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The ARIA Radiation Therapy Management product is a treatment plan and image management application. It enables the authorized user to enter, access, modify, store and archive treatment plan and image data from diagnostic studies, treatment planning, simulation, plan verification and treatment. ARIA Radiation Therapy Management also stores the treatment histories including dose delivered to defined sites and provides tools to verify performed treatments.

ARIA Radiation Therapy Management (ARIA RTM) manages several treatment information such as images and treatment data to prepare plans created for treatment and review post-treatment images and records. It also provides quality assurance options. ARIA RTM does not directly act on the patient. ARIA RTM is applied by trained medical professionals in the process of preparation and management of radiotherapy treatments for patients.

The provided FDA 510(k) summary for ARIA Radiation Therapy Management System (18.1) does not contain the detailed information necessary to fully answer your request regarding acceptance criteria and the study proving device performance.

Here's a breakdown of what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

The submission states: "Test results demonstrate conformance to applicable requirements and specifications." However, it does not provide a specific table of acceptance criteria or reported device performance metrics. It implies that underlying V&V documentation exists that confirms the software meets its design requirements, but these details are not present in this summary.

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

The document states: "No animal studies or clinical tests have been included in this pre-market submission." This indicates that the validation was likely based on non-clinical software testing, not patient data. Therefore, there is no patient-specific test set sample size or data provenance (e.g., country of origin, retrospective/prospective) mentioned. The testing would have involved simulated data, test cases, or internal datasets to verify software functionalities.

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

Since the testing was non-clinical software verification and validation, there is no mention of experts establishing ground truth for a test set in the traditional sense of clinical evaluation. Software testing typically relies on predefined requirements, specifications, and expected outputs, rather than expert-adjudicated ground truth from medical images or patient cases.

4. Adjudication method for the test set

Similarly, because there are no clinical trials or expert-adjudicated test sets, there is no adjudication method (e.g., 2+1, 3+1) 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

The document explicitly states: "No animal studies or clinical tests have been included in this pre-market submission." Therefore, no MRMC comparative effectiveness study was conducted or reported for this submission. The device is a radiation therapy management system, not explicitly an AI-assisted diagnostic tool for human readers.

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

The submission indicates that the software underwent "Software Verification and Validation Testing" and was considered a "major" level of concern. This implies extensive standalone algorithm (software) testing to ensure it meets its functional and safety requirements. However, specific details of these tests (e.g., test cases, scenarios, and their results) are not provided in this summary. The device "does not directly act on the patient" and is "applied by trained medical professionals," suggesting it's an assistive tool within a human workflow, but its core functionalities are tested in a standalone manner.

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

Given the non-clinical nature of the testing, the "ground truth" would have been established by the software requirements and specifications, test case design, and expected outputs defined by the developers. This is typical for software verification and validation, where the goal is to confirm the software performs as designed.

8. The sample size for the training set

The submission does not mention any training set as there is no indication of machine learning or AI models with external data training involved that would require such information. The changes appear to be feature enhancements to an existing software system.

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

As no training set is mentioned, this information is not applicable.

In summary, the provided document focuses on the regulatory aspects of a software update (v18.1) to an existing device (v18.0) and highlights software verification and validation as the primary evidence of performance. It explicitly states that no clinical or animal studies were included. Therefore, the detailed performance metrics, test set characteristics, and expert involvement typically associated with clinical efficacy studies are not present.

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Filling material as a treatment for dental caries

The subject devices are a mixture (alloy) of silver and several other metals, used by dentists to make fillings for tooth cavities. Amalgam alloys have been the most commonly used direct restorative filling material for over a 100 years.

The provided text describes a 510(k) summary for several dental amalgam devices, asserting their substantial equivalence to a predicate device. This submission focuses on comparing the physical and chemical properties of the devices to establish this equivalence, rather than on the performance of an AI-powered diagnostic device.

Therefore, many of the requested categories (e.g., sample size for test set, data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone performance, training set sample size, ground truth for training set) are not applicable to this document as it does not describe a study involving an AI-powered diagnostic device that requires such metrics for validation.

Here's the information that can be extracted relevant to acceptance criteria and performance:

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: Not specified in terms of distinct numerical values (e.g., number of amalgam samples tested for each property). The testing was completed in accordance with ISO 24234, which would specify sample size requirements for each test.
• Data Provenance: Not explicitly stated. The submitter is World Work Srl from Italy, so the testing was likely conducted in Europe or by contractors for the Italian company. The document does not indicate if it was retrospective or prospective.

