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STAGE is a post-processing software medical device intended for use in the visualization of the brain. STAGE analyzes input data from MR imaging systems. STAGE utilizes magnitude and phase data acquired with specific parameters to generate enhanced Tl weighted images, susceptibility weighted imaging (SWI) images, susceptibility weighted image map (SWIM) images, pseudo-SWIM (pSWIM) images, modified pSWIM (mpSWIM) images, true SWI (tSWI) images, MR angiography (MRA) images, simulated dual-inversion recovery (DIR) images, and maps of TI, R2*, and proton density (PD).

When interpreted by a trained physician, STAGE images may provide information useful in determining diagnosis.

STAGE is indicated for brain imaging only and should always be used in combination with at least one other conventional MR acquisition ( e.g., T2 FLAIR).

STAGE works as a comprehensive brain imaging post-processing solution. The STAGE system consists of a client supplied dedicated computer with an ethernet connection to the client's existing local network. The STAGE software will operate within a virtual machine environment (virtual STAGE module) on this dedicated computer. The computer receives DICOM data from a specific MRI 3D GRE scan protocol (i.e., the STAGE protocol) and then outputs back numerous DICOM datasets with different types of contrast to the PACS server. The data transfer is initiated by the user's current DICOM viewing software. STAGE has been modified from the predicate to include CROWN, a white noise filtering algorithm intended to improve specific STAGE outputs.

The provided document is a 510(k) premarket notification for SpinTech, Inc.'s "STAGE" device. It outlines the device description, indications for use, comparison to a predicate device, and a summary of non-clinical testing. However, it explicitly states that clinical testing was not necessary to demonstrate substantial equivalence, and therefore, does not contain information about a study proving the device meets specific acceptance criteria based on human reader performance, expert-established ground truth, or MRMC studies.

Here's a breakdown of the information available based on your request, highlighting the missing elements:

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

The document does not provide a specific table of quantitative acceptance criteria and corresponding reported device performance metrics in the format requested (e.g., sensitivity, specificity, AUC values).

It states: "All predefined acceptance criteria for the performance testing were met. The results from the performance testing executed on STAGE produced results consistently according to its intended use." This is a general statement that the device passed internal testing, but no specific metrics are quantified or presented.

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

This information is not provided as there was no clinical testing. The non-clinical testing focused on software verification and validation.

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 information is not provided as there was no clinical testing involving expert-established ground truth for a test set.

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

This information is not provided as there was no clinical testing (and thus no test set adjudicated by experts).

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done as explicitly stated, "Clinical testing was not necessary to demonstrate substantial equivalence of STAGE to the predicate device." Therefore, no effect size of human reader improvement with or without AI assistance is reported.

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

The document states: "STAGE was tested in accordance with SpinTech's verification and validation procedures," and "All predefined acceptance criteria for the performance testing were met." This implies standalone testing of the algorithm's performance against internal acceptance criteria (e.g., accuracy of calculations, image generation quality). However, specific metrics (like quantitative measures of accuracy, precision for the generated images or maps) for this standalone performance are not detailed. The focus is on the device generating outputs according to its intended methodology and not on diagnostic accuracy in a clinical context without human interpretation. The device is a "post-processing software medical device intended for use in the visualization of the brain" and "When interpreted by a trained physician, STAGE images may provide information useful in determining diagnosis." This indicates it's designed to be used with human interpretation, not as a standalone diagnostic tool.

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

For the non-clinical performance testing, the "ground truth" would likely be based on established computational models, mathematical correctness of transformations, or comparison to reference data generated by known methods, rather than clinical ground truth like pathology or expert consensus. Specific details on the type of ground truth used for the verification and validation are not provided.

8. The sample size for the training set

This information is not provided. The document describes the device as a software that analyzes input data to generate various images and maps. While some methodologies might involve "fitting" (e.g., lease squares fitting), it's not explicitly stated if a machine learning model requiring a distinct "training set" was utilized in a conventional sense for the image generation process, beyond the CROWN filtering algorithm being "intended to improve specific STAGE outputs." The document details the methodologies as unchanged from the predicate, and focuses on the generation of images/maps based on specific signal processing and mathematical transformations, rather than a machine learning model that would require a large training dataset for learning.

