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

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

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

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

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

Derived Acceptance Criteria and Reported Device Performance (Inferred):

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

Study Details based on the provided text:

1. Sample Size and Data Provenance:

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

2. Number of Experts and Qualifications:

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

3. Adjudication Method:

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

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

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

5. Standalone (Algorithm Only) Performance Study:

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

6. Type of Ground Truth Used:

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

7. Training Set Sample Size:

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

8. How Ground Truth for Training Set was Established:

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

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The FDR Visionary Suite is intended to generate digital or conventional radiographic images of the skull, spinal column, chest, abdomen, extremities, and other body parts of human anatomies in all routine radiography examinations. The FDR Visionary Suite enables radiographic or tomographic exposures of the whole body of all ages including pediatric patients. Exposures may be taken with the patient sitting, standing, or lying in the prone or supine position. The FDR Visionary Suite uses portable or integrated flat panel detectors to generate diagnostic images by converting x-rays into electronic signals. The device is also designed to be used with conventional film/screen or computed radiography (CR) cassettes. The Tomosynthesis option is intended to generate tomographic images of human anatomies. Tomosynthesis technique is used to produce a specific cross-sectional plane of the body by reconstruction of tomographic acquisition. The device is intended to be used in hospitals, clinics, imaging centers, and/or other healthcare facilities by qualified/trained professionals. The device is not intended for mammographic applications.

The FDR Visionary Suite is an X-ray radiography system that is mainly used for the radiography of various regions of the patient's body in a standing or recumbent position. The FDR Visionary Suite can be used in a wide range of applications from general radiography using X-ray film or Computed Radiography (CR) cassettes, to digital radiography. The FDR Visionary Suite consists of an Xray high voltage generator, X-ray tube support and collimator. The system can be configured with radiographic table, radiographic stand and digital radiography system as well. Optionally, the device is also used to perform tomosynthesis radiography by two different reconstruction modes. Filtered Back-Projection (FBP) mode is used to obtain a tomosynthesis image by performing back-projection after correcting the projection data. Shift Addition (SA) mode is used to obtain a tomosynthesis image at an arbitrary slice plane height by shifting each image according to projection angle of the tube based on the reconstruction height, and by applying image addition processing to them. FBP mode is generally recommended for all body parts. In case an artifact is observed at joints and other similar places, SA mode may remedy this artifact. In case metal artifact is obviously displayed.

The provided text does not contain acceptance criteria for device performance related to a specific diagnostic task (e.g., disease detection accuracy), nor does it describe a study proving the device meets such criteria.

The document is a 510(k) Premarket Notification from the FDA to Shimadzu Corporation for their FDR Visionary Suite. It primarily focuses on demonstrating substantial equivalence to a previously cleared predicate device (K152294).

Here's a breakdown of what the document does provide in relation to your questions, and what it does not:

What the document does provide:

• Device Description: The FDR Visionary Suite is an X-ray radiography system for various body parts and ages, including pediatric patients. It can perform general radiography and tomosynthesis.
• Modifications from Predicate: The submission outlines changes from the previous version of the FDR Visionary Suite (K152294). These changes are primarily hardware upgrades (larger LCD displays, added camera, wireless controls, updated flat panel detectors) and infrastructure software changes (programming language, OS) while maintaining the same functionality.
• Performance Testing (Non-Clinical):
  • User Manuals: Modified to reflect changes.
  • Software Validation: Performed according to FDA guidance for basic documentation level, including cybersecurity considerations.
  • EMC and Electrical Safety Testing: Performed according to IEC standards (EN 60601-1-2 and IEC 60601-2-54).
• Conclusion: Based on risk analysis, software verification/validation, and non-clinical testing, the new device is considered substantially equivalent to the predicate, with no impact on clinical safety or effectiveness due to the changes.

What the document does NOT provide (and why it's missing in this context):

The questions you've asked are typically relevant for devices that employ AI/ML algorithms where diagnostic performance (e.g., sensitivity, specificity, AUC) is a key aspect of their functionality and a basis for 510(k) clearance or de novo classification.

This document describes an X-ray system where the core imaging technology and its functional performance (image acquisition, processing, generation) are being compared to a predicate. The software changes mentioned are related to the infrastructure and user interface, not a new AI-powered diagnostic algorithm.

