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SmartGuard technology is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible Medtronic continuous glucose monitors (CGMs), and alternate controller enabled (ACE) pumps to automatically adjust the delivery of basal insulin and to automatically deliver correction boluses based on sensor glucose values.
SmartGuard technology is intended for the management of Type 1 diabetes mellitus in persons 7 years of age and older requiring insulin.
SmartGuard technology is intended for single patient use and requires a prescription.

Predictive Low Glucose technology is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible Medtronic continuous glucose monitors (CGMs), and alternate controller enabled (ACE) pumps to automatically suspend delivery of insulin when the sensor glucose value falls below or is predicted to fall below predefined threshold values.
Predictive Low Glucose technology is intended for the management of Type 1 diabetes mellitus in persons 7 years of age and older requiring insulin.
Predictive Low Glucose technology is intended for single patient use and requires a prescription.

SmartGuard Technology, also referred to as Advanced Hybrid Closed Loop (AHCL) algorithm, is a software-only device intended for use by people with Type 1 diabetes for ages 7 years or older. It is an interoperable automated glycemic controller (iAGC) that is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible interoperable Medtronic continuous glucose monitors (CGM) and compatible alternate controller enabled (ACE) pumps to automatically adjust the delivery of basal insulin and to automatically deliver correction boluses based on sensor glucose (SG) values.
The AHCL algorithm resides on the compatible ACE pump, which serves as the host device. It is meant to be integrated in a compatible ACE pump and is an embedded part of the ACE pump firmware.
Inputs to the AHCL algorithm (e.g., SG values, user inputs) are received from the ACE pump (host device), and outputs from the AHCL algorithm (e.g., insulin delivery commands) are sent by the algorithm to the ACE pump. As an embedded part of the firmware, the AHCL algorithm does not connect to or receive data from compatible CGMs; instead, sensor glucose (SG) values or other inputs received by the ACE pump from compatible CGMs via Bluetooth Low Energy (BLE) technology are transmitted to the embedded AHCL algorithm.
The AHCL algorithm works in conjunction with the ACE pump and is responsible for controlling insulin delivery when the ACE pump is in Auto Mode. It includes adaptive control algorithms that autonomously and continually adapt to the ever-changing insulin requirements of each individual.
The AHCL algorithm requires specific therapy settings (target setpoint, insulin-to-carb ratios and active insulin time) that need to be established with the help of a health care provider (HCP) before activation. It also requires five (5) consecutive hours of insulin delivery history, a minimum of two (2) days of total daily dose (TDD) of insulin, a valid sensor glucose (SG) and blood glucose (BG) values to start automated insulin delivery.
When activated, the AHCL algorithm adjusts the insulin dose at five-minute intervals based on CGM data. A basal insulin dose (auto basal) is commanded by the AHCL algorithm to manage glucose levels to the user's target setpoint of 100 mg/dL, 110 mg/dL or 120 mg/dL. The user can also set a temporary target of 150 mg/dL for up to 24 hours. In addition, under certain conditions the algorithm can also automatically command correction boluses (auto correction bolus) without user input.
Meal boluses are the responsibility of the user. The AHCL algorithm includes an integrated bolus calculation feature for user-initiated boluses for meals. When the user inputs their carbohydrate intake, the AHCL algorithm automatically calculates a bolus amount based off available glucose information, entered carbohydrate amount and other patient parameters.
The AHCL algorithm contains several layers of "safeguards" (mitigations) to provide protection against over-delivery or under-delivery of insulin to reduce risk of hypoglycemia and hyperglycemia, respectively.
The AHCL algorithm is a software-only device and does not have a user interface (UI). The compatible ACE pump provides a UI to the user to configure the therapy settings and interact with the algorithm. The AHCL-related alerts/alarms are displayed and managed by the pump.

Predictive Low Glucose Technology, also referred to as the Predictive Low Glucose Management (PLGM) algorithm is a software-only device intended for use by people with Type 1 diabetes for ages 7 years or older. It is an interoperable automated glycemic controller (iAGC) that is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible interoperable Medtronic continuous glucose monitors (CGM) and compatible alternate controller enabled (ACE) pumps to automatically suspend delivery of insulin when the sensor glucose value falls below or is predicted to fall below predefined threshold values.
The PLGM algorithm resides on the compatible ACE Pump, which serves as the host device. It is meant to be integrated in a compatible ACE pump and is an embedded part of the ACE pump firmware.
Inputs to PLGM algorithm (e.g., sensor glucose values, user inputs) are received from the ACE pump (host device), and outputs from PLGM algorithm (e.g., suspend/resume commands) are sent by the algorithm to the ACE pump. As an embedded part of the ACE pump firmware, the PLGM algorithm does not connect to or receive data from compatible CGMs; instead, sensor glucose (SG) values or other inputs are received by the ACE pump from compatible CGMs via Bluetooth Low Energy (BLE) technology are transmitted to the embedded PLGM algorithm.
The PLGM algorithm works in conjunction with the ACE pump. When enabled, the PLGM algorithm is able to suspend insulin delivery for a minimum of 30 minutes and for a maximum of 2 hours based on current or predicted sensor glucose values. It will automatically resume insulin delivery when maximum suspend time of 2 hours has elapsed or when underlying conditions resolve. The user is also able to manually resume insulin at any time.
The PLGM algorithm is a software-only device and does not have a user interface (UI). The compatible ACE pump provides the UI to configure therapy settings and interact with the algorithm. The PLGM-related alerts/alarms are displayed and managed by the pump.

