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The Anumana Low Ejection Fraction AI-ECG Algorithm is software intended to aid in screening for Left Ventricular Ejection Fraction (LVEF) less than or equal to 40% in adults at risk for heart failure. This population includes, but is not limited to:
· patients with cardiomyopathies

• patients who are post-myocardial infarction
• · patients with aortic stenosis
• · patients with chronic atrial fibrillation
• · patients receiving pharmaceutical therapies that are cardiotoxic, and
  • postpartum women.

Anumana Low Ejection Fraction Al-ECG Algorthm is not intended to be a stand-alone diagnostic device for cardiac conditions, should not be used for monitoring of patients, and should not be used on ECGs with a paced rhythm.

A positive result may suggest the need for further clinical evaluation in order to establish a diagnosis of Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. Additionally, if the patient is at high risk for the cardiac condition, a negative result should not rule out further non-invasive evaluation.

The Anumana Low Ejection Fraction AI-ECG Algorithm should be applied jointly with clinician judgment.

The Low Ejection Fraction AI-ECG Algorithm interprets 12-lead ECG voltage times series data using an artificial intelligence-based algorithm. The device analyzes 10 seconds of a single 12lead ECG acquisition, and within seconds provides a prediction of likelihood of LVEF (ejection fraction less than or equal to 40%) to third party software. The results are displayed by the third-party software on a device such as a smartphone, tablet, or PC. The Low Ejection Fraction AI-ECG Algorithm was trained to predict Low LVEF using positive and control cohorts, and the prediction of Low LVEF in patients is generated using defined conditions and covariates. The Low Ejection Fraction AI-ECG Algorithm device is intended to address the unmet need for a point-of-care screen for LVEF less than or equal to 40% and is expected to be used by cardiologists, front-line clinicians at primary care, urgent care, and emergency care settings, where cardiac imaging may not be available or may be difficult or unreliable for clinicians to operate. Clinicians will use the Low Eiection Fraction AI-ECG Algorithm to aid in screening for LVEF less than or equal to 40% and making a decision for further cardiac evaluation.

Here's a breakdown of the acceptance criteria and the study proving the device meets those criteria, based on the provided FDA 510(k) clearance letter for the Low Ejection Fraction AI-ECG Algorithm:

Low Ejection Fraction AI-ECG Algorithm: Acceptance Criteria and Performance Study

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: The clinical validation study included 16,000 patient records initially, though 2,040 records were excluded due to quality checks, resulting in a final analysis sample of 13,960 patient-ECG pairs.
• Data Provenance: The data was retrospective, collected from 4 health systems across the United States.

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

The document does not specify the number of experts or their qualifications used to establish the ground truth for the clinical validation test set. The ground truth (LVEF <= 40% or > 40%) was derived from transthoracic echocardiogram (TTE) measurements. While TTE interpretation requires expertise, the document doesn't detail the method of expert review or consensus for these TTE results themselves for the test set.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1) for the ground truth for the test set. The ground truth was established by TTE measurements.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The study evaluated the standalone performance of the AI algorithm against a ground truth without human readers in the loop.

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

Yes, a standalone performance study was done. The reported sensitivity and specificity values are for the algorithm's performance alone in detecting low LVEF.

7. The Type of Ground Truth Used

The type of ground truth used for both training and validation was objective clinical measurements from Transthoracic Echocardiogram (TTE), specifically the Left Ventricular Ejection Fraction (LVEF) measurement. An LVEF of $\le$ 40% was defined as the disease cohort, and > 40% as the control cohort.

8. The Sample Size for the Training Set

The training set for the algorithm development consisted of 93,722 patients with an ECG and TTE performed within a 2-week interval. These were split into:

• Training dataset: 50% of the 93,722 patients.
• Tuning dataset: 20% of the 93,722 patients.
• Set-aside testing dataset: 30% of the 93,722 patients (used for internal validation during development, distinct from the independent clinical validation study).

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

The ground truth for the training set was established using LVEF measurements obtained from transthoracic echocardiograms (TTE). Specifically, for each patient, the LVEF measurement from the earliest TTE within a 2-week interval of an ECG was paired with the closest ECG recording. LVEF $\le$ 40% defined the disease cohort, and LVEF > 40% defined the control cohort. This data was identified from a research-use authorized clinical database from Mayo Clinic.

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The Merit Low Profile Companion Sheath is indicated to be used for the introduction of interventional and diagnostic devices into the peripheral (and coronary) vasculature.

The Low Profile Companion Sheath is a sterile, disposable device consisting of (a) a coil reinforced shaft with and the distal end; (b) a hemostasis valve with a side port and (c) a tapered tip dilator with snap-fit hub at the proximal end.

