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The Senographe DS and Senographe Essential FFDM systems generate digital mammographic images that can be used for screening and in the diagnosis of breast cancer. The Senographe DS and Senographe Essential FFDM systems are intended to be used in the same clinical applications as traditional film-based mammographic systems.
Premium View is an image-processing algorithm, which increases the visibility of breast structures. The main advantage is to provide a single breast image, where the contrast in the fatty tissues is similar to that obtained by setting WW (window width) and WL (window level) for optimum visualization of fatty tissues, and the contrast in the fibro-glandular tissue is similar to that obtained by setting WW and WL for optimal visualization of fibro-glandular tissues.
PV i is an option that can simplify the presentation of mammographic images, improve workflow, and streamline the review process of images with very dark or bright areas by presenting the image with the WW and WL optimized for review with minimal need for the user to make adjustments for the various tissue areas. This could be especially useful with patients who have very dense breasts, or implants that currently require multiple adjustments of WW and WL to review.

The Senographe DS and Senographe Essential are both full field digital mammography systems consisting of a digital detector, a dual track x-ray tube (molybdenum / rhodium) and an x-ray generator with control. The digital detector is a flat panel of amorphous silicon on which cesium iodide is deposited to maximize the detection of x-rays. The x-ray filter is a wheel with both a molybdenum and a rhodium filter to allow various combinations of xray tube track and filter when imaging breasts of different radiological densities. The system includes a feature called Automatic Optimization of Parameters (AOP) that automatically selects the kVp, the optimal x-ray tube track and beam filtration and then terminates the exposure based on the breast density to provide consistent image quality for the user across a wide range of breast sizes and densities.
The subject of this submission will introduce a modification to a previously approved (P990066 / S015 and P990066 S020) image processing algorithm called Premium View. Premium View is an image-processing algorithm, which increases the visibility of breast structures. The main advantage is to provide a single breast image, where the contrast in the fatty tissues is similar to that obtained by setting WW (window width) and WL (window level) for optimum visualization of fatty tissues, and the contrast in the fibro-glandular tissue is similar to that obtained by setting WW and WL for optimal visualization of fibro-glandular tissues.
Premium View i, when utilized with the Senographe DS or Senographe Essential introduces a software change that applies different LUT values during the image processing prior to display of a very dense breast, or one with implants. This Premarket Notification will implement this technology on GE Healthcare's existing Full Field Digital Mammography systems as an upgrade to existing systems, or as an option to new installations.

The provided document describes a 510(k) premarket notification for the "Premium View i" (PVi) image processing option for GE Healthcare's Full Field Digital Mammography (FFDM) systems (Senographe DS and Senographe Essential). The document focuses on demonstrating substantial equivalence to predicate devices and describes the intended use and technical aspects of the PVi algorithm. However, it does not contain specific details about acceptance criteria, a formal study proving explicit device performance against those criteria, or quantitative results of such a study.

The document mentions "clinical testing to quantify the clinical acceptance of images that had been retrospectively processed with this image processing algorithm," but it does not provide the results, metrics, sample sizes, expert qualifications, or adjudication methods for this clinical testing.

Therefore, for the information requested, I can only extract what is explicitly stated or can be reasonably inferred from the provided text. Many specific details about the study design and results are missing.

Here's a summary based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state specific acceptance criteria in terms of quantitative performance metrics (e.g., sensitivity, specificity, AUC) or provide a table directly comparing these criteria to the device's reported performance.

Instead, the conclusion states: "GE Healthcare considers the Premium View i option to be as safe, as effective, and performance is substantially equivalent to the predicate device(s)." This implies that the 'acceptance criteria' were met by demonstrating substantial equivalence, which is the primary regulatory pathway for 510(k) submissions. However, no specific performance metrics are given.

The closest to "performance" stated is a qualitative description of the algorithm's effect:
"Premium View is an image-processing algorithm, which increases the visibility of breast structures. The main advantage is to provide a single breast image, where the contrast in the fatty tissues is similar to that obtained by setting WW (window width) and WL (window level) for optimum visualization of fatty tissues, and the contrast in the fibro-glandular tissue is similar to that obtained by setting WW and WL for optimal visualization of fibro-glandular tissues."

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

• Sample Size for Test Set: Not specified. The document only mentions "clinical testing to quantify the clinical acceptance of images."
• Data Provenance: Retrospective ("retrospectively processed with this image processing algorithm"). The country of origin is not specified.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The document only refers to "clinical testing" for "clinical acceptance."

4. Adjudication method for the test set:

• Adjudication Method: Not specified.

5. Multi-Reader Multi-Case (MRMC) comparative effectiveness study:

• MRMC Study Done: Not specified. The document does not mention an MRMC study or any comparison of human readers with vs. without AI assistance. The focus is on the image processing algorithm itself.

6. Standalone (algorithm only without human-in-the-loop performance) study:

• Standalone Study Done: Yes, implicitly. The "clinical testing to quantify the clinical acceptance of images that had been retrospectively processed with this image processing algorithm" suggests an evaluation of the algorithm's output (processed images) rather than a human-in-the-loop study comparing diagnostic accuracy. However, no specific performance metrics are provided.

