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Leksell GammaPlan® is a computer-based system designed for Leksell Gamma Knife® treatment planning.

Leksell GammaPlan® is a powerful, computer-based treatment planning system specifically designed for the simulation and planning of stereotactic Leksell Gamma Knife® radiosurgery based on tomographic and projectional images.

The basis of treatment planning is the acquisition and processing of digital images by a computer workstation running the treatment planning application software. The program is capable of handling a range of different imaging modalities. Images from tomographic sources such as Computer Tomography (CT), Magnetic Resonance (MR) and Positron Emission Tomography (PET) scanners can be used as well as projectional images from angiograms (AI). This allows the direct comparison between vascular structures in projectional images and tissue structures in CT and MR.

Digital images can be imported into the system via the computer network.

The treatment planning application has the ability to plan a patient's treatment protocol based on a single target or multiple targets.

The basic elements of treatment planning are:

• defining the cranial target or targets .
• . devising the configuration of the collimators to be used during treatment
• determining the parameters of the radiation shots to be delivered by Leksell • Gamma Knife®.

The provided FDA 510(k) summary for the Leksell GammaPlan® (LGP) (K232854) does not contain specific details regarding acceptance criteria and the comprehensive study results in the way requested for a typical AI/ML device.

This submission is for an updated version of a treatment planning system, which is generally software for medical device operation, rather than an AI/ML algorithm that performs autonomous diagnostic or prognostic tasks. As such, the performance testing focuses on software verification and validation against requirements, rather than clinical performance metrics in terms of sensitivity, specificity, etc., with a ground truth established by experts.

However, I can extract the information that is present and indicate where the requested information is not available in the document.

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

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

• Sample Size for Test Set: Not specified in the document. The testing described is software verification and validation, not a clinical trial with a "test set" in the sense of patient data for AI model evaluation.
• Data Provenance: Not specified, as it's not a clinical data-driven study. The document mentions "clinically equivalent device" for validation, implying internal testing and possibly simulated or anonymized data, but details are absent.

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)

• Number of Experts: Not specified.
• Qualifications of Experts: The document mentions "competent and professionally qualified personnel" performed design and usability validation, but no specific professional qualifications (e.g., radiologist, medical physicist) or experience levels are provided. Ground truth in the context of a treatment planning system primarily relates to the accuracy of computational models and adherence to clinical guidelines, rather than expert interpretation of images for diagnosis.

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

• Adjudication Method: Not specified. It's unlikely this type of adjudication was performed, as the evaluation focused on software functionality and adherence to specifications, not on resolving disagreements in expert clinical assessment.

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 study was not performed. This device is a treatment planning system, not an AI diagnostic or assistance tool intended to improve human reader performance in interpreting medical images. The document states, "No animal or clinical tests were performed to establish substantial equivalence with the predicate device."

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

• Standalone Performance: The device itself is software for treatment planning. Its "standalone" performance would be its ability to correctly calculate and display treatment plans based on inputs, which is covered by the mentioned verification and validation testing. However, this is not "standalone AI algorithm performance" in the typical sense of a diagnostic AI. The system is inherently "human-in-the-loop" as it requires a clinician to define targets and parameters.

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

• Type of Ground Truth: For the type of software described (treatment planning), the "ground truth" for verification and validation typically involves:
  • Specification Compliance: Verification that the software performs according to its written requirements and design specifications.
  • Industry Standards: Conformance to relevant medical device software (e.g., IEC 62304) and medical physics standards.
  • Phantom/Physical Measurement Comparisons: For dose calculations, comparison with physical measurements in phantoms or known analytical solutions (though not explicitly detailed for this specific 510(k)).
  • Clinical Equivalence/Usability: Validation that the software supports intended clinical use, as performed by "competent and professionally qualified personnel."
    The document does not specify "expert consensus," "pathology," or "outcomes data" as ground truth for this submission, as these are more relevant for diagnostic or prognostic AI.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable and not mentioned. This device is a treatment planning system, not a machine learning model that requires a training set in the conventional sense.

