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Green X (Model : PHT-75CHS) is intended to produce panoramic, cephalometric or 3D digital x-ray images. It provides diagnostic details of the dento-maxillofacial, ENT, sinus and TMJ for adult and pediatric patients. The system also utilizes carpal images for orthodontic treatment The device is to be operated by healthcare professionals.

Green X (Model : PHT-75CHS) is an advanced 4-in-1 digital X-ray imaging system that incorporates PANO, CEPH(optional), CBCT and MODEL Scan imaging capabilities into a single system. Green X (Model : PHT-75CHS), a digital radiographic imaging system, acquires and processes multi-FOV diagnostic images for dentists. Designed explicitly for dental radiography. Green X is a complete digital X-ray system equipped with imaging viewers, an X-ray generator and a dedicated SSXI detector.

The digital CBCT system is based on a CMOS digital X-ray detector. The CMOS CT detector is used to capture 3D radiographic images of the head, neck, oral surgery, implant and orthodontic treatment. Green X (Model : PHT-75CHS) can also acquire 2D diagnostic image data in conventional PANO and CEPH modes.

The provided text describes the Green X (Model: PHT-75CHS) dental X-ray imaging system and its substantial equivalence to a predicate device. However, it does not contain detailed information about a study proving the device meets acceptance criteria for an AI feature with specific performance metrics such as sensitivity, specificity, or AUC calculated on a test set, nor does it describe an MRMC study.

The document discusses improvements and additions to the device, including "Endo mode," "Double Scan function," "Insight PAN 2.0," and the availability of FDK and CS reconstruction algorithms. It mentions some quantitative evaluations for these features, primarily focusing on image quality metrics and stitching accuracy, but not clinical performance metrics typical for AI algorithms (e.g., detection of specific pathologies).

Based on the provided text, here's an attempt to answer the questions, highlighting where information is missing for AI-specific criteria:

Acceptance Criteria and Device Performance (Based on available information):

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

• Test Set Sample Size: Not explicitly stated for any of the described evaluations (Endo mode, Double Scan, Insight PAN 2.0, FDK/CS algorithms, or general image quality). The evaluations seem to be based on a limited number of clinical images/test phantoms rather than large-scale patient datasets.
• Data Provenance: Not specified. It indicates "clinical images generated in Endo mode" and "3D clinical consideration" for Double Scan, and "clinical evaluation" for Insight PAN 2.0. There is no mention of country of origin or whether the data was retrospective or prospective.

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

• Endo Mode: "A US licensed dentist" evaluated the clinical images. The number of dentists is not specified (could be one or multiple). No specific years of experience or sub-specialty are mentioned beyond "US licensed dentist."
• Double Scan Function: "3D clinical consideration and evaluation" was performed. No specific number or qualifications of experts are mentioned.
• Insight PAN 2.0: "Clinical evaluation was performed." No specific number or qualifications of experts are mentioned.
• Other evaluations: The document refers to "satisfying standard criteria" (IEC 61223-3-5, IEC 61223-3-4) and measurements on phantoms, which typically do not involve expert ground truth in the same way clinical AI performance studies do.

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

• None specified. The evaluations appear to involve a single "US licensed dentist" for Endo mode, and "clinical evaluation" without detailing the adjudication process for other features. This is not a typical AI performance study setup where multiple readers independently review and a consensus process might be employed.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• No, an MRMC comparative effectiveness study was not done. The document describes performance evaluations of the device's features (e.g., image quality, stitching accuracy, clinical utility) but not a comparative study where human readers' performance with and without AI assistance is measured. Thus, no effect size for human improvement is reported.

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

• This is not explicitly an AI-only device where the "algorithm" performs diagnostic tasks autonomously. The features described (Endo mode, Double Scan, Insight PAN 2.0, reconstruction algorithms) are functionalities of an X-ray imaging system that produce images for human interpretation. The "evaluations" described are largely for image quality metrics and technical performance, not for algorithmic detection or classification of disease. Therefore, a standalone performance study in the context of an AI diagnostic aid is not applicable in the way it might be for, say, an algorithm that flags lesions.