7. Type of ground truth used:

• Type of Ground Truth: Not specified. Given the context of "clinical acceptance of images," it's likely based on radiologists' subjective evaluation of image quality or visibility of structures rather than definitive pathology or outcome data.

8. Sample size for the training set:

• Sample Size for Training Set: Not specified. The document describes the algorithm's functionality and its development processes (risk analysis, requirements reviews, design reviews, various levels of testing) but does not detail the training set size for the algorithm itself. It's possible the algorithm was developed through iterative refinement rather than a single distinct "training set" like in modern deep learning contexts.

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

• Ground Truth for Training Set: Not specified. The document outlines general quality assurance measures applied during development but does not detail how ground truth was established for "training" the image processing algorithm.

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Contrast Enhanced Spectral Mammography (CESM) is an extension of the existing indication for diagnostic mammography with the Senographe Essential or Senographe DS. The CESM application shall enable contrast enhanced breast imaging using a dual energy technique. This imaging technique can be used as an adjunct following mammography and ultrasound exams to localize a known or suspected lesion.

The Senographe DS and Senographe Essential are both full field digital mammography systems consisting of a digital detector, a dual track x-ray tube (molybdenum / rhodium) and an x-ray generator with control. The digital detector is a flat panel of amorphous silicon on which cesium iodide is deposited to maximize the detection of x-rays. The x-ray filter is a wheel with both a molybdenum and a rhodium filter to allow various combinations of x-ray tube track and filter when imaging breasts of different radiological densities. The system includes a feature called Automatic Optimization of Parameters (AOP) that automatically selects the kVp, the optimal x-ray tube track and beam filtration and then terminates the exposure based on the breast density to provide consistent image quality for the user across a wide range of breast sizes and densities.

The subject of this submission is a modification that will introduce a new imaging option based on a method of image acquisition involving a x-ray exposures at two energy levels. The two exposures will be completed at the simultaneously using a technique known as "dualenergy". This x-ray acquisition methodology has been previously cleared by GE Healthcare in K013481, although that clearance excludes mammography. This Premarket Notification will implement this technology on GE Healthcare's existing Full Field Digital Mammography systems as an upgrade to existing systems, or as an option to new installations. The dualenergy exposures will be done with a single breast compression and will be following an iodine based contrast injection of an existing approved x-ray contrast agent, using the approved rate, route of administration, and dosage of the contrast agent. The new mode of operation is referred to as Contrast Enhanced Spectral Mammography (CESM) due to the nature of taking an exposure with the x-ray spectrum optimized for general mammographic imaging and a second exposure with the x-ray spectrum optimized for the iodine based contrast image. The modification also includes the implementation of an additional x-ray beam filter. The change in x-ray exposure energy, plus the change in beam filtration allow the system to optimize the x-ray spectrum for the iodine based contrast when acquiring the second exposure of the dual-energy acquisition. This filtration change is done by rotating the filter wheel and changing the beam filter selected for the high energy exposure.

These two images are combined to allow visualization of the breast tissue in a way that is typical and familiar for mammographic imaging, while being able to visualize the x-ray contrast enhancement in the breast at the same time.

The provided text is a 510(k) Summary for a medical device called Contrast Enhanced Spectral Mammography (CESM). It describes the device, its intended use, and the regulatory review process. However, it does not contain the detailed clinical study information needed to fill out most of the requested table and answer the study-specific questions.

The document states, "The subject of this premarket submission, Contrast Enhanced Spectral Mammography, included clinical testing to quantify the effect of dual energy acquisition and CESM's contribution when compared to standard FFDM mammography and ultrasound breast imaging." However, it does not report the results, acceptance criteria, sample sizes, ground truth methodology, or expert qualifications from that clinical testing.

Therefore, I can only provide limited information based on the text.

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size for Test Set: Not specified in the provided text. The text only mentions "clinical testing."
• Data Provenance (e.g., country of origin of the data, retrospective or prospective): Not specified in the provided text.

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

• Not specified in the provided text.

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

• Not specified in the provided text.

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:

• An MRMC study is not explicitly mentioned, nor is any AI involved in this device description. The device is described as "Contrast Enhanced Spectral Mammography," a hardware and software modification for image acquisition and processing following an iodine-based contrast injection. It is not an AI-assisted interpretation tool. Therefore, an effect size of human readers with vs. without AI assistance is not applicable based on this document.

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

• This is not an AI algorithm in the context of standalone performance for interpretation. It's an imaging technique. The "clinical testing" mentioned would likely involve evaluation of the images produced by the CESM system, potentially by human readers, but not in a standalone algorithm-only context for diagnosis.

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

• Not specified in the provided text.

8. The sample size for the training set:

• This document describes a modification to an existing mammography system and its associated clinical testing. It does not explicitly discuss a "training set" in the context of machine learning. The non-clinical tests mentioned include "Performance testing (Verification)" and "Simulated use testing (Validation)," which are general software/system validation terms. If "training set" refers to data used to develop the image reconstruction algorithm, that information is not provided.

