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Pure Impact is indicated to be used for:

• Improvement of abdominal tone, for strengthening of the abdominal muscles, for development of firmer abdomen.
• Improvement of muscle tone and firmness, for strengthening muscles in arms, thighs and buttocks areas.
• The Pure Impact is intended to stimulate healthy muscles in order to improve or facilitate muscle performance. The Pure Impact is not intended to be used in conjunction with therapy or treatment of medical diseases or medical conditions of any kind. The various types of muscle work that the Pure Impact can impose on the stimulated muscles are able to improve or facilitate muscle performance. The Pure Impact may therefore be considered a technique of muscle training.

Pure Impact device provides both training and physical therapy using Electrical Muscle Stimulation (EMS). The EMS is provided through electrodes that are controlled by the Touch Control Module.

The system includes:

• Touch Control Module
• Pure Impact™ Wireless Stimulation Modules
• Pure Impact™ Electrode Pads
• Pure Impact™ Charging Station

The Touch Control Module runs the Pure Impact™ program and displays the user interface, which provides the full range of controls to identify the patient and the required treatment. The Pure Impact™ Wireless Stimulation Modules provide EMS to selected body locations on the patient and communicate to the Touch Control Module through Bluetooth Low Energy (BLE). Each Stimulation Module contains a main stimulation unit and several stimulation subunits, each of which is attached to a Pure Impact™ Electrode Pad placed on the patient. The Pure Impact™ Electrode Pads are disposable and intended for single patient, single use only. They contain a hydrogel adhesive to attach to the selected body locations on the patient and magnetic connectors to attach to the Stimulation Modules. The Pure Impact™ Charging Station charges the Stimulation Modules.

This FDA 510(k) clearance letter pertains to the Pure Impact device, a powered muscle stimulator. The clearance is based on demonstrating substantial equivalence to a predicate device, the Pure Impact Module of the SofWave System (K250146).

The document states that the Pure Impact device is a modification of the predicate, with the only changes being related to the standalone PC, GUI, and software updates. It explicitly states that no changes were made to the Pure Impact Charging Station Unit, Pure Impact Wireless Stimulation Modules, or Pure Impact Electrode Pads. Furthermore, the change does not affect the treatment protocols, stimulation parameters, or energy delivery.

Therefore, the performance data and acceptance criteria for electrical muscle stimulators (EMS) typically revolve around safety and technical specifications, rather than clinical efficacy or diagnostic accuracy, which would involve more complex studies like MRMC or ground truth assessment from expert consensus or pathology. This is because EMS devices, particularly those for muscle conditioning, are often cleared based on demonstrating that their technical characteristics (e.g., waveform, output, safety features) are equivalent to a legally marketed predicate device, and that these characteristics do not raise new questions of safety or effectiveness.

Given the information provided, which heavily emphasizes direct technical comparison and safety testing (electrical safety, EMC, software V&V), the "acceptance criteria" and "study that proves the device meets the acceptance criteria" are primarily framed around these technical and safety aspects, and the device's functional equivalence to its predicate. There is no mention of a clinical study assessing improvement in muscle tone or firmness with quantified metrics from human subjects, or any study involving human readers/experts for performance evaluation, as would be common for diagnostic AI/ML devices.

Here's a breakdown of the requested information based solely on the provided FDA 510(k) clearance letter:

1. Table of Acceptance Criteria and Reported Device Performance

For this device, the "acceptance criteria" are predominantly framed as mirroring the technical specifications and safety standards met by the predicate device, and ensuring the modified device continues to meet these.

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The Impact PEEK Union Nail System is indicated for maintenance of alignment and fixation of bone fractures, osteotomies, arthrodeses or bone grafts in the presence of appropriate additional immobilization (e.g. rigid fixation implants, cast, brace).

The Impact PEEK Union Nail System contains 3.0mm - 4.5mm diameter, 50mm long pins manufactured from HA Enhanced PEEK (ASTM F2026). The implants are designed with strategically placed ridges to improve initial stability and cannulated structure to simplify insertion over a k-wire. Additionally, it was designed to include tantalum pins to ensure optimal imaging visibility for device placement accuracy.

The provided document is a 510(k) clearance letter from the FDA for a medical device called the "Impact PEEK Union Nail System." This letter details the administrative aspects of the clearance, including the device name, manufacturer, regulatory classification, and the FDA's determination of substantial equivalence to predicate devices.

However, it does not contain the information requested regarding acceptance criteria and the study that proves the device meets those criteria for software-based or AI/ML-driven medical devices.