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

• Primary Ground Truth:
  • Phantom Measurements: For quantitative image quality metrics (Noise, Contrast, CNR, MTF, line pair resolution, low contrast resolution) according to IEC standards.
  • Calculated Metrics: For stitching accuracy (SSIM, RMSE).
  • Clinical Evaluation: For confirming "diagnostic quality" (Endo mode) and "clinical efficacy" (Double Scan, Insight PAN 2.0), which relies on expert judgement of the generated images, rather than independent pathology or outcomes data. It functions more as a qualitative assessment of the image's utility.

7. The sample size for the training set:

• Not applicable/Not provided. The document describes a traditional X-ray imaging system with new features, some of which might involve algorithms (e.g., stitching algorithm, reconstruction algorithms) but doesn't explicitly state that these features are "AI" in the sense of requiring a large, labeled training dataset of images to learn to perform a diagnostic task. If these features involve machine learning (e.g., for image enhancement or reconstruction), the training data for those specific algorithms is not detailed.

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

• Not applicable/Not provided for the reasons stated above.

Summary of the Device and Evaluation Context:

The FDA 510(k) clearance process for the Green X (Model: PHT-75CHS) system focuses on demonstrating substantial equivalence to a predicate device. The performance evaluations described are primarily related to the physical and technical performance of the X-ray imaging system and its new functionalities (Endo mode, Double Scan, Insight PAN 2.0, FDK/CS algorithms). These evaluations confirm that the device produces images of sufficient quality, that spatial and contrast resolutions meet standards, and that new features like image stitching are accurate.

Crucially, this is not a submission for an AI/ML-driven diagnostic medical device that would typically involve large, diverse test sets, multiple expert readers, detailed ground truth establishment (like pathology or clinical outcomes), and comparative effectiveness studies to measure how much AI improves human reader performance for a specific diagnostic task (e.g., detecting a particular disease from the image). The "performance data" provided relates to the image acquisition capabilities and processing algorithms of the imaging system itself, which are fundamental to any diagnostic interpretation by a human professional rather than an algorithmic diagnosis or detection.

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PHT-30LFO is a computed tomography x-ray system intended to produce panoramic, cephalometric or cross-sectional images of the oral anatomy on a real time basis by computer reconstruction of x-ray image data from the same axial plane taken at different angles. It provides diagnostic details of the anatomic structures by acquiring 360 rotational image sequences of oral and maxillofacial area for a precise treatment planning in adult and pediatric dentistry . The device is operated and used by physicians, dentists, and x-ray technicians.

PHT-30LFO, a dental radiographic imaging system, consists of three image acquisition modes; panoramic, cephalometric and cone beam computed tomography. Specifically designed for dental radiography of the teeth or jaws, PHT-30LFO is a complete dental X-ray system equipped with x-fay tube, generator and dedicated SSXI detector for dental panoramic, cephalometric and cone beam computed tomographic radiography. The dental CBCT system is based on CMOS digital X-ray detector. CMOS CT detector is used to capture radiographic diagnostic images of oral anatomy in 3D for dental treatment such as oral surgery or implant. The device can also be operated as the panoramic and cephalometric dental x-ray system based on CMOS X-ray detector.

The provided text is a 510(k) Premarket Notification for the Vatech Co., Ltd. PaX-i3D Smart (PHT-30LFO) dental computed tomography x-ray system. The crucial aspect of this document for your request is demonstrating substantial equivalence to a predicate device (PHT-6500), rather than proving the device meets acceptance criteria through a rigorous clinical study of its diagnostic performance against a ground truth.

The document focuses on comparing the technological characteristics and performance of the new device to a previously cleared predicate device. This is a common approach for 510(k) submissions, where direct clinical superiority or a groundbreaking new diagnostic capability is not typically being claimed or required. Instead, the goal is to show that the new device is as safe and effective as a legally marketed device.

