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syngo.CT LVO Detection is a radiological post-processing application for the analysis of CT angiography (CTA) head images. syngo.CT LVO Detection supports computer-aided triage, and it addresses vascular abortions in the CTA of the brain, commonly referred to as large vessel occlusion (LVO), in the ICA, M1, and M2 segment. It is intended for all patient populations of age ≥ 22 years, without any of the following contraindications: old infarcts or other diseases impacting the brain vasculature (for example, brain tumors), metal artifacts (for example, coils), surgical signs in the images. The output for triage is intended for informational purposes only. It is not intended for diagnostic use and does not alter the original medical image.

The subject device syngo.CT LVO Detection is an image processing software that utilizes artificial intelligence learning algorithms to support qualified clinicians (Radiologists, Neuroradiologists, Neurologists) in prioritization of CT-angiography images by algorithmically identifying findings suspicious of a large vessel occlusion and providing notification to the user. syngo.CT LVO Detection provides a reproducible detection of large vessel occlusions (LVO) on contrast-enhanced CT examinations of the head for detection of ICA, M1, and M2 vessel occlusions in patients suspected of having stroke related circulation occlusion. syngo.CT LVO Detection analyses CT-angiography (CTA) images of the head. The subject device provides a pipeline for the analysis and identification of potential LVO The output which can be send to an external notification device does not highlight or direct attention of the reading physician to any portion of the image.

Here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) clearance letter for syngo.CT LVO Detection:

Acceptance Criteria and Reported Device Performance

Study Details

2. Sample size used for the test set and the data provenance

• Sample Size: 602 retrospective CT data sets from 602 individual patients.
• Data Provenance:
  • Country of origin: US (from 4 different clinical sites).
  • Retrospective or prospective: Retrospective.
  • Demographics (for known cases): Median patient age 66 years (IQR: [54 years, 76 years]), 51.9% female. Ethnicity known for 290 cases (48.2%): 66.2% White, 26.9% Black/African American, 4.1% Hispanic, 2.8% Others. NIHSS score known for 296 cases (49.2%) with median 10 (IQR: [4,19]).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Number of experts: Three.
• Qualifications of experts: US-board certified neuroradiologists.

4. Adjudication method for the test set

• Adjudication method: Two experts independently assessed the cases. In case of disagreement, a third expert performed adjudication. (This is a 2+1 adjudication method).

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

• The provided document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was done to assess human reader improvement with AI assistance. The study focuses purely on the standalone performance of the AI algorithm for triage.

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

• Yes, a standalone performance study was done. The study evaluated the syngo.CT LVO Detection algorithm's performance (sensitivity and specificity) in identifying LVOs on CT angiography images without human intervention in the initial detection or triage decision.

7. The type of ground truth used

• Type of ground truth: Expert consensus (established by US-board certified neuroradiologists). Specifically, it was based on the independent assessment and subsequent adjudication by these experts.

8. The sample size for the training set

• The document does not explicitly state the sample size used for the training set. It only describes the test set.

9. How the ground truth for the training set was established

• The document does not explicitly state how the ground truth for the training set was established. It only details the ground truth establishment for the test set.

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