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

This information is not provided, as details about a distinct training set for a machine learning model are absent.

In summary, this 510(k) submission primarily focuses on demonstrating substantial equivalence to a predicate device through non-clinical software verification and validation, without the need for clinical studies involving human observers or detailed performance metrics against clinical ground truth.

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STAGE is a post-processing software medical device intended for use in the visualization of the brain. STAGE analyzes input data from MR imaging systems. STAGE utilizes magnitude and phase data acquired with specific parameters to generate enhanced T1 weighted images, susceptibility weighted imaging (SWI) images, susceptibility weighted image map (SWIM) images, pseudo-SWIM (pSWIM) images, modified pSWIM ) images, true SWI (tSWI) images, MR angiography (MRA) images, simulated dual-inversion recovery (DIR) images, and maps of T1, R2*, and proton density (PD).

When interpreted by a trained physician, STAGE images may provide information useful in determining diagnosis.

STAGE is indicated for brain imaging only and should always be used in combination with at least one other conventional MR acquisition (e.g., T2 FLAIR).

STAGE works as a comprehensive brain imaging post-processing solution. The STAGE system consists of a dedicated medical grade computer (STAGE Module) connected to the user's local area network. The computer receives DICOM data from a specific MRI 3D GRE scan protocol (i.e., the STAGE protocol) and then outputs back numerous DICOM datasets with different types of contrast to the PACS server. The data transfer is initiated by the user's current DICOM viewing software.

Here's a breakdown of the acceptance criteria and study information for the STAGE device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly list specific quantitative acceptance criteria in a table format, nor does it provide a numerical "reported device performance" against those criteria. Instead, it makes general statements about meeting predefined acceptance criteria for image quality and clinical user needs.

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

The document states: "A reader study was conducted to demonstrate acceptable diagnostic image quality and equivalent radiologic finding classes compared to the predicate device."

• Test Set Sample Size: The exact number of cases or images in the test set for the reader study is not specified in the provided text.
• Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective.

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

The document does not specify the number of experts used to establish ground truth or their qualifications. It mentions that "When interpreted by a trained physician, STAGE images may provide information useful in determining diagnosis." but this does not directly address the ground truth establishment for the reader study.

4. Adjudication Method for the Test Set

The document does not specify the adjudication method used for the test set in the reader study.

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

The document states: "A reader study was conducted to demonstrate acceptable diagnostic image quality and equivalent radiologic finding classes compared to the predicate device." This suggests a comparative study was performed, but it does not explicitly mention whether it was an MRMC study in the standard sense (comparing human readers with and without AI assistance).

• Effect Size of Human Readers with vs. without AI: This information is not provided in the document. The study aimed to show "equivalent radiologic finding classes compared to the predicate device," which implies a comparison between STAGE outputs and outputs from a predicate device, rather than a direct comparison of human reader performance with and without STAGE assistance on the same case.

6. Standalone (Algorithm Only) Performance Study

The document describes STAGE as "post-processing software medical device intended for use in the visualization of the brain" and states that "When interpreted by a trained physician, STAGE images may provide information useful in determining diagnosis." It clarifies that "STAGE is indicated for brain imaging only and should always be used in combination with at least one other conventional MR acquisition."

This suggests that STAGE is intended as a tool for a physician and not for standalone diagnostic use by the algorithm itself. Therefore, a standalone (algorithm-only) performance study, in the sense of the algorithm making a diagnosis without human input, was likely not performed or reported as part of this submission, given its described role as a "post-processing software."

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for the test set (e.g., expert consensus, pathology, outcome data). It only mentions demonstrating "acceptable diagnostic image quality and equivalent radiologic finding classes compared to the predicate device," implying comparison against established clinical understanding or predicate device outputs.

8. Sample Size for the Training Set

The document does not specify the sample size used for the training set. It only mentions the "Methodology" of STAGE and its use of input data.

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

The document does not specify how ground truth for the training set was established. It only describes the technical process of STAGE, stating it "analyzes input data from MR imaging systems" and uses "magnitude and phase data acquired with specific parameters to generate" various images and maps. This implies that the algorithms learn from MR imaging parameters and derived data, but the explicit method for establishing ground truth for training data is not detailed.

