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The AnyScan 3.0 NM Scanner Family is intended for use 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 or additional uses.

SPECT: The SPECT subsystem is intended to provide projection and cross-sectional images through computer reconstruction of the data, representing radioisotope distribution in the patient body or in a specific organ using planar and tomographic scanning modes for isotopes with energies up to 588 keV.

CT: CT component is intended to provide cross sectional images of the body by computer reconstruction of x-ray transmission data providing anatomical information.

PET: The PET component is intended to provide cross- sectional images representing the distribution of tomographic scanning modes.

SPECT+CT: The SPECT and CT components used together acquire SPECT/CT images. The SPECT images can be corrected for attenuation with the CT images, and can be combined (image registration) to merge the patient's physiological (SPECT) and anatomical (CT) images.

PET+CT: The PET and CT components used together acquire PET/CT images. The PET images can be corrected for attenuation with the CT images, and can be combined (image registration) to merge the patient's physiological (PET) and anatomical (CT) images.

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

Software: The Nucline software is an acquisition, display and analysis package intended to aid the clinician to extract diagnostic information supported by image assessment tools, image enhancement features and image quantification of pathologies in images produced from SPECT, CT, PET and other imaging modalities.

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 AnyScan 3.0 NM Scanner Family will enable clinicians to utilize the device to perform separate studies in SPECT-CT, PET-CT, SPECT, PET and multi-slice CT modalities.

The AnyScan 3.0 NM Scanner Family includes the following products:
AnyScan 3.0 NM Scanner Family

The partial product names 'TRIO' and 'DUO' only differentiate the number of built-in SPECT detectors.

The partial product names 'IQMAX' and 'TheraMAX' only differentiate the type of built-in SPECT detector. The SPECT gamma camera generates nuclear medicine images based on the uptake of radioisotope tracers in a patient's body, and supports integration with CT's anatomical detail for precise reference of the location of the metabolic activity.

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 component 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 combination of SPECT, CT, and PET in a single device has several benefits. The SPECT subsystem images biochemical function while the CT subsystem images anatomy. The combination enables scans that not only indicate function, e.g., how active a tumor is, but precise localization, e.g., the precise location of that tumor in the body.

Combined SPECT and CT subsystems are intended for SPECT imaging enhanced with spatially registered CT image-based corrections, anatomical localization of tracer uptake and anatomical mapping. CT can be used to correct for the attenuation in SPECT acquisitions. Attenuation in SPECT is an unwanted side effect of the gamma rays scattering and being absorbed by tissue. This can lead to errors in the final image. The CT directly measures attenuation and can be used to create a 3D attenuation map of the patient which can be used to correct the SPECT images. The SPECT-CT scanner can be used to image and track how much dose was delivered to both the target and the surrounding tissue. The system maintains independent functionality of the CT and SPECT subsystems.

Combined PET and CT subsystems are intended for PET imaging enhanced with spatially registered CT image-based corrections, anatomical localization of tracer uptake and anatomical mapping. system maintains independent functionality of the CT and PET subsystems, allowing for single modality CT and/or PET diagnostic imaging.

A patient positioning light marker is generated by a low-power (Class II per IEC 60825-1) red laser.

Nucline software is installed on acquisition workstation to perform patient management, data management, scan control, image reconstruction and image archival and evaluation. All images conform to DICOM imaging format requirements.

The systems also include display equipment, data storage devices, patient and equipment supports, software, and accessories.

InterView XP; InterView FUSION (K221984) and software is integrated for DICOM image visualization and post-processing.

ClariCT (K212074) software is integrated for DICOM CT de-noising.

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The device is a diagnostic imaging system that combines Positron Emission Tomography (PET) and X-ray Computed Tomography (CT) systems. The CT component produces cross-sectional images of the body by computer reconstruction of X-ray transmission data. The PET component images the distribution of PET radiopharmaceuticals in the patient body. The PET component utilizes CT images for attenuation correction and anatomical reference in the fused PET and CT images.

This device is to be used by a trained health care professional to gather metabolic and functional information from the distribution of the radiopharmaceutical in the body for the assessment of metabolic and physiologic functions. This information can assist in the evaluation, detection, localization, diagnosis, staging, restaging, follow-up, therapeutic planning and therapeutic outcome assessment of (but not limited to) oncological, cardiovascular, neurological diseases and disorders. Additionally, this device can be operated independently as a whole body multi-slice CT scanner.

