Search Results

SciMoCa is a software product intended to provide quality assurance of a radiotherapy dose calculated by a treatment planning system by allowing a clinician to re-calculate the dose with an independent dose calculation algorithm and compare the two doses.

SciMoCa is not a treatment planning system or a radiation delivery device. It is to be used only by trained radiation oncology personnel for quality assurance purposes.

SciMoCa is a standalone software product that allows clinicians to perform quality assurance of a radiotherapy treatment dose generated by a treatment planning system via recalculation of the dose with an independent Monte Carlo dose calculation algorithm. SciMoCa is implemented in a service-oriented client-server architecture that allows one or more clients to communicate calculation requests to a central dose calculation server. SciMoCa is not a treatment planning system or a radiation delivery device. It is to be used only by trained radiation oncology personnel for quality assurance purposes.

The provided text does not contain detailed information about the acceptance criteria and the comprehensive study that proves the device meets those criteria. It mentions "Validation testing involved simulated clinical workflows and environment, as well as algorithm testing" and that the product "passed verification testing and was deemed safe and effective for its intended use." However, it lacks specific metrics, study designs, or methodologies that would allow for a complete answer to your detailed questions.

Therefore, many of the requested fields cannot be filled from the provided text.

Here's an attempt to answer based on the limited information available:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not specified. The document mentions "Over 700 test procedures were executed," but this refers to the number of test procedures, not the number of distinct cases or data samples in a test set.
• Data Provenance: Not specified. There is no information about country of origin or whether the data was retrospective or prospective.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication method 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

There is no mention of an MRMC study or any assessment of human reader improvement with or without AI assistance. The device, SciMoCa, is described as an "independent dose calculation algorithm" for quality assurance, not as an AI-assisted diagnostic tool for human readers.

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

Yes, based on the description, the primary validation would likely be standalone performance. The device's function is to "re-calculate the dose with an independent dose calculation algorithm and compare the two doses." This implies the algorithm's output is compared against a reference (the treatment planning system's calculated dose), which is a form of standalone evaluation. However, specific performance metrics for this standalone performance (e.g., accuracy, deviation limits) are not provided in the text.

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

The ground truth for dose calculation in radiotherapy QA typically involves comparing the device's recalculated dose against an established reference dose (e.g., from a validated treatment planning system or a benchmark phantom calculation). The text states SciMoCa "allows a clinician to re-calculate the dose with an independent dose calculation algorithm and compare the two doses," implying the "ground truth" or reference is the dose from the primary treatment planning system. However, the exact methodology for establishing this comparison and what constitutes "ground truth" for the validation itself is not detailed.

8. The sample size for the training set

• Training Set Sample Size: Not specified. The document refers to "algorithm testing" but provides no details on training data.

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

• Ground Truth Establishment for Training Set: Not specified.

Ask a specific question about this device

QualiFormeD LINACWATCH / LINACVIEW is stand-alone software that analyzes radiation therapy machine (linear accelerator) performance for each fraction automatically using log files provided by the radiation therapy machine. It is to be used by trained radiation oncology personnel as a guide to alert this professional to potential machine delivery issues that can affect treatment quality and workflow.

QualiFormeD LINACWATCH / LINACVIEW is not primary treatment planning or 3D dose calculation software, and cannot be used to generate radiotherapy treatment plans.

QualiFormeD LINACWATCH / LINACVIEW is a standalone software product analyzes logfiles generated by Varian and Elekta linear accelerators during radiation treatments. Logfile analysis can be with respect to the "prescription" DICOM radiotherapy (RT) Plan or with respect to the first logfile in a series of logfiles for each treatment and each beam. Initial logfiles that may be created for pre-treatment intensity-modulated radiation therapy (IMRT) quality assurance (QA) can be excluded from treatment session analysis.

The preferred method of running QualiFormeD LINACWATCH / LINACVIEW is in "batch" mode. In this mode, QualiFormeD LINACWATCH / LINACVIEW automatically processes logfiles and analyzes them, sending emails to designated staff for non-compliant results over an internet connection.

QualiFormeD LINACWATCH / LINACVIEW allows the user to verify for every treatment, in real time, with email notifications on alert or failure, the following parameters:

• The position of each moving multi-leaf collimator (MLC) leaf; ●
• The position of MLC carriages and jaws;
• The percentage of monitor units (MUs) delivered; ●
• The gantry rotation angle; ●
• The collimator rotation angle;
• The beam on/off statistics; and
• . The reconstructed integrated fluence.