The document specifically states:

• "No FDA performance standards have been established for the Impact PEEK Union Nail System."
• The non-clinical testing performed includes "Static and Dynamic Bending, Axial Pullout (ASTM F1264-16, ASTM F543-17)" and "Shear Testing (ASTM D2344)." These are mechanical and material property tests common for orthopedic implants, not studies designed to assess the performance of a software algorithm or AI model in an clinical setting.
• The "Equivalence to Predicate Devices" section emphasizes "similar design and similar dimensions," manufacturing material (HA Enhanced PEEK), "same intended use," and "similar technological characteristics," all referring to the physical implant rather than computational performance.

Therefore, I cannot extract the requested information from this document because it pertains to a physical orthopedic implant, not an AI/ML or software-driven medical device. The questions you've asked (e.g., sample size for test/training sets, data provenance, number of experts for ground truth, MRMC studies, standalone algorithm performance) are highly relevant to the validation of AI/ML medical devices but are not applicable to the type of device described in this 510(k) clearance letter.

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MULTIX Impact E is a radiographic system used in hospitals, clinics, and medical practices. MULTIX Impact E enables radiographic exposures of the whole body including: skull, chest, abdomen, and extremities and may be used on pediatric, adult and obese patients. Exposures may be taken with the patient sitting, standing, or in the prone position. MULTIX Impact E uses digital detectors for generating diagnostic images by converting X- rays into image signals. MULTIX Impact E is also designed to be used with conventional film/screen or Computed Radiography (CR) cassettes. MULTIX Impact E is not intended for mammography.

The MULTIX Impact E Radiography X-ray system is a modular system of X-ray components (floor-mounted x-ray tube, bucky wall stand, bucky table, x-ray generator, portable wireless detector) based on the predicate device, the MULTIX Impact E (VB10) (K220919). The following modifications have been made to the predicate device: 1) A new elevating table (option) 2) Upgraded software version to VB20 to support hardware modifications. The modified system will be branded as the MULTIX Impact E.

Please note, this document pertains to the MULTIX Impact E, which is a stationary X-ray system, and not an AI-powered diagnostic device. The submission focuses on demonstrating substantial equivalence to a predicate device, highlighting hardware modifications and software upgrades to support these changes. Therefore, the questions related to AI device performance, such as MRMC studies, ground truth establishment for AI, and training/test set sample sizes, are not applicable to this specific submission.

The acceptance criteria and performance are based on general safety and effectiveness of X-ray systems, primarily through compliance with recognized standards and non-clinical testing.

Here's a breakdown based on the provided document, addressing the applicable points:

Acceptance Criteria and Device Performance for MULTIX Impact E (X-Ray System)

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

The document does not present explicit "acceptance criteria" in a quantitative performance table for a diagnostic outcome (like sensitivity/specificity for a disease). Instead, the acceptance is based on demonstrating substantial equivalence to a predicate device through meeting regulatory standards, functional performance of components, and safety considerations. The performance is reported in terms of comparison to the predicate device for various attributes.

Here's an inferred "acceptance criteria" based on the document's structure, which is "Substantially Equivalent to Predicate Device (MULTIX Impact E VB10, K220919) and Reference Device (MULTIX Impact VA21, K213700) with no new safety risks":

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

This document describes a premarket notification (510(k)) for a conventional X-ray system, not an AI/ML-driven device. The "test set" here refers to non-clinical verification and validation testing of the device hardware and software against engineering specifications and regulatory standards.

• Sample Size: Not applicable in the context of patient imaging data for an AI algorithm. The testing involves system-level and component-level verification, functional tests, and safety tests performed on representative units of the device. The document does not specify a "sample size" of devices tested, but rather indicates that such testing was "successfully completed."
• Data Provenance: Not applicable in the context of patient data. The "data" comes from engineering tests and measurements on the device itself, not from clinical images.

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. Ground truth as typically defined for medical image analysis (e.g., diagnosis of disease) is not established for this type of device submission. The "ground truth" for the performance of an X-ray system revolves around its physical characteristics, image quality parameters, and safety compliance, which are measured and evaluated by engineers and quality assurance personnel against established technical specifications and regulatory standards.

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

Not applicable. Adjudication methods are relevant for clinical studies where expert consensus is needed to establish ground truth for image interpretation. This submission is based on engineering and manufacturing verification and validation, not clinical image interpretation.

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 device, and no MRMC study was performed or required for this type of submission.

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

Not applicable. This is not an AI algorithm.