AiCE-i for PET is intended to improve image quality and reduce image noise for FDG whole body data by employing deep learning artificial neural network methods which can explore the statistical properties of the signal and noise of PET data. The AiCE algorithm can be applied to improve image quality and denoising of PET images.

Deviceless PET Respiratory gating system, for use with Cartesion Prime PET-CT system, is intended to automatically generate a gating signal from the list-mode PET data. The generated signal can be used to reconstruct motion corrected PET images affected by respiratory motion. In addition, a single motion corrected volume can automatically be generated. Resulting motion corrected PET images can be used to aid clinicians in detection, localization, evaluation, diagnosis, staging, restaging, follow-up of diseases and disorders, radiotherapy planning, as well as their therapeutic planning, and therapeutic outcome assessment. Images of lesions in the thorax, abdomen and pelvis are mostly affected by respiratory motion. Deviceless PET Respiratory gating system may be used with PET radiopharmaceuticals, in patients of all ages, with a wide range of sizes, body habitus and extent/type of disease.

The Cartesion Prime (PCD-1000A/3) V10.21 combines a high-end CT and a high-throughput PET designed to acquire CT, PET and fusion images.

The high-end CT system is a multi-slice helical CT scanner with a gantry aperture of 780 mm and a maximum scan field of view (FOV) of 700 mm. The high-throughput PET system has a digital PET detector utilizing SiPM sensors with temporal resolution of < 250 ps (238 ps typical). Cartesion Prime (PCD-1000A/3) V10.21 is intended to acquire PET images of any desired region of the whole body and CT images of the same region (to be used for attenuation correction or image fusion), to detect the location of positron emitting radiopharmaceuticals in the body with the obtained images. This device is used to gather the metabolic and functional information from the distribution of radiopharmaceuticals in the body for the assessment of metabolic and physiologic functions. This information can assist research, detection, localization, evaluation, diagnosis, staging, restaging, follow-up of diseases and disorders, as well as their therapeutic planning, and therapeutic outcome assessment. This device can also function independently as a whole body multi-slice CT scanner.

The subject device incorporates the latest reconstruction technology, AiCE-i for PET (Advanced Intelligent Clear-IQ Engine- integrated), intended to improve image quality and reduce image noise for FDG whole body data by employing deep learning artificial neural network methods which can more fully explore the statistical properties of the signal and noise of PET data. The AiCE algorithm will be able to better differentiate signal from noise and can be applied to improve image quality and denoising of PET images compared to conventional PET imaging reconstruction.

A Deviceless PET Respiratory gating system has been implemented for use with the subject device. With this subject device, respiration is extracted using a pre-trained neural network. Respiratory-gated reconstruction is performed at a speed equal to or faster than that with "Normal".

Here's a breakdown of the acceptance criteria and study details for the Cartesion Prime PET-CT System, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance for Cartesion Prime PET-CT System (K251370)

The submission describes two primary feature enhancements: AiCE-i for PET (AiCE2) and Deviceless PET Respiratory gating system (DRG2).

1. Table of Acceptance Criteria and Reported Device Performance

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

For AiCE-i for PET (AiCE2) - Clinical Images:

• Sample Size: 10 PET DICOM clinical 18F-FDG whole body cases.
• Data Provenance: Not explicitly stated, but the submission notes "selected to cover characteristics common to the intended U.S. patient population." The training data for AiCE2 is mentioned to have over half acquired from the U.S.

For Deviceless PET Respiratory Gating (DRG2) - Clinical Images:

• Sample Size: 10 patients.
• Data Provenance: Not explicitly stated, but the submission notes "selected to cover characteristics common to the intended U.S. patient population." The training data for DRG2 was acquired entirely from the U.S.

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

For AiCE-i for PET (AiCE2) - Clinical Images:

• Number of Experts: Three (3) physicians.
• Qualifications: At least 20 years of experience in nuclear medicine.

For Deviceless PET Respiratory Gating (DRG2) - Clinical Images:

• Number of Experts: Three (3) physicians.
• Qualifications: At least 20 years of experience in nuclear medicine.

