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This device is indicated to acquire and display cross-sectional volumes of the whole body (abdomen, pelvis, chest, extremities, and head) of adult patients.

TSX-501R has the capability to provide volume sets. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

CT Scanner TSX-501R/1 V11.1 employs a next-generation X-ray detector unit (photon counting detector unit), which allows images to be obtained based on X-rays with different energy levels. This device captures cross sectional volume data sets used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician. This system is based upon the technology and materials of previously marketed Canon CT systems.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided 510(k) clearance letter.

It's important to note that a 510(k) summary typically doesn't provide the full, granular detail of a clinical study report. The information often indicates what was tested and the conclusion, but less about the specific methodologies, statistical thresholds for acceptance, or detailed performance metrics.

Understanding the Context: 510(k) Clearance

This document is a 510(k) clearance letter for a new CT scanner (CT Scanner TSX-501R/1 V11.1). The primary goal of a 510(k) submission is to demonstrate "substantial equivalence" to a legally marketed predicate device, not necessarily to prove absolute safety and effectiveness through extensive new clinical trials (which is more typical for a PMA - Premarket Approval). Therefore, the "acceptance criteria" and "study" described here are geared towards demonstrating this equivalence.

The core technology difference is the shift from an Energy Integrating Detector (EID) in the predicate to a Photon Counting Detector in the new device. The testing focuses on ensuring this new detector performs equivalently or better in terms of image quality and safety.

Acceptance Criteria and Reported Device Performance

Given the nature of a 510(k) for a CT scanner's hardware update (new detector), the "acceptance criteria" are implicitly tied to demonstrating equivalent or improved image quality and safety compared to the predicate device. The performance is assessed through bench testing with phantoms and review of clinical images.

Table of Acceptance Criteria and Reported Device Performance:

• Contrast-to-Noise Ratios (CNR)
• CT Number Accuracy
• Uniformity
• Pulse Pile Up
• Slice Sensitivity Profile (SSPz)
• Modulation Transfer Function (MTF)
• Standard Deviation of Noise and Pulse Pile
• Noise Power Spectra (NPS)
• Low Contrast Detectability (LCD) | "It was concluded that the subject device demonstrated equivalent or improved performance, compared to the predicate device, as demonstrated by the results of the above testing." |
  | Fundamental Properties of the Photon Counting Detector (using phantoms) | Effectiveness and equivalent performance compared to expected or predicate device for:
• Detector resolution and noise properties (MTF and DQE)
• Artifact analysis
• Count rate vs. current curve
• Pulse pileup or maximum count rate
• Lag/residual signal levels
• Stability over time
• Bad pixel map | "These bench studies utilized phantom data and achieved results demonstrative of equivalent performance in comparison with the predicate device." |
  | Clinical Image Quality (Human Review) | Reconstructed images using the subject device are of diagnostic quality. | "It was confirmed that the reconstructed images using the subject device were of diagnostic quality." |
  | Safety & Standards Conformance | Conformance to relevant electrical, radiation, software, and cybersecurity standards and regulations. | "This device is in conformance with the applicable parts of the following standards [list provided]... Additionally, this device complies with all applicable requirements of the radiation safety performance standards..." |
  | Risk Analysis & Verification/Validation | Established specifications for the device have been met, and risks are adequately managed. | "Risk analysis and verification/validation activities conducted through bench testing demonstrate that the established specifications for the device have been met." |
  | Software Documentation & Cybersecurity | Adherence to FDA guidance documents for software functions and cybersecurity. | "Software Documentation for a Basic Documentation Level... is included... Cybersecurity documentation... was included..." |

Study Details:

1. Sample Size Used for the Test Set and Data Provenance:

   • Test Set (Clinical Images): The specific number of clinical images/cases reviewed is not provided. The text states "Representative chest, abdomen, brain and MSK diagnostic images." This implies a selection of images from various body regions.
   • Data Provenance: The document does not specify the country of origin for the clinical images. It also does not explicitly state whether the data was retrospective or prospective, though for a 510(k) supporting equivalence, retrospective data collection for image review is common.

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

   • Number of Experts: The document states "reviewed by American Board-Certified Radiologists." The specific number is not provided.
   • Qualifications: "American Board-Certified Radiologists." This indicates a high level of qualification and experience in medical imaging interpretation.

