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The decimal Bolus product is a solid piece of material (rigid or rubber-like) that will be placed on the skin of a patient with the intended use and primary purpose of helping control the dose received by that patient when undergoing radiation therapy treatment. decimal Bolus devices are designed by radiation therapy professionals to a unique shape that is specific to each patient being treated. The device is intended to modify the dose delivered during a radiation therapy treatment. As this product is a simple general purpose bolus device, the intended patient population and indications for use are quite broad. The most common indications for use are for the treatment of patients receiving radiation therapy, which encompasses a wide range of potential disease types and locations. As such, these devices will be required to have a wide range of potential shapes, sizes, and material properties and each device must be tested and approved by the radiation therapy professional prior to use on a patient.

The decimal Bolus product is a solid piece of material (rigid or rubber-like) that will be placed on the skin of a patient with the intended use and primary purpose of helping control the dose received by that patient when undergoing radiation therapy treatment. decimal Bolus devices will be manufactured according to the unique, patient-specific shape requested by a clinical customer. Trained radiation therapy professionals will create the bolus device design. In the most common use case, the devices are designed to increase the dose that will be delivered at the patient's skin surface for the treatment of superficial tumors. In this case the decimal Bolus operates as a "build-up" region, which is necessary as radiation dose is typically at its maximum strength slightly below the entrance surface. Such treatments may occur anywhere on a patient's body. As such, these devices will be required to have a wide range of potential shapes and sizes and material properties.

The provided text details the FDA 510(k) clearance for the ".decimal Bolus" device, a custom-manufactured bolus for radiation therapy. However, the document primarily focuses on the regulatory aspects, device description, and a comparison to a predicate device. It explicitly states:

"Clinical testing was not performed as part of the development of this product. Clinical testing is not advantageous in demonstrating substantial equivalence, safety, or effectiveness of the device since testing can be performed such that no human subjects are exposed to risk."

Therefore, the document does not contain the information required to answer your questions about acceptance criteria and a study proving the device meets those criteria, as no clinical study was conducted. The non-clinical testing summary mentions "Clinically oriented validation test cases," but it does not provide specific acceptance criteria or detailed study results needed to complete the requested table and answer the subsequent questions.

Based on the provided text, I cannot describe:

1. A table of acceptance criteria and the reported device performance: The document mentions "well-fitting bolus devices, with homogeneous material composition, performing as well or better than the predicate device in all areas tested," but does not quantify these "areas tested" or provide specific acceptance thresholds or metrics.
2. Sample sizes used for the test set and the data provenance: No test set details are provided as no clinical study was performed.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable, as no clinical study with human data requiring expert ground truth was conducted.
4. Adjudication method for the test set: Not applicable.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: The text explicitly states no clinical testing was performed, so no MRMC study was done.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable, as this is a physical device, not an AI algorithm.
7. The type of ground truth used: Not applicable, as no clinical study with human data requiring ground truth was conducted.
8. The sample size for the training set: Not applicable, as no AI model or training set is mentioned.
9. How the ground truth for the training set was established: Not applicable.

The document indicates that the substantial equivalence determination for the .decimal Bolus was based on its similarity to a predicate device (.decimal Bolus Compensator K091911) and the concept that the changes (flexible material, custom manufacturing) do not impact safety or risk, especially since clinical end-users are required to test and approve the devices prior to use. The "Non-Clinical Testing" section states that ".decimal personnel and hospital-based testing partners" executed "clinically oriented validation test cases," but no specifics on these tests (e.g., number of tests, specific metrics, acceptance criteria) are provided in this document.

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The primary purpose and intended use of this device is to improve the efficiency of designing patient specific radiation therapy beam-shaping block devices through the use of optical scanning technology. This device will serve as a direct replacement to the current processes for designing such radiotherapy devices in cases where a "clinical patient set up" is used (i.e. cases where the treatment field is determined by direct physician examination, not by internal imaging technology).

This product is not intended to replace CT imaging or other internal imaging modalities and should be used only in cases where a qualified radiation oncologist has made appropriate determination of the acceptability of a "clinical patient set up" approach, independent of any information provided by this application. In other words, the role of this product is to simply ensure efficient and accurate ordering of a patient-specific beam-shaping block device from our company, in cases where a licensed radiation oncologist has predetermined that such a device and treatment approach is appropriate for the patient at hand. Thus this device's indications for use include patients with a variety of cancer and disease conditions, which will be treated under the direct supervision and guidance of a radiation oncologist that has prescribed a desired dose of radiation to be delivered to the patient.