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

The "ground truth" for this device's performance is established by engineering specifications, compliance with recognized industry standards (e.g., IEC, ISO, NEMA for X-ray systems), and adherence to manufacturing quality controls. This is demonstrated through physical measurements, electrical tests, safety circuit validation, software functionality tests, and image quality measurements, rather than clinical outcomes or diagnostic interpretations.

8. The sample size for the training set

Not applicable. This document does not describe an AI/ML device with a training set.

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

Not applicable. This document does not describe an AI/ML device with a training set.

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ImPACT is intended for use as a computer-based neurocogntive test battery to aid in the assessment and management of concussion.

ImPACT is a neurocognitive test battery that provides healthcare professionals with objective measure of neurocognitive functioning as an assessment aid and in the management of concussion in individuals ages 12-80.

ImPACT® (Immediate Post-Concussion Assessment and Cognitive Testing) is a computer-based neurocognitive test battery that allows healthcare professionals to conduct a series of tests on individuals to gather data related to the neurocognitive functioning of the test subject. This test battery measures various aspects of neurocognitive functioning including reaction time, memory, attention, spatial processing speed, and records symptoms of a test subject. ImPACT Version 4 is similar to the paper-and-pencil neuropsychological tests that have long been used by psychologists to evaluate cognition, and memory related to a wide variety of disabilities.

The device is not intended to provide a direct diagnosis or a return-to-activity recommendation, it does not directly manage or provide any treatment recommendations, and any interpretation of the results should be made only by qualified healthcare professional. The neurocognitive assessment represents only one aspect of assisting healthcare professionals in evaluating and managing individuals with cognitive function impairment related to TBI (concussion).

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Reported Device Performance

The document primarily focuses on demonstrating substantial equivalence to a predicate device (ImPACT Version 4, K202485) rather than explicitly listing quantitative acceptance criteria for each neurocognitive measure. However, the core of the performance demonstration rests on the establishment of a normative database for iPad use and test-retest reliability consistent with previous versions.

Implied Acceptance Criteria and Performance:

Detailed Study Information:

1. Sample Size and Data Provenance:

   • Test Set (Normative Data):
     • Sample Size: 1495 subjects.
     • Data Provenance: Prospectively collected from 4 different sites across the US in 2022 and 2023.
     • Type: Prospective.
   • Test Set (Test-retest Reliability):
     • Sample Size: 116 individuals, a subset of the normative sample.
     • Data Provenance: Prospectively collected from the same US sites as the normative data, in 2022 and 2023.
     • Type: Prospective.

2. Number of Experts and Qualifications for Ground Truth:

   • The document does not explicitly state the number or qualifications of experts used to establish a "ground truth" in the traditional sense of a diagnostic consensus.
   • For the normative and test-retest studies, the "ground truth" is primarily based on the self-reported health status of the participants (e.g., "not suffering from a concussion or being treated for a concussion in the past 6 months," "no known physical, neurological, behavioral or psychological impairment"). The inclusion criteria, which participants had to meet, effectively defined the "healthy" or "normal" population against which the device's performance is normed.
   • Clinical experts were part of the "cross-functional team" involved in "Walkthroughs and design reviews of mock-ups and prototypes" during software verification, but not explicitly for ground truth establishment for the clinical study data itself.

3. Adjudication Method for the Test Set:

   • The document does not describe an adjudication method for the clinical study data. The studies focused on collecting representative data from healthy individuals to establish norms and assess reliability, rather than evaluating the device's diagnostic accuracy against a separate, adjudicated clinical diagnosis.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No, an MRMC comparative effectiveness study was not performed. This device is a neurocognitive test battery, not an imaging AI diagnostic aid meant to be used by multiple readers on the same cases. The "human-in-the-loop" aspect is not about human readers interpreting AI output, but rather healthcare professionals using the device's objective measures to aid in assessment and management.

5. Standalone Performance:

   • The device ImPACT is a "computer-based neurocognitive test battery." The performance described (normative data collection, test-retest reliability) is inherently the "algorithm only" performance, where the "algorithm" refers to the test battery itself and its scoring methodology. The device provides "objective measure of neurocognitive functioning," which is its standalone output. The interpretation of these results is then done by a qualified healthcare professional.
   • So, yes, a standalone performance assessment was done in the form of normative data collection and test-retest reliability, demonstrating the device's intrinsic ability to measure cognitive function consistently in an uninjured population.

6. Type of Ground Truth Used:

   • The primary ground truth for the normative database was the self-reported clinical status and inclusion/exclusion criteria of the participants, aiming for a "healthy" or "uninjured" cognitive state. This serves as the reference for "normal" cognitive functioning.
   • For the test-retest reliability, the implicit ground truth is that the cognitive state of the healthy individuals should not have changed significantly between the two tests, allowing for an evaluation of the device's consistency.

