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The Flexible Bronchoscopes have been designed to be used with the video processor, endotherapy accessories and other ancillary equipment for endoscopy within the airways and tracheobronchial tree.

The Flexible Bronchoscope is for use in a hospital environment. The Flexible Bronchoscope is a single-use device designed for use in adults.

The Flexible Bronchoscope (Model: BS27U-12EU, BS27U-12US, BS38U-20EU, BS38U-20US) is intended to be used with the Portable Video Processor (cleared via K243497). The Flexible Bronchoscope is inserted through the airways and tracheobronchial tree during bronchoscopy, and when used with the compatible video processor and monitor, the endoscope system can be operated as intended and indicated. The Flexible Bronchoscope is a single-use endoscope, which consists of a Handle, an Insertion Section, and an Endoscope Connector. The handle includes a deflection lever, a lever lock, a push button for picture taking/video recording, a push button for suction, a connector for suction tubing, a Luer port for insertion of accessory devices and irrigation to the working channel and a LED for illumination. The insertion section contains one working channel, wiring to transmit the image signals to the video processor, and two optical fibers to transmit illumination from the handle to the distal tip. The distal bending section of the insertion section is controlled by the user via the deflection lever on the handle. The distal end of the insertion section contains a CMOS sensor for capturing image and transmitting it to the video processor, optical fibers for transmitting illumination from the LED inside the handle, and the distal opening of the working channel. The Endoscope Connector connects the endoscope handle to the video processor, which provides power and processes video signals from the endoscope. Same as the predicate, the subject device is also provided in 2 deflection versions (US/EU deflection).

This document is an FDA 510(k) clearance letter for a Flexible Bronchoscope, indicating substantial equivalence to a predicate device. However, it does not contain the level of detail requested regarding acceptance criteria and a specific study proving the device meets those criteria, particularly in the context of an AI/algorithm-driven device.

The provided text focuses on the physical and performance characteristics of the bronchoscope itself, its intended use, technological comparison to a predicate device, and various non-clinical tests (electrical safety, photobiological safety, mechanical/optical performance, biocompatibility, sterilization, shelf life, and package validation).

There is no mention of an AI component, an algorithm, or any study involving human readers, ground truth establishment, or performance metrics like sensitivity, specificity, or AUC. The "device performance" in this context refers to the bronchoscope's mechanical and optical functionality, not the diagnostic accuracy of an AI algorithm.

Therefore, most of the requested information cannot be extracted from this document.

Here's an attempt to answer based on the provided text, highlighting what is missing:

1. Table of acceptance criteria and reported device performance

The document does not present a formal table of "acceptance criteria" for a diagnostic algorithm with corresponding "reported device performance" in terms of clinical accuracy metrics (like sensitivity, specificity, AUC). Instead, it states that various non-clinical tests were performed to demonstrate compliance with recognized standards and that the device is "as safe, as effective, and performs as well as the legally marketed device identified above."

Below is a table summarizing the types of tests and the general conclusion, but without specific quantitative acceptance criteria or performance metrics related to diagnostic accuracy.

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

• Sample Size for Test Set: Not applicable/Not mentioned. This document describes a medical device (bronchoscope) itself, not an AI algorithm performing a diagnostic task on a dataset. The "tests" mentioned are non-clinical engineering and biological safety tests, not tests on a dataset.
• Data Provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable/Not mentioned.

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 "ground truth" in the context of an AI algorithm's diagnostic performance for which experts would be needed. The "ground truth" for the non-clinical tests would be the established performance specifications and standards for a bronchoscope.

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

• Not applicable. As no expert ground truth establishment for a diagnostic AI is mentioned, no adjudication method would be relevant.

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 describes the clearance of a flexible bronchoscope, a physical medical device. It does not mention any AI component or a MRMC study.

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

• Not applicable. There is no mention of an algorithm in this document that would perform as a standalone device.

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

• Not applicable. Ground truth in the context of diagnostic AI is not mentioned. For the non-clinical tests, the "ground truth" is compliance with established engineering and safety standards.

8. The sample size for the training set

• Not applicable. There is no mention of an AI model or a training set.

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

• Not applicable. There is no mention of an AI model or a training set.

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The NeuroEars-Anna™ system provides information to assist in the nystagmographic evaluation, diagnosis, and documentation of vestibular disorders. Nystagmus of the eye is recorded using a head-mounted display equipped with eye-tracking cameras. These images are measured, recorded, displayed, and stored in the software. This information can then be used by trained medical professionals to assist in diagnosing vestibular disorders.

