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The VenusX radiotherapy delivery system is intended to provide precision radiotherapy for lesions, tumors, and conditions anywhere in the body where radiation treatment is indicated.

The VenusX Radiotherapy System is a medical linear accelerator that delivers therapeutic radiation to patient in accordance with the physician's prescription. It supports CRT /IMRT Treatment Techniques (Mode). The system consists of a photon therapeutic 6 MV X-ray radiation beam producing component with a photon diagnostic kV X-ray radiation beam producing component that is installed in a radiation-shielded vault and a control console area located outside the treatment room.

The provided text describes a medical device called VenusX, a radiotherapy delivery system, and its submission for FDA clearance. However, it does not include detailed information about specific acceptance criteria related to its performance in terms of accuracy or clinical effectiveness as one would expect for a diagnostic AI device or a device with a direct measurable output related to patient diagnosis/treatment efficacy (e.g., sensitivity, specificity, accuracy).

Instead, the document focuses on demonstrating substantial equivalence to predicate devices (Varian UNIQUE and Varian On Board Imaging) by comparing technological characteristics and ensuring adherence to various safety, electrical, software, and biocompatibility standards.

Therefore, many of the requested points cannot be answered based on the provided text. I will answer the points that can be addressed from the document and explicitly state when information is not available.

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

The document primarily discusses compliance with various standards and safety requirements rather than performance metrics like sensitivity, specificity, or quantifiable clinical outcomes. Therefore, a table listing "acceptance criteria" and "reported device performance" in the typical sense for a clinical study is not possible from this text.

The "acceptance criteria" mentioned are related to:

• Electrical Safety and EMC: Compliance with IEC 60601-1-2:2014+AMD1:2020. Reported performance: "The system complies with the following standards."
• Bench Testing: Conformance to various IEC and ANSI AAMI standards (e.g., ANSI AAMIES/IEC60601-1, IEC 60601-1-3, IEC 60601-2-1, IEC 60601-2-68, IEC 62274, IEC 62366-1, IEC 60601-1-6, IEC 60976, IEC 61217). Reported performance: "Test results met all the pre-determined acceptance criteria."
• Cybersecurity: Compliance with security requirement testing, threat mitigation testing, closed box vulnerability scanning, and penetration testing, with reference to IEC 62443-4-1 Section 9.4. Reported performance: "The results of the cybersecurity testing showed that VenusX meets the cybersecurity requirements under Section524B(b) of FD&C Act."
• Hardware and Software V&V: Conformance to 21 CFR §820, ISO 13485, ISO 14971, and FDA guidance "Content of Premarket Submissions for Device Software Functions." Reported performance: "Test results showed conformance to applicable requirements specifications and assured hazard safeguards functioned properly." Also, "there were no software DRs (Discrepancy Reports) remaining which had a priority of Safety Intolerable or Customer Intolerable." The software is considered "enhanced" level of concern.
• Biocompatibility: Compliance with ISO 10993-1, ISO 10993-5, ISO 10993-10 for the Carbon fiber couch top. Reported performance: "Test results met all the pre-determined acceptance criteria."

The document states that the VenusX device "provides precision radiotherapy" and its "Cone Beam CT Imager device (CBCT) is used for verification of correct patient position in relation to isocenter and verification of the treatment fields in relation to anatomical and/or fiducial landmarks." It does not provide quantitative performance metrics for these functions.

A comparison of technological characteristics between the VenusX and predicate/reference devices is provided (pages 5-8), indicating that the VenusX has similar intended use, indications for use, and core functionalities (e.g., photon energy, maximum treatment field size, patient support, microwave source, interface for external system gating, image registration, data interface, record treatment delivery results, treatment techniques). Some differences are noted, such as FFF mode dose rates, SAD, number of MLC leaves (101 leaves in two orthogonal layers vs. 120), maximum leaf over travel, average leaf transmission (≤ 0.05% for VenusX vs. ≤ 2.0% for predicate), MV Detector size (larger for VenusX: 43cm x 43cm vs. 30cm x 40cm), and MV Pixel (higher resolution for VenusX: 2816x2816 vs. 1024x768). For CBCT, the VenusX also uses the FDK algorithm, with a dose per acquisition ≤ 2cGy (vs. ≤ 1.4cGy for reference device) and a 512x512 reconstruction matrix / 360° acquisition mode.