7. Sample Size for the Training Set:

   • The document does not explicitly describe a separate "training set" for an AI or machine learning model in the context of this 510(k) submission.
   • The term "normative database" sometimes functions similarly to a reference or training set in that it establishes what "normal" looks like. In this case, the 1495 subjects for the normative database building represent the data used to define the expected range of performance.
   • The "training" of the neurocognitive test itself, being a standardized battery, is more akin to its initial development and validation stages prior to the specific modifications addressed in this 510(k). The current submission focuses on adapting an existing, validated test to a new platform (iPad) and updating its normative reference.

8. How the Ground Truth for the Training Set Was Established:

   • As mentioned above, if the normative database is considered the "training set" for defining normal performance, its "ground truth" was established by prospectively enrolling individuals who met strict inclusion criteria indicating they were healthy, English-speaking, not suffering from or recently treated for a concussion, and without other known neurological/physical impairments that would affect test performance. This was overseen by ethics boards and involved multiple US sites.

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MOBILETT Impact is a mobile device intended to visualize anatomical structures of human beings by converting an Xray pattern into a visible image. MOBILETT Impact is not intended for mammography examinations.

MOBILETT Impact is a complete X-ray imaging system on wheels. It contains a single tank high voltage generator with an X-ray tube and collimator attached to the end of a telescopic support arm connected to a swiveling column. The system is a manually driven system, with no motor support for movement. The system includes a digital image acquisition system with an image display and graphical user interface. The digital detector, Max wi-D can be stored in the built-in docking station in the system. In addition, the system can be used with detectors Max mini and Core-L. All three detectors are equipped with rechargeable batteries, which can be charged by external battery chargers. The system can perform X-Ray when it is connected to mains. Exposure can be released by a hand switch or remote control. The included system batteries only power the imaging system if it is not connected to the mains. Besides the detectors and image system, the hardware of the same as for the predicate device SEDECAL SM-V, which was cleared on 11/21/2022 with K222951.

The provided text describes the Siemens MOBILETT Impact, a mobile X-ray system. However, it does not detail acceptance criteria or a study proving the device meets specific performance metrics for image quality or diagnostic accuracy in the way an AI/CADe device would.

Instead, the document focuses on demonstrating substantial equivalence to a predicate device (SEDECAL SM-V, K222951) and a reference device (MULTIX Impact, K213700) based on regulatory compliance, technological characteristics, and safety.

The summary highlights:

• Regulatory Compliance: Adherence to various IEC, ANSI AAMI, ISO, NEMA, and FDA standards for medical electrical equipment, radiation protection, usability, risk management, software life cycle, and digital imaging.
• Technological Equivalence: Comparison of features like regulation description, product code, indications for use (similar), high voltage generator, X-ray tube, tube voltage/current, collimator, touch screen control, movement (non-motorized), US Performance Standard, and power source (all same as predicate or similar).
• Safety and Effectiveness Concerns: General statements about IFU, safety features (visual/audible warnings), error monitoring, and adherence to recognized industry practices to minimize electrical, mechanical, and radiation hazards.

Regarding the specific information requested in your prompt for an AI/CADe device:

1. Table of acceptance criteria and reported device performance: This information is not provided in the document. The document confirms regulatory compliance with various standards, but it doesn't state specific device performance metrics like sensitivity, specificity, or AUC for diagnostic tasks, as would be expected for an AI device.

2. Sample size used for the test set and data provenance: This information is not provided. A "Customer Use Test (CUT)" was performed, but no details on sample size, data type, or provenance are given.

3. Number of experts used to establish the ground truth for the test set and qualifications: This information is not provided. The CUT involved "gathering feedback on the device's usability in the clinical environment," but it doesn't describe a ground truth establishment process by experts for a diagnostic task.

4. Adjudication method for the test set: This information is not provided.

5. Multi-reader multi-case (MRMC) comparative effectiveness study: This information is not provided. The device is a mobile X-ray system, not an AI/CADe system designed to improve human reader performance.

6. Standalone performance (algorithm only without human-in-the-loop performance): This information is not provided. The device is a hardware system for X-ray imaging, not a standalone algorithm.

7. Type of ground truth used: For the "Customer Use Test (CUT)," the "ground truth" implicitly involved qualitative feedback on "system function and performance-related clinical workflow, image quality, ease of use, and overall performance and stability." This is not a quantitative ground truth for a diagnostic task like pathology or outcomes data.

8. Sample size for the training set: This information is not provided. There is no mention of a training set as this is a hardware device.

