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The AiORTA - Plan tool is an image analysis software tool for volumetric assessment. It provides volumetric visualization and measurements based on 3D reconstruction computed from cardiovascular CTA scans. The software device is intended to provide adjunct information to a licensed healthcare practitioner (HCP) in addition to clinical data and other inputs, as a measurement tool used in assessment of aortic aneurysm, pre-operative evaluation, planning and sizing for cardiovascular intervention and surgery, and for post-operative evaluation in patients 22 years old and older.

The device is not intended to provide stand-alone diagnosis or suggest an immediate course of action in treatment or patient management.

AiORTA - Plan is a cloud-based software tool used to make and review geometric measurements of cardiovascular structures, specifically abdominal aortic aneurysms. The software uses CT scan data as input to make measurements from 2D and 3D mesh based images. Software outputs are intended to be used as a measurement tool used in assessment of aortic aneurysm, pre-operative evaluation, planning and sizing for cardiovascular intervention and surgery, and for post-operative evaluation.

The AiORTA - Plan software consists of two components, the Analysis Pipeline and Web Application.

The Analysis Pipeline is the data processing engine that produces measurements of the abdominal aorta based on the input DICOM images. It consists of multiple modules that are operated by a trained Analyst to preprocess the DICOM images, compute geometric parameters (e.g., centerlines, diameters, lengths, volumes), and upload the results to the Web App for clinician review. The Analyst plays a role in ensuring the quality of the outputs. However, the end user (licensed healthcare practitioner) is ultimately responsible for the accuracy of the segmentations, the resulting measurements, and any clinical decisions based on these outputs.

The workflow of the Analysis Pipeline can be described in the following steps:

• Input: the Analysis Pipeline receives a CTA scan as input.
• Segmentation: an AI-powered auto-masking algorithm performs segmentation of the aortic lumen, wall, and key anatomical landmarks, including the superior mesenteric, celiac, and renal arteries. A trained Analyst performs quality control of the segmentations, making any necessary revisions to ensure accurate outputs.
• 3D conversion: the segmentations are converted into 3D mesh representations.
• Measurement computation: from the 3D representations, the aortic centerline and geometric measurements, such as diameters, lengths, and volumes, are computed.
• Follow-up study analysis: for patients with multiple studies, the system can detect and display changes in aortic geometry between studies.
• Report generation: a report is generated containing key measurements and a 3D Anatomy Map providing multiple views of the abdominal aorta and its landmarks.
• Web application integration: the outputs, including the segmented CT masks, 3D visualizations, and reports, are uploaded to the Web App for interactive review and analysis.

The Web Application (Web App) is the front end and user facing component of the system. It is a cloud-based user interface offered to the qualified clinician to first upload de-identified cardiovascular CTA scans in DICOM format, along with relevant demographic and medical information about the patient and current study. The uploaded data is processed asynchronously by the Analysis Pipeline. Once processing is complete, the Web App then enables clinicians to interactively review and analyze the resulting outputs.

Main features of the Web App include:

• Segmentation review and correction: Clinicians can review the resulting segmentations from the Analysis Pipeline segmentations by viewing the CT slices alongside the segmentation masks. Segmentations can be revised using tools such as a brush or pixel eraser, with adjustable brush size, to select or remove pixels as needed. When clinicians revise segmentations, they can request asynchronous re-analysis by the Analysis Pipeline, which generates updated measurements and a 3D Anatomy Map of the aorta based on the revised segmentations.
• 3D visualization: The aorta and key anatomical landmarks can be examined in full rotational views using the 3D Anatomy Map.
• Measurement tools: Clinicians can perform measurements directly on the 3D Anatomy Map of the abdominal aorta and have access to a variety of measurement tools, including:
  • Centerline distance, which measures the distance (in mm) between two user-selected planes along the aortic centerline.
  • Diameter range, which measures the minimum and maximum diameters (in mm) within the region of interest between two user-selected planes along the aortic centerline.
  • Local diameter, which measures the diameter (in mm) at the user-selected plane along the aortic centerline.
  • Volume, which measures the volume (in mL) between two user-selected planes along the aortic centerline.
  • Calipers, which allow additional linear measurements (in mm) at user-selected points.
• Screenshots: Clinicians can capture images of the 3D visualizations of the aorta or the segmentations displayed on the CT slices.
• Longitudinal analysis: For patients with multiple studies, the Web App allows side-by-side review of studies. Clinicians have access to the same measurement and visualization tools available in single-study review, enabling comparison between studies.
• Reporting: Clinicians can generate and download reports containing either the default key measurements computed by the Analysis Pipeline or custom measurements and screenshots captured during review.

Here's a breakdown of the acceptance criteria and the study details for the AiORTA - Plan device, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Explanation for Lengths and Diameters that did not meet initial criteria:
For the following measurements which did not meet the initial acceptance criteria:

• Length: renal to left iliac bifurcation (7.0mm vs ≤ 6.0mm)
• Length: renal to right iliac bifurcation (6.6mm vs ≤ 6.0mm)
• Diameter: wall right iliac (2.5mm vs ≤ 2.3mm)

A Mean Pairwise Absolute Difference (MPAD) comparison was performed. The device-expert MPAD was smaller than the expert-expert MPAD in all three cases, indicating that the device's measurements were more consistent with experts than the experts were with each other.