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

• Not applicable as a "training set" in the machine learning sense is not described.

Summary Limitations:

The provided 510(k) Summary focuses on the device description, intended use, and substantial equivalence argument. It outlines that clinical testing was performed but does not provide the details of that clinical testing or its results, which would typically be found in a more comprehensive clinical study report. The questions largely pertain to specifics of clinical trial design and outcomes that are beyond the scope of this particular summary document.

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The Veo reconstruction option is intended to produce cross-sectional images of the head and body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial and Helical (Volumetric) acquisitions for all ages.

When used, it allows for an alternate reconstruction method designed to reduce image noise, increase resolution and improve low contrast detectability in images produced using raw Computed Tomography data from GE CT scanners. The Veo reconstruction option can be used to reduce noise in diagnostic images and also to reduce the dose required for routine imaging, including CT scans of the head, chest, abdomen and pelvis. The Veo reconstruction option may also improve the image quality of low dose non-diagnostic Filtered Backprojection images such that they become diagnostic.

Currently, Veo is for use with the Discovery CT750 HD CT Scanner.

The Veo Reconstruction Option is composed of Server hardware and reconstruction software. The Veo reconstruction Server is connected to the CT system's operator console via a dedicated Ethemet connection to receive raw scan data for processing and send back the reconstructed image data when completed. Additionally, some minor changes to the CT system software are made to include functionality for installation and operation of the Veo Reconstruction Option. The option when used with the Discovery CT750 HD system (K081105) is an evolutionary modification and performs as well as or better than the computed tomography devices currently on the market. The product changes are primarily associated with the new reconstruction software and hardware. The Veo Reconstruction Option provides another method for reconstruction to that already provided by the CT system.

The Veo Reconstruction Option is intended for head and whole body CT scans when higher image quality and/or lower dose acquisitions are desired for challenging cases. The GE Veo Reconstruction Option when used with the CT System uses virtually the same materials and identical CT imaging principles as our existing marketed product. Discovery CT750 HD.

The provided document is a 510(k) summary for the GE Veo Reconstruction Option, which is an add-on to existing CT systems. It does not contain a detailed study report with specific acceptance criteria and performance metrics in the format requested. The document primarily focuses on establishing substantial equivalence to a predicate device and outlining the intended use and device description.

Therefore, I cannot provide a table of acceptance criteria and reported device performance, nor can I elaborate on sample sizes, ground truth establishment, expert qualifications, or multi-reader multi-case studies, as this information is not present in the provided text.

The document states: "The Veo Reconstruction Option when used with a GE CT system is an evolutionary modification and performs as well as or better than the computed tomography devices currently on the market." This suggests that the "acceptance criteria" were likely based on demonstrating that the Veo option did not degrade the performance of the CT system and potentially improved certain aspects like noise reduction, resolution, and low contrast detectability, compared to conventional reconstruction methods. However, specific quantitative acceptance criteria or detailed study results are not provided.

Here's what can be extracted and inferred from the text regarding the study and evaluation, acknowledging the significant gaps in detail:

1. Table of Acceptance Criteria and Reported Device Performance
This information is not explicitly provided in the document. The submission focuses on demonstrating substantial equivalence rather than reporting specific performance metrics against pre-defined acceptance criteria.

• Inferred Performance Claims (Qualitative):
  • Reduce image noise
  • Increase resolution
  • Improve low contrast detectability
  • May improve the image quality of low dose non-diagnostic Filtered Backprojection images such that they become diagnostic.
  • Allows for an alternate reconstruction method.
  • Performs as well as or better than the computed tomography devices currently on the market.

2. Sample size used for the test set and the data provenance
The document does not specify any sample size for a test set or data provenance (e.g., country of origin, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not mentioned in the provided text.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not mentioned in the provided text.

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 does not describe an MRMC comparative effectiveness study or any effect size related to human reader improvement with or without AI assistance. The Veo Reconstruction Option is described as a reconstruction method, not specifically an AI algorithm with human-in-the-loop performance evaluation.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The document describes the device as a "reconstruction option" that processes raw CT data. It is implied that the performance of this reconstruction is evaluated in terms of image quality metrics (noise, resolution, low-contrast detectability) rather than a diagnostic algorithm's standalone performance on a specific task.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not mentioned in the provided text. Given the focus on image quality parameters (noise, resolution, contrast), the evaluation likely involved quantitative phantom studies and potentially qualitative assessment by experts on clinical images, but the method of establishing ground truth is not detailed.

8. The sample size for the training set
Not mentioned in the provided text. As this is a reconstruction option, the "training set" concept (as typically applied in machine learning) might not be directly applicable in the conventional sense, or if used, it is not disclosed.

