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The intra-aortic balloon is placed in the descending aorta just below the subclavian artery and is intended to improve cardiovascular functioning during the following situations:

Refractory ventricular failure Cardiogenic shock Unstable refractory angina Unstable angina Septic shock Impending infarction Cardiac support for high risk surgical patients Ischemic related intractable ventricular arrhythmias Mechanical complications due to acute myocardial infarction

The intra-aortic balloon is placed in the descending aorta just below the subclavian artery and is intended to improve cardiovascular functioning during the following situations:

Refractory ventricular failure Cardiogenic shock Unstable refractory angina Unstable angina Septic shock Impending infarction Cardiac support for high risk surgical patients Ischemic related intractable ventricular arrhythmias Mechanical complications due to acute myocardial infarction

This document is a 510(k) premarket notification for a medical device (Datascope Percor STAT-DL® 9.5Fr. Intra-Aortic Balloon) and does not describe a study involving algorithms, AI, or performance metrics typically associated with such systems. Instead, it focuses on demonstrating substantial equivalence to a predicate device based on non-clinical (in-vitro) tests.

Therefore, most of the requested information regarding acceptance criteria, study design for AI/algorithm performance, ground truth, and expert evaluation is not applicable to this document.

However, I can extract the relevant information concerning the device's characteristics and the basis for its safety and effectiveness claim.

Here's a summary based on the provided text, indicating where information is not applicable (N/A) due to the nature of the submission:

Acceptance Criteria and Device Performance

The document states that the device is "substantially equivalent" to predicate devices. The "acceptance criteria" here are implicitly tied to demonstrating this substantial equivalence through non-clinical testing.

Details Regarding the "Study" (Non-Clinical Tests)

• Sample size used for the test set and the data provenance: Not specified. The document only mentions "in-vitro tests." No information on sample size or data provenance (country of origin, retrospective/prospective) for these tests is provided.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. This was a non-clinical, in-vitro testing scenario, not one requiring expert human evaluation for ground truth in the context of diagnostic accuracy.
• Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.
• 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 is not an AI/algorithm-based device.
• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an AI/algorithm-based device.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc.): The "ground truth" for the in-vitro tests would be the established performance and safety specifications of the predicate devices. The new device's performance was measured against these predefined physical and functional parameters.
• The sample size for the training set: Not applicable. This is not an AI/algorithm-based device, so there is no training set in the context of machine learning.
• How the ground truth for the training set was established: Not applicable.

Key takeaway: This document describes a traditional medical device submission focused on demonstrating substantial equivalence via non-clinical (in-vitro) testing against predicate devices, rather than an AI/algorithm performance study. Therefore, many of the requested fields are not relevant.

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