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The Surfacer Inside-Out Access Catheter System is intended to obtain central venous access to facilitate catheter insertion into the central venous system for patients with upper body venous occlusions or other conditions that preclude central venous access by conventional methods.

The Surfacer Inside-Out Access Catheter System (Figure 1) is used to gain central venous access by directing a needle from inside the vein to skin, as opposed to the traditional approach of inserting a needle inwards. The Surfacer Device is percutaneously introduced into the right femoral vein. It is advanced up the inferior vena cava, via the superior vena cava (SVC), to the location of the occlusion. When the occlusion has been visualized under fluoroscopy, a needle wire is oriented to advance through or to cross the occlusion and to exit to a pre-determined external target. A new central venous catheter is then back-loaded into a peel-able introducer sheath and positioned below the occlusion for central venous access.

The Surfacer Inside-Out Access Catheter System is comprised of four components: A Workstation Sheath for percutaneous access via the femoral vein; a Surfacer Device, comprising (b)(4). | Needle Wire and Needle Guide, which is advanced to the supraclavicular al space; an Exit Target, which provides fluoroscopic guidance to mark the exit point; and a | 10(4) (D) (4) Peel-able Introducer Sheath, which is introduced over the Needle Wire to access the central venous system. Once the access is obtained and a catheter is in place, the Surfacer System is removed. Please refer to the Instructions for Use for additional details.

The provided text describes the Surfacer Inside-Out Access Catheter System, a medical device, and the studies conducted to demonstrate its safety and effectiveness. The information focuses on clinical trials and performance testing for the device itself, rather than an AI/ML powered device. As such, several requested items regarding AI/ML models (e.g., training set sample size, number of experts for ground truth, MRMC study, standalone performance) are not applicable or cannot be extracted from the provided text.

However, I can provide the acceptance criteria and device performance based on the clinical studies described for this medical device.

1. A table of acceptance criteria and the reported device performance

Based on the SAVE-US IDE Study (US) and the International Post Market SAVE Registry:

Note on Bench Testing Acceptance Criteria: The document also lists extensive bench testing, but these are general performance metrics (e.g., "[Sheath inner diameter] passes a 0.035" GW", "Verification of the following dimensions to specifications:", "Visual inspection for corrosion", "Joint strengths all (b)(4)", "Needle wire cycles (b)(4) without any failures"). Specific numerical acceptance criteria and reported values for all these individual bench tests are not provided in a summary table; instead, it is implied that the device "meets" these criteria.

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

• Test Set (Clinical Data):
  • United States SAVE-US IDE Study: 30 subjects. Data provenance is prospective, multi-center, and from the United States.
  • International Post Market SAVE Registry: 30 subjects. Data provenance is prospective, multi-center, and from Germany, Italy, Austria, and Paraguay.

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

• Not Applicable in the AI/ML sense. The "ground truth" in this context refers to clinical outcomes (e.g., successful CVC placement, occurrence of adverse events) which are objectively observed and recorded during the medical procedure and follow-up. These observations are likely made by the treating physicians and study staff according to established medical protocols, rather than requiring expert consensus to establish a "ground truth" for an algorithm's output. The studies were monitored by an Independent Data Safety and Monitoring Board (DSMB), reflecting oversight of clinical data. Physicians performing the procedures are assumed to be qualified by the nature of such clinical trials.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

• Not explicitly stated in the multi-reader sense. Clinical outcomes are typically recorded directly as observed. The primary safety endpoint for the SAVE-US study was assessed by the absence of procedural complications observed by site personnel and verified during follow-up. An Independent Data Safety and Monitoring Board (DSMB) monitored the study. This implies a level of independent oversight and review of reported events and outcomes, but not a formal "adjudication" in the sense of multiple independent readers judging data for ground truth as might be done for image-based AI studies.

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:

• No MRMC study was done. This device is a physical medical device (catheter system), not an AI/ML software. Therefore, the concept of comparing human reader performance with or without AI assistance is not applicable.

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

• Not Applicable. This is a physical medical device, not an algorithm. Therefore, "standalone performance" in the context of an algorithm is not relevant.

7. The type of ground truth used:

• The "ground truth" for the clinical studies is based on observed clinical outcomes and procedural success/failure, as recorded by physicians and study staff. This includes direct observation of CVC placement, patency, and the occurrence or absence of adverse events. For cadaver testing, it involved physician evaluation. This is analogous to "outcomes data" or "clinical observation" in research.

8. The sample size for the training set:

• Not Applicable. This device is a physical medical device, not an AI/ML algorithm that requires a training set. The clinical studies described are for demonstrating the safety and effectiveness of the device itself, not for training a model.

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

• Not Applicable. As there is no AI/ML model or training set, this question is not relevant.

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