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ResolutionMD™ Mobile is a software-based Picture Archiving and Communication System (PACS) used with general purpose computing servers and specific mobile devices. It provides for communication, storage, reformatting, rendering on the server component and communication and display of DICOM 3.0-compliant medical images as well as reports on the mobile device.

ResolutionMD Mobile provides wireless and portable access to medical images. The device is intended for use as a diagnostic, review, and analysis tool by trained professionals such as radiologists, physicians and technologists. This device is not intended to replace full workstations and should be used only when there is no access to a workstation.

ResolutionMD Mobile is not to be used for mammography.

The ResolutionMD™ Mobile software is a software-based Picture Archiving and Communication System (PACS) used with general purpose computing servers and highresolution Apple Inc. iOS and Google Inc. Android OS-based wireless mobile devices for the display and advanced visualization of medical image data. It provides for communication, storage, processing, rendering on the server and the display of DICOM 3.0 compliant image data on the mobile device.

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

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated in a quantifiable manner (e.g., "accuracy must be >90%"). Instead, the primary acceptance criterion for the clinical testing appears to be agreement among radiologists that the mobile devices running ResolutionMD Mobile provide image quality and diagnostic confidence equivalent or comparable to a predicate PACS workstation for clinical use across various modalities, leading to the same diagnosis.

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

• Sample Size for Test Set: The document mentions "a series of typical yet challenging X-ray, ultrasound, PET and SPECT cases." The exact number of cases is not specified, only that there were "individual cases" for which agreement was reached.
• Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It just refers to "typical yet challenging X-ray, ultrasound, PET and SPECT cases."

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

• Number of Experts: Nine (9) board-certified radiologists.
• Qualifications of Experts: Board-certified radiologists in the United States. No further detail on experience (e.g., "10 years of experience") is provided, but board-certification implies a certain level of expertise.

4. Adjudication method for the test set

The adjudication method appears to be a form of consensus or agreement. The study states, "All nine radiologists agreed that the iOS and Android mobile devices... were either 'equivalent' or 'comparable'..." and "For all the individual cases, there was agreement by all reviewers that the same diagnosis would be made..." This suggests a process where all radiologists had to concur, but the specific mechanics (e.g., independent review followed by discussion, or a sequential review) are not detailed. It is not explicitly a 2+1 or 3+1 method where disagreements are resolved by an additional reader.

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

• MRMC Comparative Effectiveness Study: Yes, a comparative assessment was done. The study specifically compared diagnostic confidence on mobile devices with ResolutionMD Mobile to a predicate PACS workstation. This constitutes a comparison of human readers using different display/interpretation systems.
• Effect Size of Human Readers Improvement with AI vs. without AI Assistance: This device is a PACS (Picture Archiving and Communication System) viewer for mobile devices, not an AI-powered diagnostic assist tool. Therefore, the study does not evaluate improvement with AI assistance. It evaluates the equivalence of a mobile viewing platform to a full workstation. The "effect size" is expressed qualitatively as being "equivalent" or "comparable" to the predicate PACS workstation.

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

No, a standalone (algorithm only) performance study was not done for diagnostic interpretation. ResolutionMD Mobile is a display and communication system, intended for use by trained professionals, not to autonomously interpret images. The clinical testing specifically involved human radiologists making diagnoses using the device.

7. The type of ground truth used

The ground truth was established by expert consensus among the panel of nine board-certified radiologists. They made comparative assessments and agreed on the diagnostic equivalence between the mobile device and the predicate PACS workstation for the tested cases. There is no mention of pathology or outcomes data being used as the definitive ground truth reference.

8. The sample size for the training set

The document does not provide information on a "training set" for the ResolutionMD Mobile software in the context of diagnostic interpretation. As a PACS viewer, it's not a machine learning model that is "trained" on images in the same way an AI algorithm for disease detection would be. The software is developed and verified through standard software engineering practices.

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

Not applicable, as there is no mention of a training set for the diagnostic function of the device in the context of machine learning. The "ground truth" for the software's functionality would be adherence to DICOM standards, performance metrics (like luminance), and usability, as confirmed by verification and validation testing, rather than medical ground truth established from labeled medical images for training purposes.