Study Details for Device Performance Evaluation:

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

   • Auto-segmentation masks and Landmark Identification: The document does not explicitly state the sample size for this specific test, but it mentions using "clinical data, including aortic aneurysm cases from both US and Canadian clinical centers."
   • Diameters and Lengths: The document does not explicitly state the sample size for this specific test, but it mentions using "clinical data, including aortic aneurysm cases from both US and Canadian clinical centers."
   • Volumes: 40 CT scans. The data provenance is "clinical data, including aortic aneurysm cases from both US and Canadian clinical centers." The studies were retrospective, as they involved existing clinical data.

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

   • Auto-segmentation masks and Landmark Identification: 3 US-based board-certified Radiologists.
   • Diameters and Lengths: 3 US-based board-certified Radiologists.
   • Volumes: The ground truth for volumes was established using a reference device (Simpleware ScanIP Medical), not directly by human experts, although the input segmentations for both the device and the reference device were analyst-revised.

3. Adjudication method for the test set:

   • Auto-segmentation masks and Landmark Identification: Ground truth was "annotations approved by 3 US-based board-certified Radiologists." This implies consensus or a primary reader with adjudication, but the exact method (e.g., 2+1, 3+1) is not specified.
   • Diameters and Lengths: Ground truth was "annotations from 3 US-based board-certified Radiologists." Similar to above, the specific consensus method is not detailed.
   • Volumes: Ground truth was established by a reference device, Simpleware ScanIP Medical.

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 MRMC comparative effectiveness study was explicitly mentioned in the provided text. The testing focused on the standalone performance of the AI-powered components and the consistency of the device's measurements with expert annotations, not on human reader improvement with AI assistance.

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

   • Yes, a standalone performance evaluation of the auto-masking algorithm (prior to analyst correction) was performed for auto-segmentation masks and landmark identification. The results demonstrated the performance of the auto-masking algorithm "independently of human intervention."
   • However, for diameters and lengths, the measurements were "based on segmentations that underwent Analyst review and correction, ensuring that the verification reflects real-world use conditions." This suggests a semi-automatic, human-in-the-loop performance evaluation for these specific metrics.

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

   • Expert Consensus: Used for auto-segmentation masks, landmark identification, diameters, and lengths. The consensus involved 3 US-based board-certified Radiologists.
   • Reference Device: Used for volumes, comparing against results from Simpleware ScanIP Medical.

7. The sample size for the training set:

   • The document does not explicitly state the sample size for the training set. It mentions "critical algorithms were verified by comparing their outputs to ground truth data to ensure accuracy and reliability. Algorithms were first verified using synthetic data...Subsequent verification was performed using clinical data, including aortic aneurysm cases from both US and Canadian clinical centers." This refers to verification data, not necessarily the training data size.

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

   • The document does not provide details on how the ground truth for the training set was established. It only describes the ground truth for the verification/test sets. It can be inferred that similar expert review or other validated methods would have been used for training data, but this is not explicitly stated.

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Vitaform Procedural Masks are intended to be worn to protect both the patient and the healthcare worker from transfer of microorganisms, body fluids, and particulate material. They are single-use and intended for use in infection control practices to reduce potential exposure to blood and body fluids.

Vitaform Procedural Mask (henceforth referred to as "Vitaform")

The Vitaform Procedural Mask is a single-use, three-layered, fish-shaped surgical mask with ear loops and an aluminum nose piece. The inner and outer layers are made of spunbond polypropylene, and the middle filter layer consists of meltblown polypropylene. The ear loops are made of PET and Spandex material, and worn around the ears to keep the mask close to the face. The nose piece is a flexible aluminum strip that is fitted over the nose so that the mask conforms better to the user's face.

The Vitaform Procedural Mask is provided in the color blue. It is non-sterile and intended to be a single-use, disposable medical device.

Vitaform Procedural Mask with Shield (henceforth referred to as "Vitaform-FS")

The Vitaform Procedural Mask with Shield is a single-use, fish-shaped surgical mask with ear loops and an aluminum nose piece. The inner and outer layers are made of spunbond polypropylene, and the middle filter layer consists of meltblown polypropylene. The insertion layer that provides structural support is made of thermal-bonded polypropylene. The ear loops are made of PET and Spandex material, and is worn around the ears to keep the mask close to the face. The nose piece is a flexible aluminum strip that is fitted over the nose so that the mask conforms better to the user's face. The mask also contains a face shield (FS) made from a polyethylene terephthalate film, and an anti-reflective flap. The face shield is welded to the upper half of the mask to cover the upper part of the face.

The Vitaform Procedural Mask with Shield is provided in the color blue. It is non-sterile and intended to be a single-use, disposable medical device.

N/A

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The VSH101 is designed to record, transmit, and store single channel electrocardiogram (ECG) data via Bluetooth communication to compatible Bluetooth enabled devices. The device is intended for use by healthcare professionals, individuals with known or suspected cardiac conditions, and health-conscious users. The ECG data serves as a supplementary source of patient information and is not intended for automated analysis.

VitalSigns 1-Lead Holter is an ambulatory and Bluetooth-based wireless communication ECG measurement solution designed to allow users to record, store, transmit, and display single-channel ECG data.

VitalSigns 1-Lead Holter consists of the following three components:

1. VS Electrode Patch "VSP101"
2. ECG Recorder Host "VSH101"
3. iOS APP "VSHealth"

When the ECG Recorder Host "VSH101" is fully charged and connected wirelessly via Bluetooth to the iOS app "VSHealth", it can instantly obtain the user's ECG data. VSHealth assists in transmitting, displaying, recording, and storing ECG data.