(a) Shaft. The coil reinforced, multi-layer polymer shaft contains a tapered tip at the distal end. A continuous inner PTFE tube forms the core of the shaft and provides a circular working lument through which devices can be passed. A hydrophilic coating is applied to the entire outer surface of the shaft. A radiopaque marker made of platinum iridium is embedded 5mm from the dista At the proximal end of the shaft, a female, winged luer hub is over-molded onto the shaft to support handling and to provide for the connection of the hemostasis valve.

(b) Hemostasis valve. A removable hemostasis valve is thread onto the proximal end of the shaft. Inside the valve housing, a lubricated, silicone slit disc provides a seal around devices passed through the sheath, thereby preventing blood leakage through the valve. Just distal of the valve housing is connected to a side port leading to a three-way stopcock valve. The sideport is used for flushing the introducer sheath.

(c) Dilator. The dilator made of a polypropylene blend contains a full-length round lumen to allow placement over a guidewire. The distal end of the dilator is configured as a tapered tip that extends about 2 cm beyond the end of the dilator is fully inserted through the sheath.

The Low Profile Companion Sheath is a prescription medical device that is used only in healthcare facilities and hospitals. The device is placed in patients for up to 24 hours.

This is a 510(k) summary for a medical device called the "Low Profile Companion Sheath." This document describes the device and claims substantial equivalence to a previously cleared device (predicate device). For such submissions, the acceptance criteria and study information provided generally focus on demonstrating that the new device performs comparably to the predicate or meets established performance specifications. The details for acceptance criteria and studies are typically more concise than for novel device approvals.

Here's an analysis of your requested information based on the provided text:

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

Based on the text, the acceptance criteria are implicitly tied to meeting "predetermined specifications" for the "changed dimensions of the device." The specific numerical acceptance criteria are not explicitly provided in this summary, but the general categories of tests and their successful outcome are stated.

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

The document does not explicitly state the sample size used for the design verification tests. It mentions "design verification tests" were performed. The "data provenance" (country of origin, retrospective/prospective) is also not specified, as these are typically bench-top engineering tests rather than clinical studies.

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)

This is a technical device submission, not an AI/diagnostic software submission where expert adjudication is common for ground truth. Therefore, this information is not applicable and not provided in the document. The "ground truth" here is defined by engineering specifications and physical measurements.

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

As this is a technical device submission involving engineering measurements and performance testing, an "adjudication method" in the context of expert review for diagnostic accuracy is not applicable and not mentioned. The tests would likely be performed by qualified engineers/technicians according to established protocols.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study is relevant for diagnostic software (often AI-powered) where human interpretation is involved. This submission is for a physical medical device (catheter introducer sheath). Therefore, such a study was not performed and is not applicable to this device.

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

This refers to AI algorithm performance. Since this is a physical medical device, there is no AI algorithm involved, and thus no standalone performance was evaluated in this context.

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

For the design verification tests, the "ground truth" would be the predetermined engineering specifications and physical measurements for the device's dimensions and characteristics. This is not "expert consensus," "pathology," or "outcomes data" in the clinical sense.

8. The sample size for the training set

This question refers to the training of an AI algorithm. Since this is a physical medical device and does not involve an AI algorithm, there is no "training set."

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

Again, this question is relevant for AI algorithm development. As there is no AI algorithm or training set for this physical medical device, this information is not applicable.

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The Low Dose CT Lung Cancer Screening Option for Canon/Toshiba CT systems is indicated for using low dose CT for lung cancer screening. The screening must be conducted with the established program criteria and protocols that have been approved and published by a governmental body, a professional medical society and/or Canon.

Information from professional societies related to lung cancer screening can be found, but is not limited to: American College of Radiology® (ACR)-resources and technical specification; accreditation American Association of Physicists in Medicine (AAPM) - Lung Cancer Screening Protocols; radiation management.

The low dose lung cancer screening option is an indication being added to the following existing, previously FDA-cleared scanners: [List of Aquilion and Lightning CT scanner models and their corresponding 510(k) numbers]. No functional, performance, feature, or design changes are being made to the devices that will be indicated for low dose lung cancer screening. The devices already include low dose lung screening protocols, intended for use in the review of thoracic CT images within the established inclusion criteria of programs/protocols that have been approved and published by either a governmental body or professional medical society.

The provided text describes a 510(k) premarket notification for a "Low Dose CT Lung Cancer Screening Option" from Canon Medical Systems Corporation. The submission seeks to add this indication to existing, previously FDA-cleared CT scanners. The key claim is substantial equivalence to a predicate device (Aquilion RXL, K121553, which is a successor to the Aquilion 16 used in the National Lung Screening Trial - NLST). The device's performance is demonstrated through bench testing only, not a clinical study involving human subjects or AI-assisted readings.

Therefore, the following information can be extracted/inferred:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document states that performance was "substantially equivalent" to the predicate. Specific numerical values for the reported performance are not provided in this regulatory summary.

