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The TrueTear Intranasal Tear Neurostimulator provides a tear production during neurostimulation to improve dry eye symptoms in adult patients with severe dry eye symptoms.

The TrueTear device is a prescription only, non-implantable, electrostimulation device intended to increase tear production for improvement in dry eye symptoms. The device's technological principle is the application of low-level electrical stimulation to sensory neurons located in the nose to acutely increase tear production and improve dry eye symptoms. The device consists of three distinct non-sterile subassemblies - a base unit which controls and produces electrical stimulation waveform and enables the patient to control the neurostimulation, a disposable tip that is inserted into the nose and provides the contact surface for the stimulation to the target tissue located in the nose, and a charger case that protects the device and replenishes the sealed battery inside the base unit between uses.

The provided text describes a 510(k) summary for the TrueTear Intranasal Tear Neurostimulator, which is a medical device and not an AI/ML algorithm. Therefore, many of the requested categories in the prompt, such as data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone performance, and training set information (which are relevant to AI/ML studies), are not applicable to this device submission.

The submission focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance data, primarily related to electrical output specifications, biocompatibility, electrical safety, software validation, wireless communications, and mechanical durability. The document states that no animal or clinical studies were required due to the nature of the modifications and the equivalence to the predicate device whose clinical safety and effectiveness were previously established.

Here's an attempt to answer the prompt given the available information, noting the inapplicability of AI/ML specific criteria:

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

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

This information is not applicable as the submission primarily relies on bench testing and comparisons to a predicate device, rather than a "test set" in the context of an AI/ML algorithm or a clinical trial with human subjects for novel device performance. The performance data listed (electrical output, biocompatibility, safety, software, wireless, mechanical)是由实验室测试得出的。

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)

Not applicable. The device performance is assessed against engineering and regulatory standards (e.g., ISO, IEC) through bench testing, not expert-adjudicated ground truth as would be relevant for an AI/ML diagnostic device.

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

Not applicable. This is relevant to expert review processes for AI/ML ground truth, not device performance testing against engineering standards.

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

Not applicable. This is not an AI/ML device that assists human readers.

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

Not applicable. This is a physical neurostimulation device, not an algorithm.

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

The "ground truth" for the device's performance is compliance with established engineering and safety standards (e.g., electrical output parameters, biocompatibility standards, EMC standards, software validation standards) and mechanical design specifications, demonstrated through verified bench testing and documented in the 510(k) submission.

8. The sample size for the training set

Not applicable. There is no AI/ML algorithm or "training set" for this device.

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

Not applicable. There is no AI/ML algorithm or "training set" for this device.

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The TrueTear Intranasal Tear Neurostimulator provides a temporary increase in tear production during neurostimulation to improve dry eye symptoms in adult patients with severe dry eye symptoms.

The TrueTear Intranasal Tear Neurostimulator is a non-surgical, non-implantable device intended for the application of low level electrical stimulation to sensory neurons of the nasal cavities to increase tear production to improve dry eye symptoms in adult patients with severe dry eye symptoms. The device consists of four distinct non-sterile subassemblies: Disposable Tips, a reusable Base Unit, a reusable Charging Station, and a reusable Cover. The device design has not been significantly modified from the device granted under DEN160030, with technical changes including enabling Bluetooth functionality for one-way wireless communication, removal of material used in the disposable tip, disabling of the tip detection feature in the Base Unit, and addition of an optional mobile application (TrueTear App) for reading and summarizing device usage data.

Here's a breakdown of the acceptance criteria and the study details for the TrueTear Intranasal Tear Neurostimulator, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly defined as pass/fail thresholds in the provided text for overall device performance regarding dry eye symptom improvement. Instead, the FDA's decision to grant De Novo classification is based on a benefit-risk determination and the device's ability to demonstrate "meaningful improvement in symptoms." The reported device performance is presented as the proportion of subjects achieving a clinically important change (MCID) in their OSDI score.

• At Day 7 (MCID 7.3): 42.86% (33/77) improved, 12.99% (10/77) worsened
• At Day 30 (MCID 7.3): 38.67% (29/75) improved, 12.00% (9/75) worsened
• At Day 7 (MCID 13.4): 23.38% (18/77) improved, 5.19% (4/77) worsened
• At Day 30 (MCID 13.4): 25.33% (19/75) improved, 6.67% (5/75) worsened

Conclusion: "There were more participants with severe dry eve symptoms that had a meaningful improvement in symptoms as measured with the Ocular Surface Disease Index (OSDI) than the number with clinically significant worsening of symptoms at day 7 and at day 30." |
| Probable benefits outweigh probable risks. | Based on the above OSDI results, and a low incidence of self-limited, non-serious adverse events, the FDA concluded: "The probable benefits outweigh the probable risks for the TrueTear Intranasal Tear Neurostimulator." |

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

• Test Set Sample Size:
  • Overall enrolled participants: 97
  • Participants with severe dry eye symptoms at screening (used for primary analysis): 77
  • Participants with severe dry eye symptoms seen at Day 30: 75
  • The moderate dry eye group's sample size was "too small to make meaningful inferences."
• Data Provenance: The study was a prospective clinical trial (OCUN-010) conducted by the applicant (Allergan). The document does not specify the country of origin of the data, but Allergan is based in Irvine, California.

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

The ground truth for device effectiveness was established using patient-reported outcomes via the Ocular Surface Disease Index (OSDI). Therefore, traditional "experts" like radiologists establishing a ground truth in image analysis are not applicable here. The "ground truth" for severity categories was based on OSDI scores (Severe dry eye: OSDI total score of 33 or more).

4. Adjudication Method for the Test Set

Not applicable. The study relied on a patient-reported outcome measure (OSDI) rather than expert adjudication of clinical findings.

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

No, an MRMC comparative effectiveness study was not conducted. This device is not an AI diagnostic tool that assists human readers in interpreting cases. It's a medical device for direct patient treatment. The study design focused on the patient's self-reported improvement in symptoms.

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

Yes, the effectiveness study (OCUN-010) evaluated the device's performance standalone, meaning its direct effect on patients (human-with-device) without a human interpreter in the loop. The device itself is the "standalone" treatment. The study assessed the device's ability to improve dry eye symptoms through neurostimulation. While patient training was involved, the performance being measured was that of the device and the patient's physiological response, not an AI algorithm's diagnostic accuracy.

7. The Type of Ground Truth Used

The primary ground truth used was patient-reported outcomes data, specifically the Ocular Surface Disease Index (OSDI). Clinically meaningful change was defined using published Minimal Clinically Important Difference (MCID) thresholds from Miller et al. (Arch Ophthalmol. 2010;128(1):94-101).

8. The Sample Size for the Training Set

No separate "training set" is mentioned in the context of clinical efficacy for this device. The clinical trials (OCUN-009 and OCUN-010) are described as pivotal studies for effectiveness and safety. For software development and validation, specific training set information is not provided, but it states "The submission contained all the elements of software documentation corresponding to the "Moderate" level of concern, as outlined in the "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices."

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

As there's no explicitly defined "training set" for clinical efficacy in the provided text, this question is not directly applicable. For software ground truth, internal specifications and hazard analyses would have guided the verification and validation (V&V) testing.

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