 MBq/kg radiopharmaceutical injection conditions, combined with 23 minutes whole-body scanning (4~6 bed positions), achieves comparable diagnostic image quality.
• Clinical evaluation by radiologists showed images sufficient for clinical diagnosis, with uExcel DPR exhibiting lower noise, better contrast, and superior sharpness compared to OSEM. |
  | OncoFocus | Volume relative to no motion correction (∆Volume). | Calculate the volume relative to no motion correction images. | The ∆Volume value is less than 0%. | Pass |
  | | Maximal standardized uptake value relative to no motion correction (∆SUVmax) | Calculate the SUVmax relative to no motion correction images. | The ∆SUVmax value is larger than 0%. | Pass |
  | DeepMAC | Quantitative evaluation | For PMMA phantom data, the average CT value in the affected area of the metal substance and the same area of the control image before and after DeepMAC was compared. | After using DeepMAC, the difference between the average CT value in the affected area of the metal substance and the same area of the control image does not exceed 10HU. | Pass |

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

• DeepRecon.PET:

  • Phantoms: NEMA IQ phantoms.
  • Clinical Patients: 20 volunteers.
  • Data Provenance: "collected from various clinical sites" and explicitly stated to be "different from the training data." The document does not specify country of origin or if it's retrospective/prospective, but "volunteers were enrolled" suggests prospective collection for the test set.

• uExcel DPR:

  • Phantoms: Two NEMA IQ phantom datasets, two uniform cylindrical phantom datasets.
  • Clinical Patients: 19 human subjects.
  • Data Provenance: "derived from uMI Panvivo and uMI Panvivo S," "collected from various clinical sites and during separated time periods," and "different from the training data." "Study cohort" and "human subjects" imply prospective collection for the test set.

• OncoFocus:

  • Clinical Patients: 50 volunteers.
  • Data Provenance: "collected from general clinical scenarios" and explicitly stated to be "on cases different from the training data." "Volunteers were enrolled" suggests prospective collection for the test set.

• DeepMAC:

  • Phantoms: PMMA phantom datasets.
  • Clinical Patients: 20 human subjects.
  • Data Provenance: "from uMI Panvivo and uMI Panvivo S," "collected from various clinical sites" and explicitly stated to be "different from the training data." "Volunteers were enrolled" suggests prospective collection for the test set.

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

The document does not explicitly state that experts established "ground truth" for the quantitative metrics (e.g., SUV, CNR, BV, CR, ∆Volume, ∆SUVmax, HU differences) for the test sets. These seem to be derived from physical measurements on phantoms or calculations from patient image data using established methods.

• For qualitative evaluation/clinical diagnosis assessment:

  • DeepRecon.PET: Two American Board of Radiologists certified physicians.
  • uExcel DPR: Two American board-certified nuclear medicine physicians.
  • OncoFocus: Two American Board of Radiologists-certified physicians.
  • DeepMAC: Two American Board of Radiologists certified physicians.

  The exact years of experience for these experts are not provided, only their board certification status.

4. Adjudication Method for the Test Set

The document states that the radiologists/physicians evaluated images "independently" (uExcel DPR) or simply "were evaluated by" (DeepRecon.PET, OncoFocus, DeepMAC). There is no mention of an adjudication method (such as 2+1 or 3+1 consensus) for discrepancies between reader evaluations for any of the functionalities. The evaluations appear to be separate assessments, with no stated consensus mechanism.

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

• The document describes qualitative evaluations by radiologists/physicians comparing the AI-processed images to conventionally processed images (OSEM/no motion correction/no MAC). These are MRMC comparative studies in the sense that multiple readers evaluated multiple cases.
• However, these studies were designed to evaluate the image quality (e.g., diagnostic sufficiency, noise, contrast, sharpness, lesion detectability, artifact reduction) of the AI-processed images compared to baseline images, rather than to measure an improvement in human reader performance (e.g., diagnostic accuracy, sensitivity, specificity, reading time) when assisted by AI vs. without AI.
• Therefore, the studies were not designed as comparative effectiveness studies measuring the effect size of human reader improvement with AI assistance. They focus on the perceived quality of the AI-processed images themselves.

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

• Yes, for DeepRecon.PET, uExcel DPR, OncoFocus, and DeepMAC, quantitative (phantom and numerical) evaluations were conducted that represent the standalone performance of the algorithms in terms of image metrics (e.g., SUV bias, BV, SNR, CNR, CR, COV, ∆Volume, ∆SUVmax, HU differences). These quantitative results are directly attributed to the algorithm's output without human intervention for the measurement/calculation.
• The qualitative evaluations by the physicians (described in point 3 above) also assess the output of the algorithm, but with human interpretation.