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

• Test Set Sample Size: Not applicable in the traditional sense of a clinical test set with patient data. The "test set" consists of bench testing data from representative scanners from different CT system families. One device from each of the three identified families (Aquilion 16/32/64/RXL, PRIME/PRIME SP, ONE/ViSION/Genesis, and Lightning) was used for bench testing.
• Data Provenance: The data is from bench testing performed by Canon Medical Systems Corporation. The document does not specify the country of origin for this bench testing data, but the manufacturer is Canon Medical Systems Corporation (Japan) with a U.S. agent. The original NLST data (which the predicate is compared against) was from a large-scale, prospective clinical trial conducted in the United States.

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

Not applicable. This submission relies on bench testing for substantial equivalence, not a clinical study requiring expert ground truth for image interpretation.

4. Adjudication Method for the Test Set

Not applicable, as no human readers or clinical image interpretation were part of the presented performance data.

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 submission is for a CT scanner's indication for low-dose lung cancer screening, not an AI-powered diagnostic assist device. The performance demonstration is based on the physical imaging characteristics of the CT system.

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

Not applicable. This is for a CT imaging device, not a standalone algorithm.

7. The Type of Ground Truth Used

The "ground truth" for this substantial equivalence argument is the performance of the predicate device (Aquilion RXL), which is stated to have similar technological characteristics and performance equivalent to the Aquilion 16 used in the NLST. The "ground truth" for the benefit of low-dose CT lung cancer screening itself comes from clinical literature, specifically referencing the National Lung Screening Trial (NLST) results, which demonstrated reduced mortality from lung cancer with low-dose CT screening. However, the device's performance itself is measured against established phantom-based image quality metrics.

8. The Sample Size for the Training Set

Not applicable. This is a CT imaging device, not an AI/ML algorithm that requires a training set of data.

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

Not applicable.

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The Low Dose CT Lung Cancer Screening Option for the SCENARIA and SUPRIA CT systems is indicated for using low dose CT for lung cancer screening. The screening must be conducted with the established protocols that have been approved and published by a governmental body, a professional medical society, and/or Hitachi.

There are not any functional, performance, feature, or design changes required for the CT systems which the option is applied:

• SCENARIA Phase 3 Whole-Body X-ray CT System K150595
• SUPRIA Whole-body X-ray CT System Phase 3 K163528
  Because neither of the CTs will require hardware or software modifications the subject device will include:
• Three reference LCS protocols (small, average, large patient) for each CT System
• Protocols will be loaded onto the system, there will be no need for installation instructions
• Low Dose CT Lung Cancer Screening Option instruction manual
  The reconstruction method for the LCS protocols is Filtered Back Projection with no iterative reconstruction method. The reconstruction algorithm used was a 21 Lung which is common to demonstrate lung tissues nodules and other lung pathology.
  In addition, Beam Hardening Correction is utilized in the reconstruction process. The beam hardening correction applied to the lung reconstruction algorithm corrects image quality degradation due to radiation hardening caused by metals and other dense subject matter such as shoulders, etc. Hitachi does not apply any other tools or software in the reconstruction process.

The provided text describes the substantial equivalence determination for the "Low Dose CT Lung Cancer Screening Option" from Hitachi Healthcare Americas. The submission focuses on demonstrating that the new option, which consists of reference LCS protocols for existing SCENARIA and SUPRIA CT systems, performs equivalently to a legally marketed predicate device (Philips Multislice CT System with Low Dose CT Lung Cancer Screening - K153444).

Here's an analysis based on the provided information:

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

The document does not explicitly state "acceptance criteria" with numerical thresholds set for a new device. Instead, it defines "Image Quality Metric CTQs" (Critical to Quality) as important parameters for lung cancer screening and subsequently compares the subject devices (Hitachi SCENARIA and SUPRIA CT systems with the LCS option) to the predicate device (Philips Brilliance CT 64-channel) based on these metrics. The goal was to prove "substantial equivalence," meaning the new device performs similarly to the predicate.

Here's a table summarizing the image quality metrics and the reported comparative performance:

Conclusion on Acceptance: Hitachi concluded that the comparison demonstrated "substantial equivalence" based on these metrics, meaning the subject device performs as effectively and safely as the predicate.

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

• Sample size: The non-clinical testing was performed using phantom studies. The document specifies repetition and slice counts for the phantom measurements:
  • SUPRIA and SCENARIA scanners: 15 repetitions one slice, 15 slices one study.
  • Philips scanner: 17 repetitions one slice, 25 slices one study.
• Data provenance: This was a non-clinical bench study comparing CT scanners in a lab setting, not human data. Therefore, country of origin or retrospective/prospective classification (as typically applied to clinical trials) is not applicable. The study was conducted by Hitachi Healthcare Americas.

