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510(k) Data Aggregation

    K Number
    K201300
    Date Cleared
    2021-01-26

    (256 days)

    Product Code
    Regulation Number
    874.4680
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    Airway Mobilescope

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    This instrument has been designed to be used with a Suction Pump and other ancillary equipment for airway management, which includes diagnosis and observation to access airway anatomy, endotracheal/endobronchial intubation and management.

    This instrument has been designed to be used with a Suction Pump and EndoTherapy accessories and other ancillary equipment for airway management, which includes endoscopic treatment, diagnosis, and observation to access airway anatomy, endotracheal/endobronchial intubation and management.

    Device Description

    The AIRWAY MOBILESCOPE OLYMPUS MAF-DM2/GM2/TM2 are all in one mobile endoscopes that enables efficient airway management. These endoscopes comprises a 3.5" monitor, LED light source, battery and recording features in a handy single unit, which enable observation without peripherals or cables, allowing them to be used in Intensive Care Unit (ICU), Operating Room (OR) and emergency procedures and so on.

    AI/ML Overview

    This document is a 510(k) Summary for the Olympus AIRWAY MOBILESCOPE MAF-DM2, MAF-GM2, and MAF-TM2. It details a premarket notification for these devices, asserting their substantial equivalence to previously marketed predicate devices.

    Here's an analysis of the provided information concerning acceptance criteria and supporting studies:

    1. Table of acceptance criteria and the reported device performance:

      The document does not explicitly list a table of quantifiable acceptance criteria with corresponding performance results. Instead, it states that "design verification tests and their acceptance criteria were identified and performed as a result of this risk management" (Section 7.8). The performance data section describes various types of tests conducted, implying that the devices met the acceptance criteria for each test type.

      Here's a summary of the types of performance tests conducted:

      Test TypeObjective/PurposeReported Performance (Implicit)
      Reprocessing Validation TestingValidate reprocessing instructions and methods.Conducted as per FDA guidance, implies successful validation.
      Biocompatibility TestingAssess biological compatibility with human tissue.Conducted as per ISO 10993-1, included cytotoxicity, intracutaneous, and sensitization tests, implies satisfactory results.
      Software Verification and Validation TestingEnsure software performs as intended and meets specifications.Conducted as per FDA guidance for software and cybersecurity, implies successful verification and validation.
      Electrical Safety and Electromagnetic Compatibility (EMC) TestingEnsure compliance with electrical safety and EMC standards.Complies with ANSVAAMI ES 60601-1:2005/(R)2012, A1:2012, IEC 60601-2-18:2009, and IEC 60601-1-2:2014.
      Bench Performance TestingEnsure the device performs as intended and meets design specifications.Conducted for: Mechanical durability, Thermal safety, Depth of field, Direction of view, Image performance resolution, Signal to noise ratio, Dynamic range, Photobiological safety, Color performance, Image intensity uniformity. Implies all tests met design specifications.
      Risk ManagementIdentify and mitigate risks, inform design verification tests and acceptance criteria.Performed in accordance with ISO 14971:2007.
      Human Factors ValidationAssess usability and human-device interaction.Conducted in accordance with FDA Guidance, implies satisfactory results.
    2. Sample size used for the test set and the data provenance:

      The document does not specify sample sizes for any of the test sets (e.g., number of devices tested for mechanical durability or image performance). All tests appear to be in-vitro bench tests performed on the physical devices or their components. No information is provided regarding the country of origin of the data, but given a major manufacturing site is in Japan (Aizu Olympus Co., Ltd., Fukushima, Japan), some testing may have occurred there. The data is prospective in the sense that it was generated for this specific submission to demonstrate equivalence of the new devices.

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

      This information is not provided. The performance data consists of engineering and safety tests rather than clinical evaluations requiring expert interpretation of ground truth (e.g., medical image diagnosis).

    4. Adjudication method for the test set:

      This information is not provided. As the tests are largely objective engineering and safety assessments, a formal adjudication method for ground truth establishment as seen in clinical studies is 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 comparative effectiveness study was conducted. The device is a bronchoscope, a direct observation tool, and not an AI-powered diagnostic system that would typically undergo such a study.

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

      No standalone algorithm performance study was done. The device is a medical imaging instrument (bronchoscope), not an algorithm or AI. All performance is inherently "human-in-the-loop" as it is operated by a healthcare professional.

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

      For the engineering and safety tests performed (e.g., depth of field, resolution, electrical safety), the "ground truth" would be the design specifications established by the manufacturer and relevant international and national standards (e.g., IEC, ISO, AAMI). For example, a depth of field test would compare the measured depth of field to the specified acceptable range.

    8. The sample size for the training set:

      Not applicable. The devices are physical medical instruments (bronchoscopes), not AI/ML models that require a training set.

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

      Not applicable. As above, the devices are hardware instruments and do not involve AI/ML training sets.

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