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The Galaxy System and its accessories are intended to provide bronchoscopic visualization of and access to patient airways for diagnostic and therapeutic procedures.

The Galaxy System is designed with the intent to enable articulation and precise control of a flexible, single-use disposable bronchoscope under continuous and direct control by a physician operator. The Galaxy System™ includes full procedure navigation that integrates a pre-operative computed tomography scan to display scope tip location relative to the pre-operative scan anatomy. Additionally, Galaxy integrates a tomosynthesis spin to update the scope and target position to overcome any changes in anatomy not reflected in the pre-op CT scan.

The provided document focuses on the substantial equivalence of the Noah Medical Galaxy System to a predicate device (Monarch Endoscopy Platform) and a reference device (superDimension Navigation System). It details various performance tests conducted to support this claim, but it does not explicitly define specific acceptance criteria with numerical thresholds for device performance, nor does it present a study that reports numerical device performance against such criteria.

Instead, the document broadly states that the device has been "successfully tested" and "meets the intended user needs". It also mentions a simulated use study where users were able to "navigate to within 30 mm of the lesion, complete tomosynthesis, and acquire tissue". However, this is described as a capability demonstrated rather than a performance metric achieved against a pre-defined acceptance criterion.

Therefore, I cannot fully complete the table for acceptance criteria and reported device performance as requested, as these specific numerical values and direct comparisons are not present in the provided text.

Here's a breakdown of the available information based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

As stated above, no explicit numerical acceptance criteria or corresponding reported device performance metrics are provided in the document. The performance data section describes general testing and findings without quantifiable acceptance thresholds.

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

The document mentions "human cadaver and animal models" for "Design Validation testing" and "Simulated Use" testing. It also refers to "representative users" for "Human Factor Usability Testing."

• Test Set Sample Size: Not explicitly stated as a number. The document refers to "human cadaver and animal models" and "representative users" without specifying the quantity.
• Data Provenance: The testing was performed by Noah Medical, implying it's internal study data. The document does not specify the country of origin of the cadavers or animals, nor does it explicitly state whether the study was retrospective or prospective, though design validation and usability testing are typically prospective.

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

The document does not describe a process for establishing ground truth for a test set through expert consensus in the context of clinical performance evaluation against a specific gold standard. The focus of the performance testing described is on functional verification, safety, and usability.

• For the simulated use, "The user group included physicians who have been trained and are actively using the predicate and reference devices." The number of these physicians is not specified, and their specific role in establishing "ground truth" (as opposed to being subjects performing tasks) is not detailed.
• Qualifications of these physicians are generally described as "trained and actively using the predicate and reference devices," suggesting experience with similar bronchoscopic navigation systems.

4. Adjudication Method for the Test Set

No adjudication method is described, as the testing focuses on functional and usability aspects rather than diagnostic or clinical accuracy requiring expert review and adjudication of findings.

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

The document does not mention a MRMC comparative effectiveness study or any effect size of human readers improving with AI vs. without AI assistance. The performance data focuses on the device's functionality, safety, and usability, demonstrating substantial equivalence to predicate devices, not on the comparative effectiveness of AI-assisted vs. unassisted human performance.

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

The document does not explicitly describe a standalone algorithm performance study. The Galaxy System's navigation component is integrated and described as used by physicians: "The Galaxy System enables a physician to utilize electro-mechanical articulation for precise control... By utilizing electromagnetic field-based sensors on the tip of the endoscope tip location relative to the pre-operative scan anatomy is continuously displayed." The software verification and validation section confirms that "Galaxy System software conforms to the design specifications and meets the intended use and needs of the intended users," but this is about software correctness rather than a standalone performance metric against a clinical ground truth.

7. Type of Ground Truth Used

For the simulated use testing, the "ground truth" for navigation was implicitly the ability to "successfully navigate to a peripheral lung lesion" and reach "within 30 mm of the lesion." This appears to be based on an expected outcome in the cadaver/animal models, rather than a clinical ground truth like pathology or outcomes data.

8. Sample Size for the Training Set

The document does not provide any information regarding a training set sample size. This suggests the described performance testing is for validation, not for the training of a machine learning model, or if there was training, its details are not included in this summary.

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

As no information about a training set is provided, how its ground truth was established is also not available in this document.

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The LungVision System is intended to enable users to segment previously acquired 3D CT datasets and overlay and register these 3D segmented data sets with fluoroscopic live X-ray images of the same anatomy in order to support catheter/device navigation during pulmonary procedures.

The Lung Vision System (K163622) is designed to enable users to segment previously acquired 3D CT datasets and overlay and register these 3D segmented data sets with live X-ray images of the same anatomy in order to support catheter/device navigation during pulmonary procedures.