The provided FDA 510(k) clearance letter and supporting documentation detail the acceptance criteria and the studies conducted to prove that Medtronic's SmartGuard Technology and Predictive Low Glucose Technology meet these criteria.

It's important to note that the provided text focuses on demonstrating substantial equivalence to a predicate device, as is typical for 510(k) submissions, rather than establishing de novo acceptance criteria for an entirely novel device. The "acceptance criteria" here refer to the performance benchmarks that demonstrate safety and effectiveness comparable to the predicate and compliance with regulatory special controls.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

The acceptance criteria are generally implied by the comparative data presented against the predicate device (Control-IQ Technology) and the compliance with "iAGC Special Controls requirements defined in 21 CFR 862.1356." The clinical study primarily aims to demonstrate non-inferiority or beneficial outcomes in key glycemic metrics compared to baseline (run-in period).

Table of Acceptance Criteria and Reported Device Performance

Given that this is a 510(k) submission showing substantial equivalence, the "acceptance criteria" are largely derived from the performance of the predicate device and clinical guidelines (e.g., ADA guidelines for Time Below Range). While specific numerical thresholds for acceptance are not explicitly listed as "acceptance criteria" in a table format within the provided text, the results presented serve as the evidence that these implicit criteria are met.

For the purpose of this response, I will synthesize the implied performance targets from the "Pivotal Study Observed Results" and "Supplemental Clinical Data" sections and present the device's reported performance against them.

Table 1: Implied Acceptance Criteria (via Predicate Performance/Clinical Guidelines) and Reported Device Performance for SmartGuard Technology (AHCL Algorithm)

Table 2: Implied Acceptance Criteria and Reported Device Performance for Predictive Low Glucose Technology (PLGM Algorithm)

Study Details

1. Sample Sizes and Data Provenance

Test Set (Clinical Studies):

• SmartGuard Technology (AHCL Algorithm) - Pivotal Study:

  • Adults (18-80 years): 110 subjects enrolled (105 completed).
  • Pediatrics (7-17 years): 112 subjects enrolled (107 completed).
  • Total: 222 subjects enrolled (212 completed).
  • Provenance: Multi-center, single-arm study conducted across 25 sites in the U.S. This implies prospective data collection, specifically designed for this regulatory submission. Home-setting study.

• Predictive Low Glucose Technology (PLGM Algorithm) - Clinical Testing:

  • Adults (14-75 years): 71 subjects. In-clinic study.
  • Pediatrics (7-13 years): 105 subjects. In-clinic evaluation.
  • Total: 176 subjects.
  • Provenance: Multi-center, single-arm, in-clinic studies. Location not explicitly stated but part of a US FDA submission, implying US or international sites adhering to FDA standards. Prospective data.

Training Set:

• SmartGuard Technology & Predictive Low Glucose Technology (Virtual Patient Model):
  • Sample Size: Not explicitly stated as a number of "patients" but referred to as "extensive validation of the simulation environment" and "virtual patient (VP) model."
  • Data Provenance: In-silico simulation studies using Medtronic Diabetes' simulation environment. This is synthetic data generated by computational models, validated against real patient data.

2. Number of Experts and Qualifications for Ground Truth (Test Set)

The clinical studies for both SmartGuard and PLGM technologies involved direct measurement of glucose values via CGM and blood samples (YSI for PLGM study, and HbA1c for SmartGuard study). These are objective physiological measures, not subjective interpretations requiring external expert consensus for "ground truth."

• For SmartGuard Technology: Glucose values were measured by the Simplera Sync CGM and HbA1c by laboratory tests. These are considered objective measures of glycemic control.
• For Predictive Low Glucose Technology: Hypoglycemia induction was monitored with frequent sample testing (FST) and frequent blood sampling for glucose measurements (likely laboratory-grade methods like YSI [Yellow Springs Instrument]).
• Expert involvement: While healthcare professionals (investigators, study coordinators, endocrinologists, nurses) were undoubtedly involved in conducting the clinical studies, managing patient care, and interpreting results, their role was not to establish "ground truth" through consensus or adjudication in the sense of image review. The ground truth was physiological measurements.

3. Adjudication Method for the Test Set

Not applicable in the typical sense of subjective clinical assessments (e.g., radiology image interpretation). Ground truth was established by direct physiological measurements (CGM data, HbA1c, YSI/FST blood glucose). The clinical studies were single-arm studies where subject outcomes were measured, not comparative assessments where multiple readers adjudicate on decisions.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, an MRMC comparative effectiveness study was not described. The clinical studies were single-arm (evaluating the device's performance in a standalone setting against a baseline or predefined safety/efficacy metrics) and did not involve human readers interpreting data from the device to make clinical decisions and then comparing human performance with and without AI assistance. Instead, the AI (algorithm) directly controlled insulin delivery and was evaluated based on patient physiological outcomes.

