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Saige-Dx analyzes digital breast tomosynthesis (DBT) mammograms to identify the presence or absence of soft tissue lesions and calcifications that may be indicative of cancer. For a given DBT mammogram, Saige-Dx analyzes the DBT image stacks and the accompanying 2D images, including full field digital mammography and/or synthetic images. The system assigns a Suspicion Level, indicating the strength of suspicion that cancer may be present, for each detected finding and for the entire case. The outputs of Saige-Dx are intended to be used as a concurrent reading aid for interpreting physicians on screening mammograms with compatible DBT hardware.

Saige-Dx is a software device that processes screening mammograms using artificial intelligence to aid interpreting radiologists. By automatically detecting the presence or absence of soft tissue lesions and calcifications in mammography images, Saige-Dx can help improve reader performance, while also reducing reading time. The software takes as input a set of x-ray mammogram DICOM files from a single digital breast tomosynthesis (DBT) study and generates finding-level outputs for each image analyzed, as well as an aggregate case-level assessment. Saige-Dx processes both the DBT image stacks and the associated 2D images (full-field digital mammography (FFDM) and/or synthetic 2D images) in a DBT study. For each image, Saige-Dx outputs bounding boxes circumscribing any detected findings and assigns a Finding Suspicion Level to each finding, indicating the degree of suspicion that the finding is malignant. Saige-Dx uses the results of the finding-level analysis to generate a Case Suspicion Level, indicating the degree of suspicion for malignancy across the case. Saige-Dx encapsulates the finding and case-level results into a DICOM Structured Report (SR) object containing markings that can be overlaid on the original mammogram images using a viewing workstation and a DICOM Secondary Capture (SC) object containing a summary report of the Saige-Dx results.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) clearance letter for Saige-Dx:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document indicates that the primary endpoint of the standalone performance testing was to demonstrate non-inferiority of the subject device (new Saige-Dx version) to the predicate device (previous Saige-Dx version). Specific quantitative acceptance criteria (e.g., AUC, sensitivity, specificity thresholds) are not explicitly stated in the provided text. However, the document states:

"The test met the pre-specified performance criteria, and the results support the safety and effectiveness of Saige-Dx updated AI model on Hologic and GE exams."

2. Sample Size for the Test Set and Data Provenance

• Sample Size for Test Set: 2,002 DBT screening mammograms from unique women.
  • 259 cancer cases
  • 1,743 non-cancer cases
• Data Provenance:
  • Country of Origin: United States (cases collected from 12 diverse clinical sites).
  • Retrospective or Prospective: Retrospective.
  • Acquisition Equipment: Hologic (standard definition and high definition) and GE images.

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

The document mentions: "The case collection and ground truth lesion localization processes of the newly collected cases were the same processes used for the previously collected test dataset (details provided in K220105)."

• While the specific number and qualifications of experts for the ground truth of the current test set are not explicitly detailed in this document, it refers back to K220105 for those details. It implies that a standardized process involving experts was used.

4. Adjudication Method for the Test Set

The document does not explicitly describe the adjudication method (e.g., 2+1, 3+1) used for establishing ground truth for the test set. It states: "The case collection and ground truth lesion localization processes of the newly collected cases were the same processes used for the previously collected test dataset (details provided in K220105)." This suggests a pre-defined and presumably robust method for ground truth establishment.

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

• Was it done? Yes.
• Effect Size: The document states: "a multi-reader multi-case (MRMC) study was previously conducted for the predicate device and remains applicable to the subject device." It does not provide details on the effect size (how much human readers improve with AI vs. without AI assistance) within this document. Readers would need to refer to the K220105 submission for that information if it was presented there.

6. Standalone (Algorithm Only) Performance Study

• Was it done? Yes.
• Description: "Validation of the software was conducted using a retrospective and blinded multicenter standalone performance testing under an IRB approved protocol..."
• Primary Endpoint: "to demonstrate that the performance of the subject device was non-inferior to the performance of the predicate device."

7. Type of Ground Truth Used

• The ground truth involved the presence or absence of cancer, with cases categorized as 259 cancer and 1,743 non-cancer. The mention of "ground truth lesion localization processes" implies a detailed assessment of findings, likely involving expert consensus and/or pathology/biopsy results to confirm malignancy. Given it's a diagnostic aid for cancer, pathology is the gold standard for confirmation.

8. Sample Size for the Training Set

• Training Dataset: 161,323 patients and 300,439 studies.

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

• The document states: "The Saige-Dx algorithm was trained on a robust and diverse dataset of mammography exams acquired from multiple vendors including GE and Hologic equipment."
• While it doesn't explicitly detail the method of ground truth establishment for the training set (e.g., expert consensus, pathology reports), similar to the test set, for a cancer detection AI, it is highly probable that the ground truth for the training data was derived from rigorous clinical assessments, including follow-up, biopsy results, and/or expert interpretations, to accurately label cancer and non-cancer cases for the algorithm to learn from. The implied "robust and diverse" nature of the training data suggests a comprehensive approach to ground truth.

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