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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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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.

Here's a breakdown of the acceptance criteria and the study proving the device, Lunit INSIGHT DBT, meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Standalone Performance Test Set: 2,202 DBT exams (1,100 negative/benign, 1,102 cancer cases).
  • Data Provenance: Collected at multiple imaging facilities in the US. The data was collected consecutively.
• Clinical Assessment (MRMC) Test Set: 258 DBT exams (65 cancer cases, 193 non-cancer cases, comprising 128 normal and 65 benign cases).
  • Data Provenance: Acquired from US clinical centers.

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

• Standalone Performance Test Set: The text states ground truth localization was "derived based on the radiologic review and annotation by multiple MQSA qualified ground truthers." The exact number of experts is not specified.
  • Qualifications: "MQSA qualified ground truthers."
• Clinical Assessment (MRMC) Test Set: The ground truth for the cases used in the MRMC study is implicitly established by the case classification (cancer vs. non-cancer). It's not explicitly stated how many experts established the underlying ground truth for these 258 cases, beyond the radiologists participating in the MRMC study itself. The readers for the MRMC study were "a total of 15 MQSA qualified and US board-certified radiologists."

4. Adjudication Method for the Test Set

• Standalone Performance Test Set: Ground truth was established through "binary classification of each case based on clinical supporting data, particularly pathology reports for cancer and biopsy-proven benign cases, followed by localization which was derived based on the radiologic review and annotation by multiple MQSA qualified ground truthers." This suggests an expert consensus/review process for localization, likely involving reconciliation or multiple reads. The specific type of adjudication (e.g., 2+1, 3+1) is not explicitly detailed.
• Clinical Assessment (MRMC) Test Set: The ground truth for the MRMC study seems to rely on the pre-established classification of cases (cancer/non-cancer) based on clinical and pathological data. The radiologists in the MRMC study were evaluating cases against this existing ground truth, not establishing it in an adjudicated reading session.

5. Was a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Done? If so, what was the effect size of how much human readers improve with AI vs. without AI assistance?

• Yes, an MRMC comparative effectiveness study was done.
• Effect Size of Improvement:
  • CAD-unassisted AUROC: 0.897
  • CAD-assisted AUROC: 0.915
  • Inter-test difference (effect size): 0.017 (Absolute difference in AUROC). This indicates an improvement in AUROC of 0.017 when radiologists were assisted by Lunit INSIGHT DBT. The p-value of 0.0498 indicates this improvement was statistically significant.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done

• Yes, a standalone performance study was done.
  • Performance: AUROC of 0.928 (95% Cl: 0.917 - 0.939).

7. The Type of Ground Truth Used

• Standalone Performance and Clinical Assessment Test Sets: The ground truth was established primarily through clinical supporting data, specifically pathology reports for cancer and biopsy-proven benign cases, further supplemented by radiologic review and annotation by MQSA qualified ground truthers for localization. This can be categorized as a combination of pathology and expert consensus/review.

8. The Sample Size for the Training Set

• The document states that the "dataset used in the standalone performance test was independent from the dataset used for development of the artificial intelligence algorithm." However, it does not specify the sample size of the training set used for the development of Lunit INSIGHT DBT.

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

• The document mentions that the training dataset was separate from the test dataset. While it details how the ground truth was established for the test set (pathology, biopsy, radiologic review/annotation), it does not explicitly describe how the ground truth was established for the training set. Being a deep learning model, it's highly probable the training data also relied on verified diagnoses, likely similarly derived from pathology/biopsy given the nature of CADe/x devices for breast cancer.

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