4. Adjudication Method for the Test Set

The adjudication method is not explicitly stated as 2+1, 3+1, or none. However, for both clinical image evaluations, it states that "All three physicians reported/determined that..." This implies a consensus-based adjudication among the three experts was used to reach the conclusions. It does not indicate individual disagreements were arbitrated by a fourth reader.

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

A formal MRMC comparative effectiveness study, designed to quantify the effect size of human readers improving with AI assistance, was not explicitly described in the provided text. The clinical image evaluations involved expert review and comparison, but the focus was on the algorithm's performance and image quality, not a direct measurement of human reader improvement with vs. without AI assistance.

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

Yes, standalone performance was extensively evaluated for both features:

• AiCE-i for PET:
  • Bench tests for pediatric use (CRC, BGV, CNR, artifacts, SUVmean equivalence).
  • Bench tests for image intensity (SUV max/mean accuracy, tumor volume improvement).
  • Phantom testing (NEMA NU-2, Adult and Pediatric NEMA phantoms, Small Pool phantom) comparing AiCE2 to AiCE1 and conventional methods across quantitative metrics (SUVmean, BGV, CRC, SNR) and for artifact absence.
• Deviceless PET Respiratory Gating:
  • Bench tests for AI operational mode (equivalence to external device gating, improvements in SUV max/mean, tumor volume).
  • Evaluation against predicate DRG1 using reconstructed clinical raw data and quantified outputs.

7. The Type of Ground Truth Used

• For AiCE-i for PET (AiCE2):
  • Phantom Studies: Objective, physics-based ground truth (e.g., known sphere sizes, activity concentrations) for quantitative metrics like SUV, CRC, BGV, SNR.
  • Clinical Image Evaluation: Expert consensus/opinion of three nuclear medicine physicians for subjective assessments like diagnostic quality, image sharpness, and noise levels.
• For Deviceless PET Respiratory Gating (DRG2):
  • Bench Tests/Comparison to DRG1: Quantitative measurements of SUV (max and mean) and tumor volume from reconstructed data, likely compared against a known or established ground truth from reference reconstructions.
  • Clinical Image Evaluation: Expert consensus/opinion of three nuclear medicine physicians for subjective assessments related to diagnostic quality and motion correction effectiveness.

8. The Sample Size for the Training Set

• For AiCE-i for PET (AiCE2): Subset assembled from FDG studies of sixteen (16) cancer patients.
• For Deviceless PET Respiratory Gating (DRG2): FDG studies of 27 cancer patients.

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

The text indicates that both AI algorithms (AiCE2 and DRG2) use deep learning artificial neural network methods. The ground truth for training these networks is implicitly derived from the input PET data itself, with the algorithms learning statistical properties of signal and noise or motion patterns.

• For AiCE-i for PET: The algorithm was "trained to automatically adapt to different noise levels to produce consistently high-quality images." This suggests the training data contained examples of both "noisy" input and perhaps "ideal" or "denoised" outputs (or features that guided the network to achieve denoised outputs with improved image quality), where the "ground truth" was likely the desired image characteristics or underlying signal.
• For Deviceless PET Respiratory Gating: The neural network was "trained on FDG studies... to extract motion information from acquired PET data and to generate a corresponding gating signal." This implies the "ground truth" for training involved identifying and characterizing respiratory motion within the raw PET data, possibly using external motion tracking data if available during training, or highly curated datasets where experts delineated motion patterns. The text does not explicitly state how this ground truth was established, only that it was trained on these patient studies.

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The PennPET Explorer PET system is a diagnostic imaging device that, together with the co-located IQon CT scanner, combines Positron Emission Tomography (PET) and X-ray Computed Tomography (CT) systems. The IQon CT system images anatomical cross-sections by computer reconstruction of X-ray transmission data. The PET system images the distribution of PET anatomy-specific radiopharmaceuticals in the patient. Together, these systems are used for the purposes of detecting, localizing, diagnosing, staging, re-staging, and follow-up for monitoring therapy response of various diseases in oncology, cardiology, and neurology.