The NeuroEars-Anna™ system is intended for use in individuals aged 12 years and older, based on the physical compatibility of the FOVE VR headset (FOVE Inc., Japan). While the ANSI S3.45 standard does not define age-based limitations, the FOVE VR headset is generally suitable for individuals aged 12 and above. For improved fit, soft materials such as sponge pads may be used in cases where the headset does not conform properly to the user's face. This applies to both pediatric and adult patients. Any additional padding should be used only if it does not interfere with eye-tracking performance or measurement accuracy and must follow the manufacturer's instructions for proper use.

NeuroEars-Anna™ is a standalone software device that analyzes eye movements to assist medical professionals in the nystagmographic evaluation, diagnosis, and documentation of vestibular disorders. The NeuroEars-Anna™ software is intended to be used with off the shelf hardware including the HMD, PC, and monitors.

The NeuroEears-Anna™ software is designed to perform the following vestibular tests:

• Spontaneous Nystagmus Test
• Gaze-Evoked Nystagmus Test
• Head Shaking Nystagmus Test
• Fistula Nystagmus Test
• Dix-Hallpike Test
• Positional Test
• Smooth Pursuit Test
• Random Saccade Test
• Saccadometry Test
• Optokinetic Nystagmus Test
• Subjective Visual Vertical/Subjective Visual Horizontal (SVV/SVH)
• Caloric Test
• Video Frenzel

NeuroEars-Anna™ is a software program that analyzes eye movements recorded from an eye-tracking camera mounted on a head-mounted display (HMD) with eye-tracking specifications suggested by ANSI/ASA S3.45-2009 (Reaffirmed by ANSI April 16, 2024 version). The HMD devices used can be commercial products such as the FOVE0 (powerd by FOVE Inc., Japan), which meet these minimum eye-tracking specifications. The software is intended to run on a Microsoft Windows PC platform.

Here's a breakdown of the acceptance criteria and study information for NeuroEars-Anna™, based on the provided FDA 510(k) Clearance Letter:

1. Table of Acceptance Criteria and Reported Device Performance

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UE BRONCHO Single-Use Bronchoscope: The UE BRONCHO Single-Use Bronchoscopes has been designed to be used with the UE Display, endotherapy accessories and other ancillary equipment for endoscopy within the airways and tracheobronchial tree. The UE BRONCHO Single-Use Bronchoscopes is for use in a hospital environment. The UE BRONCHO Single-Use Bronchoscopes is single-use device designed for use in adults.

UE Display: The UE Display is reusable digital monitor, intended to display live imaging data from UE Medical visualization devices.

The bronchoscope system consists of UE BRONCHO Single-Use Bronchoscopes and UE Display. The UE BRONCHO Single-Use Bronchoscopes (the bronchoscopes) are sterile, single-use flexible video bronchoscopes available in three sizes (Slim, Regular, Large). The bronchoscopes have been designed to be used with the UE Display (reusable, non-sterile), endotherapy accessories and other ancillary equipment for endoscopy within the airways and tracheobronchial tree. The bronchoscope system is designed for use by adults in a hospital environment.

The provided FDA 510(k) clearance letter details the clearance of the UE BRONCHO Single-Use Bronchoscopes and UE Display. However, it does not contain specific acceptance criteria or details of a clinical study that demonstrates the device's performance against such criteria. The document explicitly states "Clinical study: Not applicable."

The clearance is based on non-clinical data, specifically performance testing and compliance with voluntary standards, which demonstrates substantial equivalence to a predicate device (Ambu® aScope™ 4 Broncho, Ambu® aView™ Monitor - K173727).

Therefore, I cannot populate the requested table and answer questions 2-9 with the provided text. The document refers to "bench performance testing" which includes optical performance, color reproduction, geometric distortion, resolution, depth of field, image intensity uniformity, noise, dynamic range, and frame rate, comparing these aspects with the predicate device. However, it does not specify quantitative acceptance criteria for these tests nor provide the reported device performance against such criteria in the detail requested.

Here's an attempt to populate the table and address the questions based only on the information available in the provided text. Where information is missing, it will be stated as "Not provided in the text."

1. Table of Acceptance Criteria and Reported Device Performance

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

Not provided in the text. The document refers to "bench performance testing" which typically uses a limited number of physical units or simulated conditions, rather than a "test set" of patient data as might be relevant for AI/ML devices. No human patient data was used for testing.