5. Standalone (Algorithm Only) Performance

Yes, the core of the evaluation is the standalone performance of the algorithms (SmartGuard AHCL and PLGM) in managing glucose, as they are "software-only devices" that reside on the ACE pump firmware. The clinical studies directly measured the physiological impact of the algorithm's actions on glucose levels, HbA1c, and safety parameters in a human population.

6. Type of Ground Truth Used

• Clinical Studies (SmartGuard & PLGM): Objective Physiological Measurements

  • Sensor Glucose (SG) values: From compatible CGMs (Simplera Sync CGM, Guardian 4 CGM)
  • HbA1c: Laboratory measurements of glycosylated hemoglobin.
  • Frequent Sample Testing (FST) / Blood Glucose (BG) values: Clinical laboratory measurements (e.g., YSI) to confirm hypoglycemia during PLGM induction.
  • Adverse Events (AEs): Clinically reported and documented events.
  • These are considered the definitive "ground truth" for evaluating glycemic control and safety.

• In-Silico Simulation Studies: Virtual Patient Model Outputs

  • The "ground truth" for these simulations is the metabolic response of the validated virtual patient models. This computational modeling is used to extend the clinical evidence to various parameter settings and demonstrate equivalence to real-world scenarios.

7. Sample Size for the Training Set

The document does not explicitly state a numerical "sample size" for a distinct "training set" of patients in the traditional machine learning sense for the algorithms themselves. The algorithms are likely developed and refined using a combination of:

• Physiological modeling: Based on established understanding of glucose-insulin dynamics.
• Historical clinical data: From previous similar devices or general diabetes patient populations (though not specified in this document for algorithm training).
• Clinical expertise: Incorporated into the algorithm design.
• The "Virtual Patient Model" itself is a form of simulated data that aids in development and testing. The validation of this model is mentioned as "extensive validation" and establishment of "credibility," implying a robust dataset used to verify its accuracy against real patient responses.

It's typical for complex control algorithms like these to be developed iteratively with physiological models and potentially large historical datasets, but a specific "training set" size for a machine learning model isn't detailed.

8. How the Ground Truth for the Training Set was Established

As noted above, a distinct "training set" with ground truth in the conventional sense of labeling data for a machine learning model isn't described. The development of control algorithms often involves:

• Physiological Simulation: The ground truth for this is the accurate metabolic response as modeled mathematically.
• Clinical Expertise & Design Principles: The ground truth is embedded in the scientific and medical principles guiding the algorithm's control logic.
• Validation of Virtual Patient Model: The "equivalency was demonstrated between Real Patients (RPs) and Virtual Patients (VPs) in terms of predetermined characteristics and clinical outcomes." This suggests that real patient data was used to validate and establish the "ground truth" for the virtual patient model itself, ensuring it accurately mirrors human physiology. This validated virtual patient model then serves as a crucial tool for in-silico testing.

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The UNiD™ Spine Analyzer is intended for assisting healthcare professionals in viewing and measuring images as well as planning orthopedic surgeries. The device allows surgeons and service providers to perform generic, as well as spine related measurements on images, and to plan surgical procedures. The device also includes tools for measuring anatomical components for placement of surgical implants. Clinical judgment and experience are required to properly use the software.

The UNiD™ Spine Analyzer is a web-based application developed to perform preoperative and postoperative patient image measurements and simulate preoperative planning steps for spine surgery. It aims to make measurements on a patient image, simulate a surgical strategy, draw patient-specific rods or choose from a pre-selection of standard implants. The UNiD™ Spine Analyzer allows the user to:

1. Measure radiological images using generic tools and "specialty" tools
2. Plan and simulate aspects of surgical procedures
3. Estimate the compensatory effects of the simulated surgical procedure on the patient's spine

The planning of surgical procedures is done by Medtronic as part of the service of pre-operative planning. The surgical plan may then be used to assist in designing patient-specific implants. Surgeons will have to validate the surgical plan before Medtronic manufactures any implant.

The UNiD™ Spine Analyzer interface is accessible in either standalone mode or connected mode.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter for the UNiD™ Spine Analyzer:

Overview of Device and Study Focus:

The UNiD™ Spine Analyzer is a web-based application designed to assist healthcare professionals in viewing, measuring, and planning orthopedic spine surgeries. This 510(k) submission primarily focuses on the update to the AI-enabled degenerative predictive model (Degenerative Predictive model). The study aims to demonstrate that this new version is non-inferior to the previous version (predicate device).

1. Table of Acceptance Criteria and Reported Device Performance

The core of the performance evaluation for this AI-enabled software function is focused on demonstrating non-inferiority of the updated "Degenerative Predictive model" to the predicate version.

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

• Test Set Sample Size: 274 patient surgery cases.
• Data Provenance:
  • Country of Origin: US only.
  • Retrospective/Prospective: The document states "Preoperative and post operative images from 1050 patient surgery cases were collected." This implies existing data, making it a retrospective collection.