9. How the ground truth for the training set was established: This information is not provided.

In summary, the provided FDA 510(k) clearance letter and its associated summary are for a conventional mobile X-ray imaging system, not an AI/CADe product. Therefore, the detailed performance metrics and study design elements typically associated with AI/CADe device validation (e.g., sensitivity, specificity, reader studies, ground truth establishment by experts) are not present in this document. The "study" mentioned is a "Customer Use Test (CUT)" focused on usability and overall system performance in a clinical workflow, rather than a diagnostic performance study.

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SIDDHARTH-II is intended to perform image guided stereotactic radiotherapy for the lessons, tumors & conditions anywhere in the body where radiation treatment is indicated for adults and Pediatric patients.

Integrated Radiation Field Analyser (RFA) of Siddharth-II is intended to collect beam data in water under the aspect of machine QA for the following purposes:

• acceptance testing , periodic QA procedures, and/or commissioning of a radiation therapy system.

• beam data analysis according to international therapy dosimetry protocols

• acquisition, formatting and transfer of basic data to treatment planning systems

The indication for use of Immobilization & Patient Positioning devices (IMPACT) is to immobilize the patient during treatment by providing appropriate support and comfort which in turn leads to precise treatment delivery.

SIDDHARTH-II, the subject device remains same as the most recently cleared predicate device SIDDHARTH-II (K210894). In addition, with the features and specifications of existing 510k cleared equipment, the Radiation Field analyzer (RFA) has been permanently integrated with gantry of Siddharth-II to perform beam data analysis as per international therapy dosimetry protocols.

The integrated RFA is retracted and accommodated within the gantry of the radiation therapy equipment after performing data dosimetry operations. This provides an integrated solution for validating the performance of a radiation therapy equipment using an RFA which is in-built with the radiation therapy equipment that is primarily employed for providing radiation treatments to patients.

The following functionalities can be achieved by using this RFA integrated with Siddharth-II.

• . Acceptance testing, Commissioning and Periodic Quality Assurance Measurements for radiation beams emitted by radiation therapy equipment SIDDHARTH-II
• . Beam data analysis according to international therapy dosimetry protocols
• . Collection of beam data for Treatment Planning System

The RFA setup consists of RFA electromechanical arm for 3-dimensional movement of radiation detector. Ion Chamber (Radiation Detector), Built-in Electrometer, RFA water tank and Water Reservoir. The design control procedures applied to the development of the SIDDHARTH-II and its modifications include requirements reviews, risk analysis, and verification and validation testing. The results of verification and validation activities demonstrate that the acceptance criteria have been met.

All other features and technological characteristics of the SIDDHARTH-II remains as cleared by K210894.

The provided text is a 510(k) summary for the Panacea Medical Technologies Pvt Ltd. SIDDHARTH-II device, specifically addressing the integration of a Radiation Field Analyzer (RFA). It focuses on demonstrating substantial equivalence to a previously cleared predicate device (SIDDHARTH-II, K210894) rather than presenting a detailed study proving the device meets specific acceptance criteria in the context of, for example, a diagnostic performance study.

The document states that "The results of verification and validation activities demonstrate that the acceptance criteria have been met," but it does not explicitly list those criteria in a table format, nor does it provide a detailed study report that would typically include information on sample size, expert adjudication, or MRMC studies for diagnostic performance.

Based on the provided text, here's what can be extracted and what information is not available regarding explicit acceptance criteria and a study proving performance in a diagnostic or clinical efficacy sense:

The acceptance criteria and performance are discussed in the context of engineering and regulatory compliance verification and validation for the addition of the RFA feature, rather than a clinical performance study with human subjects or readers.

Acceptance Criteria and Reported Device Performance (as inferred from the document)

Since there isn't a table of diagnostic performance acceptance criteria, I will infer the performance criteria from the "Performance Data" section (VII) which focuses on non-clinical testing and regulatory compliance.

Study Details (Based on the Provided Text)

Given that this is a 510(k) for an integrated hardware/software component within a radiation therapy system, the "study" described is a non-clinical verification and validation (V&V) of the RFA component's functionality and safety, and regulatory compliance, rather than a clinical trial or performance study of a diagnostic AI.

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

   • The document mentions "non-clinical performance testing for RFA feature" involving "radiotherapy dose measurements i.e. with step by step measurements and also measurement accuracy with respect to radiation detector, positioning, reproducibility and mechanical alignment."
   • Sample Size: Not specified in terms of number of cases, but rather as test procedures on the device components (e.g., "step by step measurements"). This likely refers to quantitative measurements conducted during engineering tests, not a patient or image dataset.
   • Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). The testing would have been conducted in a lab or manufacturing environment.

