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INTENDED USE: P3TTM Cardiac is intended for use with CT Angiography. P3TTM Cardiac computes individual contrast injection protocols and scan timing, based on patient characteristics, scanner parameters and contrast concentration. The user will be required to confirm/change the suggested protocol before beginning an injection.

INDICATIONS FOR USE: P3T™ Cardiac is indicated for use with CT Angiography of the cardiac structures, coronary arteries, chambers of the heart, pulmonary vasculature, and thoracic and abdominal aorta.

The MEDRAD Stellant CT Injector System with P3T™ Cardiac is a syringe-based fluid delivery system indicated for delivery of contrast media during computed tomography procedures. The MEDRAD Stellant CT Injector System is intended for the specific purpose of injecting intravenous contrast media into humans for diagnostic studies in computed tomography (CT) applications.

MEDRAD Stellant CT Injector System operating software includes P3T™ Cardiac, an optional, password-enabled software accessory. The injector system, when used with P3T™ Cardiac, maintains the same intended use, same operational parameters, and same labeling (with the addition of the P3T™ Cardiac operation manual), and is used in the same manner as the injector system without the software accessory.

The P3T™ Cardiac software accessory provides the convenience of generating patientspecific contrast injection protocols, and increases the consistency of individualized injection protocols among technologists.

The provided document is a 510(k) summary for the MEDRAD Stellant CT Injector System with P3TTM Cardiac. This document describes an injector system with a software accessory (P3TTM Cardiac) that computes contrast injection protocols and scan timing for CT Angiography.

Based on the content, this is not a study of an AI/ML diagnostic or prognostic device that would have specific performance metrics like sensitivity, specificity, or AUC against a ground truth dataset. Instead, it's a submission for an imaging accessory that aids in optimizing injection protocols. The submission focuses on substantial equivalence to a predicate device (MEDRAD Stellant CT Injector System with P3TTM CardiacFlow (K072886)) rather than presenting a performance study against specific acceptance criteria for diagnostic accuracy.

Therefore, many of the requested elements for a performance study (like test set size, expert ground truth, MRMC study, standalone performance, training set details) are not applicable or not present in this type of submission, as the device's function is to generate protocols, not to interpret images or diagnose conditions.

Here's a breakdown based on the provided text, highlighting what is available and what is not:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not present a table of specific quantitative "acceptance criteria" and "reported device performance" in the way one would for a diagnostic algorithm (e.g., sensitivity, specificity, accuracy thresholds). Instead, the "acceptance" for this type of device is regulatory clearance based on substantial equivalence to a predicate device. The performance is indirectly supported by the device's functionalities being comparable or expanded from the predicate, and its ability to generate injection protocols as intended.

Key "performance" aspects discussed are functional equivalence to its predicate:

Summary of "Acceptance": The device was deemed "substantially equivalent" to the predicate device (K072886) by the FDA, based on the comparison of features and intended use. The extension of indications to include pulmonary vasculature and abdominal aorta (beyond gated ECG acquisition) for the P3T™ Cardiac, while maintaining the same core functionality and safety features, was accepted.

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

• Not applicable / Not provided. This document does not describe a clinical performance study using a test set of patient data for a diagnostic outcome. The P3T™ Cardiac software is an accessory that computes injection protocols, not interprets images.

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

• Not applicable / Not provided. As there is no test set for diagnostic performance, there is no need for experts to establish ground truth in this context. The "ground truth" for the device would be the accuracy of its computed injection protocols in practice, which is generally assessed through engineering validation and clinical use rather than a formal ground-truth-labeled dataset.

4. Adjudication method for the test set:

• Not applicable / Not provided.

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. This is not a diagnostic AI device. An MRMC study is not relevant here. The device assists technologists in generating protocols, but not in image interpretation or diagnosis.

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

• Not applicable / Not provided in this context. While the P3T™ Cardiac software operates standalone to compute the protocol, it's explicitly stated that "The user will be required to confirm/change the suggested protocol before beginning an injection." This indicates a human-in-the-loop for final decision-making, which is part of its intended safe use. The submission focuses on the safety and functional equivalence of the injector system with the software rather than a standalone AI performance evaluation for diagnostic output.