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

• Number of Experts: Not explicitly stated as "experts." Instead, the document mentions "highly trained Medtronic measurement technicians."
• Qualifications of Experts: "Highly trained Medtronic measurement technicians, operating within a quality-controlled environment." The specific professional background (e.g., radiologist, orthopedist) or years of experience are not provided. They were responsible for vetting image viability and performing measurements.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (like 2+1 or 3+1 for consensus). It states that "After the images were collected, they were then provided to and measured by highly trained Medtronic measurement technicians, operating within a quality-controlled environment." This suggests a single evaluation per case by these technicians, which then forms the basis for the ground truth. There's no mention of multiple technicians independently measuring and then adjudicating discrepancies.

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

• No, a formal MRMC comparative effectiveness study involving human readers assisting with AI vs. without AI assistance was not mentioned or described in this document. The study specifically focused on the AI model's performance (algorithm only) compared to its previous version, not the impact on human reader performance.

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

• Yes, a standalone (algorithm only) performance study was done. The entire "AI-enabled device software functions (AI-DSF)" section describes the evaluation of the new Degenerative Predictive model's output against the ground truth, comparing its performance (MAEs) directly to the predicate AI model. This evaluates the algorithm itself.

7. The Type of Ground Truth Used

• Derived from Measured Images by Technicians: "Ground truth was derived from the measured images." These measurements were performed by the "highly trained Medtronic measurement technicians." This is a form of expert consensus/review, albeit by technicians rather than clinicians, and described as measurements on images. It is not pathology or outcomes data.

8. The Sample Size for the Training Set

• Training Set Sample Size: 776 patient surgery cases.

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

• The document implies the ground truth for the training set was established in the same manner as the test set: through measurements performed by "highly trained Medtronic measurement technicians." The statement "Ground truth was derived from the measured images" applies to the overall data collection process before splitting into training and testing sets.

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The MiniMed 780G insulin pump is intended for the subcutaneous delivery of insulin, at set and variable rates, for the management of diabetes mellitus in persons requiring insulin.

The MiniMed 780G insulin pump is able to reliably and securely communicate with compatible, digitally connected devices, including automated insulin dosing software, to receive, execute, and confirm commands from these devices.

The MiniMed 780G insulin pump contains a bolus calculator that calculates an insulin dose based on user-entered data.

The MiniMed 780G insulin pump is indicated for use in individuals 7 years of age and older.

The MiniMed 780G insulin pump is intended for single patient use and requires a prescription.

The MiniMed 780G insulin pump is an alternate controller enabled (ACE) pump intended for the subcutaneous delivery of insulin, at set and variable rates, for the management of diabetes mellitus in persons requiring insulin. It can reliably and securely communicate with compatible digitally connected devices, including an integrated continuous glucose monitor (iCGM), interoperable Medtronic continuous glucose monitor (CGM), and interoperable automated glycemic controller (iAGC). The pump is intended to be used both alone and in conjunction with compatible, digitally connected medical devices for the purpose of drug delivery.

The MiniMed 780G insulin pump is an ambulatory, battery-operated, rate-programmable micro-infusion pump that contains pump software and houses electronics, a pumping mechanism, a user interface, and a medication reservoir within the same physical device. The pump also contains a bolus calculator that calculates an insulin dose based on user-entered data. It is comprised of several discrete external and internal components including a pump case made of a polycarbonate blend, an electronic printed circuit board assembly stacks and a drive motor system.

The MiniMed 780G insulin pump is an interoperable device that can communicate via a Bluetooth Low Energy (BLE) wireless electronic interface with digitally connected devices. The MiniMed 780G insulin pump is a host device for the iAGC and integrates iAGC algorithm into the pump firmware. The pump is then able to receive, execute, and confirm commands from an iAGC to adjust delivery of insulin. The pump receives sensor glucose (SG) data via BLE interface from a compatible iCGM or a compatible interoperable Medtronic CGM and transmits these CGM data to the embedded iAGCs.

The MiniMed 780G insulin pump can operate in one of two modes: Manual Mode or Auto Mode (also referred to as "SmartGuard Mode"). The pump provides the user with keypad pump controls, as well as a data screen for configuring therapy settings and viewing continuous real-time glucose values, glucose trends, alerts, alarms, and other information. The user interface and alerts provide the user with the ability to interact with the pump delivery system and digitally connected devices.

The provided text is a 510(k) clearance letter and summary for a medical device, specifically an insulin pump. It details the device's characteristics, intended use, comparison to predicate devices, and a summary of non-clinical performance data.

However, it does not contain the information requested regarding acceptance criteria and a study proving a device meets these criteria in the context of an AI/Machine Learning device for image analysis or diagnostics.

The request asks for details like:

• A table of acceptance criteria and reported device performance (which is present in a limited form for the pump's mechanical performance, but not for AI performance).
• Sample size for the test set and data provenance.
• Number of experts and their qualifications for ground truth.
• Adjudication method for the test set.
• MRMC study details and effect size.
• Standalone performance.
• Type of ground truth used.
• Sample size for the training set.
• How ground truth for the training set was established.