9. How the ground truth for the training set was established
Not mentioned in the provided text.

Summary of what the document DOES state regarding the study/evaluation:

• Conclusion about Performance: "The Veo Reconstruction Option when used with a GE CT system is an evolutionary modification and performs as well as or better than the computed tomography devices currently on the market." (Page 3)
• Evaluation Methodology (Implied): The device was "developed under GE's Quality System" and "Functional requirements are demonstrated via testing." (Page 3). The claims around noise reduction, increased resolution, and improved low contrast detectability suggest that these were areas of focus for the testing.
• Relationship to Predicate: "The GE Veo Reconstruction Option when combined with the GE CT system is of comparable type and substantially equivalent to GE Healthcare's currently marketed Computed Tomography X-ray Systems." (Page 3). This "substantial equivalence" is the primary regulatory pathway demonstrated.
• Intended Benefits: The option is "designed to reduce image noise, increase resolution and improve low contrast detectability" and "can be used to reduce noise in diagnostic images and also to reduce the dose required for routine imaging." It "may also improve the image quality of low dose non-diagnostic Filtered Backprojection images such that they become diagnostic." (Page 3)

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The FRAX™ 10-Year Fracture Risk software option is an accessory to currently marketed GE Lunar bone densitometer devices, which are intended to estimate the bone mineral density and body composition (lean and fat tissue mass) of patients when medically indicated by their physicians. This software option is intended to provide an assessment of 10-year fracture risk. The option provides an estimate of 10-year probability of hip fracture and 10-year probability of a major osteoporotic fracture (clinical spine, forearm, hip or shoulder fracture). This estimate is based on the patient's age, sex, country, ethnicity, height, weight, femur neck BMD T-score, and the presence of several risk factors and is computed using the FRAX™ Fracture Risk Assessment Tool endorsed by the World Health Organization (WHO). The tool has been validated for men and post-menopausal women between 40 and 90 years old. The output is provided in a separate screen display and report that can be viewed or printed or exported to an optional physician report generator tool. The results can be used by a physician in conjunction with other clinical risk factors as an aid in the diagnosis of osteoporosis and medical conditions leading to reduced bone density, and ultimately in the assessment of fracture risk.

This software option is intended to provide an assessment of 10-year fracture risk. The option provides an estimate of 10-year probability of hip fracture and 10-year probability of a major osteoporotic fracture (clinical spine, forearm, hip or shoulder fracture). This estimate is based on the patient's age, sex, country, ethnicity, height, weight, femur neck BMD T-score, and the presence or absence of several risk factors and is computed using the FRAX™ Fracture Risk Assessment Tool endorsed by the World Health Organization (WHO). The tool has been validated for men and post-menopausal women between 40 and 90 years old. The output is provided in a separate screen display and report that can be viewed or printed or exported to an optional physician report generator tool.

This response is based on the provided text. The document does not explicitly state acceptance criteria in a quantitative manner, nor does it provide a direct "device performance" in terms of clinical outcomes that are independently measured against such criteria. Instead, the submission relies on demonstrating substantial equivalence to predicate devices and conformance to published data and the WHO FRAX algorithm.

Here's a breakdown of the requested information based on the provided text:

Acceptance Criteria and Study to Prove Device Meets Acceptance Criteria

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

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

• Test Set (for software output consistency): The document states "Design verification tests of the software option were performed using a series of test cases from published data." No specific sample size for these test cases is provided, nor are details on the country of origin or whether the data was retrospective or prospective. It implies the use of existing, published examples of FRAX calculations.
• Validation Cohorts (for FRAX model itself):
  • Sample Size: 230,486 subjects.
  • Follow-up: 1,208,528 patient years of follow-up.
  • Data Provenance: "Eleven independent, population-based cohorts that did not participate in the model synthesis." The earlier description of the model development mentioned "centers in North America, Europe, Asia, and Australia," so it's reasonable to infer a similar, broad geographic distribution for the validation cohorts. The data is described as "follow up data from nine prospective, population-based cohorts" for model development, and implies the validation data is also prospective given the "patient years of follow-up."

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 for the software’s design verification test cases. The "ground truth" for these tests was the "independent outputs published that utilized the WHO 10-year fracture probability algorithm." This implies that the algorithm itself, as published by the WHO, served as the reference standard, rather than a panel of experts specifically forming a ground truth for this device's test set.

For the FRAX model's overall development and validation, experts presumably developed and endorsed the WHO FRAX algorithm, but the document does not specify their number or qualifications as part of this device's testing.

4. Adjudication method for the test set:

This information is not provided. The software performance was verified by checking consistency with "independent outputs published," which suggests a direct comparison method rather than an adjudication process involving human experts for the software itself.

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 performed or cited for this specific device. The device is an algorithm that provides a risk score, not an AI to assist human readers in image interpretation. No human-in-the-loop performance improvement data is mentioned.

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

Yes, in essence. The primary "test" performed for this device was a non-clinical evaluation comparing its algorithm's outputs to "independent outputs published that utilized the WHO 10-year fracture probability algorithm." This is a standalone performance assessment of the algorithm's consistency with the established WHO FRAX standard.