Therefore, the information available does not contain the level of detail you would typically find in a clinical study report that directly proves a device meets specific diagnostic acceptance criteria (e.g., sensitivity, specificity, AUC values against a clinical ground truth). There's no mention of a traditional diagnostic performance study with a test set of patient cases, expert readers, or ROC analysis.

However, I can extract the information that is present, framed in the context of a 510(k) submission for substantial equivalence.

Here's an attempt to answer your questions based on the provided text, acknowledging its limitations for traditional diagnostic performance evaluation:

Acceptance Criteria and Device Performance (in the context of Substantial Equivalence)

The "acceptance criteria" in this 510(k) submission are not expressed as specific diagnostic performance metrics (like sensitivity/specificity for a disease). Instead, the acceptance criteria are met by demonstrating that the new device (PHT-30LFO) is substantially equivalent to the predicate device (PHT-6500) in terms of its indications for use, fundamental technological characteristics, safety, and imaging performance.

The study proves the device meets these "acceptance criteria" by showing:

• Similar Indications for Use: Both devices are intended to produce panoramic, cephalometric, or cross-sectional images of the oral anatomy for precise treatment planning in adult and pediatric dentistry.
• Similar Technological Characteristics: The core components, x-ray parameters, and software functionalities are largely similar or improved without adverse impact.
• Non-Clinical Performance Equivalence/Improvement: Bench testing showed the new detectors performed similarly or better than the predicate's detectors in objective image quality metrics.
• Safety and Performance Standard Compliance: The device meets relevant international and FDA standards for medical electrical equipment and radiation safety.
• Software Verification and Validation: The software meets FDA guidance for medical device software.

Table of Acceptance Criteria (as implied by 510(k) substantial equivalence) and Reported Device Performance:

• Produce panoramic, cephalometric or cross-sectional images of oral anatomy.
• Provide diagnostic details for precise treatment planning in adult and pediatric dentistry. | Met: "PHT-30LFO is a computed tomography x-ray system intended to produce panoramic, cephalometric or cross-sectional images of the oral anatomy on a real time basis by computer reconstruction of x-ray image data... It provides diagnostic details of the anatomic structures by acquiring 360° rotational image sequences of oral and maxillofacial area for a precise treatment planning in adult and pediatric dentistry." (Identical to predicate) |
  | 2. Technological Characteristics Equivalence
• Similar core system components and functionalities. | Met: The proposed device and predicate device "have the same indications for use and demonstrate the similar technical characteristics." Key components (X-ray tube, generator, detectors, PC system, imaging software) are outlined, showing largely comparable specifications (e.g., focal spot size, slice width, filtration, software names). Differences (new detectors, free input voltage, different CBCT reconstruction algorithm) were addressed and deemed not to negatively impact performance. |
  | 3. Non-Clinical Image Quality Performance Equivalence/Non-Inferiority
• Image quality metrics (MTF, DQE, NPS) comparable or better. | Met: "Based on Non-Clinical Test results of the new detector Xmaru1404CF... the new Xmaru1404CF sensor has performed similarly or better than the predicate device in terms of the overall DQE performance... The new sensor also exhibits consistently better performances in terms of MTF and NPS." For the other new detector (Xmaru2301CF-O), "test results demonstrated the same characteristics in terms of MTF, NPS, and DQE performance compared to Xmaru2301 CF detector of the predicate device." Concluded: "the diagnostic image quality of the new sensor is equal or better than those of the predicate device." |
  | 4. Safety and Performance Compliance
• Adherence to relevant electrical, mechanical, environmental safety, and radiation control standards. | Met: "Electrical, mechanical, environmental safety and performance testing according to standard IEC 60601-1 (Ed. 3, 2005), IEC 60601-1-3 (Ed. 2, 2008), IEC 60601-2-63 (Ed. 1, 2012) were performed, and EMC testing were conducted in accordance with standard IEC 60601-1-2." Also meets NEMA PS 3.1-3.18 (DICOM), and EPRC standards (21 CFR 1020.30, 31, 33). "The risks of different voltage requirement of the new device is evaluated and mitigated in electrical safety test." |
  | 5. Software Verification & Validation (V&V)
• Software is safe and performs as intended. | Met: "Software verification and validation tests were conducted and documented as recommended by FDA's Guidance... The software for this device was considered as a 'moderate' level of concern... The predicate device and the proposed device utilize the identical image viewing software." "The functionality and safety of the new iterative reconstruction algorithm for the CT capture mode were assured by the company procedures that conform to accepted practices." |
  | 6. Clinical Images Comparative Assessment
• Clinical images from the new device are comparable to the predicate. | Met: "clinical images generated from the subject device were compared to a group of images taken from the predicate devices to provide further evidence... to show that the complete system works as intended and to establish substantial equivalence based on the modifications to the device." (No specific quantitative metrics or reader study details are provided, implying a qualitative assessment of overall image appearance). |