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The SpinTech, Inc. SPIN-SWI application is intended for use in the post-acquisition image enhancement of MRI acquired 3D gradient-echo images of the brain. When used in combination with other clinical information, the SPIN-SWI application may aid the qualified radiologist with diagnosis by providing enhanced visualization of structures containing venous blood such as cerebral venous vasculature.

The SPIN-SWI device includes a post-processing algorithm that enhances the contrast of tissues with different susceptibilities from 3D gradient-echo MRI images. The susceptibility of a biological tissue relates to the concentration of iron within it. which can be present in the form of deoxyhemoglobin, ferritin, hemosiderin, or other molecules. An MRI scan results in both magnitude and phase images. While magnitude is most commonly used clinically, the phase information can also be useful as it relates directly to the magnetic field. When tissues or objects of differing magnetic susceptibility are present, they perturb the field around them. This effect can be seen directly from phase images. While this perturbation already leads to signal loss in magnitude images, thus creating contrast, the phase information can still be used to enhance this contrast for local susceptibility changes. Enhancing this contrast allows us to visualize structures containing venous blood such as cerebral venous vasculature that may have not been visible prior to enhancement. Some technical challenges of SWI include eliminating the effects of unwanted background fields and choosing parameters to create optimal contrast. SPIN-SWI software works in conjunction with an FDA cleared third-party DICOM viewer as an image postprocessing solution in a PC workstation. The DICOM viewer (ORIS Visual) was FDA cleared on 4/29/2010 via K100335 and is used to transmit DICOM data and display the input and output images, the SPIN-SWI software application performs the SWI post-processing on 3D GRE input images to reconstruct the SWI output images.

Here's an analysis of the provided text to extract information about the acceptance criteria and the study proving the device meets them:

Disclaimer: The provided document is a 510(k) summary, which often focuses on demonstrating substantial equivalence to a predicate device rather than exhaustive clinical study details for novel technologies. Therefore, some information requested, particularly regarding detailed clinical study performance, may not be explicitly present or extensively elaborated upon.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present a specific table of quantitative acceptance criteria and corresponding reported device performance for a clinical outcome study. Instead, it states that "All predefined acceptance criteria for the engineering All performance testing were met for all test cases across different imaging parameters, field strength and different subjects" (page 6). This refers to internal verification and validation of the software's technical performance.

Similarly, for clinical validation, it states: "All predefined acceptance criteria for clinical validation testing, including clinical user needs testing, as a part of the SPIN-SWI performance validation testing efforts were met across all test cases. The results of the clinical validation related testing on the SPIN-SWI application demonstrates performed acceptable image quality and that all clinical user needs are met." (page 6).

This indicates qualitative acceptance regarding image quality and user needs, but not specific quantitative metrics like sensitivity, specificity, or improvement in diagnostic accuracy. The primary goal of this submission is to show substantial equivalence to a predicate device based on similar technological characteristics and performance, rather than a quantifiable improvement over existing methods or fulfilling specific performance thresholds in a clinical trial.

In summary, there is no discrete table of acceptance criteria and performance data as you might expect from a full clinical trial report. The acceptance criteria are broadly described as meeting "all performance testing" and "all clinical user needs."

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

• Sample Size for Test Set: Not explicitly stated in terms of number of cases or patients. The document refers to testing "across different imaging parameters, field strength and different subjects" (for non-clinical testing) and "all test cases" for clinical validation.
• Data Provenance: Not specified. It's not mentioned if the data was retrospective or prospective, nor the country of origin. Given the focus on substantial equivalence, it's likely pre-existing or simulated data was used for much of the non-clinical testing, and possibly a limited set of clinical cases for validation.

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

• Number of Experts: Not specified.
• Qualifications of Experts: The device's intended user is a "qualified radiologist" (page 2, 5). The "clinical user needs testing" suggests involvement of such experts, but their number, specific qualifications (e.g., years of experience, subspecialty), or their role in establishing ground truth are not detailed.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. Given the lack of detail on specific expert involvement and ground truth establishment (other than "clinical user needs testing"), no adjudication method (e.g., 2+1, 3+1) is mentioned.