These specific points are highly relevant to the validation of AI/ML-enabled medical devices, particularly those that interpret or analyze data (like images) to aid in diagnosis or treatment decisions. The MiniMed 780G insulin pump, while having advanced software and interoperability, is primarily an infusion pump with a bolus calculator, not an AI/ML diagnostic or image analysis tool. Its software functions relate to pump control, communication, and basic dose calculation, not complex pattern recognition or inference typically associated with AI in medical devices that would require the detailed validation described in the prompt.

Therefore, I cannot extract the requested information from the provided text because the text describes a different type of medical device and its associated validation. The validation methods mentioned (Delivery Volume Accuracy, Occlusion Detection, Drug Stability, Cybersecurity, Human Factors) are appropriate for an insulin pump but do not align with the AI/ML-specific validation criteria outlined in your prompt.

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The Rialto™ SI Fusion System is intended for sacroiliac joint fusion for conditions including sacroiliac joint disruptions and degenerative sacroiliitis.

The subject RIALTO™ SI Fusion System consists of cannulated devices available in various lengths, used to provide stabilization when fusion of the sacroiliac joint is desired. Autograft and/or allograft may be placed in conjunction with the RIALTO™ SI Fusion System. The RIALTO Screws are made using Titanium Alloy and are 40mm- 60mm in length with a diameter of 12mm. This device may be implanted via a minimally invasive approach using fluoroscopy or navigated instruments compatible with Medtronic StealthStation® and IPC® POWEREASE®.

The provided FDA 510(k) clearance letter for the Rialto™ SI Fusion System does not contain information about the acceptance criteria and study proving a device meets those criteria in the context of an AI/Software as a Medical Device (SaMD).

The document describes a medical implant (Rialto™ SI Fusion System) and its mechanical and MRI safety performance, not an AI or software device. The studies mentioned (ASTM F2182-19e2, F2052-21, F2213-17, F2119-24, F2503-23) are for evaluating the safety and compatibility of passive implants in the Magnetic Resonance (MR) Environment, which are standard non-clinical tests for physical medical devices.

Therefore, I cannot provide the requested information for acceptance criteria and study details related to an AI/SaMD, as the provided input does not pertain to such a device.

If you have a document describing the clearance of an AI/SaMD, I would be happy to analyze it for the requested information.

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This cannula is intended for use in venous drainage via the right atrium and inferior vena cava simultaneously during cardiopulmonary bypass surgery up to six hours or less.

The MC2™ Two-Stage Venous Cannula and MC2X™ Three-Stage Venous Cannula models feature wire wound polyvinyl chloride (PVC) bodies with side ports in the distal tip, a ported atrial basket drainage site located along the length of the cannula body, and a 3/8-inch (0.95 cm) to 1/2-inch (1.27 cm) connection site. The overall length of each cannula body is approximately 15¼ inch (38.7 cm). Insertion depth marks are provided to aid in positioning of the cannula. Each cannula is nonpyrogenic, is intended for single use, and has been sterilized using ethylene oxide.

This document is a 510(k) clearance letter for a medical device: the MC2™ Two-Stage Venous Cannula and MC2X™ Three-Stage Venous Cannula. It does not contain any information about an AI/ML-driven medical device, nor does it discuss acceptance criteria, test sets, ground truth establishment, or human reader studies related to AI performance.

The clearance is for a physical device used in cardiopulmonary bypass surgery, and the summary of performance data refers to pre-clinical bench testing related to material formulation changes, not algorithmic performance.

Therefore, I cannot provide the requested information based on the provided text. The prompt asks for details about AI/ML device performance, which are entirely absent from this 510(k) clearance.

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Stedi Guidewire is intended for use to introduce and position catheters during interventional procedures within the chambers of the heart, including transcatheter aortic valve replacement (TAVR).

The Medtronic Stedi™ Extra Support Guidewire (herein after referred as Stedi Guidewire) is design for use to introduce and position catheters during interventional procedures within the chambers of the heart, including transcatheter aortic valve replacement (TAVR) procedures.

The Stedi Guidewire has a 0.035" diameter and is 275cm in length and composed of two primary components: a core, and a coil. Both components are made of stainless steel. The core wire component is a piece of stainless-steel wire which is ground on the distal end to fit into the coil and provide flexibility. The coil and the ground core are joined in two locations: a proximal bond and a distal weld. The distal end of the Stedi Guidewire is comprised of a preformed 540° curved tip is available in 2 sizes (3cm and 4cm). The Stedi Guidewire has a polytetrafluoroethylene (PTFE) coating applied to the entire length of the device in order to aid in lubricity.

The Stedi Guidewire is sterilized using ethylene oxide, nonpyrogenic, disposable, and for single use only.

Based on the provided FDA 510(k) clearance letter for the Medtronic Stedi Extra Support Guidewire, here's a detailed breakdown of the acceptance criteria and the study information.