Additionally, after any number of treatments has been analyzed, QualiFormeD LINACWATCH / LINACVIEW allows the user to create RT Plans that can simulate the composite behavior of the analyzed logfiles. These RT Plans can be imported into the treatment planning system (TPS) to compare intended doses with those actually delivered, although this estimated dose does not include dosimetric changes due to patient setup or geometric changes. Adobe Acrobat® PDF reports documenting machine performance can be created at any point during treatment.

At the end of a course of treatment when the treatment series is closed, a composite RT Plan is automatically created that summarizes the composite behavior of all analyzed logfiles as well as two (2) final reports, a short and a comprehensive report, that documents all aspects of the treatment course.

Step and Shoot, Sliding Window, and Volumetric Modulated Arc Therapy (VMAT) IMRT techniques are supported.

The provided text is an FDA 510(k) summary for a medical device called QualiFormeD LINACWATCH/LINACVIEW. This document focuses on demonstrating substantial equivalence to a predicate device and covers software development processes, rather than detailed performance study results with specific acceptance criteria and metrics.

Therefore, many of the requested details regarding acceptance criteria, specific performance metrics, sample sizes, expert involvement for ground truth, and MRMC studies are not present in this document. The document primarily states that the software was "rigorously verified and validated" and that "Test results indicated that QualiFormeD LINACWATCH / LINACVIEW complies with predetermined specifications." It does not provide the details of those specifications or the empirical performance data against them.

Here's a breakdown of what can and cannot be extracted from the provided text based on your request:

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

• Acceptance Criteria: Not explicitly stated as quantifiable metrics in the document. The document only mentions that the device "complies with predetermined specifications." It does not list what those specifications are (e.g., specific accuracy, sensitivity, or specificity thresholds).
• Reported Device Performance: No specific numerical performance metrics (e.g., accuracy percentages, error rates, or deviations) are reported. The document states that the testing showed compliance with specifications but does not provide the results themselves.

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

• Sample Size: Not specified. The document states "QualiFormeD LINACWATCH / LINACVIEW was tested in accordance – Bench with internal specifications," but the number of test cases or log files used is not mentioned.
• Data Provenance: Not specified. There is no information on where the log files used for testing originated (e.g., specific countries, clinical sites).
• Retrospective or Prospective: Not specified. The nature of the data collection for testing is not 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 specified.
• Qualifications of Experts: Not specified. Given the device's function (analyzing linear accelerator log files), the "ground truth" would likely be established through physical measurements, simulation, or adherence to clinical standards by medical physicists or radiation oncology personnel, rather than radiologists. However, this is not detailed.

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

• 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. This device is described as "stand-alone software that analyzes radiation therapy machine (linear accelerator) performance." It is not an AI-assisted diagnostic tool for human readers but rather an automated analysis system for machine log files. Therefore, an MRMC study comparing human reader performance with and without AI assistance is not applicable to this type of device and was not conducted or reported.

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

• Standalone Performance: Yes, implicitly. The description focuses on the software's automated analysis of log files and its ability to alert personnel to potential issues. The testing performed ("Bench with internal specifications") would assess the algorithm's performance in this standalone capacity. However, the results of this standalone performance (e.g., specific metrics like accuracy in detecting non-compliant events) are not provided. The function is entirely "algorithm only" in its analysis.

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

• Type of Ground Truth: Not explicitly stated, but for a device analyzing linear accelerator log files, the "ground truth" would almost certainly involve comparing the device's output (e.g., detected deviations in MLC position, gantry angle, MUs delivered) against:
  • Machine specifications: What the machine should be doing according to its design and calibration.
  • Physical measurements: Measurements taken directly from the linear accelerator during operation.
  • Reference log files: Known good or known problematic log files.
  • Clinical standards/tolerances: Pre-defined acceptable deviations for various parameters in radiation therapy.
    The document only broadly states "Test results indicated that QualiFormeD LINACWATCH / LINACVIEW complies with predetermined specifications."

8. The sample size for the training set

• Not applicable/Not specified. This document describes a software application that analyzes log files. While software undergoes development and testing, it's not explicitly stated whether this particular application uses machine learning algorithms that require a distinct "training set" in the typical sense for AI/ML models. The focus is on validation against specifications rather than deep learning model training. If it were a machine learning model, the training set size would be crucial, but the text doesn't indicate this.

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

• Not applicable/Not specified, for the same reasons as point 8.

