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ProFound™ AI V2.1 Software is a computer-assisted detection and diagnosis (CAD) software device intended to be used concurrently by interpreting physicians while reading digital breast tomosynthesis (DBT) exams from compatible DBT systems. The system detects soft tissue densities (masses, architectural distortions and asymmetries) and calcifications in the 3D DBT slices. The detections and Certainty of Finding and Case Scores assist interpreting physicians in identifying soft tissue densities and calcifications that may be confirmed or dismissed by the interpreting physician.

ProFound AI V2.1 detects malignant soft-tissue densities and calcifications in digital breast tomosynthesis (DBT) images. ProFound AI V2.1 has the same performance with the DBT systems cleared for use with ProFound AI V2; furthermore, it provides support for additional DBT systems. The ProFound AI V.2.1 Software allows a radiologist to quickly identify suspicious soft tissue densities (masses, architectural distortions and asymmetries) and calcifications by marking the detected areas in the tomosynthesis images. When the ProFound AI V2.1 marks are displayed, the marks will appear as overlays on the 3D tomosynthesis images. For 3D tomosynthesis cases and depending on the functionality offered by the viewing/reading application, the ProFound AI V2.1 marks may also serve as a navigation tool for users because each mark can be linked to the tomosynthesis slice where the detection was identified. Each detected region is also assigned a "score" that corresponds to the ProFound AI V2.1 algorithm's confidence that the detected region is malignant (certainty of finding). Each case is also assigned a case score that corresponds to the ProFound AI V2.1 algorithm's confidence that a case is malignant. The certainty of finding scores are represented as an integer in range of 0 to 100 to indicate the CAD confidence that the detected region or case is malignant. The higher the certainty of finding or case score, the more likely the detected region or case is to be malignant.

Here’s a summary of the acceptance criteria and the study details for the ProFound™ AI Software V2.1, based on the provided FDA 510(k) summary.

1. Table of Acceptance Criteria and Reported Device Performance

The document states that "Case-Level Sensitivity, Lesion-Level Sensitivity, FP Rate in Non-Cancer Cases, and Specificity met design specifications" for both Siemens Standard and Empire Reconstruction datasets. However, the specific numerical acceptance criteria are not explicitly provided in the text. The document refers to "design specifications" and "the detailed results are in the User Manual," implying these numerical targets exist but are not included in the 510(k) summary provided.

For the comparison studies, the acceptance criterion was "the difference between the control group [Hologic] and the test group [Siemens Standard/Empire] is within the margin of non-inferiority for Sensitivity and AUC, and FPPI." The reported performance was that "Each of the three measures produced differences that were within the margin of non-inferiority." Again, specific numerical margins for non-inferiority are not detailed.

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

• Siemens Standard Reconstruction Dataset:
  • Sample Size: 694 cases (238 cancer, 456 non-cancer)
  • Provenance: Not explicitly stated (e.g., country of origin). The study is described as a "screening population dataset," implying it is collected for screening purposes. The terms "stratified bootstrap procedure was used to estimate performance over a screening patient population" suggest it's representative of a screening population. Whether it's retrospective or prospective is not explicitly stated, but "dataset consisted of" typically implies retrospective collection for testing.
• Siemens Empire Reconstruction Dataset:
  • Sample Size: 322 cases (140 cancer, 182 non-cancer)
  • Provenance: Not explicitly stated (e.g., country of origin). Similar to the Standard Reconstruction dataset, it is described as a "screening population dataset," implying it is collected for screening purposes. Whether it's retrospective or prospective is not explicitly stated, but "dataset consisted of" typically implies retrospective collection for testing.
• Hologic (Control Group for Comparison): The document references "baseline performance of ProFound AI for DBT V2.0 with Hologic DBT images." While a control group is mentioned, the specific sample size for the Hologic dataset used in the comparison is not provided in this excerpt, only that the performance was used as a reference for non-inferiority.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not explicitly state the number of experts used or their qualifications for establishing ground truth for the test sets.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method used for the test sets (e.g., 2+1, 3+1).

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study (AI vs. without AI assistance) is not described in this document. The studies presented are standalone performance evaluations of the AI system and non-inferiority comparisons of the AI system's performance across different DBT acquisition systems. The "concurrently by interpreting physicians" in the indication for use suggests a human-in-the-loop interaction, but a specific MRMC study to quantify human improvement with AI is not detailed here.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, standalone (algorithm only without human-in-the-loop performance) studies were done.

• The "ProFound AI for DBT V2.1 Siemens Standard Screening Population Dataset" study explicitly states: "Standalone testing was performed on tomosynthesis slices only."
• Similarly, the "ProFound AI for DBT V2.1 Siemens Empire Screening Population Dataset" study states: "Standalone testing was performed on tomosynthesis slices only."
• The comparison studies ("Standalone Hologic Comparison Test Results") also involve comparing "the standalone performance of ProFound AI for DBT V2.0 with Hologic DBT images to the performance of ProFound AI for DBT V2.1 with Siemens Standard/Empire Reconstruction DBT images."

7. Type of Ground Truth Used

The type of ground truth used is not explicitly stated in this excerpt. However, in the context of screening population datasets for cancer detection, ground truth is typically established by:

• Pathology (biopsy results) for positive cases.
• Long-term follow-up (e.g., 1-2 years of negative imaging) for negative cases.

8. Sample Size for the Training Set

The document does not specify the sample size used for the training set.

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

The document does not specify how the ground truth for the training set was established. It only mentions that the "ProFound AI 2.1 algorithm uses deep learning technology to process feature computations and uses pattern recognition to identify suspicious breast lesions." This implies a training process based on labeled data, but details about the origin and establishment of those labels are not provided in this excerpt.

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