7. The type of ground truth used:

• For the device's software verification: The ground truth was the published outputs of the WHO 10-year fracture probability algorithm.
• For the underlying FRAX model's validation: The ground truth was observed fracture events (hip fractures and other osteoporotic fractures) recorded during "person years of follow-up" in large, population-based cohorts.

8. The sample size for the training set:

• For the FRAX model development: The model was "developed from baseline and follow up data from nine prospective, population-based cohorts... consisting of 46,340 people and 189,852 person years of follow-up." This represents the core "training" data for the FRAX algorithm itself.
• For the GE Lunar device's software: No specific "training set" for the software is mentioned, as it implements an existing, published algorithm (FRAX).

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

• For the FRAX model development:
  • Clinical Risk Factors: "Appropriate clinical risk factors for fracture were determined from eleven large cohort studies."
  • Outcome Data: The ground truth was the occurrence of actual fracture events (hip fractures and other osteoporotic fractures) observed during the "189,852 person years of follow-up." These events were "reported," implying clinical diagnosis and documentation.
  • The data was "generally randomly selected and analyzed according to accepted epidemiological practices" and "published in peer-reviewed journals," suggesting rigorous methodology for establishing these outcomes.

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Diagnostic ultrasound imaging or fluid flow analysis of the human body as follows: Fetal / Obstetrics, Abdominal (including renal and GYN), Pediatric, Small Organ (breast, testes, thyroid), Neonatal Cephalic, Adult Cephalic, Cardiac (adult and pediatric), Peripheral Vascular (PV), Musculo-skeletal Conventional, Urology (including prostate), Transesophageal, Transvaginal (TV), and Intraoperative (abdominal, thoracic, & vascular).

The GE Vivid E9 Ultrasound is a full featured echocardiography imaging and analysis system. It consists of a mobile console with multiple electronic array transducers that provides digital acquisition, processing and display capability. The user interface includes a floating and variable height user control panel with specialized controls, high resolution LCD display and separate LCD touch panel. This modification offers improved performance and productivity for users in a smaller and lighter weight package.

This document describes the Summary of Safety and Effectiveness for the GE Vivid E9 Ultrasound system, prepared in accordance with 21 CFR Part 807.92(c).

Acceptance Criteria and Device Performance:

The document primarily focuses on demonstrating substantial equivalence to a predicate device (GE Vivid 7 Diagnostic Ultrasound) rather than establishing specific quantitative acceptance criteria for device performance. The "acceptance criteria" are implied to be adherence to medical device safety standards and comparable performance to the predicate.

The table below summarizes the claimed performance characteristics and indications for use, noting that these are "previously cleared by FDA" (P) or "new indication" (N) for specific transducers. The core acceptance criterion for the device itself is its safety and effective performance, which is stated to be consistent with traditional clinical practice and FDA guidelines.

Study Details:

A formal clinical study to prove the device meets acceptance criteria was not required or performed for this 510(k) submission.

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

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

  • No clinical test set was used, as clinical tests were stated as "None required."
  • Therefore, no sample size, data provenance, or retrospective/prospective nature of the data is reported.

• 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, as no clinical test set was utilized and no ground truth was formally established for direct comparison of the new device's performance against clinical outcomes. The equivalence relies on the established safety and efficacy of diagnostic ultrasound and its predicate.

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

  • Not applicable, as no clinical test set was utilized.

• 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. This submission is for an ultrasound imaging system, not an AI-powered diagnostic aid. The device is a diagnostic ultrasound system, and the submission emphasizes its substantial equivalence to a predicate device and adherence to safety standards, not an improvement in human reader performance through AI assistance.

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

  • Not applicable. This device is a diagnostic ultrasound system that requires human operation, not a standalone AI algorithm.

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

  • Not applicable. No new ground truth for performance evaluation was established for this submission, as clinical tests were not required. The "ground truth" for the device's acceptable safety and effectiveness is implied through its adherence to established medical device safety standards and the long history of safe and effective performance of diagnostic ultrasound as a modality.

• 8. The sample size for the training set:

  • Not applicable. This device is a hardware/software system, not a machine learning model requiring a training set in the typical sense.

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

  • Not applicable, as no training set for a machine learning model was used.

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Diagnostic ultrasound imaging or fluid flow analysis of the human body as follows: Fetal; Abdominal; Pediatric; Small Organ (breast, testes, thyroid); Neonatal Cephalic; Cardiac (adult and pediatric); Peripheral Vascular; Musculo-skeletal Conventional and Superficial: Urology (including prostate): Transesophageal (TE): Transrectal (TR); Transvaginal (TV); and Intraoperative (abdominal, thoracic, vascular and neurological).

The GE LOGIQ P6 is full-featured, general-purpose diagnostic ultrasound system consisting of a mobile console approximately 49 cm wide, 64 cm deep and 135-141 cm high that provide digital acquisition, processing and display capability. The user interface includes a computer keyboard. specialized controls and color video LCD display. The modification combines features of the unmodified system with additional features from other systems currently marketed by GE Healthcare to provide users with improved imaging capability and acoustic control.