Here are answers to your specific questions, largely reiterating the limitations for a "diagnostic performance study" as typically understood today:

1. A table of acceptance criteria and the reported device performance:
   (See table above) – The "acceptance criteria" are implied by the requirements for substantial equivalence in a 510(k) submission, focusing on safety, effectiveness, and comparable performance to a predicate device, rather than specific diagnostic accuracy metrics.

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

   • Test Set Sample Size: Not specified for "clinical images." The document mentions "clinical images generated from the subject device were compared to a group of images taken from the predicate devices." No numerical sample size is given for this comparison. For non-clinical tests, it implies the new detectors (Xmaru1404CF, Xmaru2301CF-O) were tested, which would be singular units of the device's components.
   • Data Provenance: Not explicitly stated (e.g., country of origin). The submitter (Vatech Co., Ltd.) is based in the Republic of Korea. It is a retrospective comparison of images rather than a prospective trial with new patients.

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

   • No explicit ground truth establishment process involving multiple experts is described for a diagnostic performance test set. The "clinical images" comparison was made by "qualified individuals employed by the sponsor" for "further evidence" of substantial equivalence. No number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience") are mentioned. This is typical for a 510(k) where diagnostic performance isn't being quantitatively proven against a clinical ground truth.

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

   • No adjudication method is described, as there was no formal reader study or establishment of ground truth by multiple readers for diagnostic purposes. The comparison of clinical images appears to be a qualitative assessment by the sponsor's "qualified individuals."

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, a multi-reader multi-case (MRMC) study was not conducted. This document describes a new imaging device comparing its technical performance (image quality, safety) to a predicate, not an AI-powered diagnostic tool requiring a human-in-the-loop performance study.

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

   • This is a medical imaging device (CT scanner), not a standalone diagnostic algorithm. The "algorithm" mentioned is "CBCT reconstruction algorithm," which is an integral part of the image production, not a separate diagnostic algorithm. The performance of this algorithm is evaluated as part of the overall image quality metrics (MTF, DQE, NPS) and verified through V&V.

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

   • For the "clinical images" comparison, no formal "ground truth" (like histopathology or long-term outcomes) is mentioned. The comparison was likely based on visual assessment of image quality and anatomical representation being "as intended" and "similar to the predicate device." For non-clinical performance, the "ground truth" is the physical properties of test phantoms used to measure MTF, DQE, and NPS.

8. The sample size for the training set:

   • Not applicable/Not specified. This document is about a hardware device with inherent image reconstruction algorithms, not a machine learning model that requires a distinct "training set" of images in the typical sense.

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

   • Not applicable, as there is no mention of a machine learning "training set" in the context of this 510(k) submission for a CT imaging system.

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PaX-Flex3D is a computed tomography x-ray system intended to produce panoramic, cephalometric or cross-sectional images of the oral anatomy on a real time basis by computer reconstruction of x-ray image data from the same axial plane taken at different angles. It provides diagnostic details of the anatomic structures by acquiring 360° rotational image sequences of oral and maxillofacial area for a precise treatment planning in adult and pediatric dentistry. The device is operated and used by physicians, dentists, and x-ray technicians.