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

• Not applicable / Not provided. The "ground truth" for an injection protocol computation device would involve whether the computed protocols lead to optimal image quality and patient outcomes, which is generally assured through engineering validation, simulated environments, and clinical experience/studies (not usually detailed in a 510(k) summary focused on substantial equivalence).

8. The sample size for the training set:

• Not applicable / Not provided. The P3T™ Cardiac software computes protocols based on patient characteristics and scanner parameters using presumably pre-programmed algorithms or models, not a machine learning "training set" in the sense of image recognition or diagnostic AI.

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

• Not applicable / Not provided.

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The MEDRAD Stellant CT Injector System is intended for the specific purpose of injecting intravenous contrast media into humans for diagnostic studies in computed tomography (CT) applications.

The Stellant P3T CardiacFlow software accessory is intended for use in CT angiography of cardiac structures, including coronary arteries, chambers of the heart, and thoracic and abdominal aorta during gated ECG acquisition.

The Stellant P3T CardiacFlow software accessory computes individual contrast injection protocols and scan timing, based on patient characteristics, scanner parameters and contrast concentration. The user will be required to confirm/change the suggested protocol before beginning an injection.

MEDRAD Stellant CT Injector System operating software has been modified to include the P3T CardiacFlow accessory. P3T CardiacFlow is an optional, password-enabled accessory. The injector system, when used with P3T CardiacFlow, maintains the same intended use, same operational parameters, and same labeling (with the addition of a P3T CardiacFlow operations manual). This device is also used in the same manner as the predicate devices.

The MEDRAD Stellant CT Injector System with P3T CardiacFlow is a syringe-based fluid delivery system indicated for delivery of contrast media during computed tomography procedures. The MEDRAD Stellant CT Injector System is intended for the specific purpose of injecting intravenous contrast media into humans for diagnostic studies in computed tomography (CT) applications.

MEDRAD Stellant CT Injector System operating software has been modified to include the P3T CardiacFlow accessory. MEDRAD Stellant CT Injector System with P3T CardiacFlow maintains the same intended use, operational parameters, and labeling as the MEDRAD Stellant CT Injector System, with the addition of P3T CardiacFlow Instructions For Use. The P3T CardiacFlow accessory provides the convenience of generating patient-specific contrast injection protocols, and increases the consistency of individualized injection protocols among technologists. Stellant CT Injector System with P3T CardiacFlow is used in the same manner as the predicate device (Stellant CT Injector System without P3T CardiacFlow).

The provided document, K072886, describes the MEDRAD Stellant CT Injector System with P3T CardiacFlow software accessory. This is a premarket notification (510(k)) and primarily focuses on establishing substantial equivalence to predicate devices, rather than an independent clinical study demonstrating specific performance metrics against defined acceptance criteria. Therefore, the details requested for a clinical study proving acceptance criteria compliance are not fully available in this document.

However, based on the information provided, we can infer the acceptance criteria and some aspects of performance. The substantial equivalence argument itself serves as the "study" in this context for regulatory clearance.

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is a 510(k) for substantial equivalence, the "acceptance criteria" are implicitly met by demonstrating that the proposed device is as safe and effective as the predicate devices. The performance is reported in terms of feature comparison.

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

The document does not describe a clinical study with a "test set" in the traditional sense of patient data used to evaluate AI performance. The evaluation is based on a comparison of device features, intended use, and operational parameters against existing legally marketed predicate devices. The provenance of any underlying data that informed the development of the P3T CardiacFlow algorithm (e.g., patient characteristics, scanner parameters, contrast concentration relationships) is not specified. It is likely based on engineering principles, scientific literature, and potentially in-house data/simulations rather than a distinct, prospectively collected, and adjudicated clinical "test set" for regulatory submission.

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

Not applicable, as this device's regulatory review is based on substantial equivalence to existing devices and its operational safety and effectiveness, rather than a diagnostic AI algorithm requiring ground truth established by experts on a test set. The efficacy of the P3T CardiacFlow algorithm itself (meaning its ability to accurately compute protocols for optimal cardiac CT angiography) would presumably be verified through internal engineering and potentially pre-clinical or limited clinical testing, but these details are not provided in the 510(k) summary.

4. Adjudication Method for the Test Set

Not applicable for the reasons stated in point 3.