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)

• For the non-clinical phantom study, there were no human experts establishing ground truth in the traditional sense of clinical interpretation. The "ground truth" was the physical properties of the phantom and the objective numerical measurements derived from the CT images of the phantom.
• The analysis was done using MATLAB, implying objective quantitative assessment.

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

• Since the test set involved objective phantom measurements and not human interpretation of clinical images, an adjudication method for expert consensus is not applicable. The measurements were quantitative and compared directly.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• No MRMC study was done. The submission states: "Hitachi has determined that additional clinical data for our LCS feature is not needed and that comparative phantom analysis is sufficient to demonstrate substantial equivalence."
• This device is not an AI algorithm adding assistance to human readers. It's a set of low-dose protocols for CT systems that are already cleared. The comparison is between the performance of the CT system with these protocols to a predicate CT system with similar protocols, using phantom measurements. Therefore, the question about AI assistance and reader improvement is not relevant to this submission.

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

• This device is not an algorithm in the sense of a standalone AI diagnostic tool. It is a set of acquisition protocols for a CT scanner. The "standalone" performance in this context would refer to the image quality produced by the CT system using these protocols without human intervention in the acquisition process, which was assessed via the phantom study.

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

• The ground truth for the non-clinical testing was the physical properties of the phantom itself. The phantom contains materials of known density and structures of known size. The measurements (e.g., CT numbers, contrast, small object visibility) derived from the CT images are compared against these known physical properties and also against the performance of the predicate device.

8. The sample size for the training set

• This submission focuses on protocols for existing CT systems, not a new algorithm that requires a "training set" in the context of machine learning or AI. The protocols were developed to take advantage of existing CT system capabilities, and their effectiveness was demonstrated by comparing their image quality metrics to a predicate device. Therefore, a "training set" as commonly understood in AI/ML is not applicable.

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

• As a training set is not applicable, establishing corresponding ground truth is also not applicable. The protocols themselves were designed based on engineering principles and NEMA XR25 guidelines to optimize dose reduction while maintaining image quality. Their performance was then validated through the comparative phantom study.

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The Low Profile Non-Balloon Feeding Device is indicated for use in patients who require long term feeding, are unable to tolerate oral feeding, who are at low risk for aspiration, require gastric decompression and/or medication delivered directly into the stomach through a secured (initial placement) or formed (replacement) stoma.

The Low Profile Non-Balloon Feeding Device is used to provide nutrition, medication, and decompression access into the stomach through a secured (initial placement) or formed (replacement) stoma. The device may be placed using one of two insertion methods including an 'obturator' insertion method and a 'capsule' insertion method.
Capsule Insertion Method: The internal bolster of the device is encased by water dissolvable capsule. The capsule technology provides a low-profile option that is delivered by lubricating the capsule with lubricant, inserting the device through the stoma site, and pulling on a pull tab that pulls a suture through the capsule and deploys the internal dome. Once the capsule is deployed, the internal dome returns to its uncompressed shape and the device is held in place.
Obturator Insertion Method: A T-Handle and ratcheting obturator rod (Snap Arm Assembly) is used to extend the internal dome, decreasing the outer profile of the dome. When the dome is fully elongated, the internal dome maintains a narrow profile that can easily be inserted into the stoma site with proper lubrication. Once inserted into the stoma site, the ratcheting rod and T-handle are removed and device is held in place by the internal bolster.
Once placed, both devices provide access to nutrition, medication, and decompression into the stomach via a Mini ONE® feeding port. Both devices are identical after placement regardless of the insertion method chosen.
Removal of feeding device can be performed by using a removal tool assembly (Snap Arm and T-handle with removal tool reinforcer). Removal is done by elongating the internal bolster to reduce the outer diameter of the dome, providing for an easier removal process. The Low Profile Non-Balloon Feeding Device will be offered in several different diameters including14 Fr, 18 Fr, 20 Fr, and 24 Fr and will be available in stoma lengths ranging from 1.0 cm to 4.4 cm.
The Low Profile Non-Balloon Feeding Device is provided sterile for single use only. The molded body of the Non-Balloon feeding device is made of medical silicone. The Low Profile Non-Balloon Feeding Device consists of an external bolster, feeding catheter, and internal retention bolster similar to the predicate devices. The external bolster consists of a feeding port for access to the stomach through the tubing of the device and a strap with a plug to close the feeding port while not in use. In addition, an anti-reflux valve is included in the feeding port area to prevent backflow of stomach contents while not in use. The feeding catheter is inserted into the stomach through a stoma and is held in place with the internal retention bolster. There are two different insertion methods for the proposed device, the 'capsule' and the 'obturator' method. The 'capsule' version includes a tapered capsule enclosing the internal bolster at one end that allows ease of insertion and an obturator rod is pre-loaded in to the shaft prior to use. The 'obturator' version uses a T-Handle and snap arm assembly for controlled insertion and elongation through the device shaft. The Low Profile Non-Balloon Feeding Device and the predicate devices are provided in a number of sizes to accommodate different stoma diameters and lengths.