After the "VSH101" is fully charged, the device, through the VSHealth app, allows users to continuously use and record ECG data for 24 hours in their daily routines, whether at home or in a work environment.

The provided FDA 510(k) clearance letter and summary for the VitalSigns 1-Lead Holter (VSH101) primarily focus on demonstrating substantial equivalence to a predicate device through comparison of features and adherence to various safety and performance standards. However, it does not include explicit acceptance criteria tables or detailed study results for specific device performance metrics that would be typically found in a clinical or performance validation report.

The document states that "software validation, performance test and usability test have been completed to demonstrate that the differences between these parameters would not impact the safety and effectiveness of the subject device. The subject device has also undergone all safety and performance tests, and the results complied with the test requirements." It also mentions "Performance testing - IEC 60601-2-47 test" and "Disposable ECG electrode test."

Given the information provided in the 510(k) summary, I will infer the acceptance criteria based on the mentioned compliance standards and the general nature of an ECG Holter device, and then describe what is stated about the testing.

Inferred Acceptance Criteria and Reported Device Performance

The 510(k) summary for the VitalSigns 1-Lead Holter (VSH101) does not provide a specific table of acceptance criteria with corresponding performance metrics. Instead, it relies on demonstrating compliance with relevant international standards and equivalence to a predicate device. Based on the mentioned standards (e.g., IEC 60601-2-47 for ambulatory ECG recorders, ANSI/AAMI EC12 for disposable ECG electrodes), the general acceptance criteria for such a device would relate to the accuracy, signal quality, and reliability of ECG signal acquisition.

Here's a table based on the inferred acceptance criteria from the context of ECG device standards and the information stated in the document:

Study Proving Device Meets Acceptance Criteria

The provided 510(k) summary describes that comprehensive testing was conducted, primarily focusing on compliance with recognized consensus standards and demonstrating substantial equivalence to a predicate device, rather than a single large-scale clinical/performance study with detailed outcome metrics.

Here's an analysis based on the information provided:

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

   • As detailed above, a direct table is not provided in the 510(k) summary. The acceptance criteria are inferred from the listed compliance standards (e.g., IEC 60601-2-47, ANSI/AAMI EC12). The reported performance is generally stated as "complied with the test requirements," "met its pre-defined criteria," or "confirmed compliance."

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

   • Sample Size: Not explicitly stated for performance testing. For compliance with standards like IEC 60601-2-47 or ANSI/AAMI EC12, testing typically involves a sufficient number of device units or electrodes to statistically demonstrate compliance with the standard's requirements (e.g., a batch of electrodes, multiple device samples). The document does not specify if patient data was used for performance testing beyond what is implied by the "24 hours" recording capability.
   • Data Provenance (Country of Origin, Retrospective/Prospective): Not specified. Standard compliance testing is typically done in a lab setting rather than involving patient data in a "retrospective" or "prospective" clinical study design for 510(k) submissions unless a specific clinical performance claim needs to be proven. The focus here is on engineering verification and validation against standards.

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

   • This information is not provided in the 510(k) summary. The nature of the testing described (compliance with standards like IEC 60601-2-47 for signal quality, ANSI/AAMI EC12 for electrodes, and general electrical safety) suggests controlled laboratory testing and engineering validation, which typically does not involve human expert adjudication of ECG readings as a "ground truth" for device performance in this context. The device's indication for use explicitly states, "The ECG data serves as a supplementary source of patient information and is not intended for automated analysis," implying that human interpretation remains key. Therefore, ground truth establishment by experts for automated analysis is not applicable.

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

   • None is explicitly described. Based on the content, there was no clinical study described that would require a ground truth panel or adjudication method for ECG event interpretation. The testing focuses on technical performance compliance.

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 or described. The device is a "1-Lead Holter" that records ECG data; it does not perform "automated analysis" or include AI assistance. Its purpose is to provide raw ECG data for supplementation of patient information, not for diagnostic interpretation by an algorithm. Therefore, an MRMC study assessing AI assistance is not relevant to this device's claims or function.

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

   • Based on the indication for use, "The ECG data serves as a supplementary source of patient information and is not intended for automated analysis," there is no algorithm for diagnostic interpretation. Therefore, no standalone algorithm performance study was done or described.

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

   • As noted above, for the type of device and the presented summary, complex ground truth derived from expert consensus, pathology, or outcomes data is not applicable for the performance testing described. The "ground truth" for compliance testing is largely defined by the technical specifications and requirements within the IEC/AAMI standards themselves (e.g., known input signals for signal quality, chemical assays for biocompatibility).

8. The sample size for the training set:

   • Not applicable / Not provided. The device records raw ECG data and does not perform automated analysis using a trained algorithm. Therefore, no "training set" for AI/ML model development is mentioned or required for this type of device based on its intended use.

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

   • Not applicable. As there is no AI/ML model for automated analysis that requires a training set, the establishment of ground truth for such a set is not discussed.

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Rota-Trach Disposable Standard Tracheostomy Tube is indicated for airway maintenance of tracheostomized patients.

Rota-Trach Disposable Standard Tracheostomy Tube is a respiratory device inserted into the patient's trachea through a stoma, making direct contact with the tracheal mucosal membrane tissue. It may be with or without a cuff attaching around the tube of the device. The design of the cuff could seal the space between the tube and the patient's trachea from inhaling undesired or foreign matters and for assistance of positive pressure ventilation.