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. This is a material science characterization, not a diagnostic study requiring expert ground truth establishment in the clinical sense. The "ground truth" here is the adherence to established international standards (ISO 24234) and their specified test methodologies.

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

• Not applicable. The evaluation is based on compliance with a standard (ISO 24234), not on human interpretation or adjudication of diagnostic findings.

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 is not an AI-powered diagnostic device.

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

• Not applicable. This is not an AI-powered diagnostic device.

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

• The "ground truth" for the device's performance is adherence to the physical and chemical property specifications set forth by ISO 24234. This standard provides the benchmark against which the device's characteristics are measured.

8. The sample size for the training set

• Not applicable. This is not an AI-powered device that undergoes a training phase.

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

• Not applicable. This is not an AI-powered device that undergoes a training phase.

Study Description (Summary of the "study" conducted):

The "study" conducted for these dental amalgam devices was non-clinical performance testing according to ISO 24234. The purpose was to demonstrate that the physical and mechanical properties of the subject devices meet the requirements of this international standard.

The tests carried out included:

• Package & Capsule contamination
• Chemical composition and purity of the dental amalgam alloy
• Large particles in the dental amalgam alloy powder
• Loss of mass from the capsule during mixing
• Yield of amalgam from the capsule
• Consistency of the dental amalgam from capsule to capsule
• Physical properties (Creep, dimensional change, compressive strength, corrosion)

Based on the data from these tests, the manufacturer concluded that the subject devices are substantially equivalent to the predicate device (Permite Dental Amalgam Alloy K801639) by demonstrating compliance with the ISO 24234 standard.

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The ARIA Radiation Therapy Management product is a treatment plan and image management application. It enables the authorized user to enter, access, modify, store and archive treatment plan and image data from diagnostic studies, treatment planning, simulation, plan verification and treatment. ARIA Radiation Therapy Management also stores the treatment histories including dose delivered to defined sites and provides tools to verify performed treatments.

The ARIA Radiation Therapy Management product is a treatment plan and image management application. It enables the authorized user to enter, access, modify, store and archive treatment plan and image data from diagnostic studies, treatment planning, simulation, plan verification and treatment. ARIA Radiation Therapy Management also stores the treatment histories including dose delivered to defined sites and provides tools to verify performed treatments. ARIA Radiation Therapy Management supports the integration of all data and images in one central database including archiving and restoration. The different ARIA Radiation Therapy Management features support the visualization, processing, manipulation and management of all data and images stored in the system. Images can also be imported through using DICOM, the available image import filters or by means of film digitizers.

The provided text is a 510(k) summary for Varian Medical Systems' ARIA Radiation Therapy Management (v18.0) device. It describes the device, its intended use, comparison to a predicate device, and performance data from non-clinical testing.

However, the document does not contain the following information necessary to describe acceptance criteria and the study that proves the device meets those criteria:

• Specific acceptance criteria: The document mentions "applicable requirements were met" and "safeguards against hazards functioned properly" but does not detail what these specific quantitative or qualitative acceptance criteria were for the software verification and validation.
• Reported device performance: While it states "test results showed that applicable requirements were met," it does not provide any specific performance metrics or data (e.g., accuracy, precision, error rates) that were measured and compared against acceptance criteria.
• Sample size and data provenance for test set: No information is given about a test set, as all testing was non-clinical software verification and validation.
• Experts for ground truth and adjudication method: These are typically relevant for studies involving human interpretation or clinical endpoints, which this submission explicitly states were not conducted.
• MRMC comparative effectiveness study: The document clearly states that "No data from animal studies or clinical tests have been included" and "no animal or clinical studies were conducted for the subject device." Therefore, no MRMC study was performed.
• Standalone performance: Since no clinical studies were performed, there are no reported standalone performance metrics in the context of interpretation or diagnosis. The performance mentioned refers to software functionality.
• Type of ground truth: Ground truth is usually associated with clinical or pathological verification in studies, which are absent here.
• Training set information: As this is primarily a software update and management system, not a machine learning algorithm that requires a "training set" in the conventional sense, this information is not applicable and not provided.