The provided text describes the 510(k) summary for the "Low Profile Non-Balloon Feeding Device." It outlines the device's characteristics, intended use, and substantial equivalence to predicate devices, supported by performance testing. However, the document does not contain the level of detail typically found in a study description for an AI/ML device, especially regarding acceptance criteria directly linked to a specific study with statistical results.

The document discusses performance testing in general terms for a medical device (a feeding tube), but not for an AI/ML component. Therefore, much of the requested information regarding AI/ML study specifics (e.g., sample size for test sets, data provenance, expert ground truth, MRMC studies, standalone performance, training set details) is not applicable or cannot be extracted from this text.

Here's a breakdown of what can and cannot be extracted based on your request and the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The text lists various performance tests conducted for the device. It states, "Testing found that all components and materials met or exceeded design specifications established by AMT." However, it does not explicitly state quantitative acceptance criteria for each test, nor does it provide the specific numerical results for each test. Instead, it offers a general statement of compliance.

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

• Sample Size: Not specified. The document mentions "samples of the Low Profile Non-Balloon Feeding Device" were used for bench tests, but no specific number of samples is provided for any test.
• Data Provenance: Not applicable in the context of clinical data. These are bench tests conducted on physical device samples. The company conducting the tests is Applied Medical Technology, Inc. (AMT), based in Brecksville, OH, USA. The tests are prospective in the sense that they were conducted for the purpose of this 510(k) submission.

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

• This is not applicable as the performance testing described is for a physical medical device (feeding tube) through bench testing, not for an AI/ML algorithm requiring expert interpretation of data for ground truth.

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

• Not applicable for physical bench testing. The results would be based on direct measurement or observation against predetermined specifications by test engineers.

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 is not an AI/ML device.

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

• Not applicable. This is not an AI/ML device.

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

• For the bench tests, the "ground truth" would be the engineering design specifications and expected physical properties and performance characteristics of each component and the assembled device. These are objective measurements rather than expert consensus on clinical data.

8. The sample size for the training set

• Not applicable. This is not an AI/ML device. While presumably, some prototypes or earlier versions of the device would have been tested during development, the concept of a "training set" for an algorithm does not apply.

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

• Not applicable. This is not an AI/ML device.

In summary, the provided document details the regulatory submission for a physical medical device. It confidently states that the device met its design specifications through various bench tests, but it does not delve into the specifics of AI/ML performance evaluation as requested in the prompts.

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The Low Profile Balloon Feeding Device is indicated for use in patients who require long term feeding, are unable to tolerate oral feeding, who are at low risk for aspiration, require gastric decompression and/or medication delivered directly into the stomach through a secured (initial placement) or formed (replacement) stoma. The Low Profile Balloon Feeding Device is intended for all age groups.

The Low Profile Balloon Feeding Device is used to provide nutrition, medication, and decompression access into the stomach through a secured (initial placement) or formed (replacement) stoma. The Low Profile Balloon Feeding Device consists of an external bolster, feeding catheter, and internal retention balloon, similar to the predicate devices. The feeding catheter is inserted into the stomach through a stoma and is held in place with an internal retention balloon. The device can have an MR Conditional balloon fill-valve or will be MR Safe when assembled with the Invisi-Valve. The devices are provided in a number of sizes to accommodate different stoma diameters and lengths and can have either a blunt or tapered tip balloon. The catheter tubes range in diameter from 10Fr to 24Fr, which is the same as the primary predicate. The stoma lengths are available in a wider range of options, from 0.5cm to 10.0 cm.

The provided text describes a 510(k) submission for a "Low Profile Balloon Feeding Device" and does not contain information about acceptance criteria or a study that proves the device meets those criteria in the context of an AI/ML medical device.

The document is a traditional medical device submission (K161413) to the FDA, demonstrating substantial equivalence to predicate devices, rather than an AI/ML device requiring performance evaluation against specific metrics like sensitivity, specificity, or AUC.

Therefore, I cannot extract the requested information (points 1-9) which are relevant to AI/ML device studies. The document focuses on:

• Device Description: What the device is and how it works.
• Intended Use: The medical purpose of the device.
• Technological Characteristics: Materials, sizes, and features.
• Biocompatibility Testing: Ensuring the materials are safe for patient contact, detailed with ISO standards used.
• Performance Testing: Bench tests to confirm the physical integrity and function of the device components, such as balloon burst strength, pullout strength, and flow rates.
• Substantial Equivalence: Comparison to existing devices on the market.