The provided text describes a Special 510(k) Premarket Notification for the GE LOGIQ P6 Ultrasound System. This type of submission focuses on demonstrating substantial equivalence to a predicate device, rather than on proving performance against specific acceptance criteria for a new medical indication through clinical trials.

Therefore, the document explicitly states "Clinical Tests: None required." This means a study proving the device meets acceptance criteria as typically understood for new clinical claims was not performed or deemed necessary for this submission. The substantial equivalence argument relies on the device being comparable to an existing, legally marketed device (GE LOGIQ P5 Diagnostic Ultrasound System).

Here's an analysis based on the information provided, specifically addressing why certain criteria cannot be met:

Acceptance Criteria and Device Performance

Since no clinical tests demonstrating performance against specific acceptance criteria were required or conducted for this 510(k) submission, a table of acceptance criteria and reported device performance cannot be generated from the provided text. The submission focuses on demonstrating "substantial equivalence" based on technological characteristics and intended uses, rather than empirical performance metrics for a novel clinical claim.

Study Information (Based on provided document)

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

   • Not applicable. No clinical test set was used, as "Clinical Tests: None required." The device's safety and effectiveness were established by comparison to a predicate device.

2. 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. No test set requiring expert ground truth was established, as "Clinical Tests: None required."

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

   • Not applicable. No test set requiring an adjudication method was used, as "Clinical Tests: None required."

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. An MRMC comparative effectiveness study was not done. The device is a diagnostic ultrasound system, not an AI-based interpretation tool that assists human readers.

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

   • Not applicable. The GE LOGIQ P6 is a diagnostic ultrasound system operated by a qualified physician, not a standalone algorithm. Its performance is inherent to its image acquisition and processing capabilities.

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

   • Not applicable. No ground truth for clinical performance was established, as "Clinical Tests: None required." The ground for substantial equivalence is the safety and effectiveness of the predicate device (GE LOGIQ P5).

7. The sample size for the training set:

   • Not applicable. This document pertains to regulatory clearance for an ultrasound system, not the development or training of an AI algorithm. Therefore, there is no mention of a "training set" in the context of machine learning.

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

   • Not applicable. As above, no training set for an AI algorithm is referenced in this regulatory document.

In summary: The GE LOGIQ P6 Ultrasound System gained market clearance through a Special 510(k) pathway by demonstrating substantial equivalence to a predicate device (GE LOGIQ P5). This pathway does not typically require new clinical studies to establish individual performance metrics against acceptance criteria for novel clinical claims. Instead, it relies on the established safety and effectiveness of the predicate device and the new device's comparable technological characteristics and intended uses. The document explicitly states "Clinical Tests: None required."

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The GE EchoPAC workstation is indicated for diagnostic review and analysis of ultrasound images acquired under various modes of operation including B, M, Color M modes, Color, Power, Pulsed & CW Doppler modes, Code Pulse, Harmonic and Realtime 3D. Specific clinical applications include: Fetal; Abdominal; Urology (including prostate); Pediatric; Small Organ (breast, testes, thyroid); Neonatal Cephalic; Adult Cephalic; Cardiac (adult and pediatric); Musculo-skeletal Conventional; Peripheral Vascular; Transesophageal (TEE); Transrectal (TR); Transvaginal (TV); and Intraoperative (abdominal, thoracic, & vascular).

The GE EchoPAC is an Ultrasound image Analysis and Review Workstation optimized for ultrasound images that are acquired primarily via the GE Vivid family of diagnostic ultrasound systems. It is sold either with computer hardware only with hardware specifications.

This document, K072952, describes the GE EchoPAC Ultrasound Workstation, a medical image management device. It's important to note that this is a Special 510(k) Premarket Notification, meaning it leverages a previously cleared device (K060542) and likely involves minor changes. Therefore, the depth of performance data might differ from a standard 510(k).

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

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: Not explicitly stated. The document refers to "evaluation in a clinical setting" but does not provide details on the number of cases or patients used for this evaluation.
• Data Provenance: Not explicitly stated. The "clinical setting" implies real-world data, but the country of origin and whether it was retrospective or prospective data are not mentioned. Given the nature of a Special 510(k) for an updated workstation for existing ultrasound systems, it's possible this involved internal testing or smaller-scale evaluations rather than a large clinical trial.

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

• Not specified. The document does not describe the methodology for establishing ground truth for any test set or the involvement of experts for this purpose.

4. Adjudication Method for the Test Set:

• Not specified. No information about expert adjudication or consensus is provided.

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

• No, an MRMC comparative effectiveness study was not done. The document explicitly states "Clinical Tests: None required to confirm safety and effectiveness." The evaluation mentioned is for "reliability within the intended network environment," which is not an MRMC study.

6. Standalone (i.e., algorithm only without human-in-the-loop performance) Study:

• No standalone study was conducted or described in the provided text. The device is an "Image Analysis and Review Workstation," implying interaction with a human user. The performance claims focus on its design, software, compatibility, and safety standards, rather than the standalone diagnostic accuracy of an algorithm.

