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Peregrine Endoscopy System is intended to visualize the internal cavities of the ear, airways, nose and sinus cavities during diagnostic and therapeutic endoscope procedures.

The Peregrine Endoscopy System is a single-use flexible ENT (ear, nose & throat) endoscope (provided sterile) which allows the user to steer through the anatomy and visualize it. The device is used in patients in whom endoscopic evaluation of, or intervention in, the ear, airways, nose, and sinus cavities is indicated.

The modified Peregrine endoscopy system consists of:

• A Single-use Endoscope - includes a distal CMOS imager, an illumination source, and a working channel which enables irrigation and suction. The endoscope is provided sterile. The multi-use handle and the endoscope attachment cable, which were separate components in the cleared 3NT Endoscopy System, are now an integral part of the single-use endoscope, thus eliminating all multi-use components from the system.
• A Video Console (formerly named Camera Control Unit, or CCU) which includes a video board and a tablet-based display, connects to the endoscope through the endoscope cable to receive video images from the endoscope and display them.

Based on the provided text, the Peregrine Endoscopy System is a medical device, and the document is a 510(k) summary for its premarket notification to the FDA. This document does not describe an AI medical device or a study involving AI assistance for human readers.

Therefore, many of the requested points, such as sample sizes for test/training sets, blinding, adjudication methods, number of experts for ground truth, MRMC studies, standalone AI performance, and AI-specific ground truth establishment, are not applicable to this document. The document describes a traditional medical device (an endoscope system) and its performance against acceptance criteria largely focused on safety, electrical compatibility, software validation, and bench testing.

Here's an analysis based on the information available in the document:

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

The document provides a table summarizing bench testing, but it doesn't explicitly list specific quantitative acceptance criteria alongside the numeric results for all tests. Instead, it generally states "The tests passed and all acceptance criteria were met" or "substantial equivalence is determined."

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

• Sample Size: Not specified in terms of patient data. For bench testing, it refers to the number of devices or components tested. The document does not provide raw numbers but implies enough for a statistically sound conclusion for the type of testing performed (e.g., "The Peregrine Endoscopy System passed all the functionality and simulated use tests.").
• Data Provenance: The tests are "bench tests," meaning they were conducted in a controlled lab environment on the device itself, not on patient data. The company is based in Israel (3NT Medical Ltd., Rosh Ha'ayin, Israel).

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

• This question is not applicable as the document describes performance data for a medical device (endoscopy system), not an AI algorithm. Ground truth, in the context of diagnostic AI, refers to confirmed disease status, which isn't relevant here. The "ground truth" for this device's performance is established by objective engineering and safety standards (e.g., passing electrical safety tests, meeting specified dimensions, or achieving certain image quality parameters).

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

• Not applicable. This relates to human expert consensus for diagnostic tasks, which is not relevant to the acceptance criteria for an endoscope system.

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 document does not mention an MRMC study or any study involving human readers or AI assistance. The device is an endoscope system, not an AI diagnostic tool.

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

• Not applicable. The device is a physical endoscope system, not a standalone AI algorithm. While it has software, its "performance" is tied to its function as a visualization tool, not independent algorithmic interpretation.

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

• The "ground truth" for this device's acceptance is based on compliance with established international and national standards (e.g., ISO, IEC, FDA guidelines) for medical device performance, safety, and functionality, as verified through bench testing. For example:
  • Biocompatibility: Conformance to ISO 10993-1.
  • Electrical Safety: Conformance to IEC 60601-1, IEC 60601-2-18.
  • EMC: Conformance to IEC 60601-1-2.
  • Software V&V: Conformance to IEC 62304.
  • Physical/Optical Properties: Meeting predefined engineering specifications and visual/dimensional inspections.
  • Image Quality: Objective measurements like sRGB/AdobeRGB coverage, MTF, and color accuracy, likely against defined benchmarks for endoscopic visualization.

8. The sample size for the training set

• Not applicable. This device is not an AI/ML algorithm that requires a training set. The software mentioned underwent verification and validation, but this refers to traditional software engineering V&V, not machine learning model training.

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

• Not applicable, as there is no mention of a training set for an AI/ML model in this document.

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The Colibri Endoscopy System is intended to visualize the internal cavities of the ear, airways, nose and sinus cavities during diagnostic and therapeutic endoscope procedures.