Therefore, the following information is not present for the reasons stated:

1. A table of acceptance criteria and reported device performance (e.g., diagnostic accuracy metrics): Not provided because this is an updated X-ray system, not a new diagnostic AI. The performance criteria relate to hardware safety, electrical compatibility, and software validation of its infrastructure, not diagnostic output.
2. Sample size used for the test set and data provenance: No clinical test set involving patient data for diagnostic performance evaluation is described. The tests were non-clinical (e.g., electrical safety, software validation).
3. Number of experts used to establish ground truth & qualifications: N/A, as there's no diagnostic ground truth being established for an AI algorithm.
4. Adjudication method for the test set: N/A.
5. Multi-reader multi-case (MRMC) comparative effectiveness study: Not done. This is typical for AI-assisted diagnostic tools, not for a general X-ray system hardware/software update where the functionality remains unchanged. The statement "The software functionality remains unchanged" (page 5) reinforces this.
6. Standalone (algorithm-only) performance: Not applicable, as there's no distinct diagnostic algorithm being evaluated independently.
7. Type of ground truth used: N/A.
8. Sample size for the training set: N/A, as this is not an AI algorithm being trained. The software changes are infrastructure updates, not machine learning model training.
9. How ground truth for the training set was established: N/A.

In summary, the provided FDA document is for a conventional X-ray system seeking clearance for minor hardware and infrastructure software updates via the 510(k) substantial equivalence pathway. It relies on non-clinical testing (safety, EMC, software validation) to demonstrate that the updated device is as safe and effective as its predicate, rather than clinical performance studies of an AI diagnostic algorithm.

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The RADspeed PRO is intended to generate digital or conventional radiographic images of the skull, spinal column, chest, abdomen, extremities, and other body parts of human anatomies in all routine radiography examinations. The RADspeed Pro enables radiographic or tomographic exposures of the whole body of all ages including pediatric patients. Exposures may be taken with the patient sitting, standing, or lying in the prone or supine position. The RADspeed PRO uses portable or integrated flat panel detectors to generate diagnostic images by converting x-rays into electronic signals. The device is also designed to be used with conventional film/screen or computed radiography (CR) cassettes. The Tomosynthesis option is intended to generate tomographic images of human anatomies. Tomosynthesis technique is used to produce a specific cross-sectional plane of the body by reconstruction. The device is intended to be used in hospitals, clinics, imaging centers, and/or other healthcare facilities by qualified/trained professionals. The device is not intended for mammographic applications.

The RADspeed PRO is an X-ray radiography system that is mainly used for the radiography of various regions of the patient's body in a standing or recumbent position. The RADspeed PRO can be used in a wide range of applications from general radiography using X-ray film or Computed Radiography (CR) cassettes, to digital radiography. The RADspeed PRO consists of an X-ray high voltage generator, X-ray tube unit, X-ray tube support and collimator. The system can be configured with radiographic table, radiographic stand and digital radiography system as well. Optionally, the device is also used to perform tomosynthesis radiography by three different reconstruction modes. Filtered Back-Projection (FBP) mode is used to obtain a tomosynthesis image by performing back-projection after correcting the projection data. Shift Addition (SA) mode is used to obtain a tomosynthesis image at an arbitrary slice plane height by shifting each image according to projection angle of the tube based on the reconstruction height, and by applying image addition processing to them. Iteration (IR) mode is used to reduce metal artifact in tomosynthesis image. FBP mode is generally recommended for all body parts. In case an artifact is observed at joints and other similar places, SA mode may remedy this artifact. In case metal artifact is obviously displayed, IR mode is recommended to reduce metal artifact.

The provided document is a 510(k) premarket notification for the Shimadzu Corporation's RADspeed PRO, referencing K233722. It primarily focuses on demonstrating substantial equivalence to a predicate device (K152244) rather than presenting a performance study against specific acceptance criteria for a new clinical indication or AI algorithm.

The modifications to the RADspeed PRO are described as mainly cosmetic and updates to hardware components (larger displays, updated imaging panels that are themselves cleared 510k devices) and software infrastructure (programming language, operating system) without changes to the underlying software functionality or core technological features. The device does not appear to incorporate a new AI algorithm for diagnostic purposes that would require a study with clinical performance metrics like sensitivity, specificity, or reader improvement.