The system is intended to image the whole body, heart, brain, lung, gastrointestinal, bone, lymphatic, and other major organs for a wide range of patient types, sizes, and extent of diseases. The CT scanner can also be operated as fully functional, independent diagnostic tool, including for use in radiation therapy planning.

The PennPET Explorer is based on the PET technology of its predicate device, the Philips Vereos PET/CT scanner, but follows the model of its reference device, the previous Philips Gemini TF PET/CT by having co-located—yet separated—PET and CT scanners served by a common patient table. The PennPET Explorer uses a newly designed 142 cm axial field-of-view (AFOV) PET gantry and is intended to be used with a co-located Philips IQon multi-energy CT and patient table.

The PennPET Explorer PET gantry is a modular system comprising six PET detector rings stacked axially, yielding a 142 cm axial FOV. This allows imaging of the human head, torso, and upper legs in a single frame without moving the patient. The entire imaging chain of components from the detectors to the data acquisition computers is supplied by Philips and consists of components that are used in the Vereos PET scanner. The mechanical structure and data processing software have been modified and developed to handle the additional data from all six PET rings simultaneously.

Each of the six detector rings is substantially equivalent to a Philips Vereos PET scanner.

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Hybrid Viewer is a software application for nuclear medicine and radiology. Based on user input, Hybrid Viewer processes, displays and analyzes nuclear medicine and radiology imaging data and presents the results to the user. The results can be stored for future analysis.

Hybrid Viewer is equipped with dedicated workflows which have predefined settings and layouts optimized for specific nuclear medicine investigations.

The software application can be configured based on user needs.

The investigation of physiological or pathological states using measurement and analysis functionality provided by Hybrid Viewer is not intended to replace visual assessment. The information obtained from viewing and/or performing quantitative analysis on the images is used, in conjunction with other patient related data, to inform clinical management.

Hybrid Viewer is a software application which provides 2D and 3D viewing, processing and analysis for nuclear medicine investigations.

The studies can be loaded from patient browsers (e.g., GOLD) or PACS (Picture Archiving and Communication System).

Hybrid Viewer provides general tools which include scrolling, zooming, panning, filtering, motion correction, fusion, registration, triangulation drawing regions of interest, synchronization of studies and performing mathematical operations. Specific investigation areas for Hybrid Viewer are Neurology (BRASS), Cardiology, Gastroenterology, Hepatology, Pneumology, Osteology and Nephrology.

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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

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

The document indicates that "a total of five images were obtained, including those from both patients and a normal control group" for the clinical evaluation.

The provenance of this data (e.g., country of origin, retrospective or prospective) is not explicitly stated in the provided text.

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

Ground truth for the clinical acceptability of the five images was established by "a nuclear medicine physician". The exact number of physicians is not explicitly stated beyond "a physician," implying one. No specific years of experience or other qualifications are provided for this expert. It does not mention experts establishing a "ground truth" for the NEMA phantom performance tests, as these are objective measurements.

4. Adjudication method for the test set

The document states that the images were "assessed by a nuclear medicine physician for clinical acceptability." This implies a direct assessment by this physician. There is no mention of an adjudication method such as 2+1 or 3+1, suggesting a single expert's assessment without a formal adjudication process involving multiple readers for this specific clinical evaluation.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study evaluating human reader performance with and without AI assistance was not done or reported. The study appears to be a standalone performance evaluation of the device against objective phantom criteria and a limited clinical acceptability assessment.

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

The "Performance Testing – Bench" section, which evaluates the device against NEMA NU2:2018 and NEMA NU4:2008 standards (e.g., spatial resolution, scatter fraction, peak NECR, sensitivity, energy resolution, time resolution), represents a standalone evaluation of the device's intrinsic image acquisition and reconstruction capabilities. This can be considered a standalone performance assessment of the system. The clinical images were also reviewed by a physician, but the NEMA tests are purely objective, algorithm-only type performance.

7. The type of ground truth used

• For the bench tests (NEMA standards): The ground truth is established by the physical properties of the phantoms used in the NEMA NU2:2018 and NEMA NU4:2008 standards, and the adherence to these quantitative metrics. This is an objective, standardized ground truth.
• For the clinical evaluation: The ground truth for the five clinical images was based on the "clinical acceptability" determined by a nuclear medicine physician. This is a form of expert consensus/assessment, though it's not explicitly detailed how this "acceptability" was defined or if it referenced other diagnostic findings or pathology.