The Colibri Endoscopy System is a single-use ENT (ear, nose & throat) endoscope (provided sterile under EtO sterilization) which allows the user to steer through the anatomy and visualize it. The device is used in patients in whom endoscopic evaluation of, or intervention in, the ear, airways, nose, and sinus cavities is indicated. The Colibri endoscopy system consists of: A Single-use Endoscope includes a distal CMOS imager, an LED for illumination, and an add-on working channel which enables suction. The endoscope is provided sterile. The multi-use handle and the endoscope attachment cable, which were separate components in the cleared 3NT Endoscopy System, are now an integral part of the single-use endoscope, thus eliminating all multi-use components from the system. A Video Console (formerly named Camera Control Unit, or CCU) which includes a video board and a tablet-based display, connects to the endoscope through the endoscope cable to receive video images from the endoscope and display them.

This document describes the premarket notification (510(k)) for the Colibri Endoscopy System. The information provided outlines the system's performance data, but it does not contain the details typically found in a study proving a device meets acceptance criteria for an AI/ML algorithm, as none are mentioned. Instead, it focuses on the substantial equivalence of the Colibri Endoscopy System to a predicate device based on common medical device performance benchmarks.

Therefore, many of the requested items related to AI/ML acceptance criteria, ground truth establishment, expert adjudication, and MRMC studies cannot be answered from the provided text.

Here's a breakdown of what can be extracted and what cannot:

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

The document provides the following performance data and verification of compliance, rather than specific numerical acceptance criteria linked to AI/ML performance. The "acceptance criteria" here refer to successful completion of standard medical device tests.

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

• Sample Size for Test Set: Not specified for any of the performance tests. For bench testing, it refers to the number of devices or components tested, which is not provided.
• Data Provenance: Not specified.
• Retrospective or Prospective: Not specified.

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

Not applicable as this is not an AI/ML algorithm study involving expert review for ground truth. Image quality and optical performance comparisons were based on consensus international standards and published scientific literature by unspecified personnel.

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

Not applicable.

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 reported. This device is an endoscope, not an AI-powered diagnostic tool.

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

Not applicable. The performance data relates to the physical and functional characteristics of the endoscope itself.

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

For image quality, ground truth appears to be based on adherence to international standards and published literature for color and optical performance evaluation. For other physical/functional tests, the "ground truth" is compliance with predefined specifications and standards.

8. The sample size for the training set

Not applicable. This is not an AI/ML algorithm requiring a training set.

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

Not applicable.

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The Sinusway Dilation System is intended to access and treat the frontal, maxillary and sphenoid sinuses in sinus procedures in adults using a trans-nasal approach, by dilation and displacement of the anatomic structures along the sinus drainage pathways.

Sinusway Dilation System combines the tissue expansion effect of balloon dilation with the features of a curved sinus seeker, and by that allows the user to track the dilation device into the sinus drainage pathways. The shape of the distal end of the dilation system can be changed by inserting pre-shaped seekers into it. The system includes:

1. A Dilation Kit (Single use, provided sterile) consists of the following components:
   a. A Dilation Device includes an inflatable balloon at its distal end and connects to the inflation device at its proximal end.
   b. Three interchangeable pre-shaped Seekers (also referred to as stylets).
   c. An Inflation Device connects to the dilation device and inflates it while providing visual and tactile pressure indication.
   All Dilation Kit components are disposed of at the end of the procedure.
2. A Holder (re-processible and auto-clavable) - holds the components of the Dilation Kit in place and locks them, allowing the user to operate the dilation tool.

The provided document is a 510(k) premarket notification for the Sinusway Dilation System. The information required in the request pertains to the performance criteria and studies for a device, typically AI/ML-based software. However, the Sinusway Dilation System is a physical surgical instrument (Ear, Nose, And Throat Manual Surgical Instrument). Therefore, many of the requested fields, such as "effect size of how much human readers improve with AI vs without AI assistance," "standalone (i.e. algorithm only without human-in-the-loop performance)," and "sample size for the training set," are not applicable to this type of medical device.

The document focuses on demonstrating substantial equivalence to a predicate device through comparison of technological characteristics and bench testing, rather than clinical performance metrics typically associated with AI/ML devices.

Nevertheless, I will extract the available information related to acceptance criteria and device performance based on the provided text, while explicitly noting when requested information is not applicable.