The provided document is a 510(k) clearance letter and summary for a physical medical device (tracheostomy tube), not a software-based AI/ML device. Therefore, the document does not contain information about acceptance criteria or studies proving performance for an AI/ML device.

The questions asked pertain specifically to the validation of AI/ML models in medical devices, which typically involve metrics like accuracy, sensitivity, specificity, and detailed study designs (test sets, ground truth, expert adjudication, MRMC studies, etc.).

Since the document describes a Class II physical medical device (Tracheostomy Tube and Tube Cuff), the information regarding acceptance criteria and performance data is related to its physical and biological safety and efficacy, not AI/ML performance.

Therefore, I cannot provide the requested information because the provided input does not describe an AI/ML device or its validation.

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VitalRhythm is a cloud-based software application for continuous and automatic analysis of cardiac arrhythmias. VitalRhythm is compatible with the "Vista Solution" platform, which includes the VitalPatch biosensor and VistaCenter web application. VitalRhythm is intended to be used for outpatient cardiac telemetry and patient monitoring in non-critical healthcare settings for non-urgent clinical decision-making. VitalRhythm provides analysis of cardiac arrhythmias using ECG data and RR-interval from the VitalPatch in patients who are 18 years of age or older. Results of the VitalRhythm are displayed within the VistaCenter web application to be reviewed and confirmed by qualified healthcare professionals and/or cardiac technicians. VitalRhythm is not intended for use in life-supporting or sustaining systems or for critical care monitoring. The arrhythmia analysis results are not intended to be the sole means of diagnosis and are offered on an advisory basis only, in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms, and other diagnostic information.

VitalRhythm is a cloud-based, arrhythmia detection software application that is compatible and intended to be used with the VitalConnect "Vista Solution" platform. The Vista Solution Platform consists of the VitalPatch biosensor, a phone (VistaPhone) or tablet (VistaTablet) relay device preloaded with the VistaPoint software application, VC Cloud and the cloud-based VistaCenter user interface.

VitalRhythm is intended to be used for outpatient cardiac telemetry and patient monitoring in non-critical healthcare settings for non-urgent clinical decision-making.

VitalRhythm analyzes ECG data and RR-interval from the VitalPatch biosensor for reporting of cardiac arrhythmias to be reviewed and adjudicated by qualified healthcare professionals and/or cardiac technicians.

The cloud-based VitalRhythm software application supports the analysis of ECG data and RR-interval using a proprietary algorithm developed using deep learning techniques. The application works in the following way:

1. VitalRhythm accepts ECG and RR-interval data transmitted from the VitalPatch via a secure, cloud-based API (Application Programming Interface).

2. ECG and RR-interval data are analyzed by VitalRhythm using a proprietary algorithm, which detects the following cardiac rhythms:

   • Atrial fibrillation/atrial flutter
   • AV Block (2nd degree, Type I and II)
   • Pause
   • Paroxysmal supraventricular tachycardia (PSVT)
   • Ventricular tachycardia/run
   • Sinus bradycardia
   • Sinus tachycardia
   • Normal sinus rhythm
   • Others (inconclusive)

3. For use with Mobile Cardiac Telemetry (MCT), i.e., outpatient cardiac telemetry: any of the above listed arrhythmias that are detected by the software algorithm are displayed in VistaCenter to be reviewed and analyzed by a qualified cardiac technician in the 24/7 attended Cardiac Monitoring Center, prior to transmitting a notifiable event consistent with the prescribed notification criteria to the prescribing physician during the monitoring period. An event report is generated by VistaCenter as a result of the analysis.

For use with patient monitoring in non-critical healthcare settings: any of the above listed arrhythmias that are detected by the software algorithm are displayed and notified in VistaCenter, in accordance with the notification criteria, during the monitoring period to be reviewed by the prescribing healthcare professional for non-urgent clinical decision-making.

The features, operating procedures, and mitigations in place for the compatible devices ensure continuous data collection and transmission to support the VitalRhythm application for the intended use. Further information is provided in the VitalRhythm Instructions for Use document.

Here's a detailed breakdown of the VitalRhythm device's acceptance criteria and the study that proves it meets those criteria, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance

Study Details

1. Sample Size Used for the Test Set and Data Provenance:

   • Patient count: 3,309 patients (18 years of age or older).
   • Dataset count: 7,553 datasets.
   • Annotated arrhythmia episodes: 22,034.
   • Data Provenance: Retrospective, de-identified ECG and RR-interval data obtained from patients prescribed the VitalPatch biosensor across 7 clinical sites in the United States (US).

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications:
   The document does not explicitly state the number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience") used to establish the ground truth for the test set. However, it indicates that the results are "to be reviewed and confirmed by qualified healthcare professionals and/or cardiac technicians" in the context of device usage and implies that annotation for ground truth would follow a similar expert-driven process.

3. Adjudication Method for the Test Set:
   The document does not explicitly state the adjudication method (e.g., 2+1, 3+1, none) used for establishing the ground truth of the test set. It only states that the generated "event report is generated by VistaCenter as a result of the analysis" and is "to be reviewed and analyzed by a qualified cardiac technician in the 24/7 attended Cardiac Monitoring Center, prior to transmitting a notifiable event consistent with the prescribed notification criteria to the prescribing physician during the monitoring period." This describes post-processing of algorithm results by human readers, not the method for establishing the ground truth used for algorithm validation.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
   The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it provide an effect size of how much human readers improve with AI vs without AI assistance. The study described focuses on the standalone performance of the algorithm.