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

Not applicable, as no clinical study or expert-based ground truth establishment for a test set is mentioned. The testing was non-clinical bench testing.

4. Adjudication method for the test set

Not applicable, as no clinical study involving expert interpretation or adjudication is mentioned.

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. The document explicitly states "Clinical study: Not applicable." Therefore, an MRMC study was not performed. This device is a bronchoscope system, 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 device is a bronchoscope system. While it has software and display capabilities, it is not an algorithm that performs a diagnostic or analytical function independently of a human operator, and no standalone performance study in this context was mentioned.

7. The type of ground truth used

For the non-clinical bench tests (e.g., optical performance, physical dimensions, electrical safety), the "ground truth" would be established by direct physical measurements, adherence to engineering specifications, and compliance with recognized international standards (e.g., ISO 8600, IEC 60601 series, ISO 10993 series). There is no "ground truth" in the clinical sense (e.g., pathology, outcomes data, expert consensus) as no clinical studies were performed.

8. The sample size for the training set

Not applicable. This device is a bronchoscope system and not an AI/ML device that requires a training set of data for model development.

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

Not applicable, as no training set was used.

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The Outlook Surgical Versa One System is intended for use in examination and visualization in otolaryngology and head and neck procedures.

The Outlook Surgical Versa One System is a video nasopharyngoscopy system that provides illumination and live video stream of a patient's internal anatomy during otolaryngology and head and neck examinations and procedures. The device is composed of an endoscope, a connection cable available in 6ft or 12ft lengths, a wireless enabled display unit, and a wireless HDMI receiver. Users attach accessory instruments to the semi-flexible shaft of the endoscope enabling the user to visualize with the instrument attached.
The device will be used by clinical practitioners in a sterile field in an operating room, healthcare clinic, or medical office. The Outlook Surgical Versa One System is a reusable device initially supplied as non-sterile to the user and requiring the user to process (i.e., clean and sterilize) the device for initial use, as well as to reprocess the device after each use.
The Versa One Endoscope has a semi-flexible shaft that enables the user to attach instruments. The semi-flexible shaft is rigid near the adapter end and semi-flexible at the distal end to conform the shape of the attached instrument. When an instrument is attached to the scope, the flexible portion becomes rigid and the scope functions as a rigid endoscope. The Versa One endoscope is compatible with Versa One straight and curved sheaths.
The Outlook Surgical Versa One System live stream video can be viewed on the display unit and an off-the-shelf monitor connected to the display unit via an HDMI cable or wirelessly. The off-the shelf monitor is not provided with the device. The device operates off rechargeable battery power.
The Versa One Endoscope uses an integrated LED light source to illuminate glass fiber bundles in the shaft and a CMOS sensor at the distal tip to send the signal to a custom PCB in the endoscope handle that converts the signal to live video.

Based on the provided FDA 510(k) Clearance Letter, the "Outlook Surgical Versa One System" is a medical device for examination and visualization in otolaryngology and head and neck procedures, and not an AI/ML powered device. Therefore, the information typically required for an AI/ML device's acceptance criteria, such as a table of performance metrics (sensitivity, specificity, AUROC), sample size for test/training sets, expert qualifications, and comparison to human readers, is not present in this document.

The document discusses the substantial equivalence of the "Outlook Surgical Versa One System" to a predicate device (Stryker Precision S 4K Sinuscope) based on physical and functional characteristics, and a range of non-AI/ML specific performance tests.

Here's an analysis of what information is provided regarding the device's performance and acceptance, and why the requested AI/ML specific details are absent:

Acceptance Criteria and Device Performance (as per the document):

The acceptance criteria for this device are demonstrated through various performance tests, with the reported device performance consistently being "Passed. All acceptance criteria were met."

Regarding the specific AI/ML and clinical study requirements you listed, the document does not provide the following information:

1. Sample size used for the test set and the data provenance: Not applicable as this is not an AI/ML device with a test set of data. The "test sets" here refer to physical devices undergoing various engineering and biological tests.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an imaging device like this is primarily about validating its physical performance against standards and its ability to capture high-quality images, not expert interpretation of AI outputs. "User Validation" involved multiple users, but their qualifications are not specified beyond being "clinical practitioners" in the "use environment."
3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
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: Not applicable.
5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
6. The type of ground truth used (expert concensus, pathology, outcomes data, etc): For this device, "ground truth" is established through engineering specifications, international standards (ISO, IEC, ANSI/IEEE), and regulatory guidance documents (FDA). For instance, optical performance is validated against ISO standards, and biocompatibility against ISO 10993. The "User Validation" involved using anatomical models and cadaver models, which serve as a form of ground truth for assessing usability and visualization capability.
7. The sample size for the training set: Not applicable. This is not an AI/ML device that requires a training set.
8. How the ground truth for the training set was established: Not applicable.