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

The document states that "All tests met the predefined acceptance criteria," but it does not explicitly list the acceptance criteria or the specific numerical performance results for each test. Instead, it lists the types of bench tests performed.

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

For the bench testing, the document does not specify sample sizes for each test or the data provenance (e.g., country of origin, retrospective/prospective). It generally refers to "tests" being performed.

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

Not applicable. This device is a physical surgical instrument, and its performance was evaluated through bench testing and compliance with standards, not through comparison against expert-established ground truth in a diagnostic context.

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

Not applicable. Adjudication methods are typically relevant for clinical studies or studies involving human interpretation of diagnostic results, which is not the case for this device's performance evaluation.

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 a physical device, not an AI-assisted diagnostic tool.

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

Not applicable. This is a physical device, not an algorithm. Its functionality is dependent on human use during surgery.

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

The "ground truth" for the device's acceptable performance was established by adherence to recognized international standards (e.g., ISO 14971:2012 for risk analysis, ISO 10993-1 for biocompatibility, ISO 11135-1 for sterilization) and internal predefined acceptance criteria for bench tests. These are engineering and safety standards, rather than clinical "ground truth" for diagnostic accuracy.

8. The sample size for the training set

Not applicable. This is a physical device. The concept of a "training set" is relevant for AI/ML models.

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

Not applicable. As above, this is a physical device.

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The 3NT endoscopy system is intended to visualize the internal cavities of the ear, airways, nose and sinus cavites during diagnostic and therapeutic endoscope procedures.

3NT endoscopy system is a single-use flexible ENT (ear, nose & throat) endoscope (provided sterile) which allows the user to steer through the anatomy and visualize it.

3NT endoscopy system includes:

1. A Single-use flexible endoscope (provided sterile) includes a distal tip CMOS imager and an integrated working channel which allows irrigation through a single channel. It is the only part that comes in contact with the patient and is disposed of at the end of the procedure.
2. A Multi-use handle accessory (re-processible and auto-clavable) engages the single-use endoscope component and allows the user to mechanically control its articulation through dedicated levers.
3. A Camera control unit (CCU) includes a video processor and a white LED light source. The CCU includes an attachment cable which connects to the single-use endoscope, receives video images from the endoscope, and delivers LED light to the endoscope.

The provided text is a Premarket Notification (510(k)) summary for the 3NT Endoscopy System. The information focuses on demonstrating substantial equivalence to predicate devices, primarily through bench testing, biocompatibility, sterilization validation, software validation, electrical safety, and a cadaver study.

However, the document does not contain the detailed information required to answer the specific questions about acceptance criteria for an AI/ML-based device and the study that proves it meets those criteria.

Here's why and what information is missing:

• This is not an AI/ML device: The document describes a "3NT Endoscopy System" which is a flexible ENT endoscope system. It consists of a single-use flexible endoscope (CMOS imager), a multi-use handle, and a Camera Control Unit (CCU) with a video processor and LED light source. There is no mention of any artificial intelligence, machine learning, or algorithmic analysis of the images for diagnostic purposes. The device is a direct visualization tool.
• No acceptance criteria for AI performance: Since it's not an AI device, there are no acceptance criteria related to AI performance metrics (e.g., sensitivity, specificity, AUC). The acceptance criteria mentioned refer to the successful completion of engineering bench tests, biocompatibility tests, sterilization, and a cadaver study for visualization.
• No "ground truth" establishment for AI: The "ground truth" described is based on the ability to visualize anatomical landmarks in a cadaver study, not on expert consensus or pathology for an AI's output.
• No "test set" and "training set" for AI: The data provenance, sample sizes for test/training sets, number of experts for ground truth, and adjudication methods are details specific to the validation of AI models, which are not applicable here.
• No MRMC study: A Multi-Reader Multi-Case (MRMC) study is relevant for evaluating the impact of AI on human reader performance, which isn't applicable to a direct visualization endoscope.
• No standalone AI performance: Standalone performance refers to the AI algorithm's performance without human intervention, which is not relevant for this device.

In summary, the provided document describes a medical device clearance for an endoscope based on traditional engineering and medical device testing, not an AI/ML product. Therefore, it does not contain the information requested in the prompt regarding AI acceptance criteria and study details.

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