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

• Ground Truth for Training Set Establishment: Not applicable and not mentioned, as there is no machine learning "training set" for this device.

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Leksell GammaPlan® is a computer-based system designed for Leksell Gamma Knife® treatment planning.

Leksell GammaPlan® is designed for use with the Leksell Gamma Knife and is intended to be used for planning the dosimetry of treatments in stereotactic radiosurgery and stereotactic radiation therapy. It processes the inputs of health care professionals (e.g. neurosurgeons, radiation therapists, radiation physicists) such that the desired radiation dose is delivered by the Leksell Gamma Knife to a precisely defined volume.

Here's an analysis of the provided text regarding acceptance criteria and supporting studies:

The FDA 510(k) summary for Leksell GammaPlan® (K173791) explicitly states that clinical testing on patients was NOT required to demonstrate substantial equivalence to the predicate device (K151666). This means the submission does not contain information about studies designed to prove clinical performance against specific acceptance criteria in a patient population.

Instead, the submission focuses on non-clinical testing to demonstrate that the new version of the device maintains the same fundamental functionality and technical characteristics as the predicate.

Therefore, many of the requested elements for describing a study that proves the device meets acceptance criteria (especially those related to clinical performance, sample sizes, expert involvement, and ground truth in a clinical context) are not applicable to this particular 510(k) submission.

Here's a breakdown of the requested information based on the provided text:

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

The document does not explicitly present specific, measurable acceptance criteria in a table format that would typically be used for clinical performance. Instead, it refers to internal verification and validation against "requirement specification" and "user needs."

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

• Sample size for test set: Not applicable (no patient-based clinical test set or data described).
• Data provenance: Not applicable (no patient-based clinical data described).

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)

• Number of experts: Not applicable (no clinical test set requiring expert ground truth).
• Qualifications of experts: The document mentions "competent and professionally qualified personnel" performed design and usability validation, but provides no further details on their specific qualifications or their role in establishing ground truth for a test set. This likely refers to internal development and quality assurance personnel rather than external clinical experts.

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

• Adjudication method: Not applicable (no clinical test set described).

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. The device is a treatment planning system, not an AI-assisted diagnostic tool for human readers.
• Effect size: Not applicable.

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

• Standalone performance: The non-clinical testing included "module, integration and system level verification" against requirements. This can be interpreted as evaluating the algorithm's (software's) performance in a standalone context, ensuring its internal logic and functions work as specified. However, this is not a diagnostic algorithm's standalone performance, but rather a planning system's functional performance.

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

• Type of ground truth: For the non-clinical testing, the "ground truth" would be the software requirements specifications and design documents. The validation sought to prove the system performed according to these pre-defined specifications. There is no mention of clinical ground truth (expert consensus, pathology, outcomes data) as part of this submission.

8. The sample size for the training set

• Sample size for training set: Not applicable. This device is a treatment planning system and not an AI/ML device that requires a training set in the conventional sense for a diagnostic algorithm. Its development would involve software engineering and testing principles, not machine learning model training.

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

• Ground truth for training set: Not applicable. As with the previous point, there's no mention of a training set or associated ground truth establishment methods for an AI/ML model.

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Leksell GammaPlan® is a computer-based system designed for Leksell Gamma Knife® treatment planning.

Leksell GammaPlan® is designed for use with the Leksell Gamma Knife and is intended to be used for planning the dosimetry of treatments in stereotactic radiosurgery and stereotactic radiation therapy. It processes the inputs of health care professionals (e.g. neurosurgeons, radiation therapists, radiation physicists) such that the desired radiation dose is delivered by the Leksell Gamma Knife to a precisely defined volume.

The provided document is a 510(k) Summary for a medical device called Leksell GammaPlan®. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed study supporting specific acceptance criteria for performance.

Therefore, the requested information pertaining to acceptance criteria and performance study details cannot be fully extracted from this document.

However, based on the available information, here's what can be provided and what is missing:

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

Missing: The document does not provide specific, quantitative acceptance criteria (e.g., "accuracy must be within X%", "sensitivity Y%", etc.) or detailed quantitative performance metrics for the device. The reported performance is a general statement that criteria were met.