Based on the provided text, I can only state what is mentioned regarding performance, rather than providing the requested table and details about a clinical study:

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

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

• Sample Size: Not applicable. The submission describes software verification and validation, not a test set of patient data.
• Data Provenance: Not applicable. Testing was based on software functionality and engineering requirements.

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

• Not applicable. No test set involving human interpretation or clinical ground truth was used.

4. Adjudication method for the test set:

• Not applicable. No test set requiring expert adjudication was used.

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

• No, an MRMC comparative effectiveness study was not done. The document explicitly states: "No data from animal studies or clinical tests have been included in this pre-market submission" and "no animal or clinical studies were conducted for the subject device."

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

• Yes, in the context of software verification and validation, the performance of the software (algorithm) was assessed standalone against its functional and safety requirements. However, this is not a standalone diagnostic performance study as typically understood for AI/ML devices. The document states: "The non-clinical data support the safety of the software verification and validation demonstrate that ARIA Radiation Therapy Management should perform as intended in the specified use conditions."

7. The type of ground truth used:

• For the software verification and validation, the "ground truth" was the specified engineering requirements, functional specifications, and regulatory standards (e.g., IEC 62304, IEC 62366-1, ISO 13485, ISO 14971). There was no clinical or pathological ground truth used as no clinical studies were performed.

8. The sample size for the training set:

• Not applicable. This is a software management system, not a machine learning model that typically involves a "training set" in the AI/ML sense.

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

• Not applicable, as no training set (in the AI/ML context) was described.

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The ARIA Radiation Therapy Management product is a treatment application. It enables the authorized user to enter, access, modify, store and archive treatment plan and image data from diagnostic studies, treatment planning, simulation, plan verification and treatment. ARIA Radiation Therapy Management also stores the treatment histories including dose delivered to defined sites and provides tools to verify performed treatments.

The ARIA Radiation Therapy Management product is a treatment plan and image management application. It enables the authorized user to enter, access, modify, store and archive treatment plan and image data from diagnostic studies, treatment planning, simulation, plan verification and treatment. ARIA Radiation Therapy Management also stores the treatment histories including dose delivered to defined sites and provides tools to verify performed treatments. ARIA Radiation Therapy Management supports the integration of all data and images in one central database including archiving and restoration. The different ARIA Radiation Therapy Management features support the visualization, processing, manipulation and management of all data and images stored in the system. Images can also be imported through using DICOM, the available image import filters or by means of film digitizers.

The provided text describes the ARIA Radiation Therapy Management (v16.1 MR3) device, which is a treatment plan and image management application. It states that the device was cleared based on non-clinical testing and refers to software verification and validation as the primary performance data.

However, the text does not contain the specific information requested regarding acceptance criteria, reported device performance, details of a study (sample sizes, data provenance, expert qualifications, adjudication methods), multi-reader multi-case (MRMC) comparative effectiveness study, standalone performance, or ground truth details for testing and training sets.

The document states:

• "No data from animal studies or clinical tests have been included in this pre-market submission."
• "Since the predicate device was cleared based only on the results of non-clinical testing, no animal or clinical studies were conducted for the subject device. The non-clinical data support the safety of the software verification and validation demonstrate that ARIA Radiation Therapy Management should perform as intended in the specified use conditions."

Therefore, I cannot provide the requested information from the given text. The provided document focuses on regulatory compliance, outlining the device's intended use, comparison with a predicate device, and adherence to software verification and validation testing and various regulatory standards (e.g., IEC 62304, ISO 13485, ISO 14971).

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The ARIA Radiation Therapy Management product is a treatment application. It enables the authorized user to enter, access, modify, store and archive treatment plan and image data from diagnostic studies, treatment planning, simulation, plan verification and treatment. ARIA Radiation Therapy Management also stores the treatment histories including dose delivered to defined sites and provides tools to verify performed treatments.

The Eclipse Treatment Planning System (Eclipse TPS) is used to plan radiotherapy treatments with malignant or benign diseases. Eclipse TPS is used to plan external beam irradiation with photon, electron and proton beams, as well as for internal irradiation (brachytherapy) treatments. In addition, the Eclipse Proton Eye algorithm is specifically indicated for planning proton treatment of neoplasms of the eye.