5. Standalone Performance (Algorithm Only Without Human-in-the-Loop Performance):
   Yes, a standalone performance study was conducted. The performance metrics (Sensitivity, Specificity, PPV, NPV, Accuracy) presented in the table are explicitly for the VitalRhythm algorithm "when assessed using the independent test database," indicating standalone algorithm performance against a pre-established ground truth.

6. Type of Ground Truth Used:
   The ground truth was established through annotated arrhythmia episodes. While the specific process is not detailed, it implies expert review and labeling of ECG and RR-interval data to define the 'true' presence or absence of arrhythmias. The "results are displayed within the VistaCenter web application to be reviewed and confirmed by qualified healthcare professionals and/or cardiac technicians," suggesting human consensus or expert interpretation forms the basis of the ground truth.

7. Sample Size for the Training Set:

   • Patient count: 23,587 patients.
   • Dataset count: 81,391 datasets.

8. How the Ground Truth for the Training Set Was Established:
   The document states the training database was from 354 US clinical sites, but it does not explicitly detail how the ground truth for the training set was established. It implies a process of data collection from clinical sites and subsequent processing for training the deep learning algorithm.

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VITA YZ MULTI TRANSLUCENT is indicated for:

• · fully anatomical anterior and posterior crowns
• · fully anatomical 4 unit anterior and posterior bridges
• · fully and partially veneered single tooth and up to 4-unit bridge substructures in the anterior and posterior tooth regions
• · inlays
• · Onlays
• · veneers

VITA YZ MULTI TRANSLUCENT are zirconia blanks for reliable shade reproduction. They can be used for the production of fully and partially veneered reconstructions and monolithic bridge restorations in the anterior and posterior tooth regions. VITA YZ MULTI TRANSLUCENT is part of the VITA YZ SOLUTIONS product group.

The provided document is a 510(k) Premarket Notification from the FDA for a dental material, VITA YZ MULTI TRANSLUCENT. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than providing extensive details on a comprehensive standalone study with specific acceptance criteria, ground truth, and human reader performance that would be typical for an AI/ML medical device.

Therefore, many of the requested details about acceptance criteria, study design (e.g., sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, standalone performance), and ground truth establishment are not directly available in this document as it pertains to a material rather than an AI/ML algorithm.

However, I can extract the acceptance criteria and performance data available for the material's properties, as this document focuses on non-clinical testing of the material itself.

Here's the information that can be extracted, interpreted in the context of the material's performance:

1. Table of Acceptance Criteria and Reported Device Performance (for the material's properties):

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

• Sample Size for Test Set: Not explicitly stated for each test, but standard material testing typically involves multiple samples for statistically significant results. The summary mentions "tested samples" without providing the exact number for each parameter (e.g., number of flexural strength specimens).
• Data Provenance: The tests were conducted according to EN ISO 6872 standards for dental ceramic materials. The radioactivity test was performed at Jülich Forschungszentrum. These are typically laboratory-based, controlled material property tests, not based on patient data provenance.
  • Retrospective/Prospective: Not applicable, as these are material property tests, not clinical studies on patient outcomes.

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

• Experts: Not applicable. For material property tests, "ground truth" is established through standardized physical and chemical measurements (e.g., using calibrated instruments for flexural strength, spectrometers for chemical composition, etc.) as defined by international standards like EN ISO 6872. These are objective measurements rather than subjective expert interpretations.

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

• Adjudication Method: Not applicable. Material property tests rely on objective measurements and established scientific protocols for data collection and analysis, not on expert consensus or adjudication.

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:

• MRMC Study: No. This is a material, not an AI/ML device, so MRMC studies involving human readers and AI assistance are not relevant.

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

• Standalone Performance: Not applicable. This device is a material, not an algorithm.

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

• Type of Ground Truth: For these material tests, the "ground truth" is defined by the physical and chemical properties as measured by validated testing methods adhering to international standards (e.g., EN ISO 6872). It is based on objective laboratory measurements, not expert consensus, pathology, or patient outcomes data.

8. The sample size for the training set:

• Sample Size for Training Set: Not applicable. This is a manufactured material, not an AI/ML algorithm that requires a training set. The "development" of the material would involve iterative formulation and testing, but not a "training set" in the AI/ML sense.

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

• Ground Truth for Training Set: Not applicable, for the same reasons as #8.

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The Model 9160 VitaloQUB is a whole-body plethysmograph device, when used with the Vitalograph Model 9100, is designed for lung function testing on adults and pediatrics, 6 years and older, by trained medical healthcare professionals in a variety of professional healthcare environments e.g., primary care, hospitals, and pharmaceutical research centers.

The proposed Model 9160 VitaloQUB incorporates the cleared Model 9100 (K221030) with integrated LCD display and ComPAS2 software (K213872).

The ComPAS2 software controls valves and reads unprocessed data from the sensors in the Model 9100 and from Model 9160. The ComPAS2 software then determines respiratory parameters including the 2 new parameters.

The ComPAS2 software is unchanged from K213872. The Model 9160 and Model 9100 firmware does not determine any respiratory parameters.

The Model 9160 is adding 2 additional parameters:

• TVG – Thoracic Gas Volume
• Raw Airway resistance

The provided text describes the regulatory clearance for the VitaloQUB Model 9160, a whole-body plethysmograph device. However, it does not contain specific details on the acceptance criteria or a dedicated study report that demonstrates the device explicitly meets numerical acceptance criteria. The text focuses on establishing substantial equivalence to predicate devices.

