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cmAngio is intended to process screening mammograms to aid a qualified interpreting physician in the current manual process of identifying Breast Arterial Calcification (BAC).

cmAngio, a proprietary artificial intelligence (AI) based software, is intended to detect, at the study and breast level, the presence or absence of Breast Arterial Calcifications (BAC), an incidental finding in both Full Field Digital Mammogram (FFDM) and Digital Breast Tomosynthesis (DBT) screening mammograms. The device also marks BAC segments on thumbnails of images for localization of BAC.

The software is intended to be used by qualified interpreting physicians in parallel with breast screening mammography workflow. The device is not intended for primary interpretation of digital mammography images as used for breast cancer detection. The device results should not be used alone to make any diagnosis and/or treatment decisions.

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The provided FDA 510(k) Clearance Letter for cmAngio® (V1.6) does not contain the detailed information required to answer most of your questions about the acceptance criteria and the study that proves the device meets them. This document is primarily a clearance letter, indicating that the device has been deemed substantially equivalent to a predicate device and outlining regulatory obligations. It does not typically include the specifics of performance studies.

However, based on the information that is available, here's what can be extracted:

Indications for Use:

cmAngio is intended to process screening mammograms to aid a qualified interpreting physician in the current manual process of identifying Breast Arterial Calcification (BAC). It is an AI-based software intended to detect, at the study and breast level, the presence or absence of Breast Arterial Calcifications (BAC) in both Full Field Digital Mammogram (FFDM) and Digital Breast Tomosynthesis (DBT) screening mammograms. The device also marks BAC segments on thumbnails of images for localization. It is to be used by qualified interpreting physicians in parallel with breast screening mammography workflow and is not intended for primary interpretation or for making diagnosis/treatment decisions alone.

Given the typical content of a 510(k) clearance letter versus a full submission, the following information is not present in the provided text:

• A table of acceptance criteria and reported device performance.
• Sample size used for the test set.
• Data provenance for the test set.
• Number of experts used to establish ground truth for the test set.
• Qualifications of those experts.
• Adjudication method for the test set.
• Whether a multi-reader, multi-case (MRMC) comparative effectiveness study was done.
• The effect size of human readers improving with AI vs. without AI assistance.
• Whether a standalone (algorithm-only) performance study was done.
• The type of ground truth used (e.g., pathology, outcomes data).
• The sample size for the training set.
• How the ground truth for the training set was established.

To obtain this information, one would typically need access to the full 510(k) submission document, which includes the detailed clinical and technical performance studies. The clearance letter only summarizes the outcome of the FDA's review.

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cmAngio is intended to process screening mammograms to aid a qualified interpreting physician in the current manual process of identifying Breast Arterial Calcification (BAC).

cmAngio, a proprietary artificial intelligence (AI) based software, is intended to detect, at the study and breast level, the presence of Breast Arterial Calcifications (BAC), an incidental finding in both Full Field Digital Mammogram (FFDM) and Digital Breast Tomosynthesis (DBT) screening mammograms. The device also marks BAC segments on thumbnails of images for localization of BAC.

The software is intended to be used by qualified interpreting physicians in parallel with breast screening mammography workflow. The device is not intended for primary interpretation of digital mammography images as used for breast cancer detection and should not be used alone to make any diagnosis and/or treatment decisions.

cmAngio is for prescription use only.

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The provided document does not contain specific information regarding the acceptance criteria for the cmAngio® V1.0 device, nor details about a study that proves the device meets those criteria, beyond the general statement that it has been determined to be "substantially equivalent" to legally marketed predicate devices.

Therefore, I cannot provide the requested information in the detailed format. Specifically, the document lacks:

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

The document primarily focuses on the FDA's decision regarding the 510(k) premarket notification, classifying the device, and outlining general regulatory requirements. It states the Indications for Use of the device:

• cmAngio is intended to process screening mammograms to aid a qualified interpreting physician in identifying Breast Arterial Calcification (BAC).
• It is an artificial intelligence (AI) based software intended to detect, at the study and breast level, the presence of Breast Arterial Calcifications (BAC), an incidental finding in both Full Field Digital Mammogram (FFDM) and Digital Breast Tomosynthesis (DBT) screening mammograms.
• The device also marks BAC segments on thumbnails of images for localization of BAC.
• It is intended for use by qualified interpreting physicians in parallel with breast screening mammography workflow.
• It is not intended for primary interpretation of digital mammography images for breast cancer detection and should not be used alone for diagnosis or treatment decisions.
• It is for prescription use only.

To obtain the detailed study information and acceptance criteria, one would typically need to consult the full 510(k) submission, which is not provided in this excerpt.