The ARIA Radiation Therapy Management product is a treatment plan and image management application. It enables the authorized user to enter, access, modify, store and archive treatment plan and image data from diagnostic studies, treatment planning, simulation, plan verification and treatment. ARIA Radiation Therapy Management also stores the treatment histories including dose delivered to defined sites, and provides tools to verify performed treatments. ARIA Radiation Therapy Management supports the integration of all data and images in one central database including archiving and restoration. The different ARIA Radiation Therapy Management features support the visualization, processing, manipulation and management of all data and images stored in the system. Images can also be imported through the network using DICOM, the available image import filters or by means of film digitizers.

The Varian Eclipse™ Treatment Planning System (Eclipse TPS) provides software tools for planning the treatment of malignant or benign diseases with radiation. Eclipse TPS is a computer-based software device used by trained medical professionals to design and simulate radiation therapy treatments. Eclipse TPS is capable of planning treatments for external beam irradiation with photon, electron, and proton beams, as well as for internal irradiation (brachytherapy) treatments.

The provided text describes a 510(k) premarket notification for two medical devices: ARIA Radiation Therapy Management (v15.8) and Eclipse Treatment Planning System (v15.8).

Based on the provided text, the following information can be extracted:

• Acceptance Criteria and Device Performance: The document states that the devices were "verified and validated according to the FDA Quality System Regulation (21 CFR §820) and other FDA recognized consensus standards." It also mentions, "Test results demonstrate that the device conforms to design specifications and meets of the intended users, including assuring risk mitigations were implemented and functioned properly."
  However, the document does not provide a specific table of acceptance criteria with reported numerical device performance metrics. The performance assessment is general, confirming adherence to regulatory standards and design specifications rather than specific quantitative thresholds for accuracy, sensitivity, specificity, or other performance indicators typical for AI/ML-driven devices.

• Study That Proves the Device Meets Acceptance Criteria:
  The study that proves the device meets the acceptance criteria is Software Verification and Validation Testing.

Here's a breakdown of the specific points requested, based on the provided text:

1. A table of acceptance criteria and the reported device performance:
* Acceptance Criteria: The acceptance criteria are broadly defined as conformance to design specifications, meeting intended user requirements, and assuring risk mitigations were implemented and functioned properly. This is according to FDA Quality System Regulation (21 CFR §820) and other FDA recognized consensus standards (listed below).
* Reported Device Performance: "Test results demonstrate that the device conforms to design specifications and meets of the intended users, including assuring risk mitigations were implemented and functioned properly." No specific numerical performance metrics (e.g., accuracy, sensitivity, specificity values) are provided for a direct comparison in a table format.

2. Sample size used for the test set and the data provenance:
* Sample Size: The document does not specify the sample size (e.g., number of cases or patients) used for the software verification and validation testing.
* Data Provenance: The document does not mention the country of origin of the data, nor does it specify if 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:
* This information is not provided. The document primarily focuses on software engineering and regulatory compliance rather than clinical performance validation involving experts establishing ground truth for a test set.

4. Adjudication method for the test set:
* This information is not provided. There's no mention of a clinical test set requiring adjudication in the context of this submission.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
* No MRMC comparative effectiveness study was done. The document explicitly states: "No data from animal studies or clinical tests have been included in this pre-market submission." This indicates that the regulatory submission primarily relies on software verification and validation and comparison to predicate devices, not studies demonstrating human reader improvement with AI assistance.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
* The document implies that standalone software verification and validation testing was done, as it states "Software Verification and Validation Testing" was performed to ensure conformance to design specifications and risk mitigation. However, it does not explicitly detail "algorithm only" performance metrics in a clinical sense. The devices are described as tools for trained medical professionals, suggesting a human-in-the-loop operation, but the provided V&V is on the software itself.

7. The type of ground truth used:
* The term "ground truth" in a clinical performance context is not explicitly mentioned. For software verification and validation, ground truth would relate to the correctness of the software's output against its design specifications and expected behavior, rather than clinical outcomes or expert consensus on medical images.

8. The sample size for the training set:
* This information is not applicable and therefore not provided. These devices are not described as AI/ML systems that undergo a machine learning training phase on a dataset. They are software tools for treatment planning and management.

9. How the ground truth for the training set was established:
* This information is not applicable and therefore not provided, as these are not AI/ML training data sets.

In summary, the provided document focuses on regulatory compliance through software verification and validation and substantial equivalence to predicate devices, rather than detailed clinical performance studies often associated with novel AI/ML device submissions. The "acceptance criteria" are compliance with quality system regulations and standards, and "performance" is demonstrated by successful verification and validation tests against design specifications.