None of these sections discuss AI/ML models, algorithms, or their performance metrics.

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Low Potassium Dextran Solution with Tris Diluent is indicated for flushing, storage and transportation of isolated lungs after removal from the donor in preparation for eventual transplantation into a recipient.

Low Potassium Dextran (LPD) Solution is a sterile, clear, non-pyrogenic, physiological salt solution for hypothermic flushing, storage, and transportation of human lungs outside the human body. LPD Solution is acidic and is adjusted shortly before use to pH 7.4 by the addition of Tris Diluent.

LPD Solution with Tris Diluent is supplied as 1 liter of LPD Solution in a low density polyethylene (LDPE) bottle (8 per pack), co-packed with one single-use bottle of Tris Diluent. Tris Diluent is also supplied separately in an eight-unit pack. Each container of LPD Solution and Tris Diluent is single-use.

This document is a 510(k) summary for the "Low Potassium Dextran Solution with Tris Diluent." It's a regulatory submission to the FDA, demonstrating substantial equivalence to a predicate device, Perfadex® with THAM, rather than presenting a study to prove acceptance criteria for a new, novel device. As such, the information you've requested regarding acceptance criteria, study design, and performance metrics for a new device's evaluation against those criteria is not directly applicable in this context.

This summary focuses on showing that the new device is as safe and effective as a legally marketed predicate device. The "performance data" section primarily describes validation of the new solution's chemical and physical properties and manufacturing processes, ensuring it is comparable to the predicate.

However, I can extract information related to the comparison of the subject device to the predicate where applicable:

1. Table of Acceptance Criteria and Reported Device Performance:

This document does not present specific acceptance criteria in terms of clinical performance (e.g., sensitivity, specificity, accuracy) that a medical device (like an AI algorithm) would typically have. Instead, it focuses on demonstrating substantial equivalence to a predicate device. The "performance data" described are primarily related to product specifications and manufacturing controls.

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

• Not applicable directly. This document does not describe a clinical "test set" in the sense of a dataset for evaluating an AI algorithm or a diagnostic device's performance against clinical ground truth. The "testing" mentioned refers to laboratory and manufacturing validation activities.

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

• Not applicable. No expert ground truth establishment for a clinical test set is described.

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

• Not applicable. No adjudication method for a clinical test set is described.

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 document describes a medical solution for organ preservation, not an AI-assisted diagnostic or assistive device.

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

• Not applicable. This is not an algorithm or AI device.

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

• Not applicable in the context of clinical ground truth. The "truth" here is based on established chemical, physical, and biological standards (e.g., chemical composition, sterility, biocompatibility standards like ISO 10993-1, USP <161>).

8. The sample size for the training set:

• Not applicable. This document is for a medical solution, not a machine learning model that requires a training set.

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

• Not applicable. As above, this is not a machine learning model.

Summary of the document's intent:

The document's primary purpose is to demonstrate that the "Low Potassium Dextran Solution with Tris Diluent" is substantially equivalent to the predicate device "Perfadex® with THAM." Substantial equivalence is established by showing that the new device has:

• The identical intended use.
• Similar technological characteristics.
• Any differences in technological characteristics do not raise new questions of safety or effectiveness.

The "Performance Data" section supports the claim of similar technological characteristics and that any differences are not concerning by detailing chemical composition analysis, sterility and pyrogenicity testing, stability testing (shelf-life), biocompatibility testing, and verification of other product specifications like pH adjustment and fill volume.

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The Low Dose CT (LDCT) Lung Cancer Screening Option (LCS) for Qualified GE Systems is indicated for using low dose CT for lung cancer screening must be performed within the established inclusion criteria of programs/ protocols that have been approved and published by either a governmental body or professional medical society.

*Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.

There are not any functional, performance, feature, or design changes required for the Qualified CT system onto which the Option is applied.

Because none of the CTs will require hardware or software modifications the subject device for qualified systems in the installed base consists of:

1. a set of three reference LDCT LCS protocols (small, average, large patient) for each qualified CT System on a per CT platform basis;

2. detailed instructions on how to transfer the protocols onto the corresponding CT System: and

3. a dedicated for user's manual for LDCT LCS that covers all qualified systems.

For qualified forward production systems, the three above elements that constitute the subject device for the qualified systems in the installed will be deployed in a modified manner:

1. the 3 LDCT LCS reference protocols for the Qualified system will be loaded onto the system at the factory:

2. because the reference LDCT LCS protocols will already be on the system, there will be no need for detailed instructions on how to manually enter the protocols; and

3. the dedicated user manual for LDCT LCS may be folded in as a new separate chapter of the system's user manual.

For qualified forward production systems, the LDCT LCS "device" will be structured into the qualified systems. This will result in all qualified forward production systems always incorporating this LDCT LCS Option.