Therefore, the requested information regarding acceptance criteria and performance study results for an AI algorithm (including sample sizes, ground truth establishment, expert adjudication, MRMC studies, or standalone performance) is not present in the provided text.

The document states:

• "The fundamental technological features are the same for the subject and the predicate systems. The modifications are mainly cosmetic in nature." (Page 4)
• "The software functionality remains unchanged." (Page 5, Page 6)
• "The imaging components were replaced by more recently cleared digital x-ray receptor panels made by FUJIFILM. ... All the imaging panels have 510(k) clearance... The integration software, although updated, has the identical functionality to the predicate." (Page 5)
• "The radiation safety aspects of the device have not changed. The device remains compliant with the FDA Radiation Safety Standards." (Page 9)
• "The non-clinical data supports the safety of the device and the hardware and software verification and validation demonstrate that new device should performs as intended in the specified use. Based on our risk analysis and bench testing, the differences do not affect its clinical safety or effectiveness." (Page 9)

The performance testing mentioned (Software Validation, EMC and Electrical Safety Testing) is related to regulatory compliance and safety for the modified device, not to the clinical performance of a new diagnostic algorithm.

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

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

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

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

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

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

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

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

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

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

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable. No clinical test set.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not applicable. No MRMC study was done, nor does the device appear to include AI for interpretation or improvement of human reader performance.

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

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

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

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

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

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

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

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

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

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

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

1. Table of Acceptance Criteria and Reported Device Performance

Not Applicable (for AI/ML performance).

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

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

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

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

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

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

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

Not Applicable.

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

4. Adjudication Method for the Test Set

Not Applicable.

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

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

No. A MRMC study was not done.

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

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

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

No.

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

7. The Type of Ground Truth Used

Compliance with regulated standards and functional specifications.

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

8. The Sample Size for the Training Set

Not Applicable.

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

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

Not Applicable.

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

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

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The equipment is intended to be used for the fluoroscopy/radiography diagnosis in hospital. The equipment must only be operated by qualified personnel, such as radiography technicians or those with equivalent qualifications. The equipment is used for total patient population. The equipment is NOT intended to be used for Mammography screening. The equipment is NOT intended to be used for interventional procedure. The equipment is used for radiographic, fluoroscopic, angiographic and pediatric examinations. Stored images in the equipment can be used for re-monitoring, image processing, storing to optical media (CD/DVD), and sending to DICOM server.

The Shimadzu FLUOROspeed is a universal X-ray RF system offering radiographic, fluoroscopic, and angiographic modes. As compared to our recently cleared system the SONIALVISION G4, (K190373) the positions of the tube head and the digital x-ray receptor panel have been swapped. This was done to respond to customer demand. This configuration is identical to products offered by Siemens (K173639) and GE Healthcare (K081091). The FLUOROspeed is intended to be used as a universal diagnostic imaging system for radiographic and fluoroscopic studies. Using a flat panel detector, it can perform a range of applications including general R/F, angiography and pediatric examinations. FLUOROspeed may be used for emergency treatment on an outpatient basis, as well as for bedside examinations.

The provided text is a 510(k) summary for the SHIMADZU Corporation's FLUOROspeed device. It indicates that clinical testing was not performed to demonstrate substantial equivalence. Therefore, there is no information available regarding acceptance criteria, device performance, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, or ground truth establishment related to clinical studies.

The submission relies on non-clinical testing and compliance with recognized standards to establish substantial equivalence to predicate devices. The "acceptance criteria" in this context are adherence to these specific standards and the predicate device's characteristics.

Here's a breakdown of the specific points based on the provided document:

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

The acceptance criteria are essentially the performance and safety requirements outlined in the listed IEC and EN standards, and the device's characteristics being "SAME" as the predicate device (SONIALVISION G4 K190373) for most specifications.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Not applicable. No clinical test set. The evaluation was based on non-clinical testing and comparison to predicate device specifications.

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)

Not applicable. No clinical test set requiring expert ground truth.

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

Not applicable. No clinical test set.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. No clinical effectiveness study involving human readers or AI assistance was performed. This device is an X-ray system, not an AI-powered diagnostic tool.

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

Not applicable. This device is an X-ray system, not an algorithm, and no standalone algorithmic performance was evaluated.