This device is a software product with the primary purpose to improve the efficiency of designing patient specific radiotherapy treatment devices. It uses proven off-the-shelf optical scanning technology to replace portions of the current clinical treatment device design workflow to achieve this goal. Specifically, this device uses an off-the-shelf depth sensing scanner to accurately capture and construct a full color, 3D model of a patient's treatment area. This scanner captures dimensionally accurate depth information in realtime using a combination of a structured light field infrared projector and infrared camera, and is coupled with a color camera to provide precise, full color, 3D models of patients without exposing them to any harmful radiation.

Our decimal3D software is an iPad application that guides users through the process of capturing a scan of a patient. It then provides tools that allow users to digitize the treatment area, which is pre-drawn on the patient's skin by the radiation oncologist, on the 3D model. Users also specify other device parameters, such as their treatment machine type, applicator size, and treatment direction, which allows the decimal3D software to complete the design of their treatment device. Finally, our software allows them to view and order the device for fabrication by our company. This process is directly analogous to the current digitization process in the existing clinical workflow except the predicate software device requires the user to use a clear plastic template placed in the head of the actual treatment delivery machine to project the device shape to the patient's skin surface using a light field. This acrylic template is then scanned using a flatbed document scanner and the shape is digitized in 2D using the predicate software.

The provided text does not describe the acceptance criteria and the study that proves the device meets those criteria. It focuses on the device's indications for use, technological characteristics, and a summary of non-clinical testing, stating that "Clinical testing was not performed."

Therefore, I cannot extract the requested information from the provided text.

Here's a breakdown of why each point cannot be answered:

1. A table of acceptance criteria and the reported device performance: The text mentions non-clinical testing but does not specify any acceptance criteria or report specific performance metrics for the device against these criteria.
2. Sample sized used for the test set and the data provenance: Since no clinical testing was performed, there is no test set in the context of human data. The non-clinical testing mentioned doesn't detail sample sizes or data provenance.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable as no clinical test set with human ground truth was mentioned.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable as no clinical test set with human ground truth was 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: Not applicable. The device is for designing beam-shaping blocks and is not an AI-assisted diagnostic or interpretation 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 text mentions "Clinically oriented validation test cases were written and executed by .decimal personnel and hospital-based testing partners where this device was deemed safe and effective for clinical use." However, it doesn't specify if this was a standalone algorithm performance test. The device itself is described as an "iPad application that guides users through the process...and provides tools that allow users to digitize the treatment area," implying a human-in-the-loop process for most of its functionality.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not specified for the non-clinical tests.
8. The sample size for the training set: No information about a training set for an AI/ML algorithm is provided. The device uses "proven off-the-shelf optical scanning technology" but the software itself is described in terms of tools and workflows rather than an AI model requiring a training set.
9. How the ground truth for the training set was established: Not applicable as no training set information is provided.

The document mainly serves as an FDA 510(k) clearance letter and summary, which focuses on demonstrating substantial equivalence to a predicate device and outlining the device's intended use and technological characteristics rather than providing detailed study results with acceptance criteria.

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The Astroid Planning App is an interactive end user application for proton treatment planning for the intended use and primary purpose of enabling radiotherapy professionals to efficiently design and analyze proton radiotherapy treatment plans. This Astroid Planning App leverages the existing .decimal Astroid Dosimetry App [FDA 510(k) K150547), which is a library of treatment planning functions accessed through the Thinknode® cloud services framework, for device creation, dose calculation, optimization, and all other dosimetry and processing calculations. Since the Astroid Dosimetry App is responsible for performing the calculations, the scope of this Astroid Planning App is to be the user interface for end users to input treatment planning data and review the results. Typical indications for the treatment of persons with cancer, over a wide range of potential disease locations. In the most common use case of the software, users will import patient data from existing imaging software programs, manage physician prescription and intent information, develop a proton treatment plan, and analyze the plan to determine how well it meets the physician's goals. Since the critical treatment planning functions are handled outside this software application, by a software of known quality and pedigree, the primary and most frequently used functions of this software are the record keeping service (for patient data storage), user interface controls, and visualization tools.

Furthermore, since the accuracy of information computed and displayed by an application such as this is very important to the proper treatment of patients, it is critical that users have the appropriate educational and clinical experience backgrounds to adequately understand and use the product. Additionally, since each radiotherapy treatment machine produces a unique beam of radiation, there is much responsibility on the end users to adequately commission and test this software over the full range of expected treatment conditions before the system is utilized for patient treatment.