The provided document is a 510(k) Premarket Notification Submission for a "Low Dose CT Lung Cancer Screening Option for Qualified GE CT Systems". It describes the device's indications for use, technological characteristics, and arguments for substantial equivalence to predicate devices, rather than presenting a study specifically designed to establish acceptance criteria and prove the device meets those criteria with detailed performance metrics.

However, from the document, we can infer some "acceptance criteria" through the image quality metrics used for evaluating substantial equivalence and the general claim that the device is "safe and effective". The study conducted to support this is a bench test using phantoms, rather than a clinical trial with human subjects.

Here's an attempt to structure the information based on your request, with the understanding that not all specific details you asked for are explicitly provided in this type of regulatory document.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly derived from the ability of the qualified GE CT systems, using the new LDCT LCS protocols, to maintain image quality comparable to or better than predicate devices used in past clinical trials (like NLST) and to allow for the detection and sizing of lung nodules. The performance is assessed via phantom studies.

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

• Test Set (Phantom Study): The test set primarily consisted of:
  • Standard IQ phantoms (e.g., Catphan for NPS).
  • An anthropomorphic clinical simulation lung phantom with 5mm solid and nonsolid nodules.
• Sample Size:
  • For the IQ phantoms, it's not specified how many scans or instances were used, but rather the type of phantoms.
  • For the anthropomorphic lung phantom, specific nodules (5mm solid and nonsolid) were evaluated.
  • The "test set" also implicitly refers to the representative CT systems chosen: LightSpeed 16, Discovery CT590 RT, LightSpeed VCT and Optima CT 660, Discovery CT750 HD, and Revolution CT.
• Data Provenance: The data was generated through bench testing (phantom studies) conducted by GE Medical Systems, LLC (GE Healthcare). The location of the testing is not explicitly stated but presumed to be internal GE facilities. This is a prospective generation of data for the 510(k) submission.

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

• Number of Experts: "Experienced imaging physicists and applications specialists" were used. The exact number is not specified, but the plural "specialists" suggests more than one.
• Qualifications: "Experienced imaging physicists and applications specialists." No specific number of years of experience or board certifications (like radiologist) are mentioned, as the evaluation was of phantom images, not clinical images.

4. Adjudication Method for the Test Set

• The document states, "In all cases, the small, lowest contrast nodule was easily seen" by the experienced imaging physicists and applications specialists. This suggests a consensus or affirmation rather than a formal adjudication method (like 2+1 or 3+1). Since it was a detectability assessment on phantom images rather than a diagnostic decision, a formal adjudication protocol appears to have been deemed unnecessary.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No, an MRMC comparative effectiveness study was not done for this specific 510(k) submission to demonstrate the effectiveness of the device itself.
• The submission references large clinical trials like the National Lung Screening Trial (NLST) and I-ELCAP to establish the safety and effectiveness of LDCT Lung Cancer Screening in general, performed within established protocols, for which GE CT systems were previously used. However, these trials were not conducted to compare human readers with and without the specific GE device option at hand.
• Effect size of human reader improvement: Not applicable, as no such study was performed.

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

• Not applicable. The "Low Dose CT Lung Cancer Screening Option" is not an AI algorithm for nodule detection or diagnosis; rather, it is a set of optimized acquisition protocols and a user manual for existing GE CT systems to be used for LDCT lung cancer screening. Its performance is assessed by the physical image quality metrics it produces, which then aids human readers.

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

• For the phantom study, the "ground truth" for the nodules (size, type, location) was inherent in the design of the anthropomorphic lung phantom. The detectability was then visually confirmed by experienced imaging physicists and applications specialists.
• For the broader claim of the safety and effectiveness of LDCT LCS, the submission relies on the ground truth established by large-scale clinical trials (e.g., NLST) and medical professional society guidelines, which are based on clinical outcomes and expert consensus.

8. The Sample Size for the Training Set

• This device is not an AI/ML algorithm that requires a "training set" in the conventional sense. The "training" for developing the new LDCT LCS protocols involved:
  • Reviewing existing reference protocols for Chest CT on GE CT systems.
  • A literature review of current guidance on appropriate CT acquisition parameters, reconstructions, and system functional performance capabilities for LDCT LCS.
  • Synthesizing this information and guidance recommendations to determine acquisition and reconstruction attributes.
  • Using these attributes and GE CT system knowledge to develop the specific LDCT LCS Scan Parameters for each qualified CT system.
• Therefore, there isn't a quantifiable "sample size" for a training set as would be found in an AI/ML device submission.

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

• As noted above, there isn't a traditional "training set" with ground truth in the context of an algorithm. Instead, the protocol development was based on:
  • Existing product specifications and performance data: From GE CT systems.
  • Published clinical literature and guidelines: Reference publications, clinical trials (like NLST, I-ELCAP), medical professional society guides and recommendations (e.g., USPSTF, CMS decisions). These sources provided the "ground truth" (or accepted best practices) for what constitutes effective and safe LDCT LCS.
  • Expert knowledge: Internal GE CT system knowledge and the expertise of their engineers and applications specialists.