7. Type of Ground Truth Used:

• Not specified. Since no clinical tests for confirming safety and effectiveness were "required" and no details of an evaluation test set (beyond "reliability within the intended network environment") are provided, the type of ground truth used is not described.

8. Sample Size for the Training Set:

• Not applicable/Not specified. The GE EchoPAC is described as an "Ultrasound image Analysis and Review Workstation." It does not appear to employ AI in the sense of a machine learning algorithm that requires a "training set" to learn to perform a diagnostic task. Its function is image management, processing, and review, not automated diagnosis. Therefore, there's no mention of a training set.

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

• Not applicable/Not specified, as there is no mention of a training set for an AI algorithm.

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The device is intended for use by a qualified physician for ultrasound evaluation of Fetal/OB: Abdominal (GYN & Urology); Pediatric; Small Organ (breast, testes, thyroid); Neonatal and Adult Cephalic; Cardiac (adult & pediatric); Peripheral Vascular; Intra-operative (abdominal, thoracic and PV), Musculo-skeletal Conventional, Transrectal and Transvaginal.

The GE Compact Ultrasound is a very compact and portable diagnostic ultrasound system having three variations: LOGIQ-i, LOGIQ-e and Vivid-e, each with options and features suited for its market niche. It has an integrated keyboard, LCD display and several interchangeable electronic-array transducers with an approximate size of 34 cm wide, 29 cm deep and 6 cm high in transport configuration and provides digital acquisition, processing and display capability. The user interface includes a computer keyboard, an intuitive layout of specialized controls, color GUI display and Doppler audio.

The provided Special 510(k) Premarket Notification for the GE LOGIQ-i/e & Vivid-e Compact Ultrasound system explicitly states that no clinical tests were required for this submission. The rationale given is: "Diagnostic ultrasound has accumulated a long history of safe and effective performance."

Therefore, the document does not contain acceptance criteria in the context of clinical performance metrics (like sensitivity, specificity, accuracy) derived from a study validating the device's diagnostic capabilities, nor does it describe a study to prove those criteria are met. The acceptance criteria and "reported device performance" in this context relate to demonstrating substantial equivalence to a predicate device through non-clinical testing and verification of intended use without introducing new safety or effectiveness concerns.

Here's a breakdown of the requested information based on the provided text, while acknowledging the absence of clinical performance data:

1. Table of Acceptance Criteria and Reported Device Performance

Since no clinical outcome-based acceptance criteria are presented or met via clinical study, the acceptance criteria here are derived from the regulatory requirements for establishing substantial equivalence for an ultrasound device.

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

• Test Set Sample Size: Not applicable. No clinical test set.
• Data Provenance: Not applicable. No clinical data was used for testing against acceptance criteria in this submission. The submission relies on non-clinical evaluations and the substantial equivalence to a previously cleared predicate device.

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. No clinical ground truth was established for a test set. The determination of "substantial equivalence" was a regulatory decision based on documented non-clinical tests and comparison to a predicate device.

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

• Not applicable. No clinical test set requiring adjudication was performed.

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. This submission is for a diagnostic ultrasound system, not an AI-assisted diagnostic tool.

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

• No standalone algorithm performance study was done. This submission is for a diagnostic ultrasound system.

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

• Not applicable. No clinical ground truth was used. The basis for clearance was a demonstration of substantial equivalence to a predicate device through non-clinical testing and design documentation.

8. The sample size for the training set

• Not applicable. This is not an AI/algorithm-based device requiring a training set.

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

• Not applicable. This is not an AI/algorithm-based device requiring a training set.

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Diagnostic ultrasound imaging or motion analysis of the human body as follows: Fetal/OB; Abdominal (GYN & Urology); Pediatric; Small Organ (breast, testes, thyroid); Neonatal & Adult Cephalic; Cardiac (adult & pediatric); Peripheral Vasculo-skeletal (conventional & superficial); Intraoperative including neurological, Transrectal; and Transvaginal.

The GE LOGIQ A3 is a small, general purpose diagnostic ultrasound system. It consists of a mobile console approximately 42 cm wide, 59 cm deep and 125 cm high that provides digital acquisition, processing and display capability. The user interface consists of a computer keyboard, specialized controls and color video LCD display. The modification provides for a more compact, lighter weight and easily maneuverable system with updated and efficient electronic hardware.

The provided document is a 510(k) premarket notification for the GE LOGIQ A3 Diagnostic Ultrasound System. It describes the device, its intended use, and its substantial equivalence to previously cleared predicate devices.

1. Acceptance Criteria and Reported Device Performance:

The document does not specify quantitative acceptance criteria or a dedicated performance study with numerical results for the GE LOGIQ A3. Instead, it relies on the concept of substantial equivalence to predicate devices (GE LOGIQ 3 and LOGIQ 200MD) and compliance with general safety and medical device standards.