Here's an analysis based on the information available and what is missing:

The submission states that "Performance testing demonstrated that the subject device met its acceptance criteria," and then lists the types of testing performed. However, it does not provide the specific numerical acceptance criteria or the reported device performance for these criteria.

1. Table of acceptance criteria and the reported device performance:

Missing Information: For VTG and Raw, while the document states performance testing was done, the specific acceptance criteria and the results demonstrating compliance are not provided. The accuracy values listed in the table are copied directly from the "Subject Model 9160" column, indicating that these are the device's inherent specifications, and the comparison section implies they are similar to or meet expectations based on 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):

This information is not provided in the document. The text mentions "Bench testing" but does not detail the sample sizes for any of the performance tests, nor does it specify if any clinical data with human subjects (and thus data provenance) was used for direct performance evaluation of VTG and Raw measurements. The comparison tables focus on technological characteristics and principle of operation similarities to predicates.

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 in the document. The device outputs objective physiological measurements, rather than interpretations requiring expert consensus as ground truth. If clinical studies were performed for the new parameters (VTG, Raw), the method of establishing ground truth would depend on the study design. However, the document provided does not detail such clinical studies or the involvement of experts in establishing ground truth.

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

This information is not provided in the document. Given the nature of the device (measuring physiological parameters rather than rendering diagnoses or classifications), an adjudication method for a test set as described is unlikely to be directly applicable in the same way as for image-based diagnostic AI. If human subject studies were conducted to compare measurements, adjudication of patient conditions might be relevant, but this is not detailed.

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:

This is not applicable to this device. The VitaloQUB Model 9160 is a pulmonary function measurement device. It measures physiological parameters and does not involve "readers" or "AI assistance" in the diagnostic interpretation sense for which MRMC studies are typically performed. The device itself performs the measurements and calculations (via the ComPAS2 software), it does not assist human interpretation of complex data (like images) in a way that would be quantified by an MRMC study.

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

The device is inherently a standalone measurement system in terms of calculating the parameters. The ComPAS2 software, which is part of the system, outputs the respiratory parameters. This is the "algorithm only" performance. The document states: "The ComPAS2 software controls valves and reads unprocessed data from the sensors in the Model 9100 and from Model 9160. The ComPAS2 software then determines respiratory parameters...". The performance testing mentioned ("Bench testing", "ATS / ERS (2002, 2005, 2013, 2017 and 2019) Static condition") assesses the accuracy of these measurements directly from the device/software. Specific performance for VTG and Raw measurements would have been assessed in this standalone manner, but the numerical results are not provided.

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

For physiological measurement devices, the "ground truth" is typically established by physical standards, calibration gases, and established reference methods or simulated physiological conditions that adhere to recognized industry standards (e.g., ATS/ERS standards). The document mentions compliance with various ISO and ATS/ERS standards, which dictate the methods and accuracy requirements for such measurements. For example, gas concentrations for DLCO are compared against medical-grade gas mixes, and flow/volume against calibrated instruments.

8. The sample size for the training set:

This information is not provided. As the device is a measurement instrument incorporating software (ComPAS2) for calculations based on physical readings, it's not described as an AI/ML device that requires a "training set" in the conventional sense (e.g., for pattern recognition or classification). The software implements algorithms for physiological calculations rather than learning from data in a machine learning paradigm.

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

This information is not provided, and likely not applicable as the device is not described as an AI/ML system requiring a training set with ground truth in the context of machine learning. The algorithms are based on established physiological principles and equations.

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The Model 9100 PFT/DICO is a pulmonary function testing device which uses Morgan Scientific's ComPAS2 software to measure subject respiratory parameters including FVC, SVC, MVV, CPF, RMS, SNIP, DLCO, MBN2 and SBN2.

The device is PC-based and designed for lung function testing on adults and pediatrics, 6 years and older, in a variety of professional healthcare environments e.g., primary care, hospitals, pharmaceutical research centers and physicians' offices.

The Model 9100 PFT/DICO is intended for the assessment of respiratory function through the measurement of dynamic lung volumes i.e., spirometry and other lung functions i.e., diffusing capacity.

The Model 9100 PFT/DICO is composed of various sensors and valves with associated low level firmware. The firmware interfaces with the Morgan Scientific's ComPAS2 software (K213872) that resides on an on-board computer. The Model 9100 also provides for user input and present resulting data on an integral display.

The ComPAS2 software controls valves and reads unprocessed data from the sensors in the Model 9100then determines respiratory parameters including FVC, SVC, MVV, CPF, RMS (MIP and MEP), SNIP, DLCO, MBN2 and SBN2. The Model 9100 PFT/DICO firmware does not determine any respiratory parameters.

The ComPAS2 software uses flow and volume from the Vitalograph pneumotachograph spirometer to display the flow and volume information measured directly from patient effort. ComPAS2 also utilizes gas analyzer readings from the Model 9100 patient test benchmark to display dilution lung volume data and single / multi breath diffusion data measured directly from patient effort. This information is then provided in a report format.

The provided text describes the regulatory clearance of the Vitalograph Model 9100 PFT/DICO, a pulmonary function testing device, and its substantial equivalence to a predicate device. However, it does not contain information about a study proving the device meets acceptance criteria related to a machine learning or AI model's performance.