It's important to note that the provided document is a 510(k) clearance letter, which focuses on demonstrating substantial equivalence to a predicate device. For medical devices like guidewires, the "studies" primarily consist of non-clinical (bench) performance testing to ensure the new device meets established safety and performance requirements, rather than clinical trials with human subjects in the way AI/software devices typically undergo. Therefore, many of the questions related to human readers, ground truth, and training sets are not applicable in this context.

Acceptance Criteria and Reported Device Performance

The acceptance criteria for the Medtronic Stedi Extra Support Guidewire are demonstrated through various non-clinical (bench) performance tests. The FDA guidance "Coronary, Peripheral and Neurovascular Guidewires Performance Tests and Recommended Labeling (October 10, 2019)" was utilized to establish these tests. The conclusion states that the device "met all design input requirements based on the intended use."

Here's a table summarizing the types of tests conducted, which imply the acceptance criteria were met by the device's performance in these areas:

Study Details

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

   • Sample Size: The document does not specify exact sample sizes for each non-clinical test. However, it indicates "samples were analyzed according to predetermined acceptance criteria" for the various bench tests. In medical device bench testing, sample sizes are typically determined statistically to ensure sufficient power to detect differences or to demonstrate compliance with specifications.
   • Data Provenance: The data is generated from non-clinical bench testing performed by Medtronic Inc. This is not clinical data (i.e., no patient data is involved). It is prospective in the sense that the tests were designed and executed to evaluate the new device.

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

   • N/A. For this type of medical device (guidewire), ground truth is established through engineering specifications, material science standards (e.g., ISO standards), and performance benchmarks derived from predicate devices and historical data. It does not involve human expert consensus in the diagnostic sense. The "experts" are the engineers, material scientists, and testers who design and conduct the tests and interpret the results against predetermined specifications.

3. Adjudication method for the test set:

   • N/A. Adjudication methods like "2+1" or "3+1" are relevant for clinical studies or studies involving human interpretation of data (e.g., image analysis). For bench testing of a guidewire, results are quantitative or qualitative against predetermined engineering specifications, and "adjudication" typically refers to the pass/fail criteria established for each test.

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:

   • No. This is a physical medical device (guidewire), not an AI/software device that assists human readers/clinicians, so an MRMC study is not applicable.

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

   • N/A. This is a physical medical device, not an algorithm. The "standalone" performance is the device's performance in the bench tests, independent of its use in a patient for the initial testing and FDA clearance.

6. The type of ground truth used:

   • The "ground truth" for each test is based on pre-established engineering specifications, material standards (e.g., ISO), and performance characteristics derived from the predicate device and FDA guidance documents. For example, the "ground truth" for tensile strength is a minimum force value, for biocompatibility it's compliance with ISO 10993, and for dimensions it's adherence to specified tolerances.

7. The sample size for the training set:

   • N/A. This is a physical medical device undergoing non-clinical testing, not a machine learning model, so there is no "training set."

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

   • N/A. As there is no training set for a physical device, this question is not applicable.

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Grafton™ DBM and Grafton Plus ™ DBM Paste are intended for use as a bone graft extender, bone graft substitute, and bone void filler in bony voids or gaps of the skeletal system (i.e., posterolateral spine, intervertebral disc space (excluding Flex or Crunch), pelvis and extremities) not intrinsic to the stability of the bony structure. The voids or gaps may be surgically created defects or defects created by traumatic injury to the bone. When used in intervertebral body fusion procedures, Graft™ DBM (excluding Flex or Crunch) and Grafton Plus ™ DBM Paste must be used with an intervertebral body fusion device cleared by FDA for use with a bone void filler.
Grafton™ DBM and Grafton Plus™ DBM Paste are absorbed/remodeled and replaced by host bone during the healing process.

Magnifuse™ Bone Graft is intended for use as a bone graft substitute in bony voids or gaps of the skeletal system (i.e., posterolateral spine, intervertebral disc space, pelvis and extremities) not intrinsic to the stability of the bony structure. Voids or gaps may be surgically created defects or defects created by traumatic injury to the bone.
Magnifuse™ Bone Graft may be used in a manner comparable to autogenous bone or allograft bone. Magnifuse™ Bone Graft may be mixed with fluid such as bone marrow aspirate, blood, sterile water, or sterile water in order to adjust consistency and handling of bone graft material.
When used in intervertebral body fusion procedures, Magnifuse™ Bone Graft must be used with an intervertebral body fusion device cleared by FDA for use with a bone void filler.
Magnifuse™ Bone Graft is resorbed/remodeled and is replaced by host bone during the healing process.

The Grafton™ DBM, Grafton Plus™ DBM Paste, and Magnifuse™ Bone Graft devices in this submission are human bone products containing human demineralized bone matrix (DBM).

Grafton™ DBM is a human bone product that contains human DBM with an inert additive. Grafton™ DBM is produced in particular physical forms (Grafton™ DBM Gel, Grafton™ DBM Putty, Grafton™ DBM Matrix, Grafton™ DBM Orthoblend) and/or handling property. Grafton™ DBM is provided in ready-to-use form and is intended in single patient, single use containers. Grafton™ DBM is identical to the device cleared in K051195.