7. The Type of Ground Truth Used

• For Quantitative Evaluations:

  • Phantoms: The "ground truth" for phantom studies is implicitly the known physical properties and geometry of the NEMA IQ and PMMA phantoms, allowing for quantitative measurements (e.g., true SUV, true CR, true signal-to-noise).
  • Clinical Data (DeepRecon.PET, uExcel DPR): For these reconstruction algorithms, "ground-truth images were reconstructed from fully-sampled raw data" for the training set. For the test set, comparisons seem to be made against OSEM with Gaussian filtering or full-count OSEM images as reference/comparison points, rather than an independent "ground truth" established by an external standard.
  • Clinical Data (OncoFocus): Comparisons are made relative to "no motion correction images" (∆Volume and ∆SUVmax), implying these are the baseline for comparison, not necessarily an absolute ground truth.
  • Clinical Data (DeepMAC): Comparisons are made to a "control image" without metal artifacts for quantitative assessment of HU differences.

• For Qualitative Evaluations:

  • The "ground truth" is based on the expert consensus / qualitative assessment by the American Board-certified radiologists/nuclear medicine physicians, who compared images for attributes like noise, contrast, sharpness, motion artifact reduction, and diagnostic sufficiency. This suggests a form of expert consensus, although no specific adjudication is described. There's no mention of pathology or outcomes data as ground truth.

8. The Sample Size for the Training Set

The document provides the following for the training sets:

• DeepRecon.PET: "image samples with different tracers, covering a wide and diverse range of clinical scenarios." No specific number provided.
• uExcel DPR: "High statistical properties of the PET data acquired by the Long Axial Field-of-View (LAFOV) PET/CT system enable the model to better learn image features. Therefore, the training dataset for the AI module in the uExcel DPR system is derived from the uEXPLORER and uMI Panorama GS PET/CT systems." No specific number provided.
• OncoFocus: "The training dataset of the segmentation network (CNN-BC) and the mumap synthesis network (CNN-AC) in OncoFocus was collected from general clinical scenarios. Each subject was scanned by UIH PET/CT systems for clinical protocols. All the acquisitions ensure whole-body coverage." No specific number provided.
• DeepMAC: Not explicitly stated for the training set. Only validation dataset details are given.

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

• DeepRecon.PET: "Ground-truth images were reconstructed from fully-sampled raw data. Training inputs were generated by reconstructing subsampled data at multiple down-sampling factors." This implies that the "ground truth" for training was derived from high-quality, fully-sampled (and likely high-dose) PET data.
• uExcel DPR: "Full-sampled data is used as the ground truth, while corresponding down-sampled data with varying down-sampling factors serves as the training input." Similar to DeepRecon.PET, high-quality, full-sampled data served as the ground truth.
• OncoFocus:
  • For CNN-BC (body cavity segmentation network): "The input data of CNN-BC are CT-derived attenuation coefficient maps, and the target data of the network are body cavity region images." This suggests the target (ground truth) was pre-defined body cavity regions.
  • For CNN-AC (attenuation map (umap) synthesis network): "The input data are non-attenuation-corrected (NAC) PET reconstruction images, and the target data of the network are the reference CT attenuation coefficient maps." The ground truth was "reference CT attenuation coefficient maps," likely derived from actual CT scans.
• DeepMAC: Not explicitly stated for the training set. The mention of pre-trained neural networks suggests an established training methodology, but the specific ground truth establishment is not detailed.

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The Siemens Biograph systems are combined X-Ray Computed Tomography (CT) and Positron Emission Tomography (PET) scanners that provide registration and fusion of high resolution physiologic and anatomic information.

The CT component produces cross-sectional images of the body by computer reconstruction of X-Ray transmission data from either the same axial plane taken at different angles or spiral planes taken at different angles. The PET subsystem images and measures the distribution of PET radiopharmaceuticals in humans for the purpose of determining various metabolic (molecular) and physiologic functions within the human body and utilizes the CT for fast attenuation correction maps for PET studies and precise anatomical reference for the fused PET and CT images.

The system maintains independent functionality of the CT and PET devices, allowing for single modality CT and/or PET diagnostic imaging.