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

Not applicable. No clinical ground truth was established as no clinical studies were conducted. The "ground truth" for this submission is established by the specifications and performance of the legally marketed predicate device and compliance with international safety and performance standards.

8. The sample size for the training set

Not applicable. There is no mention of a training set as this is a medical imaging device, not an AI/ML diagnostic algorithm.

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

Not applicable. There is no mention of a training set or its ground truth establishment.

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The equipment is intended to be used for the fluoroscopy/radiography diagnosis in hospital. The equipment must only be operated by qualified personnel, such as radiography technicians or those with equivalent qualifications. The equipment is used for total patient population. The equipment is NOT intended to be used for Mammography screening. The equipment is NOT intended to be used for interventional procedure. The equipment is used for radiographic, fluoroscopic, angiographic and pediatric examinations. Stored images in the equipment can be used for re-monitoring, image processing, storing to optical media (CD/DVD), and sending to DICOM server. The Tomosynthesis option for the SONIALVISION G4 is intended to generate tomographic images of human anatomy including chest or extremities. Tomosynthesis technique is used to produce a specific cross-sectional plane of the body by reconstruction of tomographic acquisition. The device is not intended for mammographic applications

The Shimadzu SONIALVISION G4 is a universal X-ray RF system offering radiographic, fluoroscopic, angiographic and tomosynthesis techniques. Tomosynthesis technique is enabled by additional software option to the digital radiography system of the SONIALVISION G4. The Shimadzu SONIALVISION G4 is floor mounted table, and the system can be configured with Digital Radiography System, X-ray High Voltage Generator, Collimator and X-ray Tube. The purpose of this submission is to introduce an additional compatible digital x-ray receptor panel. This panel is manufactured by Canon. The panel was cleared by FDA for fluoroscopic use in K171194.

The provided document is a 510(k) summary for the Shimadzu SONIALVISION G4, an image-intensified fluoroscopic x-ray system. The submission is to introduce an additional compatible digital x-ray receptor panel (Canon AS-10, CXDI-401RF) to the existing SONIALVISION G4 system.

Here's an analysis of the provided information regarding acceptance criteria and the study that proves the device meets those criteria:

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

The document doesn't explicitly state "acceptance criteria" but rather presents a comparison of the technical characteristics of the modified device with the predicate device to demonstrate substantial equivalence. The implication is that if the modified device's performance is equivalent or better than the predicate, it meets the unstated acceptance criteria for safety and effectiveness.

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

• Sample Size for Test Set: Not applicable. The submission does not describe a test set of clinical images or patient data to evaluate the device's diagnostic performance in a clinical setting.
• Data Provenance: Not applicable. The study relies on non-clinical (bench) testing.

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

• Number of Experts: Not applicable. No clinical test set requiring expert ground truth was used.
• Qualifications of Experts: Not applicable.

4. Adjudication method for the test set:

• Adjudication Method: Not applicable. No clinical test set requiring adjudication was used.

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:

• MRMC Study: No, an MRMC comparative effectiveness study was not done. This device is an x-ray system, not an AI-powered diagnostic tool requiring human reader assistance evaluation.

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

• Standalone Performance: Not applicable in the context of an AI algorithm. The device itself is an imaging system. The comparison focuses on the technical specifications of the new X-ray detector panel to demonstrate its equivalence (or non-inferiority) to the predicate panel. The "standalone" performance here refers to the physical and imaging characteristics of the detector itself, which were evaluated by Canon (the manufacturer of the new panel) as referenced in K171194.

7. The type of ground truth used:

• Type of Ground Truth: The ground truth for this submission is based on non-clinical (bench) testing standards and technical specifications that define image quality and system performance for X-ray systems. The comparison is made against the established performance parameters of the predicate device and relevant industry standards.

8. The sample size for the training set:

• Sample Size for Training Set: Not applicable. This submission is for an X-ray imaging system with a new detector panel, not an AI or machine learning algorithm that requires a training set.

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

• Ground Truth for Training Set: Not applicable. There is no training set mentioned or implied in this submission, as it's not an AI/ML device.