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

• Sample Size for Test Set: Not specified. The document mentions "module, integration and system level verification" and "regression and re-test," but does not provide details on the number of test cases, plans, or simulated data used.
• Data Provenance: Not specified. No information about country of origin or whether the data was retrospective or prospective. The summary explicitly states: "Clinical testing on patients was not required to demonstrate substantial equivalence or safety and effectiveness of the device." This implies the testing was likely done using simulated data or internal test scenarios.

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)

• Number of Experts: Not specified.
• Qualifications of Experts: The document states that "Design and usability validation of the system has been performed by competent and professionally qualified personnel." No further details on their specific qualifications (e.g., neurosurgeons, radiation therapists, radiation physicists, experience levels) are provided.

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

• Adjudication Method: Not specified.

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. The document focuses on demonstrating substantial equivalence of the Leksell GammaPlan device itself (a treatment planning system) to a predicate device, rather than evaluating the impact of an AI component on human reader performance. The device is described as "a computer-based system designed for Leksell Gamma Knife® treatment planning," which processes inputs from healthcare professionals, not an AI intended to assist human readers in image interpretation.

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

• Standalone Performance: The document describes "module, integration and system level verification" and "regression and re-test" to evaluate performance against requirements. This implicitly covers standalone algorithm performance as part of the overall system validation. However, it does not explicitly decouple an "algorithm only" performance metric from the system's intended use by healthcare professionals. The Leksell GammaPlan is a tool for treatment planning by professionals, not an autonomous diagnostic algorithm.

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

• Type of Ground Truth: Not explicitly stated. Given that clinical testing was not required, the "ground truth" for the verification and validation testing would likely have involved adherence to expected computational models, dosimetry standards, and functional requirements, rather than clinical outcomes or pathology. It would be based on established physics principles and engineering validation criteria for medical device software.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable/Not specified. The document describes a "treatment planning system," not a machine learning or AI model that requires a separate training set. The system's functionality is based on predefined algorithms and physics models, not learned from a dataset in the way a deep learning model would be.

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

• How Ground Truth for Training Set was Established: Not applicable/Not specified, as there is no mention of a "training set" in the context of this device.

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Leksell GammaPlan® is a computer-based dose planning system specifically designed for use with Leksell Gamma Knife®.

Leksell GammaPlan® is a computer-based dose planning system specifically designed for use with the Leksell Gamma Knife®. Leksell Gamma Plan® is intended to be used for planning the dosimetry of treatments, in stereotactic radiosurgery and stereotactic radiotherapy.

The provided text describes the Leksell GammaPlan®, a computer-based dose planning system for Leksell Gamma Knife®, and its 510(k) submission. However, it does not contain information about acceptance criteria, a study proving device performance, sample sizes, expert qualifications, adjudication methods, MRMC studies, or how ground truth was established for training or test sets.

The document is a 510(k) summary for the device, focusing on its substantial equivalence to predicate devices (Leksell GammaPlan® K090972 and XiO RTP K092132) rather than detailed performance study results. The FDA letter confirms the substantial equivalence determination but does not present a detailed study.

Therefore, I cannot provide the requested information.

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Leksell GammaPlan® is a computer-based dose planning system specifically designed for use with Leksell Gamma Knife®.

Leksell GammaPlan® is a computer-based dose planning system specifically designed for use with the Leksell Gamma Knife". Leksell Gamma Plan® is intended to be used for planning the dosimetry of treatments, in stereotactic radiosurgery and stereotactic radiotherapy.

The provided text describes a 510(k) summary for the Elekta Instrument AB's Leksell GammaPlan® system. This document is a premarket notification for a medical device and primarily focuses on establishing "substantial equivalence" to a predicate device, rather than presenting a detailed clinical study with acceptance criteria and performance data in the way a diagnostic AI device submission might.