These systems are intended to be utilized by appropriately trained health care professionals to aid in detecting, localizing, diagnosing, staging, and restaging of lesions, tumors, disease, and organ function for the evaluation of diseases and disorders such as, but not limited to, cardiovascular disease, neurological disorders, and cancer. The images produced by the system can also be used by the physician to aid in radiotherapy treatment planning and interventional radiology procedures.

This CT system can be used for low dose lung cancer screening in high risk populations. *

• As defined by professional medical societies. 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 Biograph Vision and Biograph mCT PET/CT systems are combined multi-slice X-Ray Computed Tomography and Positron Emission Tomography scanners. These systems are designed for whole-body oncology, neurology and cardiology examinations. The Biograph Vision and Biograph mCT systems provide registration and fusion of high-resolution metabolic and anatomic information from the two major components of each system (PET and CT). Additional components of the system include a patient handling system and acquisition and processing workstations with associated software.

Biograph Vision and Biograph mCT software is a command-based program used for patient management, data management, scan control, image reconstruction and image archival and evaluation. All images conform to DICOM imaging format requirements.

The software for the Biograph Vision and Biograph mCT systems, which are the subject of this application, is substantially equivalent to the commercially available Biograph Vision and Biograph mCT software.

• Somaris Software (cleared in K230421)
  • Upgrade to the latest revision of Somaris Software (Somaris/7 syngo CT VB30) with modified software features:
    • FAST Bolus
    • FAST 4D
    • FAST Applications (FAST Spine, FAST Planning)
    • Automatic Patient Instructions
    • Additional default exam protocols
    • Additional kV setting for Tin Filtration
• PETsyngo software
  • SMART Image Framer (available for Vision 600 and X models only – cleared in K223547)
• Updated computer hardware due to obsolescence issues (cleared in K230421). These changes do not affect system performance characteristics and have no impact on safety or effectiveness.

The Biograph Vision may also use the names Biograph Vision Quantum and Peak for marketing purposes.

Here's an analysis of the provided FDA 510(k) clearance letter for Siemens Biograph Vision and mCT PET/CT Systems, focusing on acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document describes the performance of the updated software (VG85) for the Siemens Biograph Vision and Biograph mCT PET/CT Systems, comparing it to the predicate device (VG80). The "Acceptance Criteria" for the subject device are explicitly stated as "Same" as the predicate device's performance values. This implies that the updated system must perform at least as well as the predicate device across all tested metrics.

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The Aurora system is a medical tool intended for use by appropriately trained healthcare professionals to aid detecting, localizing, diagnosing of diseases and in the assessment of organ function for the evaluation of diseases, trauma, abnormalities, and disorders such as, but not limited to, cardiovascular disease, neurological disorders and cancer. The system output can also be used by the physician for staging and restaging of tumors; and planning, guiding, and monitoring therapy, including the nuclear medicine part of theragnostic procedures.

GEHC's Aurora is a SPECT-CT system that combines an all-purpose Nuclear Medicine imaging system and the commercially available Revolution Ascend system. It is intended for general purpose Nuclear Medicine imaging procedures as well as head, whole body, cardiac and vascular CT applications and CT-based corrections and anatomical localization of SPECT images. Aurora does not introduce any new Intended Use.

Aurora consists of two back-to-back gantries (i.e. one for the NM sub-system and another for the CT subsystem), patient table, power distribution unit (PDU), operator console with a computer for both the NM acquisition and SmartConsole software and another for the CT software, interconnecting cables, and associated accessories (e.g. NM collimator carts, cardiac trigger monitor, head holder). The CT sub-system main components include the CT gantry, PDU, and CT operator console. All components are from the commercially available GEHC Revolution Ascend CT system.

Here's a breakdown of the acceptance criteria and study details for the Aurora system's deep-learning Automatic Kidney Segmentation algorithm, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Study Details for Deep-Learning Automatic Kidney Segmentation Algorithm

1. Sample sized used for the test set and the data provenance:
* Sample Size: 70 planar NM renal studies.
* Data Provenance: Acquired using GEHC systems from:
* 2 hospitals in the United States
* 1 hospital in Europe
* Nature: Retrospective (the studies were "segregated, and not used in any stage of the algorithm development," implying they were pre-existing data).
* Diversity: Served a diverse patient population including a range of ethnicities and demographics, encompassing a range of dynamic renal clinical scenarios, detection technologies, collimators, tracers, scan parameters, and patient age.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
* Number of Experts for Bench Testing Ground Truth: One (1).
* Qualifications: "An experienced Nuclear Medicine physician."
* Number of Experts for Clinical Testing Evaluation: Three (3) qualified U.S. readers.
* Qualifications: "Qualified U.S. readers" (further specific qualifications like years of experience or board certification are not detailed).