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

• Was an MRMC study done? No, a traditional MRMC comparative effectiveness study was not performed or detailed. The summary explicitly states: "The subject device of this premarket notification, SPIN-SWI application, did not require clinical studies to support substantial equivalence to the predicate device" (page 6).
• Effect Size of Human Readers Improvement: Not applicable, as no MRMC study was conducted.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Was a standalone study done? Yes, to some extent, as implied by the "Non-Clinical Testing Summary." The device is a "post-processing algorithm that enhances the contrast... When used in combination with other clinical information, the SPIN-SWI application may aid the qualified radiologist with diagnosis..." (page 4, 5).
  • The "non-clinical testing" and "performance testing (V&V)" seem to refer to the algorithm's output quality independently of human interpretation, focusing on whether it "produces results consistently according to its intended use" (page 6). However, specific metrics for standalone performance (e.g., sensitivity/specificity for detecting specific pathologies) are not provided, as its role is enhancement rather than direct diagnosis.

7. Type of Ground Truth Used for the Test Set

• Type of Ground Truth: Not explicitly stated. The "clinical validation testing" and "clinical user needs testing" suggest that the ground truth was based on the judgment of qualified radiologists regarding "acceptable image quality" and whether the enhanced visualization was beneficial. It's not specified if this involved pathology, outcomes data, or a strict expert consensus process for specific findings.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not specified. As a post-processing algorithm for image enhancement, it might rely on established imaging principles and signal processing, potentially requiring less "training" data in the machine learning sense compared to a deep learning diagnostic algorithm. Even if machine learning was involved, the training set size is not disclosed.

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

• How Ground Truth Established: Not specified. Similar to the test set, if training data were used, the method for establishing their ground truth is not detailed.

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The SafetyWand™ is intended to inject local anesthetic agents subcutaneously or intramuscularly for dental applications. It is a sterile single use handpiece assembly for use with the Milestone Compudent™ computer controlled injection system. The SafetyWand™ incorporates safety engineering sharps protection features to aid in the prevention of needlesticks.

The SafetyWand™ disposable handpiece is a safety engineered syringe component designed for use with the CompuDent™ computer controlled injection system (K992819). The CompuDent® device utilizes hypodermic needles and standard 1.8mm pre-filled carpules manufactured by various third parties. The anesthetic agent reaches the needle by means of a length of flexible vinyl tubing. The hanbdpiece is made of rigid PVC. The anesthetic cartridge holder, the tubing and the handpiece are sold in a sterile condition as a disposable assembly for one-time use. The materials, principal of operation and intended use are the same as other marketed piston and cartridge svringes. The device provides audible status indicators, including optional voice announcement of injection rate, and volume of anesthetic dispensed. The SafetyWand™ handpiece incorporates a protective barrel inside of which a movable needle hub assembly can be extended and locked in place for use in performing iniections. When not in use, or for disposal, the needle can be retracted within the barrel to aid in the prevention of needlesticks.

This document is a 510(k) premarket notification for the SafetyWand™ disposable handpiece, a component of a dental anesthetic delivery system. It primarily focuses on demonstrating substantial equivalence to a predicate device rather than providing detailed acceptance criteria and a study report for a new device's performance.

Therefore, the requested information cannot be fully extracted from the provided text for the following reasons:

• This is a 510(k) submission, not a study report: A 510(k) aims to prove "substantial equivalence" to an already legally marketed predicate device, not necessarily to independently establish performance against novel acceptance criteria through extensive studies. The "study" here refers to the comparison against the predicate.
• Focus on substantial equivalence: The document emphasizes that the SafetyWand™ handpiece is "substantially equivalent" to the company's Wand® handpiece (K992819) and the IntegriTech Safety Injection System (K962999). This means its performance is considered acceptable if it matches the predicate's established performance, implicitly meeting the same unstated "acceptance criteria" as those devices.
• Lack of detailed performance metrics: The document describes the device's function and safety features (retractible needle to prevent needlesticks) but does not provide specific quantitative performance metrics (e.g., accuracy, reliability, lifespan) or acceptance criteria for those metrics.