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

This information is not provided in the document. The testing described is primarily non-clinical (bench testing, electrical safety, cybersecurity, hardware/software V&V, biocompatibility). There is no mention of a clinical "test set" in the context of patient data, nor its provenance or design.

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)

This information is not provided in the document. Since no clinical "test set" with patient data requiring ground truth by experts is described, this point is not applicable.

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

This information is not provided in the document. Since no clinical "test set" requiring adjudication is described, this point is not applicable.

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

This information is not provided in the document. The VenusX is a radiotherapy delivery system, not a diagnostic AI system designed to assist human readers in interpreting images or data. Therefore, an MRMC study comparing human readers with and without AI assistance is not relevant to the described device.

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

The document refers to the device's technical performance against engineering and safety standards. It does not describe an "algorithm only" standalone performance in the sense of a diagnostic or analytical algorithm reporting a clinical outcome. The device itself (a linear accelerator) performs the function of radiation delivery, and its imaging components (MV, CBCT) are for positional verification.

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

For the non-clinical testing, the "ground truth" implicitly refers to the specified technical requirements, standards, and design specifications. For example, in electrical safety, the ground truth is adherence to the IEC 60601-1-2 standard. For hardware/software V&V, it's conformance to requirements specifications and hazard safeguards. There is no mention of ground truth established from clinical data like pathology or outcomes data.

8. The sample size for the training set

This information is not provided in the document. The document describes a medical linear accelerator system, not an AI model that requires a "training set" of data in the typical sense. The software and hardware development involves verification and validation, but not a data-driven training process in the way a machine learning model would.

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

This information is not provided in the document. As noted in point 8, there is no mention of a "training set" for an AI model.

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TiGRT IVS is intended to be used in conjunction with Linac and Digital Panel for analyzing the current patient position and calculating the patient positioning shift correction factor, as well as the treatment portal verification and record.

TiGRT IVS is intended to be used in conjunction with Linac and Digital Panel for analyzing the current patient position and calculating the patient positioning shift correction factor, as well as the treatment portal verification and record.

The provided text does not contain any information about acceptance criteria or a study proving the device meets acceptance criteria.

The document is a 510(k) premarket notification for an image viewing system called TiGRT IVS. It primarily focuses on demonstrating substantial equivalence to a predicate device (ExacTrac K072046) based on intended use, design, and performance characteristics.

Here's what the document does include:

• Device Name: TiGRT IVS
• Intended Use: To be used in conjunction with Linac and Digital Panel for analyzing the current patient position, calculating patient positioning shift correction factor, and for treatment portal verification and record.
• Predicate Device: ExacTrac (K072046)
• Classification: Class II device (21 CFR 892.5050)
• Comparison with Predicate Device: A table comparing characteristics like operating system, networking, intended use, application, and DICOM compatibility. All characteristics are either identical or considered substantially equivalent.

Therefore, I cannot provide the requested information in the table format because the source text does not contain it. This type of document (a 510(k) summary) focuses on substantial equivalence rather than explicit performance data against acceptance criteria.

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TiGRT TPS is a Radiation Therapy Treatment Planning System for radiation dose planning of patients undergoing external beam treatment in the oncology clinic. TiGRT TPS is used to plan radiation treatments with linear accelerators and other similar teletherapy devices with x-ray and/or electron energies from 1 to 25MV, as well as Cobalt-60.

TiGRT TPS is a Radiation Therapy Treatment Planning System for radiation dose planning of patients undergoing external beam treatment in the oncology clinic. TiGRT TPS is used to plan radiation treatments with linear accelerators and other similar teletherapy devices with x-ray and/or electron energies from 1 to 25MV, as well as Cobalt-60.

The provided text describes a 510(k) submission for a Radiation Treatment Planning System called TiGRT TPS. This submission focuses on demonstrating substantial equivalence to a predicate device (WiMRT, K041971) rather than presenting a detailed performance study with specific acceptance criteria and outcome metrics for standalone or human-in-the-loop performance.

Here's an analysis of the requested information based on the provided document:

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

The document does not explicitly state quantitative acceptance criteria for device performance (e.g., accuracy of dose calculation, speed of optimization) or present specific reported device performance metrics against such criteria. Instead, the acceptance is based on demonstrating substantial equivalence to a predicate device. The "performance documentation" listed is about the process of development and risk management, not a direct measurement of the device's functional output against numerical benchmarks.