8. The sample size for the training set

The provided document does not mention a training set sample size. This 510(k) pertains to a PET scanner hardware device, not an AI/ML software device that typically requires a large training dataset for model development. While the device utilizes algorithms for image reconstruction, the focus here is on the system's physical performance and output quality rather than an AI model's training.

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

As no training set is mentioned in the context of an AI/ML model for this device, this information is not applicable based on the provided text. The "ground truth" related to the device's fundamental function is based on established physics principles for PET imaging and standardized phantom measurements.

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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

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.

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

The document does not specify a "sample size" in terms of number of patient cases for the test set. Instead, it states:

• Test Set: The testing was "PET Testing in accordance with NEMA NU2-2018." NEMA NU2-2018 is a standard for performance measurements of PET scanners, which involves phantom studies, not human patient data.
• Data Provenance: The data provenance is from phantom studies conducted to NEMA NU2-2018 standards on the Siemens Biograph Vision and Biograph mCT systems. This is non-clinical testing. The document does not indicate country of origin or whether it was retrospective/prospective, as it's a controlled laboratory environment with phantoms.

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

This information is not applicable and therefore not provided in the document. Since the testing was conducted on phantoms according to NEMA NU2-2018 standards, the "ground truth" is defined by the known physical properties and configurations of the phantoms, as well as the standardized measurement protocols. There are no human experts involved in establishing a diagnostic "ground truth" for these types of physical performance measurements.

4. Adjudication Method for the Test Set

This information is not applicable and therefore not provided in the document. Adjudication methods like "2+1" or "3+1" are used in studies where human readers are interpreting images and there's a need to resolve discrepancies in their evaluations to establish a consensus ground truth. Since this study involved non-clinical phantom testing, such adjudication was not necessary.

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

No, an MRMC comparative effectiveness study was not done. The document explicitly states: "Clinical testing was not conducted for this submission." This submission focuses on the hardware and software's physical performance characteristics and its substantial equivalence to a predicate device, not on clinical utility or an AI's impact on human reader performance.

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

The document describes the performance of the PET/CT system's hardware and software components. It's a "standalone" performance study in the sense that it measures the inherent physical and technical capabilities of the device (e.g., resolution, sensitivity, image quality) without human diagnostic interpretation being part of the measurement. However, it's critical to clarify that this is not a standalone AI algorithm performance study as typically understood in the context of diagnostic AI. The "Somaris Software" and "PETsyngo software" are operational control and image reconstruction software for the PET/CT system itself, not necessarily an "AI" in the diagnostic sense that would report findings for human review.

7. The Type of Ground Truth Used

The ground truth used for this testing was physical phantom data with known properties. The NEMA NU2-2018 standard specifies phantoms with defined sizes, activity concentrations, and geometric configurations, allowing for objective measurement of the scanner's performance parameters (e.g., resolution, sensitivity, image quality spheres).

8. The Sample Size for the Training Set

This information is not provided and is likely not applicable in the context of this 510(k) submission. The document describes updates to existing established software and hardware for a medical imaging device. While software development for such systems may involve extensive internal testing and validation, a "training set" with established ground truth as used for machine learning models is not typically a concept applied to the fundamental operational and reconstruction software of a PET/CT scanner being cleared under these circumstances. The emphasis is on meeting performance standards (NEMA NU2-2018) and demonstrating substantial equivalence.

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

As stated above, a "training set" in the machine learning sense is likely not applicable. For the development and verification of the system's operational and reconstruction software, the "ground truth" would have been established through methodologies typical for medical device software engineering, including:

• Reference data/simulations: Utilizing known physical models, simulations, and calibrated reference data to test reconstruction algorithms and system functionality.
• Engineering specifications: Ensuring the software's output aligns with predefined technical specifications and physics principles governing PET/CT image formation.
• Phantom studies (internal development): Similar to the NEMA NU2-2018 testing, phantom studies would be used extensively during development to refine algorithms and establish performance characteristics.

The specific details of the software's internal development and validation, including how any "training" (if applicable for specific algorithmic components, though not explicitly an AI for diagnosis) was performed, are not part of this public 510(k) summary.

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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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