Therefore, many of the requested elements for acceptance criteria and study design are not present in this 510(k) summary because this type of submission typically demonstrates equivalence through technical specifications rather than extensive clinical performance studies.

Here's an analysis based on the provided text, highlighting what is present and what is absent:

Acceptance Criteria and Device Performance Study (as per the provided document)

1. Table of Acceptance Criteria and Reported Device Performance

This document does not contain a specific table of acceptance criteria or reported device performance in the sense of accuracy, sensitivity, specificity, or other performance metrics for a diagnostic or AI-driven device.

Instead, the "acceptance criteria" for a 510(k) submission like this are generally satisfied by demonstrating substantial equivalence to predicate devices. This means showing that the new device has the same intended use, technological characteristics, and safety/effectiveness profiles as devices already legally marketed.

The key "performance" claimed in this document is:

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

Not applicable/Not provided. This submission does not describe a clinical performance study with a test set of patient data to evaluate the device's diagnostic or predictive performance. The assessment is based on technical equivalence to predicate devices, not on a new clinical data evaluation.

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

Not applicable/Not provided. There is no mention of experts or ground truth establishment because a clinical performance study on a test set is not detailed.

4. Adjudication method for the test set

Not applicable/Not provided. No test set or adjudication method is described.

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. This device is a dose planning system for radiation therapy, not a diagnostic AI system intended to assist human readers. Thus, an MRMC study comparing human readers with and without AI assistance is not relevant to this device's function or the information provided in this 510(k) summary.

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

Not applicable/Not explicitly detailed as a 'standalone performance study'. The Leksell GammaPlan is itself a computer-based dose planning system, used by human operators. Its performance is inherent in its functionality for dose planning, which is being compared for equivalence to existing versions of the same system (predicate devices). The submission focuses on its technical characteristics being similar, implying its standalone function is equivalent.

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

Not applicable/Not provided in the context of a performance study. The "ground truth" for this 510(k) submission implicitly lies in the established safety and effectiveness of the predicate devices. The new device is deemed acceptable because it is substantially equivalent to these already-approved devices, whose 'ground truth' in terms of clinical utility and safety has already been accepted by the FDA.

8. The sample size for the training set

Not applicable/Not provided. This is not an AI/ML-driven device that underwent a distinct "training" phase with a dataset. It is a software system for dose planning.

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

Not applicable/Not provided. As there is no stated training set for an AI/ML model, the concept of establishing ground truth for it is not relevant to this document.

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Leksell GammaPlan® is a computer-based dose planning system specifically designed for use with Leksell Gamma Knife®.

Leksell GammaPlan® is a computer-based dose planning system specifically designed for Eeksell Gamma Knife®. Leksell Gamma Plan® is intended to be used for alse with the Losiments, in stereotactic radiosurgery and stereotactic radiotherapy.

I am sorry, but the provided text does not contain the information needed to describe the acceptance criteria and the study that proves the device meets the acceptance criteria. The document is a 510(k) summary and approval letter, which primarily focuses on substantial equivalence to predicate devices and regulatory classification. It does not detail specific performance studies, acceptance criteria, or the methodology used to validate the device's performance.

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GammaPlan® is a computer-based system designed for Gamma Knife® treatment planning.

GammaPlan® is a computer-based dose planning system specifically designed for use with the Gamma Knife®. Gamma Plan® is intended to be used for planning the dosimetry of treatments, in stereotactic radiosurgery and stereotactic radiotherapy.

The provided text does not contain detailed information about acceptance criteria or specific studies to prove device performance. It primarily focuses on regulatory approval (510(k) summary) rather than scientific validation studies. Therefore, much of the requested information cannot be extracted from this document.

However, based on the provided text, here's what can be gathered and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

This information is not present in the provided document. The document states that GammaPlan® is "substantially equivalent" to its predicate devices in safety and effectiveness, but it does not specify any quantitative acceptance criteria or report on specific performance metrics of the new device.

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

This information is not present. The document doesn't describe any specific "test set" or data used for performance evaluation beyond asserting substantial equivalence to predicate devices.

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

This information is not present. No details about ground truth establishment or expert involvement are mentioned.