In summary, the provided 510(k) clearance letter details the substantial equivalence of a medical imaging device (a nasopharyngoscope) by demonstrating its physical, electrical, and performance characteristics meet established safety and effectiveness standards, and are comparable to a predicate device. It is not an AI/ML device, and therefore the specific criteria, studies, and data provenance related to AI/ML device performance are not present in this document.

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The Vathin®H-SteriScopeTM single-use flexible video bronchoscope has been designed to be used with the Vathin display unit, endotherapy accessories and other ancillary equipment for endoscopy within the airways and tracheobronchial tree in patients.

The Vathin® Video Bronchoscope System is for use in a hospital environment.

The Vathin®H-SteriScopeTM single-use flexible video bronchoscope is a single-use device designed for use in adults, with the BCV1-02 and BCV1-C2 also designed for pediatric use (BCV1-02: 6 months to 6 years; BCV1-C2: 6 years and older).

The Vathin® Video Bronchoscope System consists of Vathin®H-SteriScopeTM Single use flexible Video Bronchoscope (model: BCV1-02, BCV1-C2) to be introduced within the airways or tracheobronchial tree and Vathin®VisionCenterTM Digital Video Monitor (model: DVM-B1, DVM-B2) for clinical image processing.

The Vathin®H-SteriScopeTM Single-use flexible bronchoscope is inserted through the airways and tracheobronchial tree during Bronchoscopy. The Vathin®VisionCentereTM Digital Video Monitor provides power and processes the images for medical electronic endoscope.

Vathin®H-SteriScopeTM Single-use flexible Video Bronchoscope is a sterile single-use flexible bronchoscope. Vathin®H-SteriScopeTM Digital Video Monitor is a reusable monitor.

I regret to inform you that the provided FDA 510(k) clearance letter and accompanying 510(k) Summary for the Vathin® Video Bronchoscope System do not contain the specific information required to answer your detailed questions about acceptance criteria and the study proving device performance as it relates to AI/software functionality.

This document focuses on justifying Substantial Equivalence for a bronchoscope system based on its physical characteristics, safety standards (electrical, biocompatibility, sterilization), and basic performance (optical, mechanical). It mentions "Software verification and validation" in Section 7.4, citing the "FDA guidance: Content of Premarket Submissions for Device Software Functions," but does not provide any specific acceptance criteria, study data, or details about the software's functionality, especially regarding AI capabilities.

Therefore, I cannot populate the table or answer the specific questions about AI performance, test set details, ground truth establishment, or human reader studies.

Here's why the document is insufficient for your request:

• No AI/Algorithm Claims: The document describes a video bronchoscope and its display unit. It doesn't mention any AI or advanced algorithmic functions (e.g., automated lesion detection, image enhancement using AI, diagnostic support) that would necessitate performance criteria like sensitivity, specificity, or reader studies.
• Focus on Hardware/System Performance: The performance data section (7.1-7.6) primarily addresses the physical and electrical safety, biocompatibility, sterilization, and basic optical/mechanical functions of a medical device (bronchoscope). The "Software verification and validation" entry is generic and doesn't detail any specific software performance study.
• Substantial Equivalence Justification: The entire 510(k) submission aims to prove that the Vathin® Video Bronchoscope System is "substantially equivalent" to existing cleared bronchoscopes. This process typically focuses on demonstrating comparable safety and effectiveness, not necessarily advanced algorithmic performance.

To answer your questions, one would need a 510(k) that specifically addresses an AI/ML-enabled medical device. Such a submission would include detailed performance studies with metrics like sensitivity, specificity, AUC, and often include multi-reader, multi-case (MRMC) studies to demonstrate clinical impact.

If you had provided a 510(k) document for an AI-powered device, the table and answers would look something like this (conceptual example):

Conceptual Example - This section is not based on the provided document as it does not contain the necessary information.