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The ResolutionMD™ Mobile software is a software-based Picture Archiving and Communication System (PACS) used with general purpose computing servers and specific mobile devices. It provides for communication, storage, reformatting, rendering on the server component and communication and display of DICOM 3.0-compliant CT and MR medical images as well as reports on the mobile device.

The ResolutionMD Mobile provides wireless and portable access to medical images. The device is intended for use as a diagnostic, review, and analysis tool by trained professionals such as radiologists, physicians and technologists. This device is not intended to replace full workstations and should be used only when there is no access to a workstation.

The ResolutionMD Mobile is not to be used for mammography.

The ResolutionMD™ Mobile 3.1 software is a software-based Picture Archiving and Communication System (PACS) used with general purpose computing servers and highresolution Apple Inc. iOS and Google Inc. Android OS-based wireless mobile devices for the display and advanced visualization of medical image data. It provides for communication, storage, processing, rendering on the server and the display of DICOM 3.0 compliant image data derived from CT and MRI on the mobile device.

The ResolutionMD Mobile 3.1 is a software-based Picture Archiving and Communication System (PACS) intended for use as a diagnostic, review, and analysis tool for CT and MR medical images on specific mobile devices. It is not intended to replace full workstations and should not be used for mammography.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The FDA submission for ResolutionMD Mobile 3.1 (K123186) primarily focuses on establishing substantial equivalence to a predicate device (ResolutionMD Mobile, K111346). The acceptance criteria for the new version, specifically supporting Android devices, revolve around demonstrating that the image quality and diagnostic confidence achieved on Android platforms are comparable to the previously cleared iOS platforms (predicate device) and adequate for clinical use.

• The Android mobile devices (smartphone and tablet) were comparable to the predicate iPhone and iPad devices.
• The devices were of adequate quality for clinical use.
• They were comfortable with the diagnoses made on the Android mobile devices using the ResolutionMD Mobile software.
• The overall clinical image display quality on the Android devices was equivalent to the iOS devices for the identification of clinically-relevant pathology.
• Comments on image contrast and sharpness included "very comparable" and "is diagnostic."
• No image artifacts were noted by the reviewers.
• The software and devices provide acceptable quality for regular use, and they were comfortable reviewing images. |

Study Proving Acceptance Criteria:

The study combined Performance Testing and Clinical Testing to demonstrate the device meets the acceptance criteria.

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

• Performance Testing Test Set: The sample size for the display performance tests was two mobile devices: an Android smartphone and an Android tablet.
  • Data Provenance: The tests were conducted by an ISO 17025-certified third party, implying controlled laboratory conditions and objective measurements. The data is "prospective" in the sense that the tests were specifically designed and executed to evaluate the new device's performance. The country of origin of the data is not explicitly stated, but the ISO certification and NIST traceability suggest an internationally recognized standard of testing.
• Clinical Testing Test Set: Not explicitly stated, but it involved a "series of typical CT and MR cases."
  • Data Provenance: The cases were reviewed by a panel of radiologists in the United States. The specific nature (retrospective/prospective) of the "series of typical CT and MR cases" is not detailed, but it likely involved retrospective cases from clinical archives for evaluation.

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

• Performance Testing: Ground truth was established by adherence to the AAPM Assessment of Display Performance for Medical Imaging Devices (2005) document. The "experts" in this context are the authors and contributors of the AAPM document, representing a consensus of medical physics and imaging professionals on display performance standards. The testing was carried out by an ISO 17025-certified third party, ensuring expertise in calibration and measurement.
• Clinical Testing: Three board-certified radiologists in the United States. Their specific years of experience are not mentioned, but "board-certified" indicates a recognized high level of expertise in diagnostic radiology.