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The Aria sequential circulator is a programmable sequential, pneumatic compression device intended for use by medical professionals and patients at home, for the treatment of the following conditions:

• Chronic edema
• Lymphedema
• Venous insufficiency
• Wound healing

The Aria system consists of two main components: A flow generator and a garment. The garment is to be wrapped around the limb, providing a comfortable fit. The garment has seven (7) chambers that are filled with air by the flow generator to provide compression on the extremity. The Aria system uses a compressand-release massage action, similar to the predicate, in order to stimulate lymphatic vessels in the treated area and encourage fluid clearance.

The Aria system retains similar hardware and performance features of the predicate device. Key features include flow generator, valves, A/C plug pack, lower limb garment, tubing, no-LCD User Interface and ON/OFF button. The Aria System contains a microprocessor-controlled flow generator/blower system that generates pressure from 0-45 mmHG to provide for effective treatment of the conditions described in the IFU.

The Aria flow generator has no control settings and delivers one pre-programmed therapy mode.

This document describes the Inova Labs Aria System, a programmable sequential, pneumatic compression device. It is intended for the treatment of chronic edema, lymphedema, venous insufficiency, and wound healing. The submission is a 510(k) premarket notification, indicating a claim of substantial equivalence to a predicate device.

Here's the breakdown of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" against which the device performance is reported with specific numerical targets. Instead, it describes "bench testing" which included performance comparisons to a predicate device. The results of this testing are stated to "demonstrate that the Aria system raises no new safety or effectiveness concerns and is substantially equivalent to the predicate device."

Here's a summary of the characteristics compared with the predicate device (Entre Model PD08-U, K143185):

The "bench testing" areas mentioned for the Aria System were:

• Pressure stability
• Sleeve burst test
• Sleeve leakage test
• Sleeve integrity test
• Pressure accuracy test
• Chamber filling cycle time testing
• Total therapy time

The report generally states that the Aria system "met the Aria System Specification when compared to the predicate device" for these tests, implying that the performance was either equivalent or acceptable relative to the predicate. Specific numerical values for acceptance criteria or detailed results are not provided in this summary.

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

The document primarily discusses non-clinical bench testing. There is no "test set" in the context of patient data described for evaluating device performance. The testing involved physical device units. The provenance of the device units used for bench testing (e.g., country of manufacture) is 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)

This question is not applicable as there was no clinical test set requiring expert ground truth establishment. The evaluation was based on bench testing against engineering specifications and comparison to a predicate device.

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

This question is not applicable as there was no clinical test set requiring adjudication.

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 question is not applicable. The Aria System is a pneumatic compression device, not an AI-assisted diagnostic or interpretative device that would involve human "readers." No MRMC study was performed.

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

This question is not applicable. The Aria System itself is a standalone medical device (hardware) that delivers therapy; it's not an algorithm whose performance needs to be assessed independently. While it contains a microprocessor and potentially firmware/software for control, the evaluation focuses on the overall device's physical and functional performance through bench testing.

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

For the non-clinical testing, the "ground truth" or reference for evaluating performance was based on engineering specifications for the Aria System and the performance characteristics of the legally marketed predicate device (Entre Model PD08-U, K143185). For example, pressure accuracy would be compared to a calibrated standard.

8. The sample size for the training set

This question is not applicable. This device is not an AI/ML algorithm that requires a "training set" of data for learning.

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

This question is not applicable as there was no training set.

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X-ray Bone Densitometer designed to estimate the bone mineral density of patients when medically indicated by their physicians.

• Provides an estimate of bone mineral density at various anatomical sites (Spine, Femur, Forearm). These values can then be compared to an adult reference population at the sole discretion of the physician.
• Provides an assessment of relative risk based on the patient's T-score value using the categories of fracture risk defined by the World Health Organization (WHO).
• Provides an assessment of 10-year fracture risk using WHO FRAX model.
• Provides a standardized bone density report using data from the densitometer and physician-generated assessments based on the patient's demographics, which can assist the physician in communicating scan results to the patient and the patient's referring physician.