For an AI-powered medical device, the acceptance criteria and study proving its performance would be detailed as follows:

1. Acceptance Criteria and Reported Device Performance

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TurbAlign is intended to separate the middle turbinate from the lateral nasal wall during the clinically relevant healing phase associated with sinus surgery (e.g., endoscopic sinus surgery, FESS). The implant provides short-term fixation of the middle turbinate to the nasal septum and thus minimizes the risk of adherence to the lateral nasal wall.

TurbAlign™ is a bioabsorbable, polydioxanone implant designed to hold the middle turbinate away from the lateral nasal wall during the clinically relevant healing phase associated with nasal/sinus surgery. The implant includes self-anchoring features (e.g., one "distal anchor" and multiple "proximal anchors") which enable attachment to the middle turbinates for short-term fixation of the middle turbinate to the nasal septum.

TurbAlign includes an attached surgical needle which is inserted into the lateral aspect of the middle turbinate. It is then passed through the nasal septum and then through the contralateral middle turbinate. The implant is then pulled through all three structures until the distal anchor feature is embedded in the first middle turbinate at which time the turbinate is medialized to the septum. The contralateral turbinate is then medialized to the opposite side of the septum using a freer or equivalent and held into place via the proximal anchor. The excess portion of the implant is trimmed off.

The sterile, single-use implant is delivered using standard surgical instruments, such as needle drivers. The implant provides temporary fixation and is fully resorbed over 180 days.

This document is a 510(k) Pre-market Notification for a medical device called TurbAlign. It does not describe a study based on analyzing medical images or clinical data for AI/ML device approval. Instead, it describes hardware device attributes and physical performance tests.

Therefore, the requested information regarding acceptance criteria, reported performance for an AI/ML device, sample size for test/training sets, experts, adjudication methods, MRMC studies, or specific types of ground truth cannot be extracted from this document, as it pertains to a different type of medical device submission.

The document primarily focuses on demonstrating substantial equivalence to a predicate device for an intranasal splint based on:

1. Biocompatibility Testing: Ensuring the materials are safe for human contact.
2. Distribution, Packaging, and Shelf-Life Testing: Verifying sterile barrier integrity and device performance over time.
3. Performance Testing – Bench: Evaluating mechanical integrity, anchoring features, and usability by ENT physicians in cadaver specimens.

The closest this document comes to "performance criteria" is the statement that "Mechanical integrity testing of the implant and needle properties was performed and demonstrated that the physical and functional requirements were met" and "Comparative testing... demonstrated equivalent performance of the device's anchoring features in the relevant tissue." For usability, it states that "the physicians graded the turbinate position post-TurbAlign placement and confirmed both turbinates were medialized and did not contact the lateral wall." However, these are not quantitative acceptance criteria in the format requested for an AI/ML study.

Summary of missing information for an AI/ML device:

• Table of acceptance criteria and reported device performance: Not applicable; no AI/ML performance metrics are mentioned.
• Sample size for test set and data provenance: No test set of data (images, etc.) is mentioned.
• Number of experts and qualifications: Experts (ENT physicians) were used for usability testing of the physical implant in cadavers, not for establishing ground truth on a data set.
• Adjudication method: Not applicable.
• Multi Read Multi Case (MRMC) comparative effectiveness study: Not applicable.
• Standalone (algorithm-only) performance: Not applicable; this is a physical device.
• Type of ground truth used: Not applicable for an AI/ML context. The closest is the physical observation of turbinate medialization by physicians in cadavers.
• Sample size for training set: Not applicable.
• How ground truth for training set was established: Not applicable.

This 510(k) pertains to a traditional, non-AI medical device.

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The piezoelectric surgical system is indicated for: osteotomies, osteoplasties, drilling and shaping of hard tissue. Including:

• Otolaryngology,
• Maxillofacial surgery,
• hand- & foot-surgery and
• plastic & reconstructive surgery.

The new device, the "Piezomed Pro", is an ultrasonic surgical modular system which has to be connected to W&H's AMADEO device (K213221). The new device is an ultrasonic surgical system whose basic function is the conversion of electrical energy into mechanical vibration.

In addition, a physiological saline solution is pumped to the treatment site with a displacement pump, depending on the treatment.

Using a control unit with electronics, the user can change several operating parameters within predefined limits:

• oscillation amplitude, 5 to 100% in steps of 5 (100 % corresponds to max. power)
• coolant flow rate via pump speed control; 5 to 100% in steps of 5 (100% corresponds to max. water quantity)

The device is activated or deactivated via a foot control.