Summary of Device Performance Study (as described in the document):

The 510(k) Summary for QualiFormeD LINACWATCH/LINACVIEW describes an extensive software verification and validation process in accordance with FDA guidance documents and international standards (IEC 62304).

• Study Type: Bench testing against internal specifications.
• Purpose: To establish that the software "complies with predetermined specifications" and is "as safe, as effective, and performs as well as or better than the predicate device."
• Key Findings (High-Level): "Test results indicated that QualiFormeD LINACWATCH / LINACVIEW complies with predetermined specifications." and "The results of these activities demonstrate that QualiFormeD LINACWATCH / LINACVIEW is as safe, as effective, and performs as well as or better than the predicate device."

Conclusion:

The provided FDA 510(k) summary focuses on regulatory compliance and demonstrating substantial equivalence through a robust software development and testing process (verification and validation). It affirms that the device meets its predetermined specifications but does not disclose those specific acceptance criteria, nor the quantitative results of the performance testing, the size of the test data set, or details about the ground truth establishment method. This level of detail is typically found in the full submission documents, not necessarily the publicly available summary letter.

Ask a specific question about this device

Adaptivo is a stand-alone software product that provides comparative dose information about the daily and cumulative dose received by a radiotherapy patient relative to their treatment plan. It is to be used by a radiation oncology licensed medical professional as a guide to provide pre-treatment plan delivery verification; to monitor daily treatments and indicate potential clinically relevant deviations from the intended plan delivery: to provide estimates of daily and cumulative dose delivered to the patient, accounting for patient position and anatomy changes; and to aid in determining whether a patient plan should be altered partway through the course of treatment in order to meet the treatment planning goals. Adaptivo is not a primary treatment planning software, and cannot be used to generate radiotherapy treatment plans.

Aurora Technology Development (ATD) Adaptivo is a stand-alone software product that provides comparative dose information about the daily and cumulative dose received by a radiotherapy patient relative to their treatment plan. It provides:
• pre-treatment plan delivery verification;
• the ability to monitor daily treatments and indicate potential clinically relevant deviations from the intended plan delivery;
• estimates of daily and cumulative dose delivered to the patient, accounting for patient position and anatomy changes; and
• an aid in determining whether a patient plan should be altered partway through the course of treatment in order to meet the treatment planning goals.
ATD Adaptivo is not a primary treatment planning software, and cannot be used to generate radiotherapy treatment plans.
ATD Adaptivo has two (2) primary software modules: (1) the Adaptive Dose Recalculation (ADR) module; and (2) the In-vivo Dosimetry (IVD) module. These two (2) modules integrate seamlessly to provide the user full benefit of ATD Adaptivo.

Here's a breakdown of the acceptance criteria and study information for the Adaptivo device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly list acceptance criteria in a table format with corresponding reported performance values. Instead, it states that "Test results indicated that ATD Adaptivo complies with predetermined specifications and the applicable standard" and that "Verification and validation activities were conducted to establish the performance and safety characteristics of ATD Adaptivo. The results of these activities demonstrate that ATD Adaptivo is as safe, as effective, and performs as well as or better than the predicate device."

Therefore, I cannot populate a table with specific numerical acceptance criteria and reported performance metrics based on the given information. The document focuses on demonstrating substantial equivalence to predicate devices and adherence to general software development and medical device guidelines.

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

The document does not specify the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective). It broadly mentions "Test results indicated that ATD Adaptivo complies with predetermined specifications."

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

This information is not provided in the document.

4. Adjudication Method for the Test Set

This information is not provided in the document.

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

• No, the document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was done.
• Therefore, there is no effect size given for how much human readers improve with AI vs without AI assistance.

6. Standalone (Algorithm Only) Performance Study

The document describes Adaptivo as a "stand-alone software product." The performance testing described (Bench testing and V&V activities) appears to evaluate the algorithm's performance based on internal specifications. While not explicitly using the term "standalone study," the nature of the software and testing suggests its performance was assessed independently. However, specific metrics of this standalone performance are not provided.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for testing. It refers to "predetermined specifications" and "internal specifications," implying that the device's output was compared against established baselines or expected results, but the nature of these ground truths (e.g., expert consensus, pathology, phantom measurements, etc.) is not detailed.

8. Sample Size for the Training Set

The document does not specify the sample size used for the training set.

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

The document does not explain how the ground truth for the training set was established, nor does it explicitly mention a training set, though it implies software development and validation, which often involve such stages.

Ask a specific question about this device