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

   • Not applicable / Not specified. This type of information is relevant for studies involving human interpretation or clinical data (e.g., radiologists for ground truth). The testing described is hardware and software functional and safety testing, evaluated against engineering specifications and international standards (like IEC 60731:2016 for dose measurements).

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

   • Not applicable. Adjudication is typically for clinical data consensus or discrepancies among human readers. For non-clinical device testing, the methods involve direct measurement and comparison to predefined engineering specifications and regulatory standards.

4. 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. This type of study is not mentioned or implied because the device is a radiation therapy system with an integrated QA tool (RFA), not a diagnostic AI system intended to assist human readers in image interpretation.

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

   • Yes, in essence, for the RFA component's function. The "non-clinical performance testing for RFA feature" describes evaluating the RFA's ability to perform "radiotherapy dose measurements," "measurement accuracy with respect to radiation detector, positioning, reproducibility and mechanical alignment." This would be the RFA's function operating standalone (i.e., collecting data and measuring) outside of human-in-the-loop interpretation.

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

   • Engineering specifications and international standards. For the RFA's performance, the ground truth is against the expected physical properties of radiation beams and the accuracy requirements defined by standards like IEC 60731:2016. For software, ground truth is conformity to functional requirements and safety standards (e.g., IEC 62304).

7. The sample size for the training set:

   • Not applicable / Not specified. This device is not an AI/ML system that requires a "training set" in the sense of a dataset for machine learning. The "training" for the device development would be the engineering design and programming process.

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

   • Not applicable / Not specified. (As above, no ML training set.)

Summary of what the document describes as "proof":

The document primarily relies on non-clinical verification and validation testing of the RFA module and its integration, along with demonstrating conformance to various international consensus standards (e.g., IEC 60601-1, IEC 60731, IEC 62304, ISO 14971) to establish safety, effectiveness, and substantial equivalence to the previously cleared predicate device. It explicitly states that "clinical testing was not necessary to support substantial equivalence data" (Page 11).

Therefore, the "proof" is based on adherence to predefined engineering specifications, the results of hardware and software functional tests, electrical safety, EMC tests, and compliance with the relevant medical device standards.

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DENTCA Base Resin is a light-curable resin indicated for the fabrication and repair of removable denture bases in dental laboratories, including full and partial dentures as well as immediate dentures, and baseplates. DENTCA Base Resin can also be used for the fabrication of try-in dentures for the evaluation before fabricating the final dentures. Fabrication of these prostheses with DENTCA Base Resin requires a digital denture file, additive printer, and curing light equipment. DENTCA Base Resin can be utilized as an aid in bonding the denture teeth onto denture base.

DENTCA Base Resin is a light-curable resin intended to fabricate removable dentures in a CAD/CAM system using an additive printing process. This material is used as an alternative to traditional heat cured and auto polymerizing denture base resins and is available in multiple shades. DENTCA Base Resin can also be utilized to repair the printed denture and to bond printed teeth onto denture base.

The provided text does not contain information about an AI/ML powered device, but rather a dental resin material. Therefore, I cannot extract the requested information regarding acceptance criteria and a study proving the device meets these criteria for an AI/ML device.

The document is a 510(k) premarket notification for a dental resin called "DENTCA Base Resin," which is a light-curable resin for fabricating and repairing removable denture bases. The "Performance Data" section discusses:

• Biocompatibility Testing: Conducted according to ISO 7405:2018 and FDA Guidance for medical devices in dentistry.
• Software Verification and Validation Testing: For the additive printing (3D printer) operation software, in accordance with FDA guidance for "Off-The-Shelf Software Use in Medical Devices" and "Technical Considerations for Additive Manufactured Medical Devices."
• Performance Bench Test: Conducted according to ISO 20795-1:2013, which specifies requirements for denture base polymers.

The table 5.2 "Comparison of Performance Bench Test" shows the acceptance criteria (specifications of Type 4 resin) and the reported performance of the subject device (DENTCA Base Hi-Impact and Premium), the predicate device (Halley Resin), and a reference device (DENTCA Denture Base II). All listed devices "Meet the consensus STD" or are "Not Detectable" for Residual Methylmethacrylate, indicating they meet the specified physical properties.

This document describes the testing for a physical material, not an AI/ML algorithm. Therefore, the requested details about multi-reader multi-case studies, ground truth establishment for AI, sample sizes for training sets, or expert qualifications are not applicable and not present in the provided text.