The provided FDA 510(k) clearance letter for the Piezomed Pro describes a surgical device (an ultrasonic surgical modular system), not a diagnostic AI device. Therefore, the information typically requested regarding acceptance criteria and performance studies for an AI/ML-enabled diagnostic device (such as accuracy, sensitivity, specificity, sample sizes, ground truth establishment, expert adjudication, MRMC studies) is not present in this document.

This document focuses on proving substantial equivalence for a physical medical device based on:

• Intended Use/Indications for Use: The Piezomed Pro is indicated for osteotomies, osteoplasties, drilling, and shaping of hard tissue in various surgical specialties. This is shown to be identical to the predicate device.
• Technological Characteristics: The device's operating principle (conversion of electrical energy into mechanical vibration), frequency, operating mode, electrical classification, and components are compared to predicate and reference devices, demonstrating similarity.
• Performance Data: This section details non-clinical testing for biocompatibility, electromagnetic compatibility, electrical safety, reprocessing validation, and general performance/functional testing (e.g., life-time, falling, vibration tests).
• Software and Cybersecurity: Compliance with relevant standards and guidance documents for software development and cybersecurity is stated.

Therefore, based on the provided text, it's not possible to extract the information required for an AI/ML diagnostic device performance study. The questions provided in the prompt are geared towards evaluating the performance of an AI algorithm against a ground truth, which is not the subject of this 510(k) submission for the Piezomed Pro.

If this were an AI/ML diagnostic device, the "Performance Data" section would describe clinical study results, including metrics like sensitivity, specificity, AUC, and details about the test set, ground truth, and reader studies. Since it's a surgical tool, the performance data relates to its physical and functional safety and effectiveness, rather than diagnostic accuracy.

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EVIS EXERA III BRONCHOVIDEOSCOPES OLYMPUS BF-XP190 is intended to be used with an Olympus video system center, light source, documentation equipment, monitor, Endo Therapy accessories (such as a biopsy forceps), and other ancillary equipment for endoscopy and endoscopic surgery. This instrument is indicated for use within the airways and tracheobronchial tree.

EVIS EXERA III BRONCHOVIDEOSCOPES OLYMPUS BF-P190 is intended to be used with an Olympus video system center, light source, documentation equipment, monitor, Endo Therapy accessories (such as a biopsy forceps), and other ancillary equipment for endoscopy and endoscopic surgery. This instrument is indicated for use within the airways and tracheobronchial tree.

EVIS EXERA III BRONCHOVIDEOSCOPES OLYMPUS BF-XT190 is intended to be used with an Olympus video system center, light source, documentation equipment, monitor, Endo Therapy accessories (such as a biopsy forceps), and other ancillary equipment for endoscopy and endoscopic surgery. This instrument is indicated for use within the airways and tracheobronchial tree.

BRONCHOVIDEOSCOPE OLYMPUS BF-H1100 is intended to be used with an Olympus video system center, documentation equipment, monitor, Endo Therapy accessories (such as a biopsy forceps), and other ancillary equipment for endoscopy and endoscopic surgery. This instrument is indicated for use within the airways and tracheobronchial tree.

BRONCHOVIDEOSCOPE OLYMPUS BF-1TH1100 is intended to be used with an Olympus video system center, documentation equipment, monitor, Endo Therapy accessories (such as a biopsy forceps), and other ancillary equipment for endoscopy and endoscopic surgery. This instrument is indicated for use within the airways and tracheobronchial tree.

The EVIS EXERA III BRONCHOVIDEOSCOPES (OLYMPUS BF-XP190, OLYMPUS BF-P190, and BF-XT190) and BRONCHOVIDEOSCOPE BF-H1100 and BF-1TH1100 are used for endoscopic diagnosis and treatment within the respiratory organs. These endoscopes consist of three parts: the control section, the insertion section, and the connector section.

The provided FDA 510(k) clearance documentation for the Olympus Bronchovideoscopes (K250862) primarily demonstrates substantial equivalence based on technological characteristics and bench testing. It does not contain information about comparative effectiveness studies (like MRMC studies for AI devices), expert ground truth establishment, or typical performance metrics associated with AI/software-as-a-medical-device (SaMD) clearances.