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cmTriage is a passive notification for prioritization-only, parallel-workflow software tool used by radiologists to prioritize specific patients within the standard-of-care image worklist for 2D FFDM screening mammograms, cmTriage uses an artificial intelligence algorithm to analyze 2D FFDM screening mammograms and flags those that are suggestive of the presence of at least one suspicious finding at the exam level. These flags are viewed by the radiologist via their Picture Archiving and Communication System (PACS) worklist. The decision to use cmTriage codes and how to use on Triage codes is ultimately up to the radiologist. cmTriage does not send a proactive alert directly to the radiologist.

Radiologists are responsible for reviewing each exam on a diagnostic viewer according to the current standard of care.

cm Triage is limited to the categorization of exams, does not provide any diagnostic information beyond triage and prioritization, does not remove images from the radiologist's worklist, and should not be used in lieu of full patient evaluation, or relied upon to make or confirm diagnosis.

cmTriage is for prescription use only.

CureMetrix's cmTriage is a radiological computer-assisted triage and notification software device. Digital two-dimensional (2D) mammograms are captured by a Full-Field Digital Mammography (FFDM) system and deposited on the PACS. The CureMetrix image forwarding software, acting as a PACS listener, receives a copy of the mammography DICOM image(s), creates a local copy of the image(s), de-identifies the local copy, transmits the local copy, transmits the local copy to the CureMetrix cloud, and then deletes the local copy.

Within the CureMetrix cloud, the cmTriage service receives the DICOM image(s), groups them by study, analyzes the image(s) within the study, and produces a result for each study. The results are produced in the form of DICOM Structured Report (SR) file with a cmTriage result.

The result file is encrypted and transmitted from the CureMetrix cloud back to cmEdge where it is decrypted and reassociated with the original study. The DICOM SR is then routed to the PACS.

Once the PACS receives the DICOM SR, the file is opened, the cmTriage code ("Impression Description") is extracted for the exam, and the worklist column for the exam is updated. The cmTriage code will either indicate "Suspicious" or "" (blank).

Within the PACS worklist the cmTriage code can be displayed in a separate column. Each PACS may have different features and functionality depending on the manufacturer which are outside of the scope and control of CureMetrix and cmTriage. However, in general, at a minimum, the user is able to sort their worklist based on values in columns. This sorting functionality (if present) would allow the radiologist to group Suspicious exams together. The device does not alter the original medical image and is not intended to be used as a diagnostic device.

The standard of care for breast cancer screening in the US is quickly becoming one in which both FFDM and digital breast tomosynthesis (DBT) are acquired during the exam. However, cmTriage only operates on 2D images. If a site does not use FFDM and 2D but instead only uses 3D and DBT, they will not be able to use this current device.

In summary, the cmTriage device is intended to provide a passive notification through the PACS to the radiologist indicating the existence of a case that may potentially benefit from that radiologist's prioritization.

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

Acceptance Criteria and Reported Device Performance

Study Details Proving Device Meets Acceptance Criteria:

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

   • Test Set Sample Size: 1255 mammographic studies (exams).
     • 400 biopsy-proven cancer studies (320 soft-tissue density, 122 microcalcifications - Note: The numbers 320 and 122 add up to 442, which is more than 400. This is a potential discrepancy in the source text, though it could imply that some studies had both, or it's a typo in the document. For this response, I'm reporting as written).
     • 855 normal studies (BIRADS 1 and 2 with two-year follow-up of negative diagnosis).
   • Data Provenance: Quarantined test data obtained from multiple clinical sites. The text does not specify the country of origin but implies a multi-center study. It was a retrospective study.

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

   • The document does not specify the number of experts or their qualifications used to establish the ground truth for the test set. It mentions "biopsy-proven cancer studies" and "normal studies (BIRADS 1 and 2 with two-year follow-up of negative diagnosis)" as the ground truth. This implies that the ground truth was established through clinical outcomes (biopsy results and follow-up) rather than de novo expert reading for the purpose of the study.

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

   • The document does not specify an adjudication method for the test set. Given that ground truth was established by biopsy and clinical follow-up for the test set, a reader adjudication process would typically not be explicitly needed for establishing the definitive truth labels.

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 involving human readers with and without AI assistance was not reported in this document. The study presented focused on the standalone performance of the cmTriage software. The comparison made (e.g., population-adjusted mark rate against radiologists' recall rate) is a comparison to a general historical standard, not a direct human reader study for improvement.

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

   • Yes, a standalone performance study of the algorithm was conducted. The results for sensitivity, specificity, AUC, and mark rate are all based on the algorithm's performance in isolation.

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

   • The ground truth for the test set was based on pathology (biopsy-proven cancer) and outcomes data (two-year follow-up of negative diagnosis for normal studies).

7. The sample size for the training set:

   • The document does not explicitly state the sample size for the training set. It only describes the test set.

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

   • The document does not explicitly state how the ground truth for the training set was established, as the training set details are not provided. However, typically, ground truth for training similarly relies on confirmed diagnoses (e.g., pathology, long-term follow-up) or expert annotations.

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