3. Adjudication Method for the Test Set:

   • The document does not specify an adjudication method (like 2+1 or 3+1) for the clinical image review. It simply states they were "reviewed by American Board-Certified Radiologists" and "it was confirmed that the reconstructed images using the subject device were of diagnostic quality." This implies a consensus or individual assessment of diagnostic quality, but the process is not detailed.

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

   • Was it done? No, a formal MRMC comparative effectiveness study demonstrating how human readers improve with AI vs. without AI assistance was not conducted or described for this submission. This makes sense as the device is a CT scanner itself, not an AI-assisted diagnostic software. The clinical image review was to confirm diagnostic quality of the images produced by the new scanner, not to assess reader performance with or without an AI helper.

5. Standalone (Algorithm Only) Performance:

   • Was it done? Yes, in a sense. The "bench testing" focusing on Objective Image Quality Evaluations and Fundamental Properties of the Photon Counting Detector can be considered "standalone" performance for the device's imaging capabilities. These tests used phantoms and measured technical specifications without human interpretation as the primary endpoint. The device's stated function is to acquire and display images, so its "standalone" performance is its ability to produce good images.

6. Type of Ground Truth Used:

   • Bench Testing (Phantoms): The ground truth is the physical properties of the phantoms and the expected performance characteristics based on established physics and engineering principles (e.g., a known object size for MTF, known density for CT number accuracy).
   • Clinical Images: The ground truth for confirming "diagnostic quality" is expert consensus/opinion from American Board-Certified Radiologists. It's an assessment of whether the image contains sufficient information and clarity for diagnostic purposes, not necessarily a comparison to a biopsy or long-term outcome.

7. Sample Size for the Training Set:

   • The document does not mention a training set in the context of typical AI/machine learning development. This device is a CT scanner hardware system, not an AI diagnostic algorithm that learns from training data. Therefore, the concept of a "training set" as it relates to AI models is not applicable here.

8. How Ground Truth for the Training Set Was Established:

   • As stated above, the concept of a "training set" as applied to AI/machine learning development does not directly apply to this CT scanner hardware submission. The device's performance is based on its physical design and engineering, not on learning from a large dataset.

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syngo.CT Dual Energy is designed to operate with CT images based on two different X-ray spectra.

The various materials of an anatomical region of interest have different attenuation coefficients, which depend on the used energy. These differences provide information on the chemical composition of the scanned body materials. syngo.CT Dual Energy combines images acquired with low and high energy spectra to visualize this information. Depending on the region of interest, contrast agents may be used.

The general functionality of the syngo.CT Dual Energy application is as follows:

• · Monoenergetic 1)
• · Brain Hemorrhage
• · Gout Evaluation
• · Lung Vessels
• · Heart PBV
• · Bone Removal
• · Lung Perfusion
• · Liver VNC
• · Monoenergetic Plus 1)
• · Virtual Unenhanced 1)
• Bone Marrow
• · Hard Plaques
• Rho/Z
• · Kidney Stones 2)
• · SPR (Stopping Power Ratio)
• · SPP (Spectral Post-Processing Format) 1)
• · Optimum Contrast 1)

The availability of each feature depends on the Dual Energy scan mode.

1. This functionality supports data from Photon-Counting CT scanners.

2. Kidney Stones is designed to support the visualization of the chemical composition of kidney stones and especially the differentiation between uric acid stones. For full identification of the kidney stone, additional clinical information should be considered such as patient history and urine testing. Only a well-trained radiologist can make the final diagnosis upon consideration of all available information. The accuracy of identification is decreased in obese patients.

Dual energy offers functions for qualitative and quantitative post-processing evaluations. syngo.CT Dual Energy is a post-processing application consisting of several post-processing application classes that can be used to improve the visualization of the chemical composition of various energy dependent materials in the human body when compared to single energy CT. Depending on the organ of interest, the user can select and modify different application classes or parameters and algorithms.

Different body regions require specific tools that allow the correct evaluation of data sets. syngo.CT Dual Energy provides a range of application classes that meet the requirements of each evaluation type. The different application classes for the subject device can be combined into one workflow.

Based on the provided text, the acceptance criteria and the study proving the device meets these criteria can be summarized as follows:

The document describes software verification and validation, non-clinical testing, and an evaluation of specific application classes for Photon Counting Data. However, it does not provide a quantitative table of acceptance criteria for specific performance metrics (e.g., sensitivity, specificity, accuracy) or detailed clinical study results with human readers (MRMC study). The testing described focuses on technical performance and consistency with expected phantom values and visual comparison with clinical data, rather than diagnostic accuracy or clinical effectiveness in a human-in-the-loop setting.