The Aria X-ray Bone Densitometer is designed to be a value product version of the predicate device, the Prodigy (K982267 and K161682). Like its predicate Prodigy device, the proposed Aria device is composed of a scanner and a computer that runs the software. The scanner comprises the x-ray source and detector, the patient scan table, the mechanical drive system, and the lowest level portions of the control system. The scanner is in communication with the computer, which is a standard PC. The computer runs the enCORE software, and thus controls the scanner, acquires scan data from the scanner, stores and analyzes the data, and interacts with the human operator.

Aria X-ray Bone Densitometer functions with the same software as the one that is FDA cleared under GE Lunar DXA Bone Densitometers with enCORE version 17 (K161682).

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

Based on the provided information, the Aria device is an X-ray Bone Densitometer. The study presented is a bench testing to demonstrate precision and accuracy, not a clinical study involving human subjects or AI algorithms. As such, several requested items like "Multi-reader multi-case (MRMC) comparative effectiveness study" and "Human reader improve with AI vs without AI assistance" are not applicable.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for the Test Set

• Sample Size: The text mentions "a wide range of phantoms" for precision and accuracy tests, and "a set of phantoms with different BMD and tissue composition properties." However, specific numerical sample sizes for these phantom sets are not provided.
• Data Provenance: The tests were conducted during "product development" as "bench testing." The data provenance is from phantom measurements, not human participants. No country of origin is explicitly stated for the phantom data, but it's implied to be internal testing by GE Healthcare (USA). The study is retrospective in the sense that it's based on controlled phantom measurements rather than prospective patient recruitment.

3. Number of Experts and Qualifications for Ground Truth

• Not applicable. The ground truth for the test set was established using phantoms with known bone mineral density (BMD) and tissue composition properties, not by human experts. The accuracy was verified by showing an "excellent correlation to previously released Prodigy product," implying a comparison to an established device's performance.

4. Adjudication Method for the Test Set

• Not applicable. Since the ground truth was phantom-based and not expert-driven, there was no adjudication method involving multiple experts.

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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The presented study is a bench test, and the device is a bone densitometer, not an AI-assisted diagnostic tool for human interpretation. Therefore, there's no "effect size of how much human readers improve with AI vs without AI assistance" to report.

6. Standalone (Algorithm Only) Performance Study

• Yes, a standalone performance study was done. The precision and accuracy bench testing evaluates the algorithm's (and the overall device's) ability to accurately and precisely estimate bone density from phantom measurements without human intervention in the measurement process. The results "demonstrate that Aria can accurately and precisely estimate bone density."

7. Type of Ground Truth Used

• The ground truth used was phantom-based with established, known values for bone mineral density (BMD) and tissue composition properties. These values were validated against the performance of the predicate Prodigy product.

8. Sample Size for the Training Set

• Not explicitly stated and likely not applicable in the traditional sense for a machine learning model. The Aria device is presented as a "value product version" using the "same software as the one that is FDA cleared under GE Lunar DXA Bone Densitometers with enCORE version 17 (K161682)." This suggests that the core algorithms might have been developed and "trained" (if applicable for this type of system) with data associated with the enCORE software, but the document does not provide details on a specific training set size for Aria's development.

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

• Not explicitly stated and likely not applicable in the traditional sense for a machine learning model. Given that the Aria uses the same software (enCORE v17) as its predicate, the ground truth for any underlying algorithm "training" would have been established during the development of that software. Typically for bone densitometry, this involves:
  • Phantom studies: Similar to the testing described, using phantoms with known physical properties.
  • Clinical studies: Correlating device measurements against other established methods or clinical outcomes, although this document states clinical studies were "not required" for Aria's substantial equivalence.

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The ARIA Radiation Therapy Management product is a treatment plan and image management application. It enables the authorized user to enter, access, modify, store and archive treatment plan and image data from diagnostic studies, treatment planning, simulation, plan verification and treatment. ARIA Radiation Therapy Management also stores the treatment histories including dose delivered to defined sites and provides tools to verify performed treatments.

The ARIA Radiation Therapy Management product is a treatment plan and image management application. It enables the authorized user to enter, access, modify, store and archive treatment plan and image data from diagnostic studies, treatment planning, simulation, plan verification and treatment. ARIA Radiation Therapy Management also stores the treatment histories including dose delivered to defined sites, and provides tools to verify performed treatments. ARIA Radiation Therapy Management supports the integration of all data and images in one central database including archiving and restoration. The different ARIA Radiation Therapy Management features support the visualization, processing, manipulation and management of all data and images stored in the system. Images can also be imported through the network using DICOM, the available image import filters or by means of film digitizers

The provided document is a 510(k) summary for Varian Medical Systems, Inc.'s ARIA Radiation Therapy Management 15.5 MR1. This product is a management application for radiation therapy treatment plans and images, not a diagnostic or AI-powered device that identifies or predicts conditions. Therefore, it does not involve acceptance criteria, performance metrics, ground truth establishment, or clinical studies of the type typically associated with AI-driven medical devices (e.g., sensitivity, specificity, reader studies).