The document outlines performance testing conducted for the device's electrical, mechanical, and biocompatibility aspects, as well as software verification and validation. It explicitly states that the device uses "Morgan Scientific's ComPAS2 software to measure subject respiratory parameters," but there's no indication that this software includes an AI or machine learning component that would require a study with human-in-the-loop performance, expert ground truthing, or MRMC studies typically associated with AI/ML medical devices.

Therefore, many of the requested details about acceptance criteria for an AI model's performance and associated study specifics (sample size for test/training, number of experts, adjudication, MRMC, standalone performance, ground truth type) cannot be extracted from this document.

Instead, the document focuses on demonstrating substantial equivalence to a predicate device based on similar indications for use, technological characteristics, and principles of operation, supported by standard bench testing and software validation.

Here's an attempt to answer the request based only on the provided text, highlighting the absence of AI/ML-specific details:

Device: Vitalograph Model 9100 PFT/DICO

Study Type: This document describes a 510(k) premarket notification for substantial equivalence, supported by bench testing, software verification/validation, and compliance with various standards. It is not an AI/ML performance study. The "study that proves the device meets the acceptance criteria" refers to the entire body of evidence submitted for 510(k) clearance, rather than a specific AI model's performance study.

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

The document defines performance specifications and states that testing supported the safety and performance, implying these specifications were met. The specific "acceptance criteria" for the overall device's performance are embedded in the compliance with standards and the "similar" comparisons to predicate/reference devices.

Note on "Acceptance Criteria" for AI: The document does not describe acceptance criteria for an AI or machine learning model. The stated accuracies (e.g., flow, volume, gas sensors) are for the physical measurement components of the device, not a predictive algorithm based on complex data interpretation.

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

• Sample Size for Test Set: Not specified for any performance testing, other than the implication that tests were sufficient to meet specific standards (e.g., ATS/ERS waveforms, drop tests, cleaning validations). There is no test set in the context of an AI/ML model's performance.
• Data Provenance: Not applicable in the context of typical AI/ML data provenance (e.g., country of origin, retrospective/prospective clinical data). The performance tests are largely bench-based or simulated.

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

• Not applicable. There is no mention of human experts establishing ground truth for a test set, as would be done for an AI/ML interpretation task. Ground truth for the device's measurements would be established by reference standards or highly accurate laboratory equipment.

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

• Not applicable. No expert adjudication process 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

• No, an MRMC comparative effectiveness study was not done. The device measures physiological parameters; it does not "assist" human readers in interpreting complex medical images or data.

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

• Not applicable in the context of an AI/ML algorithm. The device itself is the "standalone" entity that performs measurements. The software (ComPAS2) controls the device and processes the raw sensor data, but there's no indication of it being a standalone AI/ML interpreter.

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

• The ground truth for the device's performance relies on calibration standards, reference instruments, and established engineering/medical device testing protocols (e.g., ATS/ERS guidelines for spirometry, ISO standards for gas analysis accuracy, and various electrical/mechanical standards). There is no "expert consensus," "pathology," or "outcomes data" used for performance validation in the AI/ML sense.

8. The sample size for the training set

• Not applicable. There is no mention of an AI/ML model that would require a training set. The ComPAS2 software and device firmware are likely developed using traditional software engineering and embedded system development methods, not machine learning model training.

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

• Not applicable, as there is no AI/ML training set.

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The intended use of the Vitalograph Model 6000 Alpha is the simple assessment of respiratory function through the measurement of dynamic lung volumes i.e. spirometry. The device measures patient respiratory parameters including FVC, FEV1, FEV6, PEF, MVV and VC. The device is designed to be operated by medical professionals trained in respiratory and lung function testing on adults and pediatrics, 5 years and older, in a variety of professional healthcare environments, e.g. primary care, hospitals and occupational health centers.

The Vitalograph Alpha Model 6000 is a desktop spirometer which measures the following lung function parameters FVC, FEV1, FEV6, PEF, MVV and VC in professional healthcare environments, e.g., primary care, hospitals and occupational health centers. It is externally powered from a Class II, IEC 60601-1 compliant medical power supply. It contains a rechargeable battery powered from the external supply. The device also contains an integral 4 inch thermal printer. The device has a USB port for connection to other devices and an SD card slot for backup of stored data. The device also has wired ethernet and Wi-Fi for connection to a hospital network. Its primary functions and technology are: - Spirometry measurements using single breath and multiple-breath testing techniques, the display and recording of measured lung volumes and flow rates (including FVC, FEV1, FEV6, PEF, MVV and VC) are identical to the predicate device - Record subject data - Storage of data and test results on unit for later printing or export to Spirotrac software which was cleared under 510(k) K201562. The Flowhead utilizes a Fleisch Pneumotachograph. The operating principle is identical to the predicate K200550 - User Interface navigation via touch screen display

The provided text describes the regulatory clearance of the Vitalograph Model 6000 Alpha spirometer and details its comparison to a predicate device. It primarily focuses on the device's technical specifications, regulatory compliance, and non-clinical performance testing rather than a study proving the device meets acceptance criteria in the context of an AI/ML model for clinical decisions.

Based on the provided document, here's an analysis of the acceptance criteria and study that proves the device meets them:

This document is for a diagnostic spirometer, which is a physical medical device that measures lung function. It is not an AI/ML device for clinical decisions. Therefore, many of the typical "acceptance criteria" and "study types" associated with AI/ML devices (like MRMC studies, ground truth establishment by experts, adjudication, sample size for training sets, etc.) do not apply in this context.