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

The document does not describe a traditional "test set" in the context of clinical or performance validation data (e.g., patient cases, imaging data) for evaluating the device's diagnostic or treatment accuracy. The submission focuses on comparing the new device's specifications and design to a predicate, and the provided "Performance Documentation" relates to software development and risk management. Therefore, no specific sample size or data provenance for a performance test set is mentioned.

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

Since no specific test set involving medical data (e.g., images for diagnosis, treatment plans for review) is described, this information is not applicable and not provided in the document. The "ground truth" for this submission revolves around the technical specifications and safety profile being equivalent to an already approved device.

4. Adjudication method for the test set

As there is no described test set requiring expert review or ground truth establishment, no adjudication method is mentioned.

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

The TiGRT TPS is a Radiation Treatment Planning System, not a diagnostic AI tool that assists human readers. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not described and not relevant to this type of device. The document does not mention any AI capabilities that would directly assist human interpretation in a diagnostic sense.

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

The document does not provide details of a standalone performance study in terms of metrics like dose calculation accuracy against physical measurements or clinical outcomes for a specific patient cohort. The "performance documentation" relates to the software development process and risk analysis rather than direct performance measurement of the algorithm itself against ground truth. The acceptance is based on design and functional similarity to the predicate, implying that if the design is substantially equivalent, the performance is also considered equivalent.

7. The type of ground truth used

The concept of "ground truth" in this 510(k) submission is primarily established by:

• Predicate Device Specifications: The technical characteristics and intended use of the legally marketed predicate device (WiMRT) serve as the "ground truth" or benchmark against which TiGRT TPS is compared for substantial equivalence.
• Industry Standards and Best Practices: Implied "ground truth" for software development and risk management practices (e.g., "Hazard Analysis Report," "Verification and Validation Documents," "Software Design Description") ensure the device is developed safely and effectively, analogous to how the predicate was developed.

8. The sample size for the training set

The document does not describe any machine learning or AI components that would require a "training set" of data. The device relies on established dose calculation algorithms (Super-position Convolution) and optimization algorithms (Genetic Algorithm) which typically do not involve statistical machine learning training on a large dataset in the same way modern AI systems do. Therefore, this information is not applicable and not provided.

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

As there is no described training set for a machine learning model, this information is not applicable and not provided.

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TIGRT MLC is a dynamic multileaf collimator designed to be mounted on the linear accelerator. It is intended to shape the specific fields, either in static or dynamic mode, to assist the radiation oncologist in the delivery of radiation to well-defined target volumes while sparing surrounding normal tissue and critical organs from excess radiation.

TIGRT MLC is a dynamic multileaf collimator designed to be mounted on the linear accelerator. It is intended to shape the specific fields, either in static or dynamic mode, to assist the radiation oncologist in the delivery of radiation to well-defined target volumes while sparing surrounding normal tissue and critical organs from excess radiation.

The provided document, KO90802 from July 7, 2009, is a 510(k) summary for the TiGRT MLC (Dynamic Multileaf Collimator). This document establishes substantial equivalence to a predicate device and does not contain information about acceptance criteria or a study that specifically proves the device meets such criteria in terms of analytical or clinical performance metrics.

Instead, this submission focuses on demonstrating substantial equivalence to the DMLC (K060187) based on similarity in design, construction, intended use, and performance characteristics. The comparison table (Table 1) highlights minor differences in operating system, number of COM ports, and selectable leaf pairs, which are not presented as acceptance criteria or performance metrics derived from a study.

Therefore, I cannot fulfill your request for:

• A table of acceptance criteria and the reported device performance: This information is not present in the provided document. The table in the document is for comparing characteristics with a predicate device, not for reporting performance against acceptance criteria.
• Sample sized used for the test set and the data provenance: No test set or data provenance is mentioned as part of a performance study.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Ground truth establishment for a test set is not discussed.
• Adjudication method for the test set: No test set adjudication is mentioned.
• 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 is described. This device is a mechanical component for radiation delivery, not an AI-assisted diagnostic tool for human readers.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: No standalone algorithm performance is discussed.
• The type of ground truth used: No ground truth is mentioned in the context of a performance study.
• The sample size for the training set: No training set is mentioned.
• How the ground truth for the training set was established: No training set ground truth establishment is mentioned.

The document primarily states:

• "This device is similar in design and construction, and has the same intended use and performance characteristics to the predicate device."
• "No new issues of safety or effectiveness are introduced by using this device."

This type of submission relies on demonstrating that the new device functions similarly to an already approved device, rather than providing novel clinical or analytical performance data against specific, quantifiable acceptance criteria.

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