The Lung Vision System is designed to assist the physician in guiding endobronchial tools towards the target area of interest inside the patient lungs. Prior to the endoscopic procedure the system allows planning the target location and the path to the target area on the CT scan. During the endoscopic procedure the system overlays planned data over fluoroscopic images to support endobronchial tool navigation towards the area of interest. The system does not include the Fluoroscope, Bronchoscope or the external monitor. Lung Vision system includes a main unit and a tablet vs. the previous PC based hardware platform. Image processing algorithms are executed on the main unit and the tablet is used as a primary method of interacting with the system. The Tablet is for planning but is not for diagnostic purposes. Both can perform the following functions: segment previously acquired DICOM 3D CT image data, register DICOM 3D CT image data with live fluoroscopic X-ray image, overlay the segmented 3D CT dataset over a live fluoroscopic X-ray image of the same anatomy, obtained on a Fluoroscopic system.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text.

Important Note: The provided text is a 510(k) Summary, which is a premarket notification for new medical devices. It primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting detailed clinical trial results for efficacy or a comprehensive standalone AI performance study. Therefore, some information typically found in a clinical study report (like detailed MRMC results, specific effect sizes, or a large, prospectively collected, expert-adjudicated dataset for de novo AI training) is not present here. The performance testing described is primarily focused on demonstrating that the device functions as intended and safely, matching the predicate.

Acceptance Criteria and Reported Device Performance

The provided text doesn't explicitly state quantitative acceptance criteria in a table format with specific performance metrics (e.g., "accuracy > X%", "sensitivity > Y%"). Instead, the acceptance criteria are implicitly defined by demonstrating substantial equivalence to a predicate device and successful completion of bench testing to ensure the device meets its own "requirement specifications" and "hazards mitigations."

The performance is reported in terms of functional equivalence and safety:

Study Details

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

   • Test Set Sample Size: Not explicitly stated as a separate "test set" in the context of typical AI model validation. The "bench tests" included "verification testing of the requirements, testing of hazards mitigations and performance testing of the system." This suggests internal testing against pre-defined specifications rather than a distinct, large, clinical test dataset.
   • Data Provenance: Not specified. The document mentions "pig lungs" were used for some accuracy testing in "deformable tissue." This implies animal (in vitro/ex vivo) testing. There is no mention of human clinical data for the performance testing cited.
   • Retrospective or Prospective: Not explicitly stated for performance testing. Given the "bench tests" and no clinical testing, it's likely internal, controlled testing rather than a large-scale retrospective or prospective patient study.

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

   • Not applicable/Not stated. The "ground truth" for the performance testing described here (bench tests, pig lungs) relates to the system's technical specifications and physical performance (e.g., registration accuracy on deformable tissue) rather than diagnostic interpretations by human experts.

3. Adjudication Method for the Test Set:

   • Not applicable/Not stated. Since the "test set" described is for bench performance, there's no mention of human expert adjudication.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No. The document explicitly states: "Clinical: No clinical testing was performed." Therefore, no MRMC study comparing human readers with and without AI assistance was conducted or reported.

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

   • A standalone performance study for the algorithm itself in terms of diagnostic accuracy (e.g., detecting lesions) was not the focus or purpose of this 510(k) submission. The device is a "system, image processing, radiological" intended to support catheter/device navigation by overlaying pre-acquired CT data onto live X-ray. Its performance is evaluated on its ability to accurately segment, register, and overlay images, and perform real-time compensation, not on its ability to autonomously diagnose.
   • The "Performance Testing" section states, "We have performed bench tests and found that the Lung Vision met all requirements specifications and was found to be equivalent in comparison to the predicate. Testing includes verification testing of the requirements, testing of hazards mitigations) and performance testing of the system." This implies internal measurements of system performance (e.g., accuracy of registration or compensation) but not a standalone diagnostic outcome.

6. Type of Ground Truth Used:

   • For the bench testing, the ground truth would have been the engineering specifications of the device's functions (e.g., the expected accuracy of C-Arm based Tomography or real-time compensation).
   • For testing in "deformable tissue (pig lungs)," the ground truth would likely be physical measurements or imaging references to assess the system's ability to maintain accuracy in a dynamic environment, rather than a clinical diagnosis or pathology.

7. Sample Size for the Training Set:

   • Not stated. The document doesn't discuss the details of the training data used for any algorithms (e.g., segmentation, registration, real-time compensation). As this is primarily a system modification and not a de novo AI diagnostic device, the specifics of algorithm training or AI model validation datasets are not a required part of this 510(k) summary. The "software upgrades" are described as "an algorithm improvement" or "a standard technology," implying refinements rather than entirely new AI models requiring extensive separate training data disclosure for this type of submission.

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

   • Not applicable/Not stated. As the training set details are not provided, the method for establishing its ground truth is also not mentioned.

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The Ion™ Endoluminal System (Model IF1000) assists the user in navigating a catheter and endoscopic tools in the pulmonary tract using endoscopic visualization of the tracheobronchial tree for diagnostic and therapeutic procedures. The Ion™ Endoluminal System enables fiducial marker placement. It does not make a diagnosis and is not for pediatric use.

The Flexision™ Biopsy Needle is used with the Ion™ Endoluminal System to biopsy tissue from a target area in the lung.

The PlanPoint™ Software uses patient CT scans to create a 3D plan of the lung and navigation pathways for use with the Ion™ Endoluminal System.