This submission focuses on:

• Technological Equivalence: Showing that the devices (BF-XP190, BF-P190, BF-XT190, BF-H1100, and BF-1TH1100) are fundamentally the same as their predicates, with the primary change being compatibility with a new video system center (CV-1500) and updated labeling related to laser/high-frequency/APC systems.
• Bench Testing: Verification of physical and imaging performance parameters (e.g., thermal safety, color performance, resolution, noise, video latency) to ensure they meet specifications, particularly when combined with the new video system.
• Animal Testing: To assess the imaging modes (WLI, NBI, TXI, BAI-MAC) with the new video processor.

Therefore, the requested information about "acceptance criteria and the study that proves the device meets the acceptance criteria" in the context of AI/software performance (e.g., sensitivity, specificity, MRMC studies, ground truth establishment by experts) is not present in this document. The document explicitly states that "Software Testing and Cybersecurity" was "not performed" due to "no design, material, sterilization, reprocessing, packaging, shelf life, or software changes" (Page 29). This implies that the device itself is a hardware endoscope, and any software associated with it is considered an intrinsic part of its established functionality, not a new or significantly changed software component requiring a separate performance study with clinical endpoints or AI evaluation.

The "acceptance criteria" here relate to the successful completion of the listed bench (and limited animal) tests, demonstrating that the new combination (endoscope + CV-1500) functions as intended and safely, similar to the predicate combinations.

Below is a table summarizing the "acceptance criteria" and "reported device performance" as derived from the document's comparison tables and performance data section, which are primarily about technical specifications and functional verification, not software/AI performance metrics.

Acceptance Criteria and Reported Device Performance (Summary based on provided text)

Since this 510(k) is for existing endoscopes with a new video system and updated labeling, the "acceptance criteria" are implied by the extensive comparison tables (Tables 1-5) which show the subject devices having nearly identical technical specifications to their predicate devices, and the successful completion of specified bench and animal testing. The performance data section doesn't list specific quantitative acceptance criteria for each test but rather states that tests were conducted to "ensure that the subject device performs as intended and meet design specifications."

Study Details (based on provided text)

The document describes performance testing rather than a comparative clinical study for AI/software-as-a-medical-device.

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

   • See table above. The acceptance criteria are largely implied by the equivalence to the predicate devices in terms of physical, optical, and functional characteristics, and the successful completion of specified bench and animal tests. Quantitative metrics for these tests are not provided in this summary but would have been part of the full submission.

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

   • Test set sample size: Not specified. The document mentions "bench testing" and "animal testing." For bench tests, it typically refers to a small number of devices or engineered test setups. For animal testing, the number of animals or studies is not provided.
   • Data provenance: Not specified. It can be inferred that the testing was conducted by or on behalf of Olympus Medical Systems Corp. in Japan, given the manufacturing site and submitter location. It does not state if the data is retrospective or prospective, or from which country/region the "animal" data would originate.

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

   • Not applicable / Not specified. This type of information is typically provided for studies evaluating AI algorithms or diagnostic accuracy, where human experts establish a ground truth for imaging interpretation. The present submission is for a hardware endoscope system, and its performance evaluation relies on engineering specifications and functional testing, not expert interpretation of diagnostic images.

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

   • Not applicable / Not specified. Adjudication methods are relevant for human reader studies or expert ground truth establishment, which are not detailed here.

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, not performed / Not applicable. This submission is not for an AI companion diagnostic or an AI-software-as-a-medical-device. It's for an endoscope system. The document explicitly lists "Software Testing and Cybersecurity" and "Clinical" as "not performed" because there were no fundamental software changes beyond integration with a new video system, which itself does not constitute an AI component in the context of this 510(k) summary. The NBI, RDI, TXI, and BAI-MAC modes are imaging enhancements, not AI algorithms.

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

   • Not applicable. This relates to AI/software performance, which is not the focus of this 510(k).

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

   • Not applicable. For this device (endoscope), ground truth typically relates to engineering specifications (e.g., accurate measurements, clear image quality as determined by reproducible test patterns, proper functionality of mechanical parts). For the animal testing of imaging modes, the "ground truth" would be the direct observation of the animal's internal anatomy via the endoscope itself and comparison to expected/known characteristics, not an independent "pathology" or "outcomes" ground truth in a diagnostic sense.

8. The sample size for the training set:

   • Not applicable. This submission is not for an AI system that requires a "training set."

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

   • Not applicable. As above, no AI training set is described.

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Navient is a computerized surgical navigation system intended as an aid for precisely locating anatomical structures. The Navient system is indicated for any medical condition in which the use of stereotactic surgery may be appropriate, and where reference to a rigid anatomical structure, such as the skull, can be identified relative to a CT, MR-based anatomy model.