The document focuses on "Software Verification and Validation Testing" and "Standards conformance" to demonstrate the safety and effectiveness of the device as a management system. The "Summary of Performance Data" section explicitly states: "No clinical tests have been included in this pre-market submission."

Given this, I cannot provide the requested information. The questions you've asked are not applicable to the type of device and submission described in the provided text.

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The ARIA WiFi Smart Scale is a body analyzer that measures body weight and uses bioelectrical impedance analysis (BIA) technology to estimate body fat percentage in generally healthy individuals 10 years of age or older. It is intended for home use only.

ARIA is a body weight scale and a body fat analyzer that operates by using a low, safe, battery-generated electrical current through the body (using a bioelectrical impedance analysis technique) to provide body fat and body weight information. After the user registers their scale, the scale automatically recognizes the subject based on body weight and body fat readings. ARIA contains a WiFi module (802.11 module) that allows it to connect to the Internet in the user's home. The module provides a complementary interface to the Fitbit website. Body weight and body fat measurements are independent of internet communication after initial product registration.

The ARIA scale automatically measures body weight and body fat composition. The scale recognizes the user based on previous weight readings, and can accept up to eight (8) different users. The 16 most recent readings are kept in memory on the scale and readings are also transmitted to the user's optional fitbit.com personal account for trending. If users have similar weight, the proper identity can be selected by tapping the scale.

Here's a breakdown of the acceptance criteria and study information for the Fitbit ARIA WiFi Smart Scale, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the ARIA WiFi Smart Scale are established not through explicit numerical thresholds but by demonstrating substantial equivalence to a predicate device (Withings Smart Body Scale K121971) and showing that its body fat measurements are not statistically different from the predicate, with variation within an acceptable range.

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

• Sample Size for Test Set: 50 subjects (25 male and 25 female).
• Data Provenance: The study was a "small comparative clinical study" comparing the ARIA WiFi Smart Scale to the predicate device. The text does not specify the country of origin but implies it was conducted by the manufacturer or a contracted clinical research organization. The study design strongly suggests it was prospective as it involved collecting new data for direct comparison between the two devices.

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

The study does not establish an independent "ground truth" against which the device performance is measured in the classical sense (e.g., DEXA or underwater weighing). Instead, the performance of the ARIA device is compared directly against the predicate device (Withings Smart Body Scale K121971) as the reference. Therefore, there were no experts used to establish a separate ground truth for the test set. The predicate device's measurements serve as the comparator.

4. Adjudication Method for the Test Set

Not applicable. This was a direct comparison study between a new device and a predicate device. There was no complex labeling or interpretation by multiple human readers requiring adjudication. The study involved objective measurements of body fat percentage from both devices.

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. This device (Fitbit ARIA WiFi Smart Scale) is a standalone measurement device for body weight and body fat, not an AI-assisted diagnostic tool that aids human readers. The study performed was a direct comparison of its measurement accuracy against a predicate device.

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

Yes, a standalone performance study was done. The clinical performance testing compared the ARIA WiFi Smart Scale's measurements of body fat composition directly against the measurements from the predicate device. This is a standalone comparison as it assesses the device's output independently. The device's primary function is to measure and display these values, which are then transmitted to a user's account for tracking, but the core performance evaluation focuses on the accuracy of these direct measurements.

7. The Type of Ground Truth Used

The "ground truth" for the clinical performance study was the measurements obtained from the predicate device (Withings Smart Body Scale K121971). The study aimed to demonstrate that the ARIA device's measurements were not statistically different from those of the legally marketed predicate device. While BIA is an estimation method itself, for the purpose of demonstrating substantial equivalence, the predicate device's output serves as the comparative reference.

8. The Sample Size for the Training Set

The document does not mention a training set or any machine learning/AI model that would require a distinct training set. The ARIA WiFi Smart Scale uses Bioelectrical Impedance Analysis (BIA) technology, which is a well-established method, not typically relying on a separately described "training set" in the context of regulatory submissions for this type of device. The description focuses on its sensor technology and comparison to a predicate.