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 injector system with software to assist in protocol generation, not an AI diagnostic algorithm meant to be read by human readers. Therefore, an MRMC study is not relevant to its regulatory submission in this context. The benefit of the P3T CardiacFlow is stated as "increases the consistency of individualized injection protocols among technologists" (page 1).

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

The P3T CardiacFlow is not a standalone algorithm that operates without human intervention. The document explicitly states: "The user will be required to confirm/change the suggested protocol before beginning an injection." and "every injection protocol must be approved and locked by the practitioner. The practitioner always has the option of modifying or deleting the P3T CardiacFlow suggested protocol." (Page 1-2). This indicates a human-in-the-loop design where the algorithm provides suggestions but requires final human approval and can be overridden.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

Since this is not a diagnostic device establishing a "ground truth" for a medical condition, the concept of ground truth as typically applied to diagnostic AI is not directly applicable. For the P3T CardiacFlow software, the "ground truth" for its function would be whether the computed protocols are appropriate and effective for the intended CT angiography procedures, leading to diagnostic quality images while ensuring patient safety. This "truth" would likely be established through:

• Engineering validation: Demonstrating that the algorithm correctly applies the defined logic based on input parameters.
• Physical modeling, simulations, and phantom studies: Verifying that the proposed injection protocols achieve desired contrast enhancement profiles.
• Possibly limited clinical evaluation or literature review: Confirming that the principles underlying the algorithm lead to clinically acceptable results in practice, as informed by medical expertise in CT angiography.
  However, specific details of such validation are not provided in this 510(k) summary.

8. The Sample Size for the Training Set

Not applicable. The document does not describe a machine learning model that requires a distinct "training set" of data for learning patterns, as might be the case for image recognition algorithms. The P3T CardiacFlow computes protocols based on predefined patient characteristics, scanner parameters, and contrast concentration, likely using a programmed algorithm based on scientific principles and clinical knowledge, rather than a learned model from a large dataset.

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

Not applicable for the reasons stated in point 8.

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The MEDRAD Stellant CT Injector System is intended for the specific purpose of injecting intravenous contrast media into humans for diagnostic studies in computed tomography (CT) applications. The MEDRAD Extravasation Detection System (XDS) accessory is designed to aid in the detection of intravenous contrast media from the injection site into the surrounding tissue during diagnostic studies in computed tomography (CT) applications.

The Stellant CT Injector System with XDS accessory is a syringe-based fluid delivery system indicated for delivery of contrast media during computed tomography procedures. The MEDRAD Stellant CT Injector System is intended for the specific purpose of injecting intravenous contrast media into humans for diagnostic studies in computed tomography (CT) applications. The MEDRAD Extravasation Detection System (XDS) accessory is designed to aid in the detection of intravenous contrast media from the injection site into the surrounding tissue during diagnostic studies in computed tomography (CT) applications. The XDS accessory is designed to detect and notify the user before 20ml of fluid is extravasated within the sensors' field of view.

The provided text describes the MEDRAD Stellant CT Injector System with the Extravasation Detection System (XDS) accessory, which is designed to aid in the detection of intravenous contrast media extravasation during CT applications.

Here's an analysis of the acceptance criteria and study information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document states the intended performance of the XDS accessory without explicitly defining "acceptance criteria" in a separate table. The key performance characteristic mentioned for the XDS accessory is its ability to detect extravasation before 20ml of fluid is extravasated.

Note: The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device. It highlights the design intent and performance claims of the new accessory rather than presenting detailed study results with quantitative metrics against formal acceptance criteria in the way a clinical study report might. It implies that these performance characteristics were verified during the development and testing process for regulatory submission.

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

The provided 510(k) summary does not explicitly state the sample size used for any test set or the data provenance (e.g., country of origin, retrospective or prospective) for the studies supporting the XDS accessory. 510(k) summaries often focus on design and functional verification rather than detailed clinical trial data, especially for accessories that don't directly modify the primary therapeutic or diagnostic output of the main device. The information indicates that the XDS accessory "provides a supplemental monitoring function" and "does not impose additional risk to the patient," suggesting that extensive clinical trials might not have been deemed necessary for this type of device accessory to demonstrate substantial equivalence.