The primary purpose of the Astroid Planning App device is for facilitating the planning and analysis of proton radiation therapy treatments. The Astroid Planning App device is an interactive end user application in which the user interacts with the interface to perform proton treatment planning tasks. The data constructed in the Astroid Planning App device will be used as inputs to the Astroid Dosimetry App device [FDA 510(k) K150547], which is the foundational proton dosimetry calculation library that contains all algorithms and calculation processing for the proton treatment planning. The Astroid Planning App device composes and otherwise constructs the calculation requests required for the development of the proton treatment plan, leveraging the functions externally located in the Astroid Dosimetry App to then perform the requested calculations.

The Astroid Planning App is an interactive end-user application for proton treatment planning. It leverages the existing .decimal Astroid Dosimetry App (K150547) for device creation, dose calculation, and optimization. The Planning App's scope is to provide a user interface for inputting treatment planning data and reviewing results, while the critical treatment planning functions are handled by the Dosimetry App.

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

The provided text does not contain a specific table of quantitative acceptance criteria. However, it states that the device was evaluated against predicate devices and through validation tests. The overall reported performance is that the Astroid Planning App "performed as well as the predicate devices and that the Astroid Planning App is deemed safe and effective for clinical use when properly commissioned for a proton treatment machine."

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

The document states: "Validation tests comparing results of proton dose calculations, with the inclusion of all applicable treatment delivery devices, to experimental and analytical datasets were performed." It does not specify the exact sample size or the provenance of the experimental and analytical datasets used for comparison. It does not indicate if the data was retrospective or prospective or its country of origin.

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

This information is not explicitly provided in the document.

4. Adjudication method for the test set:

The document does not describe a formal adjudication method (e.g., 2+1, 3+1). It states that "Clinically oriented validation test cases were written and executed by .decimal personnel and hospital-based testing partners." This suggests internal validation and potentially collaboration with clinical partners, but not a formal adjudication process for establishing ground truth as might be expected in a clinical study.

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

A multi-reader multi-case study comparing human readers with and without AI assistance was not explicitly mentioned. The testing focused on the performance of the Astroid Planning App itself, comparing it to predicate devices. The device serves as a planning tool, not a diagnostic aid that would typically involve human reader performance changes.

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

The document states that "the primary and most frequently used functions of this software are the record keeping service (for patient data storage), user interface controls, and visualization tools." The critical treatment planning functions and calculations are handled by the separate Astroid Dosimetry App. Therefore, the Astroid Planning App, as described, is inherently a "human-in-the-loop" interface for the Astroid Dosimetry App, and a standalone algorithm-only performance assessment of the Planning App itself (without considering the Dosimetry App) would not be applicable to its stated function. However, the underlying Astroid Dosimetry App (K150547) would have undergone its own standalone performance evaluation. This document focuses on the Planning App's role as an interface.

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

The ground truth for the dose calculation comparisons was based on "experimental and analytical datasets." This implies physical measurements and/or established theoretical models for proton dose calculations. For other functions, the "performance as well as the predicate devices" suggests the predicate devices' established accuracy served as a benchmark for comparison.

8. The sample size for the training set:

Information regarding a specific training set size is not provided. As this is a planning application, rather than a machine learning model that would typically have a distinct training phase, the concept of a "training set" might not directly apply in the same way. The development and verification process likely involved extensive iterative testing and refinement.

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

Since a distinct training set (in the machine learning sense) is not described, the method for establishing its ground truth is not mentioned. The validation process, however, involved comparison to "experimental and analytical datasets," which would serve as the reference for accuracy.

Summary of the Study:

The study was a non-clinical testing effort focused on demonstrating that the Astroid Planning App performs comparably to predicate devices and is safe and effective when properly commissioned.

• Tests performed:
  • Validation tests comparing proton dose calculations to experimental and analytical datasets.
  • Verification and validation tests for all other externally available functions.
  • Usability testing, including analysis of all system displays and user options.
  • Plan quality studies and full end-to-end testing of the entire planning process as compared to the predicate devices.
• Personnel involved: .decimal personnel and hospital-based testing partners.
• Comparison: The device was compared to predicate devices (Eclipse Treatment Planning System K172163 and RayStation 4.0.2 K140187) in terms of technology, intended uses, and end-user profiles.
• Conclusion: The tests showed that the Astroid Planning App performed as well as the predicate devices.

