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The Medline ReNewal Reprocessed Siemens ACUSON AcuNav Diagnostic Ultrasound Catheter is inteacardiac and intra-luminal visualization of cardiac and great vessel anatomy and physiology as well as visualization of other devices in the heart of adult patients. The catheter is intended for imaging guidance only, not treatment delivery, during cardiac interventional percutaneous procedures.

The Medline ReNewal Reprocessed AcuNav Diagnostic Ultrasound Catheters 8F are licensed for single use only. The catheter is optimized for intracardiac scanning. With the catheter, the physician can maneuver the imaging plane located inside the catheter tip to see the region of interest. The physician can steer the catheter to optimize tissue visualization. The catheters are to be used only on systems with which they have been tested and found compatible.

The provided text is a 510(k) summary for a reprocessed medical device, specifically an ultrasound catheter, and does not describe an AI/ML powered device. Therefore, information regarding acceptance criteria and studies proving the device meets those criteria for an AI/ML powered device cannot be extracted from this document.

The document focuses on demonstrating substantial equivalence of a reprocessed device to a legally marketed predicate device, primarily through non-clinical testing of functional performance, material characteristics, cleaning validation, biocompatibility, and sterilization validation. It does not contain details about a study evaluating an AI/ML algorithm's performance.

To answer your request, if this were an AI/ML powered device, the following information would typically be present in a separate study report or detailed within the 510(k) submission, and would not be found in a standard summary like the one provided:

• Acceptance Criteria Table and Reported Performance: This would specify metrics like sensitivity, specificity, AUC, F1-score, accuracy, etc., and the target thresholds, along with the actual performance achieved by the AI model.
• Sample Size and Data Provenance: Details on the number of cases/samples in the test set, and whether the data was retrospective or prospective, and its geographical origin.
• Ground Truth Experts: The number and qualifications of experts who established the ground truth labels for the test set.
• Adjudication Method: How discrepancies among experts were resolved (e.g., 2+1, 3+1, majority vote).
• MRMC Comparative Effectiveness Study: If human readers were involved, details on a multi-reader, multi-case study to assess AI's impact on human performance, including effect sizes.
• Standalone Performance: If the AI algorithm's performance was evaluated independently without human intervention.
• Type of Ground Truth: The method used to establish the true diagnosis (e.g., expert consensus, biopsy, surgical findings, long-term outcomes).
• Training Set Sample Size: The number of data points used to train the AI model.
• Training Set Ground Truth: How the ground truth for the training data was established (often a multi-stage process, possibly involving automated methods, expert review, or existing clinical records).

In summary, the provided document relates to a reprocessed physical medical device and not an AI/ML enabled device. Therefore, it does not contain the information required to answer your specific questions about an AI/ML device's acceptance criteria and validation study.

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The ACUSON AcuNav™ Ultrasound Catheter is intended for intra-cardiac and intra-luminal visualization of cardiac and great vessel anatomy and physiology as well as visualization of other devices in the heart.

The AcuNav catheter is comprised of a single-use, disposable ultrasonic phasedarray imaging transducer and a catheter which is 8 Fr in diameter and 110 cm in insertable length.

The device is capable of imaging at multiple frequencies and obtaining blood flow data in multiple ultrasound modes. The distal portion of the catheter can be deflected in four directions in two orthogonal planes: left-right (in a plane perpendicular to the image rour directions in two over of the a plane coincident with the image plane). The range of pume) and a 160° in each direction. The AcuNav catheter is comprised of three major components: (1) the catheter itself; (2) the steering mechanism; and (3) the reusable system cable.

The provided document is a 510(k) premarket notification letter from the FDA for a medical device: the ACUSON AcuNav 8F Ultrasound Catheter. This type of document is typically used to demonstrate that a new device is "substantially equivalent" to a legally marketed predicate device, rather than providing extensive de novo clinical trial data with acceptance criteria and detailed study designs.

Therefore, the document does not contain the information requested for acceptance criteria and a study proving device performance in the format of a clinical study with detailed statistical results, sample sizes, expert qualifications, or comparative effectiveness.

However, I can extract information related to the device, its intended use, and the basis for its clearance, which can be interpreted within the context of a 510(k) submission.

Here's a breakdown of why the requested information is absent and what can be inferred:

• Acceptance Criteria and Reported Device Performance: This document doesn't provide specific quantitative acceptance criteria (e.g., sensitivity, specificity, accuracy targets) or a table of reported device performance metrics from a clinical study. Instead, the "acceptance criteria" for a 510(k) submission are typically related to demonstrating substantial equivalence to existing predicate devices. This means showing that the new device has the same intended use, similar technological characteristics, and raises no new questions of safety or effectiveness. The reported "performance" is implicitly tied to meeting safety and performance standards for ultrasound devices and catheters, as well as being comparable to the predicate devices.

• Sample Size, Data Provenance, Experts, Adjudication, MRMC, Standalone Performance, Ground Truth for Test Set: These are all elements of a robust clinical study to assess new diagnostic device performance. A 510(k) generally relies on bench testing, engineering analysis, and performance data compared to a predicate device, rather than a full-scale clinical trial to establish de novo performance metrics. The document references adherence to various safety and performance standards (e.g., UL, IEC, ISO, AIUM/NEMA UD-2/UD-3), which serve as the implicit "acceptance criteria" for the device's fundamental function and safety.

• Training Set Sample Size and Ground Truth: Since this is a 510(k) for an ultrasound catheter, it's not an AI/ML device that requires a "training set" in the conventional sense of machine learning. The device is hardware that processes and displays ultrasound images. The "ground truth" for its performance would be assessed through its ability to accurately visualize anatomical structures and blood flow, which is established through engineering validation and comparison to predicate devices, not typically a large, separately labeled training dataset.

Summary of available information (and absence of requested details):

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

   • Acceptance Criteria (Implied for 510(k) clearance): Substantial equivalence to predicate devices (K033650, K992631, K010950) in terms of intended use, technological characteristics, and no new questions of safety or effectiveness. Adherence to various national and international safety and performance standards (listed in the document).
   • Reported Device Performance: Not explicitly stated as quantitative metrics from a clinical study. The device is described as "capable of imaging at multiple frequencies and obtaining blood flow data in multiple ultrasound modes" (Page 6). Its performance is considered substantially equivalent to the predicate devices, which are already marketed and assumed to be safe and effective for their intended use.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):

   • Not specified. Typical 510(k) submissions for devices like this primarily rely on bench testing, engineering validation, and comparison to predicate device specifications. If clinical data were used, details are not provided in this excerpt.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience):

   • Not specified.

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

   • Not specified.

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:

   • Not applicable. This device is an ultrasound catheter, not an AI-assisted diagnostic tool.

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

   • Not applicable. This device is an ultrasound catheter, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • For an ultrasound catheter, ground truth for its functional performance would typically involve comparison to established imaging standards, physical phantoms, and potentially animal or human studies where anatomical and physiological accuracy can be verified through other means (e.g., direct observation during surgery, other imaging modalities, or known physiological parameters). The document does not detail how "ground truth" was established for any specific performance assessment.

8. The sample size for the training set:

   • Not applicable. This is not an AI/ML device requiring a training set.

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

   • Not applicable.

In conclusion, this document primarily serves as an FDA clearance letter for a medical device demonstrating substantial equivalence to existing devices, and therefore does not contain the detailed clinical study information typically required to answer the questions about acceptance criteria, study design, and performance metrics as if it were a novel diagnostic algorithm or a device requiring a de novo clinical efficacy study.

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