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

As no training set is mentioned or implied for a machine learning model, this question is not applicable. The device relies on physical principles of bioelectrical impedance.

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The ARIA Radiation Therapy Management product is a treatment plan and image management application. It enables the authorized user to enter, access, modify, store and archive treatment plan and image data from diagnostic studies, treatment planning, simulation, plan verification and treatment. ARIA Radiation Therapy Management also stores the treatment histories including dose delivered to defined sites and provides tools to verify performed treatments.

The ARIA Radiation Therapy Management product is a treatment plan and image management application. It enables the authorized user to enter, access, modify, store and archive treatment plan and image data from diagnostic studies, treatment planning, simulation, plan verification and treatment. ARIA Radiation Therapy Management also stores the treatment histories including dose delivered to defined sites, and provides tools to verify performed treatments.

ARIA Radiation Therapy Management supports the integration of all data and images in one central database including archiving and restoration. The different ARIA Radiation Therapy Management features support the visualization, processing, manipulation and management of all data and images stored in the system. Images can also be imported through the network using DICOM, the available image import filters or by means of film digitizers.

After reviewing the provided FDA 510(k) summary for "ARIA Radiation Therapy Management," it appears that the document describes a software application for managing radiation therapy data and images, rather than an AI-powered diagnostic or assistive device that would typically undergo rigorous performance studies with specific acceptance criteria, test sets, and ground truth establishment involving expert readers.

The provided text focuses on:

• Device Description and Intended Use: Managing, storing, accessing, and modifying treatment plan and image data, and storing treatment histories.
• Changes to Predicate Device: Listing minor software feature changes, such as improved rigid registration, DICOM UI, and workflow usability.
• Summary of Performance: A generic statement that "Results of verification and validation testing showed conformance to applicable requirements specifications and assured hazard safeguards testing: functioned properly."
• Standards Conformance: Listing relevant IEC standards (e.g., IEC 61217, IEC 62366, IEC 62304).

Crucially, there is no mention of:

• Specific acceptance criteria tied to a particular performance metric (e.g., sensitivity, specificity, accuracy).
• A test set size, data provenance, or details of a study involving human readers or AI performance evaluation.
• Ground truth establishment methods, expert qualifications, or adjudication.
• MRMC studies or standalone algorithm performance.

It seems this device falls under a category where conformance to software engineering standards, functionality testing, and verification/validation against specifications are the primary means of demonstrating safety and effectiveness, rather than a clinical performance study with statistical endpoints. The changes are primarily software enhancements and re-structuring, not the introduction of a new AI algorithm for detection, diagnosis, or prediction that would require such studies.

Therefore, I cannot provide the requested information regarding acceptance criteria and performance studies in the format you've outlined because the provided document does not contain that level of detail for this specific type of device and its claimed modifications.

To answer your request based only on the provided text, the response would be:

Based on the provided FDA 510(k) summary for ARIA Radiation Therapy Management (K133572), the device is a treatment plan and image management application, and the submission primarily addresses software modifications and functional capabilities, not a new AI-powered diagnostic or assistive feature that would necessitate a clinical performance study with specific acceptance criteria measured against a defined test set.

Therefore, the detailed information requested regarding acceptance criteria, study design, sample sizes, ground truth establishment, expert involvement, and MRMC studies is not present within this document.

The document broadly states:

1. A table of acceptance criteria and the reported device performance: This information is not provided in a quantifiable table format. The summary states: "Results of verification and validation testing showed conformance to applicable requirements specifications and assured hazard safeguards testing: functioned properly." This implies the acceptance criteria were likely functional and performance specifications related to data management, accessibility, storage, and processing, rather than clinical efficacy metrics.
2. Sample sized used for the test set and the data provenance: Not specified.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not specified, as ground truth in the context of clinical interpretation/diagnosis is not relevant for this device's modifications.
4. Adjudication method for the test set: Not specified.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Not specified. It is highly unlikely for this type of software management system.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not specified.
7. The type of ground truth used: Not specified, given the device's function.
8. The sample size for the training set: Not specified. (This device is not described as involving machine learning training.)
9. How the ground truth for the training set was established: Not applicable, as no machine learning training is described.

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