3. Adjudication method for the test set:
* For Bench Testing Ground Truth: The ground truth contours were reviewed and confirmed by a single experienced Nuclear Medicine physician. This suggests a form of expert consensus, but without multiple experts, it's not a multi-expert adjudication like 2+1 or 3+1. It's best described as single expert confirmation.
* For Clinical Testing: The three qualified U.S. readers independently assessed the quality of segmentation using a 4-point Likert scale. There is no mention of an adjudication process among these three readers, implying their individual assessments contributed to the overall evaluation.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:
* No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with AI assistance vs. without AI assistance was not explicitly described.
* The clinical testing involved multiple readers evaluating the quality of the algorithm's segmentation itself, rather than assessing their own diagnostic performance with and without AI. The focus was on the utility and acceptability of the AI output for the readers.

5. Effect size of how much human readers improve with AI vs without AI assistance:
* This information is not provided as a comparative effectiveness study was not explicitly conducted. The study assessed the acceptability of the AI's output, not the improvement in human reader performance.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
* Yes, a standalone performance evaluation of the algorithm was done. This is described as "Bench Testing" where the algorithm's generated contours were compared directly against the ground truth (GT) contours using the DICE similarity score. The "clinical testing" involved human readers evaluating the AI output, but the bench testing was algorithm-only.

7. The type of ground truth used:
* Expert Consensus: The ground truth for the bench testing (GT contours) was established by an "experienced Nuclear Medicine physician." While only one physician is mentioned, it's considered an expert-derived ground truth.

8. The sample size for the training set:
* The document does not explicitly state the sample size used for the training set of the deep learning algorithm. It only mentions that the 70 test studies "were segregated, and not used in any stage of the algorithm development," which implies they were distinct from the training data.

9. How the ground truth for the training set was established:
* The document does not explicitly state how the ground truth for the training set was established. It is only mentioned for the test set.

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HeartSee™ software for cardiac positron emission tomography (PET) is indicated for determining regional and global absolute rest and stress myocardial perfusion in cclmin/q. Coronary Flow Reserve and their combination into the Coronary Flow Capacity (CFC) Map in patients with suspected or known coronary artery disease (CAD) in order to assist clinical interpretation of PET perfusion images and quantification of their severity.

HeartSee™ is intended for use by trained professionals, such as nuclear technicians, nuclear medicine or nuclear cardiology physicians, or cardiologists with appropriate training and certification. The clinician remains ultimately responsible for the final assessment and diagnosis based on standard practices, clinical judgment and interpretation of PET images or quantitative data.

HeartSee is a software tool for cardiac positron emission tomography (PET) for determining regional and global absolute rest and stress myocardial perfusion in cc/min/g, Coronary Flow Reserve and their combination into the Coronary Flow Capacity (CFC) Map for facilitating the interpretation of PET perfusion images in patients with suspected or known coronary artery disease. HeartSee is intended for use by trained professionals, such as nuclear technicians, nuclear medicine or nuclear cardiology physicians, or cardiologists with appropriate training and certification.

HeartSee contains two fundamental components. First, the software imports cardiac PET images in DICOM format from PET scanners with DICOM output. These images are reoriented to cardiac axes to produce standard tomographic and topographic displays of relative uptake. Second, the software quantifies absolute rest and stress myocardial perfusion per unit tissue (cc/min/gm), Coronary Flow Reserve (CFR) as the stress/rest perfusion ratio and the Coronary Flow Capacity combining CFR and stress perfusion, all on a pixel basis for regional and global values. HeartSee and the predicate K231731 also display stress subendocardial to subepicardial ratio and subendocardial stress to rest ratio on relative activity tomograms, and stress relative topogram maps expressed as a fraction of maximum cc/min/g. called relative stress flow (RSF). Archiving output data is supported for clinical diagnostics, quality control and research.

In addition to these established measurements of perfusion in cc/min/g, CFR and CFC, HeartSee has software for determining left ventricular ejection fraction (EF) by PET-CT using Rb-82 compared to EF.

The provided text does not contain information about acceptance criteria or a study proving the device meets those criteria for the HeartSee™ Cardiac P.E.T. Processing Software.