Here's a breakdown of the available information:

1. Acceptance Criteria and Reported Device Performance

The document states that "all software specifications have met the acceptance criteria" and "The testing results support that all the software specifications have met the acceptance criteria." However, the document does not explicitly list the specific acceptance criteria in a table format with corresponding reported device performance values for metrics like accuracy, sensitivity, or specificity.

Instead, the performance data provided focuses on:

• Software Verification and Validation: Conformance with "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices," risk analysis completion, and mitigation of identified hazards.
• Non-Clinical Testing: Integration and functional tests were conducted to demonstrate the ability of included features. "The results of these tests demonstrate that the subject device performs as intended."
• Evaluation of application classes for Photon Counting Data:
  • Monoenergetic Plus application class: "calculated values from phantom scans agreed well with the expected ones. Clinical data showed no artifacts. The iodine contrast clearly increased with lower keV settings and decreased with higher ones."
  • Virtual Unenhanced application class: "demonstrated that virtual non-contrast images and iodine concentration can be calculated from spectral data acquired at the NAEOTOM Alpha." In phantom scans, "the measured iodine concentration agrees well with the known iodine concentration. The VNC values are good approximations of the expected water value for all tested iodine concentrations." In clinical data, "the image impression of the virtual non-contrast images was compared with true non-contrast images. Measurements showed good agreement of CT values in the VNCs with the values in the TNCs."

No quantitative performance metrics (e.g., sensitivity, specificity, AUC) or a direct comparison to specific numerical acceptance criteria are provided in the document.

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

The document mentions "phantom scans" and "clinical data" for the evaluation of the Monoenergetic Plus and Virtual Unenhanced application classes.

• Phantom Scans: "Multi-Energy CT Phantom (Sun Nuclear Corporation, Melbourne, Florida, USA) was scanned at a NAETOM Alpha."
• Clinical Data: Used for visual comparison and measurement of CT values. The text refers to "clinical data" in general without specifying the sample size (number of patients/cases).
• Data Provenance: Not specified (e.g., country of origin). The data from the NAETOM Alpha appears to be prospectively acquired for testing purposes. It is not stated whether the clinical data used for comparison was retrospective or prospective.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document mentions that for the Kidney Stones feature, "Only a well-trained radiologist can make the final diagnosis upon consideration of all available information." However, it does not specify the number of experts used to establish ground truth for the test set or their specific qualifications (e.g., years of experience, subspecialty) for the evaluations described (phantom studies or clinical data comparisons).

4. Adjudication Method for the Test Set

The document does not describe any formal adjudication method (e.g., 2+1, 3+1 consensus) for establishing ground truth for the "clinical data" used. The evaluations seem to rely on technical comparisons for phantom data and general observation/measurement agreement for clinical data.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC comparative effectiveness study was conducted or reported. The submission focuses on technical validation and comparison of the device's outputs to expected values and impressions, rather than measuring human reader performance with and without AI assistance.

6. Standalone (Algorithm Only) Performance Study

The study appears to be an algorithm-only performance evaluation in terms of its ability to generate specific types of images/data (monoenergetic images, virtual non-contrast images, iodine concentrations) and the agreement of these outputs with expected or true values (for phantom data) and visual/measurement comparisons (for clinical data). However, no specific standalone diagnostic performance metrics (e.g., sensitivity, specificity for disease detection) are reported.

7. Type of Ground Truth Used

• Technical/Physical Ground Truth: For phantom studies, the "known iodine concentration" and "expected" values serve as ground truth.
• Reference Image Ground Truth: For the Virtual Unenhanced application, "true non-contrast images" are used as a reference for comparison.
• Expert Interpretive Ground Truth: While "well-trained radiologist" is mentioned in the Indications for Use for Kidney Stones, the actual methodology for establishing ground truth for the clinical data used in the evaluation is not detailed beyond "image impression" and "measurements." It's an implicit expert consensus by a "well-trained radiologist" who would interpret the images, but the methodology for establishing this is not formalized in the provided text.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set used to develop the syngo.CT Dual Energy algorithms. The focus of this submission is on verification and validation of a device modification, not initial algorithm development.

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

The document does not describe how the ground truth for the training set was established, as it pertains to the validation of a device modification rather than the initial algorithm development.

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