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

The document does not provide any information regarding the number of experts used or their qualifications to establish ground truth for any test set. Given the nature of extravasation detection (a physical event during an injection), the "ground truth" during testing would likely be based on direct observation of fluid leakage, either in vitro models or potentially controlled in vivo animal studies, rather than expert interpretation of images or clinical outcomes from human subjects.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1, none) for a test set. This type of detail is usually found in clinical trial protocols where expert consensus is needed to define an endpoint or ground truth, which is not described here.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

The document does not mention or describe a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. The XDS accessory is described as a supplemental monitoring function and does not replace human diligence. Therefore, a study comparing human readers with and without AI assistance (which would be the purpose of an MRMC study if the XDS were an AI diagnostic tool) is not applicable or described in this submission. Its function is to alert, not to interpret images.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Study Was Done

While not explicitly called a "standalone study", the core functional description of the XDS accessory implies a standalone performance objective: "The XDS accessory is designed to detect and notify the user before 20ml of fluid is extravasated... Will halt the Stellant injector system in the event of an extravasation event." This suggests that the detection mechanism operates independently to identify extravasation and trigger an alert/halt, regardless of human interaction. The device's ability to detect extravasation up to 20ml and then alert/halt the injector system is its standalone performance claim. However, no specific study data or methodology for proving this standalone performance is detailed in the summary.

7. The Type of Ground Truth Used

Given the function of the device, the ground truth for extravasation detection would most likely be direct physical observation of fluid extravasation, either in in-vitro models designed to simulate antecubital injections or potentially in-vivo animal models. The document does not specify the exact method for establishing ground truth, but for a physical detection system, it wouldn't typically involve expert consensus on images or pathology as it would for a diagnostic imaging AI. It's about physically sensing the presence of extravasated contrast.

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size for a training set. The XDS accessory, based on its description (gold-plated antenna, ferro-plastic absorber), appears to be leveraging sensor technology to detect changes related to extravasation rather than being a machine learning or AI-driven system that would traditionally require a "training set" in the context of image analysis or algorithm development. Therefore, the concept of a training set as typically understood for AI/ML devices might not be directly applicable, or if any calibration/parameterization was done, it's not detailed.

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

As there's no mention of a "training set" or AI/ML components for which a training set ground truth would be established, this information is not provided in the document.

Summary of what's provided for studies:

The 510(k) summary primarily focuses on describing the device, its intended use, contraindications, and demonstrating substantial equivalence to a predicate device by comparing features and operational parameters. It states the functional goal of the XDS accessory (detecting extravasation before 20ml and halting the injector) but does not include detailed information about specific studies, sample sizes, expert involvement, or adjudication methods that would typically be found in a clinical study report for an AI/ML device or a device requiring extensive clinical validation. The nature of the device as a physical sensor-based detection system, rather than an image interpretation system, likely means the validation approach focused on engineering performance, reliability, and safety in simulated or bench environments to demonstrate its functional claims.

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The Medrad Stellant CT Injector System is intended for the specific purpose of injecting intravenous contrast media into humans for diagnostic studies in computed tomography (CT) applications. The ISI module option is intended for the specific purpose of allowing an injector to interface with a CT scanner.

The Medrad Stellant CT Injector System with ISI Module is a syringe-based fluid delivery system indicated for delivery of contrast media during computed tomography procedures. It is intended to be used for the specific purpose of injecting intravenous contrast medium into the human vascular system for computed the organy studies. The addition of the ISI Module option to the Stellant CT Injector System simply establishes an interface between the injector and the CT scanner. The Stellant CT Injector System with ISI Module is comprised of the same main components as the predicate device: an Injector Head, a Display Control Unit (DCU) and sterile disposables. The Imaging System Interface Module is added to the system as an accessory.

This is a 510(k) premarket notification for a medical device (Medrad Stellant CT Injector System with Imaging System Interface Module), not an AI/ML device. Therefore, the provided text does not contain information about acceptance criteria, study details, sample sizes, expert involvement, or ground truth relevant to AI/ML performance.

The document focuses on demonstrating substantial equivalence to a predicate device (Medrad Stellant CT Injector System K023183) by comparing features and intended use, rather than proving performance against specific acceptance criteria through a study.

Therefore, I cannot provide the requested information.