The "acceptance criteria" for a physical diagnostic device like a spirometer primarily revolve around its technical performance specifications, electrical safety, EMC, and compliance with relevant international standards.

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

The document doesn't present a formal "acceptance criteria" table in the AI/ML sense. Instead, it provides a "Comparison of Subject and Predicate Devices" (Table 1) which implicitly serves as a comparison against established performance benchmarks and standards for spirometers. The performance data section further details the testing performed to demonstrate compliance.

Here's an attempt to derive "acceptance criteria" from the Specifications reported in the comparison table and the Performance Data section:

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

• Test Set Sample Size: Not applicable in the context of patient data or clinical test sets for AI/ML validation. The testing described is bench testing using standardized methods and controlled inputs (e.g., flow/volume simulators, environmental chambers).
• Data Provenance: Not applicable as it's not a data-driven AI/ML study. The "data" here comes from direct measurements by the device itself under test conditions.

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 device performance (e.g., whether a spirometer accurately measures volume) is established by calibration against known, traceable standards and instruments, not by human expert interpretation of results. The "ground truth" for spirometry measurements comes from the physical and engineering principles of the measurement itself and the standards against which it is calibrated and tested.

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

• Not applicable. Adjudication is a process used in studies where human interpretation or clinical judgment is involved, particularly for establishing a consensus "ground truth" from multiple readers. This is a technical device performance test, not a reader study.

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. An MRMC study is relevant for AI/ML diagnostic aids where human readers interpret medical images or data. This is a fundamental diagnostic device, not an AI assistance tool for human readers.

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

• The device itself is a "standalone" diagnostic instrument. Its performance is evaluated intrinsically through bench testing against specified standards and its predicate, rather than being an "algorithm only" being evaluated for clinical decision support. Its core function is to measure parameters directly, not to provide an automated clinical interpretation that would fall under "algorithm only" performance in the AI/ML sense.

7. The type of ground truth used:

• The "ground truth" for the device's technical performance is based on established engineering standards and reference measurements, such as those defined by ATS/ERS (2019), ISO 23747, and ISO 26782. These standards specify how spirometers should measure flow and volume and define the acceptable accuracy limits. For electrical safety and EMC, the ground truth is compliance with the relevant IEC/AAMI standards.

8. The sample size for the training set:

• Not applicable. This device does not use machine learning, so there is no "training set."

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

• Not applicable. As there is no training set for machine learning.

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VITA AKZENT LC Indication range:

• Restorations made of hybrid ceramic
• · Restorations made of light-curing veneering material
• · Restorations made of CAD/CAM composites
• · Prefabricated Teeth
• · Denture bases

VITA AKZENT LC is a light-curing methacrylatebased stain/ glaze system for extraoral surface characterization of dental restorations made of hybrid ceramic, resin veneering materials, CAD/CAM composites, prefabricated teeth and denture base resins. It can also be used for internal characterization with the layering technique of veneering composites.

The document describes the VITA Akzent® LC, a light-curing methacrylate-based stain/glaze system for extraoral surface characterization of dental restorations. The acceptance criteria and the study proving the device meets these criteria are outlined in the "Non-Clinical Performance Testing" and "Biocompatibility" sections.

1. Table of Acceptance Criteria and Reported Device Performance:

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

The document does not specify a distinct "test set" in the traditional sense of a clinical study with human subjects. The evaluation was primarily based on non-clinical performance testing and biocompatibility assessment.

• For biocompatibility, the testing was conducted in-vitro/non-clinical based on recognized international standards. Specific sample sizes for these tests (e.g., number of specimens tested for cytotoxicity, irritation, etc.) are not explicitly stated in this summary.
• For adhesion performance, "bench testing" was conducted. The sample size for this testing is not explicitly stated.
• The data provenance is not explicitly stated but is presumed to be from laboratory testing as per the cited ISO standards.

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

This information is not applicable and not provided in the document because the assessment was based on non-clinical (laboratory) testing and established international standards for biocompatibility and material performance, rather than clinical efficacy studies requiring expert evaluation of patient data.

4. Adjudication Method for the Test Set:

This information is not applicable and not provided as the evaluation was based on objective laboratory testing against established standards, not human interpretation of results requiring adjudication.

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 document explicitly states: "No human clinical testing was performed to support the substantial equivalence of VITA Akzent® AC."

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:

This concept is not applicable to this device. VITA Akzent® LC is a material (a stain/glaze system), not an algorithm or an AI-powered diagnostic tool. Its performance is determined by its physical and chemical properties as evaluated through non-clinical laboratory tests.

7. The Type of Ground Truth Used:

The ground truth for demonstrating the device's performance relies on:

• Established international standards: ISO 10993-1 (Biological evaluation of medical devices) and ISO 7405 (Dentistry – Evaluation of biocompatibility of medical devices).
• Bench test results: Specifically, "adhesion performance testing" against a reference device.
• Material composition equivalence: Comparison of the chemical composition to predicate devices.

8. The Sample Size for the Training Set:

This information is not applicable and not provided. As VITA Akzent® LC is a material and not an AI/ML algorithm, there is no "training set" in the conventional sense. Its development would likely involve
iterative formulation and testing, but not machine learning training.

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

This information is not applicable and not provided, as there is no "training set" for this type of device. The formulation and development of the material are based on scientific principles of polymer chemistry and dental material science, combined with performance testing.

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