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

This information is not present. There is no description of an adjudication method, as no specific test set evaluation is detailed.

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 present. The document does not describe an MRMC study or any assessment of human reader improvement with or without AI (though GammaPlan® itself is a dose planning system, not an AI for human readers in the traditional sense).

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

This information is not present. While GammaPlan® is a computer-based system, the document does not detail specific standalone performance evaluations. It focuses on its role in "planning the dosimetry of treatments" and being "equivalent to its predicate device."

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

This information is not present. No ground truth definition is provided.

8. The sample size for the training set:

This information is not present. There is no mention of a training set or the methodology for developing the device.

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

This information is not present. As there's no mention of a training set, the ground truth establishment for it is also not discussed.

Summary of what is available from the document:

• Device Name: GammaPlan®
• Intended Use: GammaPlan® is a computer-based system designed for Gamma Knife® treatment planning. It is intended to be used for planning the dosimetry of treatments in stereotactic radiosurgery and stereotactic radiotherapy.
• Regulatory Pathway: 510(k) Premarket Notification, based on substantial equivalence.
• Predicate Devices: Leksell GammaPlan® (K973441) and Leksell GammaPlan® 4C with MultiView (K042269).
• Claim of Performance: The functionality for the GammaPlan® is equivalent to its predicate device in safety and effectiveness. The fundamental technical characteristics are similar to those of the predicate device.

To get the information requested, a more technical performance report or a validation study document would be necessary, which is typically separate from a 510(k) summary. The 510(k) summary mainly demonstrates that a new device is "substantially equivalent" to an existing legally marketed device, often by showing it performs as well or better than the predicate, but it doesn't always detail the full scientific validation studies with specific performance metrics and acceptance criteria for the new device as a standalone product.

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The Leksell GammaPlan 4C with MultiView is a computer-based dose planning system specifically designed for use with the Leksell Gamma Knife®. The Leksell GammaPlan 4C with MultiView is intended to be used for planning the dosimetry of treatments in stereotactic radiosurgey and stereotactic radio therapy. It processes the inputs from the healt care professionals such that the desired radiation dose is provided by the Leksell Gamma Knife® to a precisely defined target area within the cranium.

The Leksell GammaPlan 4C with MultiView adds support for frameless images by means of co-registration of image series. This simplifies stereotactic radiosurgery procedures as only one image series needs to be acquired with the Leksell stereotactic frame. Leksell GammaPlan 4C with MultiView also adds support for brain altas structures and visualization of color mapped PET images for use in preoperative planning.

The provided text describes a Special 510(k) submission for the "Leksell GammaPlan 4C with MultiView" device. This submission focuses on demonstrating substantial equivalence to a predicate device ("Leksell GammaPlan", K973441) and highlights new features like support for frameless images via co-registration and visualization of color-mapped PET images.

However, the document does not contain specific acceptance criteria or details of a study designed to prove the device meets such criteria. It primarily details the device description, intended use, and substantial equivalence claim, which is a regulatory pathway for devices that are similar to already legally marketed devices.

Therefore, I cannot provide the requested table or answer most of the questions because the information is not present in the provided text.

Here's an assessment based on the available information:

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

   • N/A. The document does not specify acceptance criteria or report performance against such criteria. The submission is a "Special 510(k)" which usually relies on demonstrating substantial equivalence to a predicate device, often without a detailed performance study like a traditional 510(k) might require for novel features.

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

   • N/A. No information on a test set or data provenance is provided.

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):

   • N/A. No information on experts or ground truth establishment for a test set is provided.

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

   • N/A. No information on adjudication methods for a test set is 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:

   • N/A. This device is a dose planning system, not an AI-assisted diagnostic tool for human readers. No MRMC study is mentioned.

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

   • The document implies the device is a "computer-based dose planning system" for "health care professionals," indicating human-in-the-loop operation. No standalone performance study details are provided.

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

   • N/A. No information on the type of ground truth is provided.