The Ion™ Endoluminal System, Model IF1000, is a software-controlled, electromechanical system designed to assist qualified physicians to navigate a catheter and endoscopic tools in the pulmonary tract using endoscopic visualization of the tracheobronchial tree for diagnostic and therapeutic procedures. It consists of a Planning Laptop with PlanPoint™ Software, a System Cart with System Software, a Controller, Instruments, and Accessories. The IF1000 Instruments include the Ion™ Fully Articulating Catheter, the Ion™ Peripheral Vision Probe, and the Flexision™ Biopsy Needles.

The Planning Laptop is a separate computer from the System Cart and Controller. A 3D airway model is generated from the patient's chest CT scan using the PlanPoint™ Software.

The System Cart contains the Instrument Arm, electronics for the slave portion of the servomechanism, and two monitors. The System Cart allows the user to navigate the Catheter Instrument with the Controller, which represents the master/slave relationship. For optimal viewing, the physician can position the monitors in both vertical and horizontal axes.

The Controller is the user input device on the Ion™ Endoluminal System. It provides the controls to command insertion, retraction, and articulation of the Catheter. The Controller also has buttons to operate the Catheter control states.

The Catheter Instrument has a tool channel compatible with the Peripheral Vision Probe. Flexision™ Biopsy Needles, and third-party tools. The Vision Probe provides direct visualization of the patient's airways during navigation. Accessories such as the Catheter Guide, Vision Probe Adapter, Suction Adapter, and Swivel Connector facilitate use of the IF1000 Instruments.

The provided text describes the 510(k) summary for the Ion™ Endoluminal System and Flexision™ Biopsy Needle. It details performance data but does not include a specific table of acceptance criteria with reported device performance or a standalone study with quantitative metrics for accuracy, sensitivity, or specificity.

However, I can extract information related to the device's validation and the types of studies conducted.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly provide a table of acceptance criteria with specific numerical performance metrics (e.g., sensitivity, specificity, accuracy). Instead, it states that "validation and verification testing demonstrate that the subject devices are equivalent to the predicate devices in terms of safety and effectiveness." and that "the design output meets the design input requirements" and "the System meets design specifications and user needs."

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

The document mentions "animal and cadaver testing" and a "summative validation study" for human factors.

• Animal and Cadaver Testing:

  • Sample Size: Not explicitly stated. The text notes "Cadavers" (plural) and "Live animal models" (plural).
  • Data Provenance: Prospective (simulated use conditions). No specific country of origin is mentioned, but typically, these studies are conducted within the submitting company's or collaborating research facilities.

• Human Factors Evaluation (Summative Validation Study):

  • Sample Size: Not explicitly stated. It involved "intended user groups."
  • Data Provenance: Prospective (simulated bronchoscopy suite).

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

The document does not specify the number of experts or their qualifications for establishing ground truth in the animal/cadaver testing or human factors evaluation. It implies that these studies were overseen by qualified personnel as part of the overall design verification and validation process. For the human factors study, "intended user groups" were involved, suggesting participation from physicians or clinical staff who would typically perform such procedures.

4. Adjudication Method for the Test Set

The document does not describe a formal adjudication method (e.g., 2+1, 3+1) for establishing ground truth in the performance studies mentioned. The evaluation of "safety or performance" and "accuracy" in animal/cadaver models, and the assessment of "high-risk use scenarios and essential tasks" in the human factors study, likely relied on expert observation and assessment without a multi-reader adjudication process detailed here.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size

No MRMC comparative effectiveness study is described in this document. The focus is on demonstrating the device's safety, effectiveness, and substantial equivalence to predicate devices, rather than a direct comparison of human reader performance with and without AI assistance.

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

The device is an "endoluminal system" that "assists the user in navigating a catheter and endoscopic tools." The description of the System Cart's "master/slave relationship" with the Controller and the "3D guided navigation view" indicates it is a human-in-the-loop system. While "Software Verification and Validation" was performed, a standalone algorithm-only performance study (without human-in-the-loop) for a clinical outcome is not explicitly discussed. The PlanPoint™ Software "creates a 3D plan... and navigation pathways," which is an algorithmic function, and its verification would have been part of the software testing. However, its performance as an aid to the human user is part of the integrated system.

7. The Type of Ground Truth Used

• Animal and Cadaver Testing: The ground truth for these studies would have been established through direct observation during the simulated procedures, potentially involving anatomical dissection, histological analysis of biopsied tissue (if sample quality was assessed), and clinical assessment by veterinarians or physicians during animal studies for safety and performance.
• Human Factors Evaluation: Ground truth here relates to task completion, error rates, and user interface effectiveness, observed and recorded during simulated use scenarios.

8. The Sample Size for the Training Set

The document does not provide details on sample sizes for training sets. It focuses on the validation and verification of the final product. The PlanPoint™ Software uses patient CT scans to create 3D plans, which implies an underlying model that would have been developed using a training set, but this information is not included in this submission summary.

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

Information on how the ground truth for any potential training data used for the PlanPoint™ Software (e.g., for segmentation or pathway generation models) is not provided in this document.

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