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The FIRSTSense Breast Exam® device is intended for viewing and recording heat patterns generated by the human body in the hospital, acute care settings, outpatient surgery, healthcare practitioner facilities, or in an environment where patient care is provided by qualified healthcare personnel. It is intended for use in adult patient populations. The device is for adjunctive diagnostic screening for detection of breast cancer and diseases affecting blood perfusion of tissue or organs. This device is intended for use by qualified healthcare personnel trained in its use.

The FirstSense Breast Exam® system consists of a portable device (FSBE Tester) that captures and records thermal infrared energy (heat) emitting from a person's body. There is no compression of the breast or patient contact during a screening test. The device emits no radiation to the patient. The device consists of a thermal camera, a 3D camera, a tester main body consisting of metal and plastic to safely and securely house the electronic and mechanical components and motors (to adjust cameras for various sized patients), a computer, software components and two color monitors, as well as an air cooling unit that blows cool air during part of the screening test cycle. The FSBE system also contains a cloud server for safe storage of test data. During a screening test, the FSBE Tester's thermal camera acquires thermal images, and the 3D camera acquires depth data and visible light images of the patient breasts. When a test is completed, the acquired data is uploaded to the FSBE system's cloud server, the FSM Central Server. The uploaded data becomes available to a physician when the FirstSense Data Viewer (FSDV) application downloads the data from the server to a local computer. The FSDV application allows the physician to view the thermal images, the 3D depth image and visible RGB images of the patient breasts. The depth and visible images are provided to the physician as additional information about the breasts with no quantitative data. When the FSDV application provides the thermal images, it allows the application user to select breast regions (nipple, areola, whole breast quadrants) and regions of interest on the thermal views. The FSDV application provides temperature differential data between the left and right breast regions, and temperature differential data for the regions of interest before and after blowing cool air to the breasts in Test Summary report. The FSDV allows the application user to enter threshold values to be compared to the calculated temperature differential data. The FSDV provides comments in the Test Summary Report to indicate that the temperature differential data is within or above the entered threshold value. The FSDV does not include default threshold values and does not provide comments in the Test Summary report should the user choose not to enter threshold values.

The provided document is a 510(k) summary for the FIRSTSense Breast Exam® device. It primarily focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed performance study with acceptance criteria.

Therefore, much of the requested information (acceptance criteria table, sample sizes, expert details, adjudication methods, MRMC study, ground truth details, and training set information) is not explicitly present in the provided text. The document details performance validation tests for the device's components and software, but not a study proving its diagnostic efficacy against specific clinical acceptance criteria.

Missing Information:

• A table of acceptance criteria and reported device performance related to diagnostic accuracy.
• Sample sizes used for a "test set" in the context of diagnostic performance.
• Data provenance (country of origin, retrospective/prospective) for a diagnostic performance study.
• Number and qualifications of experts for establishing ground truth.
• Adjudication method for a test set.
• Information on a multi-reader multi-case (MRMC) comparative effectiveness study or details on the effect size of AI assistance.
• Details on a standalone (algorithm only) performance study.
• The type of ground truth used for diagnostic performance (e.g., pathology, outcomes data).
• Sample size for a training set (as this is a 510(k) for a thermal imaging system, not an AI software where a training set size would be explicitly discussed here).
• How ground truth for a training set was established.

Information that can be extracted from the document:

1. A table of acceptance criteria and the reported device performance:
The document does not provide acceptance criteria in terms of diagnostic accuracy (e.g., sensitivity, specificity for breast cancer detection). Instead, it lists various engineering and functional validation tests for the device's components. Since there are no specific diagnostic acceptance criteria stated, there is no corresponding reported performance in that context. The document broadly states that "In all instances, the FIRSTSense Breast Exam® functioned as intended and the results observed and reported were as expected," for the technical performance tests.

Below is a summary of the technical performance validations mentioned:

2. Sample sized used for the test set and the data provenance: Not mentioned as the document describes technical validation tests, not a clinical diagnostic performance study with a test set of patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable/not mentioned. Technical validation tests do not typically use expert ground truth in this manner.

4. Adjudication method for the test set: Not applicable/not mentioned.

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 mentioned. The device is a "Telethermographic System," and the document describes its hardware and software functionality, not an AI-assisted diagnostic tool in the typical sense that would necessitate an MRMC study with AI.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not mentioned.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable, as detailed diagnostic performance studies with such ground truth are not presented. The "ground truth" for the technical tests would be the expected functional output or measured physical parameters.

8. The sample size for the training set: Not mentioned. This type of submission is for a medical device that captures images and provides temperature differentials, not an AI/ML diagnostic algorithm that would have a distinct training set.

9. How the ground truth for the training set was established: Not mentioned. (See point 8).

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