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The RADspeed fit is intended to generate digital or conventional radiographic images of the skull, spinal column, chest, abdomen, extremities, and other body parts of human anatomies in all routine radiography examinations. The RADspeed fit enables radiographic exposure of the whole body of all ages including pediatric patients. Exposures may be taken with the patient sitting, standing, or lying in the prone or supine position. The RADspeed fit uses portable or integrated flat panel detectors to generate diagnostic images by converting x-rays into electronic signals. The device is also designed to be used with conventional film/screen or computed radiography (CR) cassettes. The device is not intended for mammographic techniques. The indications for use remain the same as those for the predicate, except for the removal of tomographic technique.

The RADspeed fit is an X-ray radiography system that is mainly used for the radiography of various regions of the patient's body in a standing or recumbent position. The RADspeed fit can be used in a wide range of applications from general radiography using X-ray film or Computed Radiography (CR) cassettes, to digital radiography. The RADspeed fit consists of an X-ray high voltage generator, X-ray tube unit, X-ray tube support and collimator. The system can be configured with radiographic table, radiographic stand and digital radiography system as well. The X-ray generator of RADspeed fit has the capability to communicate with the digital radiography system to synchronize exposure timing, receive a code to select an anatomical program and to send X-ray technique factors and dose information after exposure.

The provided document describes a 510(k) premarket notification for the "RADspeed fit" device, which is a stationary X-ray system. The core of the submission is to demonstrate substantial equivalence to a legally marketed predicate device (K152244 RADspeed Pro) and a digital radiography system (K170332 Digital Radiography CXDI-710C Wireless / CXDI-810C Wireless).

The document does not contain information about acceptance criteria or a study that proves the device meets specific performance criteria through a clinical or non-clinical evaluation comparing the device's output to ground truth, as typically seen for AI/ML-based medical devices or devices where image quality itself is the primary novelty.

Instead, the submission focuses on demonstrating substantial equivalence through:

1. Comparison of Technological Characteristics: Showing that the new device shares fundamental technology with the predicate devices and that modifications (removed functions, added features like FPD rotation tray and calculated dose-area product display) do not affect safety or essential effectiveness.
2. Compliance with Standards: Stating that the new device conforms to relevant IEC standards (IEC60601-1, IEC60601-1-2, IEC60601-1-3) and CFR, which are general safety and performance standards for medical electrical equipment and X-ray systems.
3. Software Verification and Validation: Showing that these processes were conducted according to FDA guidance, assuring safety and effectiveness for the software modifications related to interface adaptation.
4. Claim of Substantial Equivalence for Image Quality: Since the digital radiography system (detectors) used in the new device is "exactly the same as the predicate device (K170332)," the submission argues that "the output image is substantially equivalent to the predicate device as long as the quality and profile of input X-ray are similar." The compliance of X-ray generators with IEC60601-1 is cited to ensure similar X-ray quality.

Therefore, many of the requested details regarding acceptance criteria, test set characteristics, expert involvement, and specific performance metrics (like sensitivity, specificity, or MRMC results) are not applicable or not present in this type of 510(k) submission, which relies on demonstrating equivalence through design similarity, functional comparison, and adherence to general safety standards rather than a de novo performance study against a specified ground truth for a novel function.

However, I can extract the information that is present and explain why other points are not detailed.

Summary of Device Acceptance Criteria and Performance Data (Based on provided document):

The acceptance criteria are not presented in a quantitative table with specific metrics (e.g., sensitivity, specificity, accuracy). Instead, the acceptance is based on demonstrating substantial equivalence to predicate devices by ensuring:

• Safety and Effectiveness: The device is as safe, as effective, and performs as well as the predicate device(s).
• Technological Equivalence: Key technological elements (energy emission, X-ray generation mechanism, image data acquisition/processing, hardware/software components) are the same or comparable.
• Compliance with Standards: Adherence to relevant IEC standards (IEC60601-1, 60601-1-2, 60601-1-3) and CFR for electrical, mechanical, chemical, thermal, and radiation safety, and electromagnetic compatibility.
• Software Verification and Validation: Software risks are analyzed, and V&V testing is conducted per FDA guidance to ensure its safety and effectiveness, despite modifications to adapt interfaces.
• Image Quality Equivalence: The digital radiography system (detector) is identical to a legally marketed predicate (K170332), thus ensuring "substantially equivalent" output image quality when combined with X-ray generators complying with relevant IEC standards.
• Non-introduction of New Safety/Effectiveness Concerns: Modifications (removed functions, added minor features) do not adversely impact safety or effectiveness.