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The SprintRay High Impact Denture Base resin is a light-curable polymerizable resin intended to be used for the fabrication and repair, of full and partial removable dentures and baseplates. The material is an alternative to traditional denture base material.

Fabrication of dental prosthetics with High Impact Denture Base resin requires computer-aided design and manufacturing system that includes the following components not part of the device: oral casting impression, digital denture base file created in an optical impression system, 3D printer, and curing light equipment.

The High Impact Denture Base consists of a curable dental acrylate resin that is designed to be used in conjunction with a scanned 3D image of a patient's teeth, and 3D printer assembly, to locally manufacture out a dental appliance in dental offices based on the clinician's judgment of patient need.

High Impact Denture Base Resin is intended exclusively for professional dental work. SprintRay High Impact Denture Base Resin is offered in following shades/colors:

• Light Pink .
• Original Pink ●
• . Light Meharry
• Original Meharry .
• . Dark Meharry

High Impact Denture Base is designed to meet appropriate ISO standards for flexibility, sorption, and solubility to withstand prolonged use in the oral cavity. It is delivered non-sterile, and instructions are provided on cleaning the material prior to providing it to a patient. Curing is performed with a UV lamp. The appliance is then cleaned, trimmed, and verified to fit in the dental office before the patient leaves.

The provided text describes the regulatory clearance for a dental resin, not an AI/ML powered device. Therefore, it does not contain the information requested in the prompt regarding acceptance criteria and a study that proves the device meets those criteria for an AI/ML device.

However, based on the information provided for the SprintRay High Impact Denture Base, I can extract information related to its performance and equivalence to a predicate device. This information pertains to the physical and biocompatibility characteristics of the resin, not AI/ML performance.

Here's a summary of the performance data and equivalence to predicate devices, as presented in the document:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly derived from the industry consensus standard ISO 20795-1 and the biocompatibility testing standards. The reported device performance indicates that the SprintRay High Impact Denture Base functioned as intended and the outcomes were as expected against these standards.

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

• The document does not explicitly state sample sizes for each specific test (e.g., number of samples for flexural strength). It broadly states that "High Impact Denture Base was tested for conformity with the industry consensus standard ISO 20795-1." These standards typically specify sample sizes for their respective tests.
• Data provenance is not provided (e.g., country of origin, retrospective or prospective). The tests are laboratory-based bench and biocompatibility tests, not clinical studies involving patient data.

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

• Not applicable. The "ground truth" for this device is based on established scientific and engineering standards (ISO standards) and laboratory test results, not expert consensus on medical images or clinical outcomes.

4. Adjudication method for the test set:

• Not applicable. This concept is relevant for studies involving human interpretation or clinical data, which is not the case here.

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/ML powered device.

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

• Not applicable. This is not an AI/ML powered device.

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

• The "ground truth" is defined by the performance specifications and methodologies outlined in recognized international standards for dental materials (e.g., ISO 20795-1 for physical properties, ISO 10993 for biocompatibility).

8. The sample size for the training set:

• Not applicable. This is not an AI/ML powered device, so there is no "training set."

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

• Not applicable. As above, there is no "training set" for this type of device.

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MULTIX Impact E is a radiographic system used in hospitals, clinics, and medical practices. MULTIX Impact E enables radiographic exposures of the whole body including: skull, chest, abdomen, and extremities and may be used on pediatric, adult and obese patients. Exposures may be taken with the patient sitting, standing, or in the prone position. MULTIX Impact E uses digital detectors for generating diagnostic images by converting X- rays into image signals. MULTIX Impact E is also designed to be used with conventional film/screen or Computed Radiography (CR) cassettes. MULTIX Impact E is not intended for mammography.

The MULTIX Impact E Radiography X-ray system is a modular system of x-ray components (floor-mounted x-ray tube, bucky wall stand, bucky table, x-ray generator, portable wireless detector) based on the predicate device, the MULTIX Impact (K203345). The following modifications have been made to the predicate device:

• A new X-ray Tube Assembly
• A new Collimator
• A new Generator
• Fixed patient table
• Tube-side control module (TCM)
• Upgraded software version to VB10 to support hardware modifications
  The modified system will be branded as the MULTIX Impact E.

This document describes modifications to an existing X-ray system, the MULTIX Impact E, and asserts its substantial equivalence to a predicate device, the MULTIX Impact (K203345).

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

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria here are implicitly equivalence in safety and effectiveness to the predicate device, despite modifications. The performance is assessed through verification and validation testing of the modified components to ensure they do not negatively impact safety and effectiveness and perform as intended.