The document is a 510(k) premarket notification clearance letter from the FDA. It states that:

• This device, HeartSee™ Cardiac P.E.T. Processing Software For Myocardial Perfusion and Coronary Flow Reserve (CFR) Version 4, is substantially equivalent to a legally marketed predicate device (Optional Screen Displays For HeartSee Cardiac P.E.T. Processing Software - HeartSee version 4 (K231731)).
• The primary changes from the predicate device are a software platform migration to the Windows operating system, minor software application updates, and cybersecurity enhancements.
• The core functionality of determining quantitative myocardial perfusion, Coronary Flow Reserve (CFR), and Coronary Flow Capacity (CFC) map, along with their displays and left ventricular ejection fraction (EF) determination, remains essentially the same as the predicate.
• No new clinical testing was performed in support of this 510(k) Premarket Notification. The substantial equivalence is based on nonclinical testing of the predicate device, combined with system testing and cybersecurity testing of the proposed device, due to the nature of the modifications being primarily platform and minor updates.

Therefore, I cannot provide the requested table, sample sizes, expert details, adjudication methods, MRMC study information, standalone performance, ground truth types, or training set details because this information is not present in the provided FDA 510(k) clearance letter.

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The Imaging system of positron emission and X-ray computed tomography is a PET/CT system for producing attenuation corrected PET images. It is intended to be used by qualified health care professionals for imaging the distribution and localization of any positron-emitting radiopharmaceutical in a patient, for the assessment of metabolic (molecular) and physiologic function in patients, with a wide range of sizes and extent of disease, of all ages.

Imaging system of positron emission and X-ray computed tomography is intended to image the whole body, head, heart, brain, lung, breast, bone, the gastrointestinal and lymphatic systems, and other organs. The images produced by the system may be used by physicians to aid in radiotherapy treatment planning, therapy guidance and monitoring, and in interventional radiology procedures. The images may also be used for precise functional and anatomical mapping (localization, registration, and fusion).

When used with radiopharmaceuticals approved by the regulatory authority in the country of use, the raw and image data is an aid in; detection, localization, diagnosis, staging, restaging, monitoring, and/or follow up, of abnormalities, lesions, tumors, inflammation, infection, organ function, disorders, and/or disease, such as, but not limited to, those in oncology, cardiology, and neurology. Examples of which are:

Cardiology:

• Cardiovascular disease
• Myocardial perfusion
• Myocardial viability
• Cardiac inflammation
• Coronary artery disease

Neurology:

• Epilepsy
• Dementia, such as Alzheimer's disease, Lewy body dementia, Parkinson's disease with dementia. and frontotemporal dementia.
• Movement disorders, such as Parkinson's and Huntington's disease
• Tumors
• Inflammation
• Cerebrovascular disease such as acute stroke, chronic and acute ischemia
• Traumatic Brain Injury (TBI)

Oncology/Cancer:

• Non-Small Cell Lung Cancer
• Small Cell Lung Cancer
• Breast Cancer
• Prostate Cancer
• Hodgkin disease
• Non-Hodakin Ivmphoma
• Colorectal Cancer
• Melanoma

Imaging system of positron emission and X-ray computed tomography is also intended for standalone, diagnostic CT imaging in accordance with the stand-alone CT system's cleared indications for use.

The Imaging system of positron emission and X-ray computed tomography (model: DigitMI 930) is a PET/CT diagnostic imaging system combining a Positron Emission Tomography (PET) System and a Computed Tomography (CT) System.

The Imaging system of positron emission and X-ray computed tomography (model: DigitMI 930) consists of a scanning system, power distribution unit, table system, data processing system, and console system. The PET part consists of a 72 ring LYSO detector, while the CT part has 64 physical rows of detectors.

The provided FDA 510(k) summary for the Imaging system of positron emission and X-ray computed tomography (DigitMI 930) does not contain information about specific acceptance criteria related to a clinical study or device performance against clinical metrics. Instead, it focuses on non-clinical performance specifications and regulatory standards for showing substantial equivalence to a predicate device.

Here's an analysis of the information provided, addressing your questions to the extent possible:

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

The document provides a "Performance Comparison" table that lists various physical and imaging specifications for the proposed device and compares them to the predicate device (Discovery MI, K161574). These are not clinical acceptance criteria in the sense of accuracy, sensitivity, or specificity for a diagnostic task, but rather technical performance metrics of the PET/CT system itself. The "Remark" column for these items is consistently "Analyse 1," indicating that any differences were further evaluated.