Grafton Plus™ DBM Paste is human demineralized bone matrix combined with an inert additive to yield a product having a particular physical form and/or handling property. Grafton Plus™ DBM Paste is identical to the device cleared in K043048.

Magnifuse™ Bone Graft is a human bone allograft product containing human DBM and surface demineralized cortical bone chips sealed in an absorbable mesh pouch for intraoperative handling. It is intended for use in filling bony voids or gaps or the skeletal system not intrinsic to the stability of the bony structure. Magnifuse™ Bone Graft is provided ready-to-use in various package sizes by volume or dimension and is intended for single patient use. Magnifuse™ Bone Graft is identical to the device cleared in K082615.

This FDA 510(k) clearance letter (K251193) is for bone graft materials (Grafton™ DBM, Grafton Plus™ DBM Paste, Magnifuse™ Bone Graft) and does not describe an AI/software device or a study with "acceptance criteria" based on AI performance metrics like sensitivity, specificity, or reader studies.

The document details the substantial equivalence of new product formulations/expanded indications for use to previously cleared predicate and reference devices. The "performance" section refers to pre-clinical testing and leveraging prior clearances for bone graft characteristics (e.g., DBM properties, viral inactivation, shelf-life, biocompatibility in animal models, etc.), not a clinical study involving human readers or AI algorithm performance.

Therefore, I cannot provide the information requested in your prompt as it pertains to AI device acceptance criteria and performance studies. The document does not contain:

• A table of acceptance criteria and reported device performance for an AI system.
• Sample sizes for a test set, data provenance, or expert ground truth establishment for an AI study.
• Details on MRMC studies or human reader improvement with AI assistance.
• Standalone algorithm performance.
• Description of ground truth type for an AI system.
• Training set sample size or how ground truth for training was established for an AI system.

The "Performance" section explicitly states: "The devices' performance in the intervertebral body space was supported by a robust analysis of bone grafting materials in the prior posterolateral spine fusion studies." This refers to biological and mechanical performance of the bone graft materials themselves, not an AI software.

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The Liberant Thrombectomy System is indicated for the removal of fresh, soft emboli or thrombi from the vessels of the peripheral arterial and venous systems.

The Liberant Thrombectomy System consists of the Liberant Thrombectomy Set (catheter, clotbuster, dilator, aspiration tubing and hemostasis valve), Liberant Blood Conservation Unit (BCU), Riptide Aspiration Pump and Riptide Collection Canister with intermediate tubing. The Liberant Thrombectomy Set is single use and provided sterile.

The Liberant BCU and Riptide Pump are provided separately from the Liberant Thrombectomy Set as non-sterile capital units. The single use, non-sterile Riptide Collection Canister is provided separately to the pump.

The provided FDA 510(k) clearance letter pertains to a thrombectomy system, which is a physical medical device (catheter-based system) used for removing blood clots. It is not an AI/ML device. Therefore, the request for acceptance criteria and study details relevant to AI/ML devices, such as sample size for test/training sets, data provenance, expert ground truth, adjudication methods, MRMC studies, and standalone performance, cannot be answered from the provided text.

The document focuses on the substantial equivalence of the Liberant Thrombectomy System to a predicate device (Penumbra Indigo Aspiration System) based on:

• Indications for Use: Both systems are indicated for the removal of fresh, soft emboli or thrombi from peripheral arterial and venous systems.
• Operating Principle/Technological Design: Both utilize continuous aspiration and fragmentation tools (clotbuster/separator).
• Catheter Specifications: Similar sizes, lengths, and guidewire compatibility.
• Non-Clinical Data: Extensive performance, biocompatibility, shelf-life, sterilization, packaging, software validation (for the pump control), and electrical/EMC testing were performed. This type of "software validation" typically refers to embedded software controlling the pump mechanics, not an AI/ML diagnostic or predictive algorithm.

Therefore, I cannot provide the requested information regarding AI/ML device acceptance criteria and study details because the provided document describes a non-AI medical device.

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Stealth™ Spine Clamps

When used with Medtronic computer assisted surgery systems, defined as including the Stealth™ System, the following indications of use are applicable:

• The spine referencing devices are intended to provide rigid fixation between patient and patient reference frame for the duration of the surgery. The devices are intended to be reusable.
• The navigated instruments are specifically designed for use with Medtronic computer-assisted surgery systems, which are indicated for any medical condition in which the use of stereotactic surgery may be appropriate or vertebra can be identified relative to a CT or MR based model, fluoroscopy images, or digitized landmarks of the anatomy.
• The Stealth™ spine clamps are indicated for skeletally mature patients.