The acceptance criteria are implicitly met by demonstrating that the GE LOGIQ A3:

• Has the same intended uses as the predicate devices.
• Shares comparable technological characteristics.
• Is comparable in key safety and effectiveness features.
• Utilizes similar design, construction, and materials.
• Conforms with applicable medical device safety standards through non-clinical tests (acoustic output, biocompatibility, cleaning/disinfection, thermal, electrical, and mechanical safety).
• Conforms to 21 CFR 820, ISO 9001, and 13485 quality systems.

No specific performance metrics (e.g., sensitivity, specificity, accuracy) are reported for the device as part of newly established acceptance criteria. The "performance" is considered equivalent to that of the predicate devices due to the lack of significant technological changes that would raise new questions of safety or effectiveness.

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

No clinical test set was used for the GE LOGIQ A3 in this submission. The document explicitly states: "Clinical Tests: None required." This is typical for submissions asserting substantial equivalence to predicate devices where there are no new indications or significant technological changes that would necessitate new clinical data.

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

Since no clinical test set was used, there were no experts establishing ground truth for such a set within the scope of this submission. The determination of device safety and effectiveness relies on the established safety and effectiveness of the predicate ultrasound systems and compliance with recognized standards.

4. Adjudication Method for the Test Set:

Not applicable, as no clinical test set was used.

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

No MRMC comparative effectiveness study was done. The submission for the GE LOGIQ A3 is based on substantial equivalence, not a comparative clinical trial to demonstrate improved human reader performance with or without AI assistance.

6. Standalone (Algorithm Only) Performance Study:

Not applicable. The GE LOGIQ A3 is a diagnostic ultrasound system intended for direct use by a qualified physician, not an AI algorithm operating in a standalone capacity. No standalone algorithm performance was assessed or reported.

7. Type of Ground Truth Used:

No new ground truth from pathology, outcomes data, or expert consensus was generated specifically for the GE LOGIQ A3 as part of this submission. The ground truth for the device's acceptable safety and effectiveness is implicitly derived from:

• The known safe and effective performance of previously cleared diagnostic ultrasound devices (predicates).
• Compliance with recognized industry standards for acoustic output, biocompatibility, electrical safety, etc.
• General acceptance of diagnostic ultrasound as a safe and effective imaging modality.

8. Sample Size for the Training Set:

Not applicable, as this submission pertains to a diagnostic ultrasound system and not an AI/ML algorithm that requires a training set.

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

Not applicable, as no training set was used.

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The device is intended for use by a qualified physician for ultrasound evaluation of Fetal; Abdominal; Pediatric; Small Organ (breast, testes, thyroid); Neonatal Cephalic; Adult Cephalic; Cardiac (adult and pediatric); Peripheral Vasculo-skeletal Conventional and Superficial; Urology (including prostate); Transesophageal; Transvaginal; and Intraoperative (abdominal, thoracic, and vascular).

The GE Vivid S5/S6 is a mobile ultrasound console having a wide assortment of electronic array transducers intended primarily for echocardiography with additional capability in vascular a and general ultrasound imaging. Its intuitive user interface, high level of auto-optimization along with significantly reduced size and weight make it readily maneuverable, efficient and easy to use.

The provided document is a 510(k) Premarket Notification for the GE Vivid S5/S6 Diagnostic Ultrasound system, dated July 18, 2007. This type of submission is for demonstrating substantial equivalence to a predicate device, rather than proving safety and effectiveness through extensive clinical trials. Therefore, the information requested regarding acceptance criteria based on clinical studies, sample sizes for test and training sets, expert adjudication, or MRMC studies is not typically part of this type of submission.

The document explicitly states: "Clinical Tests: None required."

However, I can extract the general acceptance criteria and the type of studies conducted to support the device's substantial equivalence to the predicate device, the GE Vivid 3 Ultrasound System (K020789).

1. Table of Acceptance Criteria and Reported Device Performance

Since no specific numerical acceptance criteria or performance metrics from clinical studies are reported, the table will reflect the general safety and performance evaluation conducted for the 510(k) submission. The acceptance criteria here refer to compliance with safety standards and substantial equivalence to the predicate device.

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 for Test Set: Not applicable. The document explicitly states "Clinical Tests: None required." The submission relies on non-clinical tests and comparison to a predicate device for substantial equivalence.
• Data Provenance: Not applicable for clinical test data. Non-clinical evaluations were conducted by GE Healthcare and independent evaluation with ongoing factory surveillance.

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

• Not applicable. No clinical test set requiring expert-established ground truth was reported.

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

• Not applicable. No clinical test set requiring adjudication was reported.

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 a 2007 submission for an ultrasound system, not an AI-assisted diagnostic device. No MRMC comparative effectiveness study was performed or reported.

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

• Not applicable. This is an ultrasound imaging system, not a standalone algorithm.

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

• Not applicable for clinical ground truth. The "ground truth" for this submission is based on adherence to recognized medical device safety standards and the established performance and safety record of the predicate device (GE Vivid 3).

8. The sample size for the training set

• Not applicable. This is not an AI/ML device that uses a training set in the conventional sense.

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

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

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