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PENTAX Medical Single Use Video Bronchoscope EB-S01 are sterible endoscopes intended for use with PENTAX Medical mobile processor, therapeutic accessories, and other ancillary equipment for endoscopy and endotherapeutic procedures within the airways and tracheobronchial tree.

PENTAX Medical Mobile Processor ONE-M is intended to be used with PENTAX Medical endoscopes and other peripheral devices for endoscopic diagnosis, treatment and video observation.

PENTAX Medical Mobile Processor Plug-in ONE-Dock is intended to the PENTAX Medical Mobile Processor to provide additional ports for hardware interface.

PENTAX Medical ONE Pulmo Single Use Video Bronchoscope System is intended to provide the optical visualization of the airways and tracheobronchial tree for diagnostics and therapeutic applications. The PENTAX Medical ONE Pulmo Single Use Video Bronchoscope System consist of the following three component devices:

• . PENTAX Medical Single Use Video Bronchoscope EB11-S01 / EB15-S01
• . PENTAX Medical Mobile Processor ONE-M
• PENTAX Medical Mobile Processor Plug-in ONE-Dock

The EB11-S01 / EB15-S01 endoscopes are connected to the ONE-M and video images captured with the bronchoscope are displayed on the touch screen of the ONE-M. The ONE-M is also connected to the Plug-in ONE-Dock, which has several interfaces, such as an external monitor to display captured images by the bronchoscope and a connection with an external network.

The provided text is a 510(k) summary for the PENTAX Medical ONE Pulmo Single Use Video Bronchoscope System. It details the device, its intended use, and a comparison to a predicate device. However, it does not contain a detailed study report with specific acceptance criteria and device performance results in the format requested.

Here's an analysis of what can be extracted and what information is missing:

The document states that the subject device (post-modification) is substantially equivalent to the predicate device (pre-modification PENTAX Medical ONE Pulmo Single Use Video Bronchoscope System, K213235). This implies that the acceptance criteria for the subject device are implicitly met by demonstrating equivalence to the predicate, which presumably had its own acceptance criteria met previously.

The document discusses performance data in the "NON-CLINICAL PERFORMANCE DATA" section, but these are general statements about successful testing rather than a table of specific criteria and quantitative results.

Here's what can be inferred or explicitly stated from the provided text, and what remains unknown based on the prompt's requirements:

1. Table of Acceptance Criteria and Reported Device Performance

• Software function for maximum light intensity (XLUM) added.
• Shelf life extended to 3 years. |
  | Other Shared Characteristics (Implied Criteria met via Predicate Equivalence) | - Scope working length
• Scope field of view
• Scope depth of field
• Scope tip angulation
• Software requirements |

Missing Information:

• Specific numerical acceptance criteria (e.g., "signal-to-noise ratio > X dB").
• Specific quantitative results from the tests (e.g., "Measured signal-to-noise ratio was Y dB").
• Detailed reports of the "equivalence" demonstrated in system and optical performance.

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

• Sample Size: Not specified for any of the performance tests. The document mentions "test sets" or "bench testing" but does not give numbers of devices tested.
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). Given that it's a 510(k) for a medical device manufacturer, it's highly likely to be internal validation data, but the specifics are not provided.

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

• Not applicable / Not specified. The reported tests are primarily non-clinical bench and engineering tests (e.g., electrical safety, mechanical durability, optical measurements, software V&V, biocompatibility). These types of tests typically do not involve human expert adjudication for ground truth in the way a diagnostic AI algorithm would.

4. Adjudication Method for the Test Set

• Not applicable / Not specified. As noted above, the reported tests are non-clinical and do not involve human adjudication of results in the context of establishing a "ground truth" for a diagnostic outcome.

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

• No. The document explicitly states: "Animal Image Capture Study: This change does not affect the image quality. As the subject and predicate scopes are identical, animal performance testing was not conducted." This indicates that no comparative effectiveness study involving human or animal subjects (which would be necessary for MRMC) was performed for this specific 510(k) submission.

6. Standalone (Algorithm Only) Performance Study

• Not applicable / Not specified. This device is hardware (bronchoscopes, mobile processor, dock) with associated software (for image display, controls, etc.), not a standalone diagnostic algorithm. The software verification and validation are for the functionality of the device, not a diagnostic algorithm's standalone performance.

7. Type of Ground Truth Used

• Technical Specifications / Engineering Standards / Regulatory Guidance. For the non-clinical tests conducted, the "ground truth" refers to meeting predefined technical specifications, industry standards (e.g., IEC 60601 series), and regulatory guidance from the FDA. For example, for "Optical Performance," the ground truth would be that the measured values for signal-to-noise, sharpness, etc., fall within an acceptable range defined by engineering specifications or are equivalent to the predicate device's performance.

8. Sample Size for the Training Set

• Not applicable / Not specified. This is not an AI/ML diagnostic algorithm that would have a "training set" in the conventional sense. The software functions (e.g., brightness control, exposure control, color adjustment, i-scan image processing) are likely deterministic or rule-based, or if they involve ML, the training data for such embedded features are not disclosed or are aggregated within the predicate device's prior development.

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

• Not applicable / Not specified. As above, a specific training set ground truth process is not relevant or described for this device's submission.

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