ModuLeX™ Shank Mounts

When used with Medtronic computer assisted surgery systems, defined as including the Stealth™ System, the following indications of use are applicable:

• The spine referencing devices are intended to provide rigid fixation between patient and patient reference frame for the duration of the surgery. The devices are intended to be reusable.
• The navigated instruments are specifically designed for use with Medtronic computer assisted surgery systems, which are indicated for any medical condition in which the use of stereotactic surgery may be appropriate or vertebra can be identified relative to a CT or MR based model, fluoroscopy images, or digitized landmarks of the anatomy.
• The ModuLeX™ shank mounts are indicated to be used with the CD Horizon™ ModuLeX™ Spinal System during surgery.
• The ModuLeX™ shank mounts are indicated for skeletally mature patients.

The Stealth™ Spine Clamps are intended to provide rigid attachment between the patient and patient reference frame for the duration of the surgery. The subject devices are designed for use with the Stealth™ System and are intended to be reusable.

The ModuLeX™ Shank Mounts are intended to provide rigid attachment between the patient and patient reference frame for the duration of the surgery. The subject devices are designed for use with the Stealth™ System and are intended to be reusable.

This document, an FDA 510(k) Clearance Letter, does not contain the specific details about acceptance criteria and study data that would be found in a full submission. 510(k) summary documents typically provide a high-level overview.

Based on the provided text, here's what can be extracted and what information is not available:

Information from the document:

• Device Type: Stealth™ Spine Clamps and ModuLeX™ Shank Mounts, which are orthopedic stereotaxic instruments used with computer-assisted surgery systems (specifically the Medtronic Stealth™ System).
• Purpose: To provide rigid fixation between the patient and a patient reference frame for the duration of spine surgery, and to serve as navigated instruments for surgical guidance.
• Predicate Devices:
  • Stealth™ Spine Clamps: StealthStation™ Spinous Process Clamps (K211442)
  • ModuLeX™ Shank Mounts: Rod Clamps (K131425)
• Testing Summary (XI. Discussion of the Performance Testing):
  • Mechanical Robustness and Navigation Accuracy
  • Functional Verification
  • Useful Life Testing
  • Packaging Verification
  • Design Validation
  • Summative Usability
  • Biocompatibility (non-cytotoxic, non-sensitizing, non-irritating, non-toxic, non-pyrogenic)

Information NOT available in the provided document (and why):

This 510(k) summary describes physical medical devices (clamps and mounts) used in conjunction with a computer-assisted surgery system, but it does not describe an AI/software device whose performance is measured in terms of accuracy, sensitivity, or specificity for diagnostic or guidance purposes. Therefore, many of the requested points related to AI performance, ground truth, and reader studies are not applicable or not detailed in this type of submission.

Specifically, the document does not contain:

1. A table of acceptance criteria and reported device performance (with specific numerical metrics for "Navigation Accuracy"): While "Navigation Accuracy" is listed as a test conducted, the actual acceptance criteria (e.g., "accuracy must be within X mm") and the quantitative results are not provided in this summary. This would typically be in a detailed test report within the full 510(k) submission.
2. Sample sizes used for the test set and data provenance: No information on the number of units tested, or if any patient data was used for "Navigation Accuracy" (it's likely bench testing).
3. Number of experts used to establish ground truth and their qualifications: Not applicable as this is a mechanical device submission, not an AI diagnostic submission. Ground truth for mechanical accuracy would be established by precise measurement tools, not human experts in this context.
4. Adjudication method for the test set: Not applicable for mechanical/functional testing.
5. Multi-Reader Multi-Case (MRMC) comparative effectiveness study: Not mentioned or applicable. This type of study is for evaluating human performance (e.g., radiologists interpreting images) with and without AI assistance.
6. Stand-alone (algorithm only) performance: Not applicable; this is not an algorithm for diagnosis or image analysis.
7. Type of ground truth used (expert consensus, pathology, outcomes data, etc.): For "Navigation Accuracy," the ground truth would be based on highly precise measurement systems (e.g., optical tracking validation) in a lab setting, not clinical outcomes or expert consensus.
8. Sample size for the training set: Not applicable; there is no "training set" as this is not a machine learning model.
9. How the ground truth for the training set was established: Not applicable.

Summary of what is known concerning acceptance criteria and proof of adherence:

• Acceptance Criteria/Proof (General): The document states that "Testing conducted to demonstrate equivalency of the subject device to the predicate is summarized as follows: Mechanical Robustness and Navigation Accuracy, Functional Verification, Useful Life Testing, Packaging Verification, Design Validation, Summative Usability, Biocompatibility."
• Implied Acceptance: The FDA's clearance (K242464) indicates that Medtronic successfully demonstrated that the new devices are "substantially equivalent" to predicate devices based on the submitted testing. This means the performance met the FDA's expectations for safety and effectiveness, likely by demonstrating equivalent or better performance against the predicates in the specified tests (e.g., meeting established benchmarks for sterility, material strength, and precision when interfaced with the navigation system). However, the specific numerical criteria for "Navigation Accuracy" are not disclosed in this summary letter.

Conclusion based on the provided text:

This 510(k) summary is for a Class II mechanical stereotaxic instrument and, as such, focuses on demonstrating mechanical, functional, and biocompatibility equivalency to predicate devices. It does not contain the detailed performance metrics, ground truth establishment methods, or human reader study results that would be pertinent to an AI/software medical device submission.

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