Here's why each point cannot be answered:

1. A table of acceptance criteria and the reported device performance: This document is about regulatory clearance (510(k) submission) based on substantial equivalence, not a performance study against specific acceptance criteria for a new AI/ML algorithm. The "reported device performance" in this context is the device's operational parameters and intended use, which are deemed equivalent to the predicate device.
2. Sample sized used for the test set and the data provenance: Not applicable. There is no "test set" in the context of an AI/ML performance study described here.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. There is no ground truth, test set, or expert review process described for an AI/ML algorithm.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. There is no test set or adjudication process for an AI/ML algorithm.
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 injector system, not an AI/ML diagnostic tool that assists human readers.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable. This is not an AI/ML algorithm.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc): Not applicable. There is no ground truth for an AI/ML algorithm described.
8. The sample size for the training set: Not applicable. There is no AI/ML training set.
9. How the ground truth for the training set was established: Not applicable. There is no AI/ML training set.

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The Medrad Stellant CT Injector System is intended for the specific purpose of injecting intravenous contrast media into humans for diagnostic studies in computed tomography (CT) applications.

The Stellant CT Injector System is comprised of the same main components as the predicate device: an Injector Head, a Display Control Unit, and sterile disposables.

The provided document is a 510(k) summary for the Medrad Stellant CT Injector System. This type of regulatory submission focuses on demonstrating substantial equivalence to a predicate device, rather than proving performance against specific acceptance criteria in the manner one might find for a novel diagnostic algorithm.

Therefore, many of the requested details about acceptance criteria, study design, ground truth, and sample sizes for AI/algorithm performance are not applicable to this document as it describes a medical device (an injector system), not an artificial intelligence or machine learning diagnostic tool.

However, I can extract the information relevant to the device's comparison to its predicate and the testing conducted for its clearance.

Acceptance Criteria and Device Performance (for Substantial Equivalence)

The acceptance criteria for the Medrad Stellant CT Injector System, as presented in this 510(k) summary, are primarily focused on demonstrating substantial equivalence to its predicate device, the Medrad EnVision CT Injector System (K934086 and K993728). This means the device must share the same intended use, similar operational parameters, similar labeling, and be used in a similar manner.

The "study" in this context is the design verification and validation testing conducted as part of Medrad's Quality System, in compliance with FDA's Quality System Regulations (QSRs). The results of these tests demonstrated that the design specifications for the Stellant CT Injector System were met and that it meets applicable requirements of international standards.

Details of the "Study" (Design Verification and Validation Testing)

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

   • Not applicable (N/A). This is a hardware device (injector system). The "test set" would refer to physical units of the device and its components undergoing engineering and performance testing rather than a dataset for an algorithm. The document does not specify the number of units tested.
   • Data Provenance: The testing was conducted by Medrad Inc. as part of its internal Quality System. The country of origin of the data would therefore be the USA, where Medrad Inc. is located. It is inherently prospective with respect to the device's development and clearance.

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

   • Not applicable (N/A). "Ground truth" in this context is established by engineering specifications, international performance standards, and comparisons against the predicate device's established performance. The "experts" would be the engineering and quality control teams at Medrad, verifying that the device meets these specifications. Their qualifications would be expertise in medical device design, manufacturing, and regulatory compliance.

3. Adjudication method for the test set:

   • Not applicable (N/A). Adjudication methods like 2+1 or 3+1 are used for human expert review of uncertain cases in diagnostic studies. For a hardware device, performance is evaluated against predefined engineering tolerances and standards. Any discrepancies would likely be resolved through further engineering analysis and re-testing until specifications are met.

4. 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 (N/A). This is not a diagnostic AI device or image reading assistance tool. Therefore, no MRMC study or AI-assistance effectiveness was performed or is relevant.

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

   • Not applicable (N/A). This device is a hardware injector system, not an algorithm. Its "standalone" performance refers to its functional operation as a medical device.

6. The type of ground truth used:

   • For a hardware device like this, the "ground truth" relates to engineering specifications, performance standards (e.g., flow rate accuracy, pressure limits, safety mechanism functionality), and comparison to the predicate device's established capabilities. It's about meeting quantifiable design outputs and demonstrating functional equivalence/improvement.

7. The sample size for the training set:

   • Not applicable (N/A). There is no "training set" in the context of an AI/ML algorithm for a hardware medical device like an injector system.

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

   • Not applicable (N/A). As there is no training set, this question is not relevant.

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