Indications:

Example procedures include but are not limited to:

ENT Procedures:

• Transsphenoidal procedures
• Maxillary antrostomies
• Ethmoidectomies
• sphenoidotomies
• Sphenoid explorations
• Turbinate resections
• Frontal sinusotomies
• Intranasal procedures
• Intranasal tumor resections
• All ENT related skull base surgery

Navient is an image guided navigational system intended to assist with preoperative planning and real-time positioning of surgical tools during stereotaxic procedures via optical tracking technology. The system is essentially composed of a computerized main unit (computer), a Navient IR CameraBox, Navient cart, Navient navigation software, and corresponding accessory set.

Navient's guidance function is based on the patient images acquired prior to the procedure, combined with optical measurements of the pose of navigated instruments relative to the patient's anatomy. To enable navigation, the reference instrument/accessory is attached to the patient to enable tracking of the patient's anatomy. The patient images are then spatially registered with the patient's anatomy by matching landmark locations marked on both the image and the patient, followed by matching a path traced by the user on the patient's anatomy with a model of patient's anatomical surface automatically generated from the image data.

The provided FDA 510(k) clearance letter and summary for the Navient Image Guided Navigation System (ENT) do not contain information about the study design or acceptance criteria for AI/algorithm-based performance evaluations. Instead, the document focuses on the system's accuracy, software validation, electrical safety, biocompatibility, and reprocessing validation, all typical for traditional medical devices rather than AI/ML-powered ones.

The document states: "Full system accuracy bench testing: Navient has been validated to the positional accuracy of ≤ 2.0 mm (mean=1.52 mm, STD=0.93 mm, 99% confidence interval of 3.68 mm), with the angular error of ≤ 2.0 deg (mean=1.13 deg, STD=0.43 deg, 99% confidence interval of 2.13 deg). This performance was determined using representative phantoms with system components that are deemed the worst-case in the Navient clinical applications."

This validation refers to the physical navigation system's accuracy in positioning, not the performance of an AI algorithm in tasks like image interpretation or diagnosis. Therefore, I cannot generate the requested table and study details related to AI acceptance criteria and performance based on the specific content provided in this 510(k) document.

The "Navient navigation software" mentioned is described as having a workflow for loading images, planning, setting up, registration, and navigation. This suggests a traditional software interface for guiding the user, rather than an AI/ML algorithm performing diagnostic or predictive functions that would require a ground truth, expert consensus, or MRMC studies.

If we were to hypothetically extract the closest equivalent to "acceptance criteria" for this device, it would be its spatial accuracy, which is a key performance metric for image-guided navigation systems.

Here's a hypothetical structure based on the provided spatial accuracy data, while acknowledging it's not AI-specific:

Hypothetical Acceptance Criteria and System Performance (based on provided spatial accuracy)

Recognizing that the provided document details a traditional image-guided navigation system and not an AI-powered diagnostic/interpretive device, the "acceptance criteria" presented here refer to the system's demonstrated physical accuracy.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test set sample size: Not explicitly stated as a "test set" in the context of an AI model. The performance data is derived from "Full system accuracy bench testing" using "representative phantoms." The number of measurements or phantom tests isn't specified.
• Data provenance: Not directly applicable as it's a bench test on phantoms, not clinical patient data. The testing was conducted internally by ClaroNav.

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

• Not applicable. The ground truth for positional and angular accuracy in bench testing is defined by precision measurement equipment and physical phantoms, not human experts.

4. Adjudication method for the test set:

• Not applicable. Bench testing does not involve human adjudication.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• No. This type of study (MRMC) is typically performed for AI devices that aid human interpretation (e.g., radiologists reading images with AI assistance). The Navient system is a guidance system, not an interpretive AI.

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

• The "Full system accuracy bench testing" represents the standalone performance of the navigation system's hardware and software integration in terms of its ability to track instruments accurately relative to images. It's not an AI algorithm performing a task without human input in the sense of a diagnostic or predictive AI.

7. The type of ground truth used:

• The ground truth for the positional and angular accuracy was established through precise measurements on representative phantoms using calibrated equipment, which is standard for validating the accuracy of surgical navigation systems.

8. The sample size for the training set:

• Not applicable. This is not an AI/ML device that undergoes a training phase on a dataset of examples. Its software processes sensor data and medical images according to deterministic algorithms.

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

• Not applicable, as there is no training set in the context of AI/ML for this device's reported validation.

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