Despite these limitations, I can infer and state what might be considered "acceptance criteria" based on the nature of the device and the 510(k) process, and then indicate that the "study" is the substantial equivalence comparison.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not explicitly stated as this is a substantial equivalence claim based on device design and features, rather than a clinical trial with a specific "test set" of patients or users. Demonstrating substantial equivalence primarily relies on technical comparisons (design, materials, intended use, energy, safety principles) to the predicate device.
• Data Provenance: Not applicable in the sense of patient data. The "data" involves device specifications and comparisons to predicate devices (K992819 and K962999).

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

• Not applicable. Ground truth for a test set is not described in a 510(k) submission focused on substantial equivalence of a physical medical device. The "truth" here is its equivalence to an already approved device.

4. Adjudication method for the test set

• Not applicable. There is no mention of a "test set" requiring adjudication in a clinical or experiential sense.

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

• Not applicable. This device is a disposable handpiece for anesthetic delivery, not an AI-assisted diagnostic tool or imaging system.

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

• Not applicable. This is a hardware device; no algorithm is described.

7. The type of ground truth used

• Not applicable in the conventional sense of clinical ground truth (pathology, outcomes). The "ground truth" for this 510(k) submission is the pre-existing regulatory approval and performance of the identified predicate devices (Wand® handpiece K992819 and IntegriTech Safety Injection System K962999). The new device is considered acceptable if it can demonstrate substantial equivalence to these devices.

8. The sample size for the training set

• Not applicable. This is not a machine learning or AI device that would have a training set.

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

• Not applicable. This is not a machine learning or AI device.

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For injection of lidocaine subcutaneously or intramuscularly for infiltration and nerve block.

The Wand™ is a computer controlled syringe consisting of a stationary motor housing which includes a metal piston whose speed of advance is regulated by a foot switch thus controlling the flow rate of anesthetic being injected. The device utilizes hypodermic needles and standard 1.8mm pre-filled carpules manufactured by various third parties. The anesthetic agent reaches the needle by means of a length of flexible vinyl tubing. The handpiece is made of rigid PVC. The holster containing the anesthetic carpule, the tubing and the handpiece are sold in a sterile condition as a disposable assembly for one-time use. The materials, principal of operation and intended use are the same as other marketed piston and cartridge syringes.

The provided document is a 510(k) summary for "The Wand™ Syringe," which is a computer-controlled syringe for anesthetic delivery. This document focuses on demonstrating substantial equivalence to predicate devices and does not contain information about a formal study with acceptance criteria, sample sizes, expert ground truth, or adjudication methods for performance evaluation.

Therefore, I cannot provide the requested information from the given text. The document's purpose is to clear the device for market based on its similarity to existing devices, not to present a detailed performance study against specific acceptance criteria.

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To inject local anesthetic agents subcutaneously or intramuscularly for dental applications.

The Wand Plus™ is a computer controlled syringe consisting of a stationary motor housing which includes a metal piston whose speed of advance is regulated by a foot switch thus controlling the flow rate of anesthetic being injected. The device utilizes hypodermic needles and standard 1.8mm pre-filled carpules manufactured by various third parties. The anesthetic agent reaches the needle by means of a length of flexible vinyl tubing. The handpiece is made of rigid PVC. The holster containing the anesthetic carpule, the tubing and the handpiece are sold in a sterile condition as a disposable assembly for one-time use. The materials, principal of operation and intended use are the same as other marketed piston and cartridge syringes. The device provides audible status indicators, including optional voice announcement, of injection rate, and volume of anesthetic dispensed.

This document describes a 510(k) submission for "The Wand Plus™ Syringe". The 510(k) summary (K992819) states that the device is "substantially equivalent" to the company's currently marketed "Wand™ computer controlled syringe." Generally, a 510(k) summary provides evidence of substantial equivalence to a predicate device rather than detailing specific acceptance criteria and a study to prove they are met.

Given the information provided, a table of acceptance criteria and reported device performance, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth information are not applicable or not present in the provided 510(k) summary document. This document primarily focuses on establishing substantial equivalence to a predicate device, which often relies on demonstrating that the new device has the same intended use, technological characteristics, and performs as safely and effectively as a legally marketed predicate device.

Therefore, I cannot extract the requested information from the provided text.

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