The "study" proving this device meets its "acceptance criteria" is primarily the comparative analysis of its design, technology, and compliance with standards against established predicate devices, as documented in the 510(k) submission.

Breakdown of Requested Information based on the provided text:

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

   • Acceptance Criteria (Implicit - Substantial Equivalence):

     • Device is as safe, as effective, and performs as well as predicate device (K152244 RADspeed Pro).
     • Digital Radiography system (Canon CXDI-710C/810C) is identical to a legally marketed device (K170332).
     • Compliance with IEC 60601-1, 60601-1-2, 60601-1-3, and CFR (for radiation safety).
     • Software V&V conducted per FDA guidance, showing no new risks.
     • No new safety or effectiveness concerns due to modifications.

   • Reported Device Performance (as stated in the document):

     • Electromagnetic compatibility: In conformity with IEC60601-1-2.
     • Materials and Bio-compatibility: In conformity with IEC 10993-1. All materials in clinical use by other commercial products.
     • Sterility: Not sterilized (same as predicate).
     • Electrical, Mechanical, Chemical and Thermal safety: Substantially equivalent to predicate by conformed standards.
     • Software: Risk Analysis and V&V testing conducted; identified level of concern, documentation consistent. "Difference does not affect its safety and effectiveness."
     • Non-clinical Image Performance: "Substantially equivalent to the predicate device" because the Digital Radiography system is identical to K170332, and X-ray generators comply with IEC60601-1.
     • Clinical Performance: "Substantially equivalent to the predicate device" for the same reasons as non-clinical performance.
     • Radiation safety: Substantially equivalent by conformities of CFR and IEC60601-1-3.

   (Note: This is not a quantitative performance table but a qualitative statement of equivalence and compliance.)

2. Sample sizes used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

   • Not applicable/Not provided in this document. This submission primarily relies on design comparison, compliance with general standards, and leveraging the prior clearance of the identical digital detector system. There is no specific test set of patient images with ground truth described for "proving" the device's image quality or diagnostic performance via a clinical trial.

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)

   • Not applicable/Not provided. As there is no described test set with ground truth established, no experts for this purpose are mentioned.

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

   • Not applicable/Not provided.

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

   • No MRMC study was done or mentioned. This device is a basic X-ray system, not an AI-assisted diagnostic tool.

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

   • Not applicable/Not done. This device is an X-ray imaging system, not an algorithm providing a standalone diagnostic output.

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

   • Not applicable/Not provided. No specific "ground truth" for diagnostic accuracy or image quality assessment (beyond compliance with technical standards) is detailed in this type of submission. The ground truth in this context is implicitly the "known good performance" of the predicate devices and the established performance of the identical digital detector.

8. The sample size for the training set

   • Not applicable/Not provided. This is a hardware device submission. While software V&V is mentioned, it's about system integration and safety, not an AI/ML algorithm that requires a "training set."

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

   • Not applicable/Not provided. (See point 8).

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The FDR Visionary Suite is intended to generate digital or conventional radiographic images of the skull, spinal column, chest, abdomen, extremities, and other body parts of human anatomies in all routine radiography examinations. The FDR Visionary Suite enables radiographic exposures of the whole body of all ages including pediatric patients. Exposures may be taken with the patient sitting, standing, or lying in the prone or supine position. The FDR Visionary Suite uses portable or integrated flat panel detectors to generate diagnostic images by converting x-rays into electronic signals. The device is also designed to be used with conventional film/screen or computed radiography (CR) cassettes.

The Tomosynthesis option is intended to generate tomographic images of human anatomies. Tomosynthesis technique is used to produce a specific cross-sectional plane of the body by reconstruction of tomographic acquisition.

The device is intended to be used in hospitals, clinics, imaging centers, and/or other healthcare facilities by qualified/ trained professionals.

The device is not intended for mammographic applications.

The FDR Visionary Suite is an X-ray radiography system that is mainly used for the radiography of various regions of the patient's body in a standing or recumbent position. The device can be used in a wide range of applications of digital radiography. The device consists of an X-ray high voltage generator, X-ray tube unit, X-ray tube support, collimator and digital radiography system. The system can be configured with radiographic table and/or radiographic stand as well.