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Degania Silicone Gastrostomy Button is a replacement gastrostomy tube indicated for long term use in a well established gastrostomy tract for feeding and/or administration of medication. May be used for gastric decompression.

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This document is a 510(k) premarket notification decision letter from the FDA for the Degania Silicone Gastrostomy Button. It is not a study report and therefore does not contain the information requested in your prompt regarding acceptance criteria and performance data for a device.

The letter confirms that the device is substantially equivalent to a legally marketed predicate device but does not provide any details about performance criteria, study design, or results.

To answer your questions, I would need access to the actual 510(k) submission (K122030) or a separate study report for the Degania Silicone Gastrostomy Button.

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The Arthrex Low Profile Screws (2.0-3.0mm solid) are intended to be used as stand-alone bone screws, or in a plate-screw system for internal bone fixation for bone fractures, fusions, osteotomies and non-unions in the ankle, foot, hand, and wrist. When used with a plate, the screw may be used with the Arthrex Low Profile and Small Fragment Plates,

The Arthrex Low Profile Screws (2.0-3.0mm cannulated) are intended to be used as standalone bone screws for internal bone fixation for bone fractures, fusions, osteotomies and nonunions in the ankle, foot, hand, and wrist.

The Arthrex Low Profile Screws (3.5mm and larger, solid) are intended to be used as stand-alone bone screws, or in a plate-screw system for internal bone fixation for bone fractures, fusions, osteotomies and non-unions in the ankle, foot, hand, wrist, clavicle, scapula, olecranon, humerus, radius, ulna, tibia, calcaneous, femur and fibula. When used with a plate, the screws may be used with the Arthrex Low Profile and Small Fragment Plates, Humeral Fracture Plates, and Osteotomy Plates.

The Arthrex Low Profile Screws (3.5mm and larger, cannulated) are intended to be used as stand-alone bone screws for internal bone fixation for bone fractures, fusions, osteotomies and non-unions in the ankle, foot, hand, wrist, clavicle, scapula, olecranon; humerus, radius, ulna, tibia, calcaneous, femur and fibula.

The Arthrex Low Profile Screw is fully or partially threaded, titanium or stainless steel, self-tapping, headed screw. The screw ranges from 2.0 mm to 4.0 mm in diameter and in length from 8 mm to 80 mm. The screw may be either solid or cannulated.

The provided text describes a 510(k) summary for the Arthrex Low Profile Screws, focusing on their substantial equivalence to predicate devices. It does not contain information about acceptance criteria or a study proving that the device meets those criteria in the context of diagnostic accuracy, which would involve concepts like sensitivity, specificity, or reader performance.

Instead, this document is a regulatory submission for a medical device (bone fixation screws) and discusses mechanical testing to demonstrate substantial equivalence, rather than clinical performance or diagnostic accuracy.

Therefore, I cannot provide the requested information about acceptance criteria, reported device performance in those terms, sample sizes for test sets (as there's no diagnostic test set), expert qualifications, adjudication methods, MRMC studies, standalone algorithm performance, or ground truth establishment in a diagnostic context.

The "study" mentioned in the document is mechanical testing. Here's what can be extracted about that:

1. A table of acceptance criteria and the reported device performance:
   The document states: "The submitted mechanical testing data demonstrated that the torque and pull-out force of the proposed devices is substantially equivalent to the torque and pull-out force of the predicate devices."

   While it indicates that mechanical testing was performed for "torque and pull-out force," it does not provide the specific numerical acceptance criteria or the reported performance values for either the proposed device or the predicate devices. It only states they were found to be "substantially equivalent."

2. Sample size used for the test set and the data provenance:
   Not specified for the mechanical testing. "Data provenance" (country of origin, retrospective/prospective) is not applicable to mechanical bench testing.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
   Not applicable. Mechanical testing does not involve human experts establishing a "ground truth" in the way a diagnostic study would.

4. Adjudication method:
   Not applicable to mechanical testing.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done:
   No. This is a medical device (bone screw) not an imaging or diagnostic device that would typically involve a MRMC study.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
   Not applicable. This is a physical medical device, not an algorithm.

7. The type of ground truth used:
   For mechanical testing, the "ground truth" would be the direct physical measurement of properties like torque and pull-out force, as measured by testing equipment. There is no biological or diagnostic "ground truth" (like pathology or outcomes data) involved for this type of testing.

8. The sample size for the training set:
   Not applicable. This is not a machine learning model, so there is no training set.

9. How the ground truth for the training set was established:
   Not applicable.

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