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The p.d software is used by radiation therapy professionals to assist in the design, manufacturing, and quality assurance testing of various radiation therapy devices used for cancer patients. The p.d software performs three distinct, primary functions which each are described below.

1. The p.d software takes a design of a compensating filter from a Treatment Planning System and converts the Treatment Planning System compensator filter files into a .decimal file format. This file can then be electronically submitted to .decimal through the software, so that we can manufacture the device.
2. The p.d software can design a beam shaping and compensating filters based on Treatment Planning System and other user supplied data. The device designs for compensating filters will be transferred back into the Treatment Planning System for final dose verification before devices are ordered and used for patient treatment.
3. The p.d software can perform quality assurance testing of the physical characteristics of treatment devices using data from various types of scanned images, including computed tomography images.

The .decimal p.d device is a software application that will enable users of various radiation treatment planning systems (TPS) to design, measure, and order beam shaping and modulating devices used in the delivery of various types of radiotherapy, including photon, electron, and particle therapy. The input from the treatment planning systems to the p.d product is generally received in DICOM file format. but other vendor specific or generic file formats are also utilized. p.d will also provide a simplified radiation dose calculator for the purpose of improving its ability to accurately create/modify patientspecific radiation beam modifying devices without the need for iteration with other treatment planning systems. However, all modulating devices will have final dose verification performed in a commissioned Treatment Planning System before devices are used for patient treatment. Additionally, the p.d software contains tools for analyzing scanned image data that aids users in performing quality assurance measurement and testing of radiotherapy devices.

The provided text describes the p.d 5.1 software, a device used in radiation therapy. However, it does not contain the detailed information required to fully answer your request regarding acceptance criteria and a specific study proving the device meets them. This document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with detailed acceptance criteria.

While the document indicates some testing was done, it doesn't provide the specifics you're asking for. Here's what can be inferred and what's missing:

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

Missing Information: The document does not provide a table of acceptance criteria with specific quantitative thresholds or reported device performance metrics. The testing described is more qualitative and focused on comparing to predicate devices and general software validation.

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

Missing Information:

• Sample Size: Not specified.
• Data Provenance: Not specified. The document mentions "hospital-based testing partners" but doesn't detail the origin or nature of the data used in validation.
• Retrospective/Prospective: Not specified.

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)

Missing Information: The document does not specify the number or qualifications of experts used for establishing ground truth in the test set. It mentions "Clinically oriented validation tests were written and executed by .decimal personnel and hospital-based testing partners," but this doesn't detail specific expert involvement for ground truth adjudication.

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

Missing Information: The document does not describe any specific adjudication method for establishing ground truth for the 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

Missing Information:

• MRMC Study: No MRMC comparative effectiveness study is mentioned.
• Effect Size: Not applicable, as no MRMC study was described. The focus is on demonstrating substantial equivalence of the software's functionality to existing tools. This device is an aid to radiation therapy professionals, not an AI to improve human reader performance in a diagnostic context.

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

Information Available (Inferred): The testing seems to have been primarily standalone, focusing on the software's ability to perform its functions (design filters, convert files, perform QA measurements) and comparing its output to predicate devices. The document states "Clinical testing was not performed... since testing can be performed such that no human subjects are exposed to risk." This suggests the validation was primarily of the software's internal logic and output, rather than its performance in conjunction with a human user in a clinical setting with real patient outcomes.

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

Information Available: The ground truth for the validation tests was established by:

• Comparing results to those of known predicate devices (p.d software version 5.0 and Eclipse TPS).
• Performing quality assurance measurements on devices of known quality.

This implies a form of "reference standard" or "known truth" derived from established systems and manufactured devices, rather than clinical pathology or patient outcomes.

8. The sample size for the training set

Not Applicable/Missing Information: The document describes software validation and verification, not the training of a machine learning model. Therefore, there is no "training set" in the context of AI/ML. If the "p.d software" incorporates algorithms that are based on machine learning, this information is not provided. The phrasing "using nearly identical algorithms and processes" to the predicate software suggests it's more of a deterministic software rather than a trained AI model.

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

Not Applicable/Missing Information: As there's no mention of a training set for an AI/ML model, this question is not applicable.

Summary of Device Performance (from the document):

The document concludes with: "These tests show that the p.d software performed equivalently to the predicate device when appropriate and that the software is deemed safe and effective for clinical use."

This is a general statement of performance, but it lacks the specific, quantifiable acceptance criteria and corresponding reported performance metrics that your request specifies. The 510(k) process primarily aims to demonstrate substantial equivalence, and the provided document reflects that focus.

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