Optionally, the device is also used to perform tomosynthesis radiography by two different reconstruction modes, e.g. Filtered Back-Projection (FBP) mode and Shift-and-Add (SA) mode. SA reconstructed images are intended to be used alongside FBP reconstructed images and not for stand-alone diagnostic purpose.

This document pertains to the FDR Visionary Suite, a stationary X-ray system. The acceptance criteria and the study proving the device meets these criteria are primarily based on non-clinical performance testing and substantial equivalence to predicate devices, rather than a clinical study with detailed performance metrics.

1. Table of Acceptance Criteria and Reported Device Performance

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

• The document does not specify a numerical sample size for the test set. It mentions "phantom tests using several different anatomical body phantoms."
• Data Provenance: Not explicitly stated, but given the manufacturer (SHIMADZU CORPORATION, Japan) and the predicate devices being from FUJIFILM CORPORATION, Japan, it is highly likely the phantom tests and image quality evaluations were conducted in a controlled, non-clinical laboratory setting, likely in Japan. This was a retrospective evaluation against existing predicate device performance, not a prospective clinical trial.

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

• The document does not specify the number of experts or their qualifications for establishing ground truth for the test set.
• Given that the testing involved "phantom tests" and "image quality evaluation," the ground truth would likely be established based on known characteristics of the phantoms and objective image quality metrics, potentially interpreted by imaging scientists or engineers rather than clinical experts.

4. Adjudication Method for the Test Set

• The document does not specify any adjudication method for the test set. As the testing appears to be non-clinical (phantom-based), a formal adjudication process involving multiple human readers for diagnostic interpretation would not typically be required or relevant in the same manner as a clinical study.

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

• No, an MRMC comparative effectiveness study was not done. The document explicitly states: "The result of non-clinical bench testing demonstrates substantial equivalence to the predicate devices. Therefore, it is our understanding that clinical testing is not required in this case."
• Consequently, there is no reported effect size of human reader improvement with or without AI assistance, as this type of study was not conducted.

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

• Yes, standalone performance testing was implicitly performed as part of the "non-clinical performance bench test and image quality evaluate substantial equivalence of image processing technique to each predicate device". These tests assess the device's image acquisition and processing capabilities independent of human interpretation for diagnostic purposes, focusing on technical image quality metrics and functional equivalence to the predicate. The "metal artifact reduction function of tomosynthesis option" testing is an example of an algorithm-only performance assessment.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• For the non-clinical performance testing, the ground truth was based on known characteristics of anatomical phantoms and objective image quality metrics. For example, in metal artifact reduction, the ground truth would be the known presence and characteristics of metal in the phantom and objective metrics of how well the artifact is reduced. For "distinction of lesion overlying bones," the ground truth would be the known presence/absence and characteristics of these features in the phantom.

8. The Sample Size for the Training Set

• The document does not provide information regarding a "training set" or its sample size. This is because the submission primarily focuses on demonstrating substantial equivalence to physically existing predicate devices through engineering and quality testing, rather than an AI/machine learning model that typically requires a large training dataset. The "software processing methods" mentioned are likely traditional image processing algorithms, not deep learning models.

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

• As no training set is described, this information is not applicable and not provided in the document.

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The RADspeed Pro is intended to generate digital or conventional radiographic images of the skull, spinal column, chest, abdomen, extremities, and other body parts of human anatomies in all routine radiography examinations. The RADspeed Pro enables radiographic or tomographic exposures of the whole body of all ages including pediatric patients. Exposures may be taken with the patient sitting, standing, or lying in the prone or supine position.

The RADspeed Pro uses portable or integrated flat panel detectors to generate diagnostic images by converting x-rays into electronic signals. The device is also designed to be used with conventional film/screen or computed radiography (CR) cassettes. The Tomosynthesis option is intended to generate tomographic images of human anatomies. Tomosynthesis technique is used to produce a specific cross-sectional plane of the body by reconstruction of tomographic acquisition.

The device is intended to be used in hospitals, clinics, imaging centers, and/or other healthcare facilities by qualified/trained professionals.

The device is not intended for mammographic applications.