PaX-Flex3D is a diagnostic imaging system which consists of multiple image acquisition modes; panorama, cephalometric, and computed tomography for implantation. Specifically designed for dental radiography of the teeth or jaws, PaX-Flex3D is equipped with extra-oral x-ray detector, panoramic radiography with an extra-oral x-ray tube, cephalometric radiography and computed tomographic radiography. The computed tomography is a system based on CMOS digital X-ray detector. CMOS CT detector is used to capture scanned images in 3D for obtaining diagnostic information of teeth for oral surgery or other treatments. The device can also be operated as the panoramic and cephalometirc dental x-ray system based on CMOS X-ray detector.

The provided 510(k) summary for PaX-Flex3D does not contain specific acceptance criteria or a dedicated study proving performance against such criteria. The submission primarily focuses on demonstrating substantial equivalence to a predicate device (PaX-Reve3D) based on shared intended use and similar technical characteristics.

Here's a breakdown of the information available and what is not present:

1. Table of Acceptance Criteria and Reported Device Performance

No explicit acceptance criteria (e.g., minimum resolution, specific accuracy metrics) or a table comparing device performance against such criteria is provided in the document. The "Performance Specification" section compares the technical specifications of the proposed device to the predicate device.

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

The document states that "Non-clinical & Clinical considerations according to FDA Guidance 'Guidance for the Submissions of 510(k)'s for Solid State X-ray Imaging Devices' was performed." However, it does not provide details on:

• The specific sample size (number of images, cases, or patients) used for any such testing.
• The data provenance (country of origin, retrospective or prospective nature).

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

This information is not provided in the document.

4. Adjudication method for the test set

This information is not provided in the document.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and its effect size

An MRMC study is not mentioned in the document. The submission focuses on device characteristics and regulatory compliance, not comparative effectiveness with human readers.

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

This device is an X-ray imaging system, not an AI or algorithm-only device. Therefore, a standalone algorithm performance study (in the context of AI) would not be applicable or expected for this type of submission. The device's performance is inherently tied to image acquisition and reconstruction.

7. The type of ground truth used

As specific performance studies are not detailed, the type of ground truth used is not specified. The primary "ground truth" implied in this 510(k) is the established performance of the predicate device.

8. The sample size for the training set

This device is an imaging system, not an AI model requiring a "training set" in the machine learning sense. Therefore, this information is not applicable and not provided.

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

As there is no "training set" for an AI model, this information is not applicable and not provided.

Summary of the Study Proving Device Meets Acceptance Criteria (as described in the document):

The submission primarily relies on design and performance specifications comparison to a legally marketed predicate device (PaX-Reve3D) and adherence to recognized electrical, mechanical, environmental safety, and performance standards, as well as FDA guidance for solid-state X-ray imaging devices.

The key "study" mentioned is the execution of "Non-clinical & Clinical considerations according to FDA Guidance 'Guidance for the Submissions of 510(k)'s for Solid State X-ray Imaging Devices'." However, the details of these considerations, including specific test methodologies, results, or data sets, are not enumerated in this summary. The conclusion states that "All test results were satisfactory," implying that the device met internal or regulatory benchmarks relevant to its classification and intended use, aligning with the predicate device's performance and safety profiles. The mechanism for "proving" acceptance is largely through demonstrating substantial equivalence to an already cleared device and compliance with relevant safety standards.

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PaX-Reve3D Plus is a computed tomography x-ray system intended to produce panoramic, cephalometric or cross-sectional images of the oral and craniofacial anatomy on a real time basis by computer reconstruction of x-ray image data from the same axial plane taken at different angles. It provides diagnostic details of the anatomic structures by acquiring 360° rotational image sequences of oral and craniofacial area for a precise treatment planning in adult and pediatric care . The device is operated and used by physicians, dentists, and x-ray technicians.