8. The sample size for the training set:

   • N/A. This document describes a software update for a medical device; it does not detail a machine learning model that would typically have a "training set."

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

   • N/A. As above, no training set is described in the context of a machine learning model.

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The Leksell GammaPlan (LGP) is designed for use with the Leksell Gamma Knife manufactured by Elekta Instrument, AB. The Leksell GammaPlan is intended to be used for planning the dosimetry of treatments in stereotactic radiosurgery and stereotactic radiation therapy. It processes the inputs of the health care professionals (Neurosurgeons, Radiation Therapists, Radiation Physicists) such that the desired radiation dose is provided by the Leksell Gamma Knife to a precisely defined target area within the cranium.

The Leksell GammaPlan® is a computer based dose planning system specifically designed for use with the Leksell Gamma Knife. The Leksell GammaPlan is intended to be used for planning the dosimetry of treatments in stereotactic radiosurgery and stereotactic radiation therapy. Digital images from CT or MR scanners, or angiograms are imported and processed for treatment planning. Patient treatment protocols are based on single or multiple targets and alternative treatment plans can be generated by the GammaPlan.

The provided documentation (K973441) is a 510(k) summary for the Leksell GammaPlan device. This type of submission focuses on demonstrating substantial equivalence to a predicate device rather than comprehensive clinical performance studies with detailed acceptance criteria and statistical analyses common in more recent submissions.

Based on the provided text, the acceptance criteria and study information are quite limited. The device is a computer-based dose planning system, and the "performance data" section focuses on software testing, not clinical performance.

Here's a breakdown of the available information according to your requested categories:

1. Table of Acceptance Criteria and Reported Device Performance

"The fundamental algorithm principle of calculation and other technical characteristics are the same as that of the predicate device."

"Calculation of the radiation dose for the treatment plan and overlay of the isodose curve is the same in the new version, as in the predicate device." |
| Software Functionality and Reliability: All software modules, their integration, and the overall system must function as intended. | The submission states: "The Leksell GammaPlan® has been demonstrated to perform as intended. Testing of the software includes Module, Integration and System testing. All test results have been included in Section 14 of this notification." (However, Section 14 is not provided in this excerpt.) |

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

• Sample Size: Not explicitly stated. The "performance data" refers to software testing, not a clinical test set.
• Data Provenance: Not applicable, as no patient or clinical data is described for testing the "device performance." The software testing would likely involve simulated data or predefined test cases.
• Country of Origin: Not specified for the software testing. The company is based in Stockholm, Sweden.
• Retrospective or Prospective: Not applicable.

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. The performance data described is related to software testing, not the establishment of ground truth by clinical experts. The indications for use mention "health care professionals (Neurosurgeons, Radiation Therapists, Radiation Physicists)" as users, implying their expertise in the application of the device, but not in validating its "ground truth" performance in a study mentioned here.

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

• Not applicable. No clinical test set and therefore no adjudication method is described.

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 device is a dose planning system, not an AI-assisted diagnostic tool for human readers. This submission is from 1998, predating widespread use of "AI" in medical devices as we understand it today.

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

• Yes, in a sense, the "performance data" described refers to the algorithm's performance as a software system. The testing mentioned ("Module, Integration and System testing") would inherently evaluate the software's ability to calculate and process data according to its design specifications, which is a standalone evaluation of the algorithm. However, this is distinct from a clinical standalone study evaluating diagnostic accuracy against a ground truth.

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

• For the software performance described, the "ground truth" would be the expected outputs based on predefined mathematical models and engineering specifications for dose calculation and image processing. It's an engineering/mathematical ground truth, not a clinical ground truth like pathology or outcomes.

8. The sample size for the training set

• Not applicable. This is a 510(k) submission for an updated version of a dose planning software. There is no mention of a "training set" in the context of machine learning algorithms. The device's "training" refers to its software development and validation against established physics principles and algorithms.

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

• Not applicable, as there is no "training set" in the context of machine learning. The "ground truth" for the device's design and functionality would be based on established physics, dosimetry principles, and the calculations implemented in the original predicate device (K914808).

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