The RADspeed Pro is an X-ray radiography system that is mainly used for the radiography of various regions of the patient's body in a standing or recumbent position. The RADspeed Pro can be used in a wide range of applications from general radiography using X-ray film or Computed Radiography (CR) cassettes, to digital radiography. The RADspeed Pro consists of an X-ray high voltage generator, X-ray tube unit, X-ray tube support and collimator. The system can be configured with radiographic table, radiographic stand and digital radiography system as well.

Optionally, the device is also used to perform tomosynthesis radiography by three different reconstruction modes. Filtered Back-Projection (FBP) mode is used to obtain a tomosynthesis image by performing back-projection after correcting the projection data. Shift Addition (SA) mode is used to obtain a tomosynthesis image at an arbitrary slice plane height by shifting each image according to projection angle of the tube based on the reconstruction height, and by applying image addition processing to them. Iteration (IR) mode is used to reduce metal artifact in tomosynthesis image. FBP mode is generally recommended for all body parts. In case an artifact is observed at joints and other similar places, SA mode may remedy this artifact. In case metal artifact is obviously displayed. IR mode is recommended to reduce metal artifact.

The provided text describes a 510(k) summary for the RADspeed Pro X-ray system. Here's a breakdown of the acceptance criteria and study details based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with quantitative performance metrics for disease detection or diagnostic accuracy. Instead, the performance studies primarily aim to demonstrate "substantial equivalence" to predicate devices, focusing on image quality and artifact reduction.

The closest to "acceptance criteria" are the conformities to various standards and the stated goal of achieving image quality comparable to predicate devices.

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

• Test Set Sample Size: The document mentions "several different anatomical body phantoms" for phantom tests, and an unspecified number of "anthropomorphic phantom images" for the tomosynthesis reconstruction review. Specific numerical sample sizes (e.g., number of phantoms, number of images reviewed) are not provided.
• Data Provenance: The studies were non-clinical performance testing using phantoms. There is no mention of human patient data (retrospective or prospective) being used for the performance evaluation detailed in Section V.

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

• Number of Experts: "A U.S. board-certified radiologist" (singular) was used for the review of the tomosynthesis anthropomorphic phantom images.
• Qualifications: "U.S. board-certified radiologist." No information about years of experience or subspecialty is provided.

4. Adjudication Method for the Test Set

The document mentions "a U.S. board-certified radiologist review" for the tomosynthesis images. This single-reader review implies no adjudication method (like 2+1 or 3+1 consensus) was conducted, as there was only one expert involved in that specific review. For other non-clinical tests (bench tests, image quality evaluations), the method of evaluation and any expert involvement or adjudication is not specified.

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

No. The document does not describe an MRMC comparative effectiveness study involving human readers with and without AI assistance. The radiologist review mentioned was for phantom images to assess image quality for substantial equivalence, not a comparative reader study to measure improvement from AI assistance.

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

Yes, implicitly. The "non-clinical performance bench test and image quality evaluate substantial equivalence of image processing technique" were conducted comparing the new device's output to predicate devices. This implies evaluating the algorithm's performance (specifically, its image processing and reconstruction capabilities) on phantom data. The metal artifact reduction function of the tomosynthesis option was also "conducted performance testing" which would be a standalone evaluation of the algorithm's output. The radiologist review of phantom images could be seen as an 'expert-in-the-loop' check on the standalone output.

7. The Type of Ground Truth Used

• Phantom Gold Standard: For the non-clinical performance testing (including general image quality, tomosynthesis reconstruction, and metal artifact reduction), the ground truth was established by the physical properties and known characteristics of the anatomical body phantoms used. The 'truth' is inherent in the phantom's design and the controlled testing environment. For Dual Energy Subtraction, it was related to the "distinction of lesion overlying bones to calcification or tubercle shade" within a phantom context.
• Expert Review of Phantom Images: For the tomosynthesis reconstruction review, a U.S. board-certified radiologist provided an evaluation, which serves as an expert opinion on the image quality rendered from the phantom data against the expected 'truth' from the phantom.

8. The Sample Size for the Training Set

The document does not provide any information about a training set or its sample size. This submission focuses on the performance testing of the modified device against predicate devices for substantial equivalence, not on the development or training of an AI algorithm from a dataset in the typical sense. While software processing is mentioned, details about machine learning training are absent.

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

As no training set is mentioned, there is no information on how its ground truth might have been established.

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