PaX-Reve3D Plus is a diagnostic imaging system which consists of multiple image acquisition modes; panorama, cephalometric, and computed tomography for implantation. Specifically designed for dental radiography of the oral and craniofacial anatomy, PaX-Reve3D Plus is equipped with extra-oral x-ray detector, panoramic radiography with an extra-oral x-ray tube, cephalometric radiography and computed tomographic radiography. The computed tomography is a system based on CMOS digital X-ray detector. CMOS CT detector is used to capture scanned images in 3D for obtaining diagnostic information for craniofacial surgery or other treatments. The device can also be operated as the panoramic and cephalometire dental x-ray system based on CMOS X-ray detector.

Here's a breakdown of the acceptance criteria and the study information for the PaX-Reve3D Plus, based on the provided text:

Important Note: The provided text is a 510(k) summary for a dental computed tomography X-ray system. These summaries typically focus on demonstrating substantial equivalence to a predicate device rather than comprehensive de novo clinical studies with detailed acceptance criteria and performance metrics against a clinical ground truth as might be found for novel AI devices or efficacy claims. The "performance" discussed here primarily refers to technical specifications and comparison to the predicate, not clinical accuracy or diagnostic improvement.

Acceptance Criteria and Reported Device Performance

The provided document doesn't explicitly state "acceptance criteria" in the traditional sense of a performance study targeting specific clinical endpoints (e.g., sensitivity, specificity, AUC). Instead, it demonstrates substantial equivalence by comparing technical specifications and intended use of the proposed device (PaX-Reve3D Plus) with a legally marketed predicate device (PaX-Reve3D). The implied "acceptance criteria" is that the new device's performance characteristics are either equivalent or improved compared to the predicate, and that it meets relevant safety and performance standards.

Here's a table summarizing the comparison:

Study Details (Relevant to 510(k) Substantial Equivalence)

The provided document describes a technical comparison and safety testing rather than a formal clinical performance study as might be conducted for an AI-powered diagnostic device.

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

   • Test Set Sample Size: Not applicable in the context of a clinical test set with patient data for diagnostic evaluation. The "test set" for this submission would be the physical device itself and its components, undergoing engineering and safety testing.
   • Data Provenance: Not specified for clinical data, as this is not a study assessing diagnostic accuracy on a patient cohort. The non-clinical and performance data refer to engineering and safety bench testing, likely conducted by the manufacturer (Vatech Co., Ltd.) in Korea Republic (manufacturer's location).

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

   • Not applicable. This type of submission relies on technical specifications and adherence to international safety and performance standards for medical devices, rather than expert-derived ground truth from interpreting medical images.

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

   • Not applicable. There was no clinical ground truth established for a test set of images requiring adjudication.

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 device is a new imaging system hardware, not an AI software intended to assist human readers. Thus, no MRMC study or AI assistance effect size was reported.

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

   • Not applicable. This is a medical imaging hardware device, not a standalone algorithm. Its "performance" is inherent in its image acquisition capabilities, image quality specifications, and adherence to safety standards.

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

   • Not applicable. The "ground truth" here is compliance with established engineering standards (e.g., IEC 60601 series) and the technical specifications of the predicate device.

7. The sample size for the training set:

   • Not applicable. This device is a hardware system, not a machine learning algorithm that requires a training set.

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

   • Not applicable, as there is no training set for a machine learning algorithm.

In summary, the provided document demonstrates substantial equivalence by:

• Comparing the intended use, technical specifications, and performance characteristics of the PaX-Reve3D Plus to a predicate device (PaX-Reve3D).
• Showing that the proposed device is either equivalent or features minor improvements (e.g., larger imaging volume options, wider tube voltage range, slightly higher panoramic resolution) that do not raise new questions of safety or effectiveness.
• Confirming compliance with a comprehensive set of international electrical, mechanical, environmental safety, and performance standards (IEC 60601 series) and relevant FDA guidance.

The "study" that proves the device meets the (implied) acceptance criteria is the documentation of these technical comparisons and the results of the aforementioned safety and performance tests.

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