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The Aurora system is a medical tool intended for use by appropriately trained healthcare professionals to aid in detecting, localizing, diagnosing of diseases and in the assessment of organ function for the evaluation of diseases, trauma, abnormalities, and disorders such as, but not limited to, cardiovascular disease, neurological disorders and cancer. The system output can also be used by the physician for staging and restaging of tumors; and planning, guiding, and monitoring therapy, including the nuclear medicine part of theragnostic procedures.

· 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 Whole body) and tomographic mode (SPECT, Gated SPECT, Whole body SPECT), Imaging modes include single photon, multi-isotope, and multi-peak, with data stored in frame/list mode. The imaging-enhancement features include assortment of collimators, gating by physiological signals, and real-time automatic body contouring.

· CT System: produces Cross sectional images of the body by computer reconstruction of X-Ray transmission data taken at different angles and planes, including Axial, Cine, Helical (Volumetric), Cardiac, and Gated acquisitions. These images may be obtained with or without contrast. The CT system is indicated for head, whole body, cardiac 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 (localization, registration, and fusion).

The Aurora system may include signal analysis and display equipment, patient and equipment supports, components and accessories. The system may include digital processing of data and images, including display, quality check, transfer, and processing, to produce images in a variety of trans-axial and reformatted planes. The images can also be post processed to obtain additional images, imaging planes, analysis results and uptake quantitation. The system may be used for patients of all ages.

GEHC's Aurora is a SPECT-CT system that combines an all-purpose Nuclear Medicine imaging system and the commercially available Revolution Ascend system. It is intended for general purpose Nuclear Medicine imaging procedures as well as head, whole body, cardiac and vascular CT applications and CT-based corrections and anatomical localization of SPECT images. Aurora does not introduce any new Intended Use.

Aurora consists of two back-to-back gantries (i.e. one for the NM sub-system and another for the CT subsystem), patient table, power distribution unit (PDU), operator console with a computer for both the NM acquisition and SmartConsole software and another for the CT software, interconnecting cables, and associated accessories (e.g. NM collimator carts, cardiac trigger monitor, head holder). The CT sub-system main components include the CT gantry, PDU, and CT operator console. All components are from the commercially available GEHC Revolution Ascend CT system. The CT gantry has been adapted for use with predicate device's NM portion. CT PDU, CT Console Keyboard and CT operator console are the same as in Revolution Ascend Plus.

The provided document does not contain details about specific acceptance criteria, a study proving device performance against those criteria, or the various methodological details requested regarding sample sizes, data provenance, expert ground truth, adjudication methods, MRMC studies, or standalone performance.

The document is a 510(k) summary for the Aurora system, indicating that it is a modification of a predicate device (Discovery NM/CT 670) and incorporates components from other cleared devices. The filing emphasizes that, due to the nature of these modifications (primarily replacing a 16-slice CT with a 64-slice CT and other workflow enhancements, while the NM system is largely carried over), clinical testing was deemed unnecessary to demonstrate substantial equivalence.

The document states:

• "Because the changes associated with Aurora do not change the Indications for Use from the predicate and reference devices, and represent equivalent technological characteristics, this type of change supports using scientific, established / standardized, engineering / physics-based performance testing, without inclusion of clinical images for determining substantial equivalence."
• "Given the above information and the type and scope of changes, particularly that the NM imaging component is identical to the predicate, and the CT component is the commercially available Revolution Ascend CT system, clinical images are not included in this submission. Clinical images are not needed to demonstrate substantial equivalence."

Instead of a clinical study, the submission relies on:

• Design control testing per their quality system (21CFR 820 and ISO 13485): including Risk Analysis, Required Reviews, Design Reviews, Testing on unit level (Module verification), Integration testing (System verification), Performance testing (Verification), Safety testing (Verification), Simulated use testing (Validation).
• Conformance to standards: IEC 60601-1 and its applicable Collateral and Particular Standards (IEC 60601-1-2, 60601-1-3, 60601-2-44), as well as performance testing per NEMA NU-1.
• Additional engineering bench testing (non-clinical testing): This was performed to support substantial equivalence, demonstrate performance, and substantiate product claims. Evaluated areas included applicability of cleared lesion detectability and dose/time reduction claims, quantitation accuracy, IQ performance with low dose CT for attenuation correction, and workflow.

Therefore, the requested information cannot be extracted from this document as a clinical validation study demonstrating performance against specific acceptance criteria with human-in-the-loop or standalone performance was not part of the submission for substantial equivalence.

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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.

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