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 extensively refers to "verification and validation testing" without specifying a distinct "test set" in terms of patient data or image data. The testing appears to be focused on hardware and software performance/functionality testing of the new components and the integrated system. There are no mentions of patient images or clinical data as a test set.

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

Not applicable. The provided document does not describe a clinical study involving experts establishing ground truth for a diagnostic test set. The testing described is technical verification and validation of the device's components and system functionality.

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

Not applicable. There is no mention of a diagnostic 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

Not applicable. This device is an X-ray system, not an AI-assisted diagnostic tool.

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

Not applicable. This is a hardware system, not a standalone algorithm.

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

Not applicable in the context of a diagnostic test set. For the verification and validation described, the "ground truth" would be the engineering specifications, performance standards (e.g., IEC, ANSI, NEMA), and safety requirements that the new/modified components and the integrated system are assessed against.

8. The sample size for the training set

Not applicable. This is not a machine learning model requiring a training set. The software mentioned is system control software, validated through standard software development lifecycle processes.

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

Not applicable.

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1. Noninflammatory degenerative joint disease including osteoarthritis and avascular necrosis
2. Rheumatoid arthritis
3. Correction of functional deformity
4. Treatment of non-union, femoral neck fracture, and trochanteric fractures of the proximal femur with head involvement, unmanageable using other techniques
5. Revision of previously failed total hip arthroplasty

The subject devices, Biomet Answer/Impact/Integral Distal Centralizer/Centering Sleeves, are cylindrical components designed to slide onto the distal end of a cemented femoral stem prior to insertion into the femoral canal.

This submission proposes a new bioburden reduction manufacturing process that includes a new contact material for the Biomet Answer/Impact/Integral Distal Centralizer/Centering Sleeve.

The provided document is a 510(k) summary for the Biomet Answer/Impact/Integral Distal Centralizer/Centering Sleeve. This document focuses on demonstrating substantial equivalence to a predicate device, rather than proving that a device meets specific acceptance criteria through a study with performance metrics in the way one might expect for a diagnostic AI or imaging device.

Therefore, the requested information, particularly regarding acceptance criteria performance, sample sizes for test/training sets, expert consensus, adjudication methods, MRMC studies, standalone performance, and ground truth types and establishment, is not applicable to this type of submission. This submission is for a physical medical device (a hip prosthesis component) and its substantial equivalence is based on technological characteristics and biocompatibility.

Here's a breakdown of what can be extracted and what is not applicable:

1. Table of Acceptance Criteria and Reported Device Performance

• Acceptance Criteria: For this 510(k) submission, the "acceptance criteria" are not reported as specific performance metrics (e.g., sensitivity, specificity). Instead, they are implied by demonstrating substantial equivalence to a predicate device (K193546 Distal Centralizers). The criteria revolve around showing that the new device shares similar technological characteristics (intended use, indications for use, materials, design features, sterilization) and biocompatibility with the predicate.
• Reported Device Performance:
  • Non-Clinical Tests: A biocompatibility assessment was performed in accordance with ISO 10993-1. The data confirmed the biocompatibility of the candidate manufacturing process flow for the device in long-term contact with patient bone and tissues. No specific numerical performance metrics are provided, as this is a qualitative assessment of biocompatibility.
  • Clinical Tests: None were provided for this submission.

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

• Not Applicable. This submission does not involve a "test set" in the context of evaluating an algorithm or AI. The biocompatibility assessment is a laboratory test on device materials, not a study on a human subject population with a test set of data.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Not Applicable. Ground truth in the context of expert consensus for diagnostics is not relevant here. Biocompatibility assessment relies on standardized testing procedures and interpretation by qualified laboratory personnel.

4. Adjudication Method for the Test Set

• Not Applicable. No test set or expert adjudication is involved.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• Not Applicable. This is a physical device, not an AI or diagnostic tool that would involve human readers.

6. If a Standalone Performance (Algorithm Only) Was Done

• Not Applicable. This is a physical device, not an algorithm.

7. The Type of Ground Truth Used

• Not Applicable. For the biocompatibility assessment, "ground truth" would be established by the results of the ISO 10993-1 tests themselves, interpreted against the standard's requirements. This is not "expert consensus," "pathology," or "outcomes data" in the typical sense applied to diagnostic performance.

8. The Sample Size for the Training Set

• Not Applicable. No training set is involved as this is not an AI/algorithm submission.

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

• Not Applicable. No training set is involved.

In summary, the provided document is a regulatory submission for a physical medical device, not a diagnostic or AI product. Therefore, most of the requested information about "acceptance criteria" and "studies" related to performance metrics, test/training sets, and expert evaluations is not applicable to this specific type of device and submission.

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