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Lunit INSIGHT DBT is a computer-assisted detection and diagnosis (CADe/x) software intended to be used concurrently by interpreting physicians to aid in the detection and characterization of suspected lesions for breast cancer in digital breast tomosynthesis (DBT) exams from compatible DBT systems. Through the analysis. the regions of soft tissue lesions and calcifications are marked with an abnormality score indicating the likelihood of the presence of malignancy for each lesion. Lunit INSIGHT DBT uses screening mammograms of the female population.

Lunit INSIGHT DBT is not intended as a replacement for a complete interpreting physician's review or their clinical judgment that takes into account other relevant information from the image or patient history.

Lunit INSIGHT DBT is a computer-assisted detection/diagnosis (CADe/x) software as a medical device that provides information about the presence, location and characteristics of lesions suspicious for breast cancer to assist interpreting physicians in making diagnostic decisions when reading digital breast tomosynthesis (DBT) images. The software automatically analyzes digital breast tomosynthesis slices via artificial intelligence technology that has been trained via deep learning.

For each DBT case, Lunit INSIGHT DBT generates an artificial intelligence analysis results that include the lesion type, location, lesion-level/case-level score, and outline of the regions suspected of breast cancer. This peripheral information intends to augment the physician's workflow to better aid in detection and diagnosis of breast cancer.

The provided text describes the 510(k) submission for Lunit INSIGHT DBT v1.1, a computer-assisted detection and diagnosis (CADe/x) software for breast cancer in digital breast tomosynthesis (DBT) exams. The document primarily focuses on demonstrating substantial equivalence to its predicate device, Lunit INSIGHT DBT v1.0.

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

Acceptance Criteria and Reported Device Performance

The core acceptance criterion explicitly mentioned for the standalone performance testing is an AUROC (Area Under the Receiver Operating Characteristic curve) greater than 0.903. This is directly compared to the predicate device's performance.

Details of the Study

The provided text only discusses a "Standalone Performance Testing" for Lunit INSIGHT DBT v1.1. It states that the protocol for this evaluation was the same as that used for the predicate device (K231470).

1. Sample Size Used for the Test Set and Data Provenance:

   • Sample Size: The document does not explicitly state the sample size (number of cases or images) used for the test set in the standalone performance study.
   • Data Provenance: The document does not explicitly state the country of origin of the data or whether it was retrospective or prospective. It only mentions that the software uses "screening mammograms of the female population."

2. Number of Experts Used to Establish Ground Truth and Qualifications:

   • The document does not specify the number of experts used or their qualifications for establishing ground truth in the standalone performance study.

3. Adjudication Method for the Test Set:

   • The document does not mention any adjudication method (e.g., 2+1, 3+1, none) used for the test set.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • The document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was done to show how much human readers improve with AI vs. without AI assistance. The study described focuses on the standalone performance of the AI algorithm.

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

   • Yes, a standalone performance study was done. The document explicitly states: "A standalone performance study of the Lunit INSIGHT DBT v1.1 assessed the detection performance of the artificial intelligence algorithm for breast cancer within DBT exams."

6. Type of Ground Truth Used:

   • The document does not explicitly state the specific type of ground truth used (e.g., expert consensus, pathology, outcomes data, etc.) for the standalone performance study. It refers to the detection of "breast cancer," implying a definitive diagnosis, but doesn't detail how this diagnosis was established as ground truth for the test cases.

7. Sample Size for the Training Set:

   • The document does not specify the exact sample size for the training set. It only mentions that the updated AI engine has "expanded training data."

8. How the Ground Truth for the Training Set Was Established:

   • The document does not explicitly detail how the ground truth for the training set was established. It states that the AI technology "has been trained via deep learning," which implies the use of labeled data, but does not describe the process of labeling or establishing that ground truth.

In summary:

The provided information focuses on demonstrating that Lunit INSIGHT DBT v1.1 meets the standalone performance AUROC criterion (0.931 > 0.903), which was the same criterion used for its predicate device. However, it lacks detailed information regarding the specifics of the data used (sample sizes, provenance), the ground truth establishment process (experts, adjudication), and the absence of an MRMC study is notable for a CADe/x device, though not explicitly required for this specific 510(k) submission that highlights substantial equivalence based on standalone performance to a predicate.

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