4. Adjudication Method for the Test Set

• Performance Testing: The adjudication method was based on whether the devices "passed all of the tests" as defined by the AAPM document. This implies a pass/fail threshold for each of the nine display performance tests.
• Clinical Testing: The adjudication method was based on consensus among all three radiologists. The document repeatedly states "All three radiologists agreed that..." and refers to their collective findings. This indicates a form of unanimous consensus. There's no mention of a 2+1 or 3+1 rule; rather, it appears all three came to the same conclusion directly.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, If So, What Was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

• No, an MRMC comparative effectiveness study was not done in the context of AI assistance. This submission is for a PACS visualization device, not an AI diagnostic algorithm. The clinical testing was a comparative assessment between human readers using the Android mobile devices vs. human readers using the predicate iOS devices to establish equivalence in image display quality and diagnostic confidence. It did not involve comparing human readers with and without AI assistance to measure an effect size related to AI improvement. The device itself is a display and analysis tool, not an AI-powered diagnostic aid.

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

• No, a standalone (algorithm-only) performance was not done. This device is a software-based PACS for image display and review by trained professionals. Its intended use inherently involves a "human-in-the-loop" for interpretation and diagnosis. The performance testing focused on the display capabilities of the device, and the clinical testing assessed human readers' diagnostic confidence and image quality perception when using the device.

7. The Type of Ground Truth Used

• Performance Testing: The ground truth was based on established technical standards and objective measurements as outlined in the AAPM Assessment of Display Performance for Medical Imaging Devices (2005). The test equipment and calibration were certified traceable to NIST, further solidifying the objectivity of the ground truth.
• Clinical Testing: The ground truth for comparative image quality and diagnostic confidence was expert consensus among the three board-certified radiologists comparing the Android display to the predicate iOS display for "clinically-relevant pathology." While the "clinically relevant pathology" implies there was a true pathological state in the cases, the primary "ground truth" for the equivalence assessment was the radiologists' agreement on the adequacy of the display for identifying that pathology.

8. The Sample Size for the Training Set

• Not Applicable / Not Provided. This submission primarily addresses the substantial equivalence of a new version of a PACS viewing software on Android devices to a previously cleared version on iOS. It describes the software's design and testing relative to established display standards and clinical usability. There is no mention of machine learning or AI components that would require a distinct "training set" in the traditional sense. The software's development would involve standard software engineering practices, verification, and validation, rather than a machine learning training phase.

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

• Not Applicable / Not Provided. As there's no mention of a distinct "training set" for a machine learning algorithm, there's no information on how a ground truth for such a set was established.

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The ResolutionMD™ Mobile software is a software-based Picture Archiving and Communication System (PACS) used with general purpose computing servers and specific mobile devices. It provides for communication, storage, reformatting, rendering on the server component and communication and display of DICOM 3.0-compliant CT and MR medical images as well as reports on the mobile device.

The ResolutionMD Mobile provides wireless and portable access to medical images. The device is intended for use as a diagnostic, review, and analysis tool by trained professionals such as radiologists, physicians and technologists. This device is not intended to replace full workstations and should be used only when there is no access to a workstation.

The ResolutionMD Mobile is not to be used for mammography.

The ResolutionMD™ Mobile software is a software-based Picture Archiving and Communication System (PACS) used with general purpose computing servers and specific mobile devices. It provides for communication, storage, reformatting, rendering on the server component and communication and display of DICOM 3.0-compliant CT and MR medical images as well as reports on the mobile device.

The provided text describes ResolutionMD Mobile, a PACS software, but it does not contain the detailed information required to answer all the questions about specific acceptance criteria and study details. The text is primarily an FDA 510(k) clearance letter and an Indications for Use statement.

Therefore, I cannot extract the following information from the provided text:

1. A table of acceptance criteria and the reported device performance
2. Sample sized used for the test set and the data provenance
3. Number of experts used to establish the ground truth for the test set and their qualifications
4. Adjudication method for the test set
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, or the effect size
6. If a standalone (algorithm only) performance study was done
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
8. The sample size for the training set
9. How the ground truth for the training set was established

The document states that the device is intended for "diagnostic, review, and analysis" and emphasizes that it "is not intended to replace full workstations and should be used only when there is no access to a workstation," and also "is not to be used for mammography." These are related to the intended use and limitations, but not specific performance metrics or acceptance criteria as would be found in a clinical study report.

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