The Codonics Safe Label System SLS 700i and related 610i, 620i, 630i series enabled to receive RFID features and safe and effective released SLS Software provides a simple computer-based bar code scanning/RFID & printing system to automatically verify drug identity from NDC and other drug vial UDI Barcodes and (when configured and available) RFID parenteral vial information and formulary information, and to print labels embedded with RFID tags for prepared drugs and other items in use on patients during surgical procedures. Drug information is human readable on labels, 2D & Linear barcoded on labels, and RFID encoded as part of secondary container medication labels.

Codonics Safe Label System SLS 700i and related 610i, 620i, 630i series enabled to receive RFID features is generally placed in, however not limited to, the peri-operative environment to identify syringes prepared for anesthesiology use during surgery. Additional uses include producing labels for IVs and other artifacts used during a surgical procedure. SLS can also be used to print "non-surgical environment" color & text labels as required. Typical users of this system are trained professionals, including but not limited to physicians, nurses, and technicians

Drug preparation and administration in the perioperative environment are integral aspects of pharmacy, patient care clinicians, and anesthesiologist's patient care responsibilities.

Drug selection, preparation, and administration errors occur at the point of care in association with parenteral vial identification, secondary CSP container labeling, and pre-administration selection. Machine readable drug IDs & information in the form of machine-readable barcode and RFID tags serve as additional means to confirm medications (beyond human readable labeling).

Best practices for safe medication preparation and labeling in support of reducing error includes font characteristics & readability, drug class/type color coding, machine readable bar and RFID codes & formulary confirmed information being part of creation & application of secondary container CSP labeling to assist pre-administration selection and dose delivery recording.

Codonics Safe Labeling System (SLS-1) 700i and related 610i, 620i, 630i, SLS-XXX RFID series is a simple, integrated system utilizing a bar code scanner and RFID reader & encoder to confirm drug identity from NDC and other drug ID Barcoded vials and safely & automatically print labels for prepared drugs and other items in use on patients during clinical and surgical procedures. In addition to K101439 series machine readable barcodes, the 700i and related 610i, 620i, 630i, SLS-XXX series configurations when equipped with suitable near field HF, UHF, or HF/UHF RFID modules & software, allows RFID information to be read and formulary information including 3rd party MDD ID content to be written ("encoded") to SLS label RFID tags (Passive) for use by third party applications. The labels are fully compliant with national standards and best practices focused on improving medication safety in the perioperative environment. As a MDD (Master Drug Database) intra-hospital networked device, emission & immunity safety as well as protections in the realm of cyber security is part of the safety and efficacy. The system is small enough to fit on the anesthesia supply cart and integrate seamlessly into the anesthesia workflow allowing use in the OR during patient care. Like the predicate SLS, barcode read NDC/UDI codes indexed to the approved formulary is retained as the "source of truth" for all drugs processed for labeling by SLS RFID series*.

The software components provide functions for scanning/reading vials, indexing against a "source of truth" hospital/healthcare environment managed/commissioned & verified formulary database, displaying on screen and audibly confirming drug type, printing color JCOAHO and ISO and ASTM compliant labels with 2-D barcodes and, when equipped with suitable HF, UHF, or HF/UHF RFID modules & software, RFID tagged information encoding on secondary container CSP labels. The system reads drug vial barcodes and produces waterproof, color labels compliant to FDA/ISMP, ASA, USP, ISO, ASTM, TJC, JCACHO.

*SLS RFID Series products maintain reliance on barcode scanned and formulary verified information to print secondary container labels and encode 2D and RFID tagged information to embedded SLS label RFID tags. The system label output can be integrated to function with 3rd party applications such as an AIMS system workflow to provide real-time documentation of drug administration when the syringe "2D Barcode" or RFID tag is read.

The Codonics Safe Labeling System (SLS) is designed to improve medication safety in perioperative and other clinical environments by verifying drug identity and printing compliant labels. The acceptance criteria and study proving its performance are detailed below, particularly focusing on the advancements in RFID technology.

1. Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: The non-clinical lab tests verifying NDC/UDI coded vials and RFID performance were conducted with a "Preliminary Verification Test SLS Configurations Safe Label System Confirmation of Functionality reporting greater than 10,000 RFID labels of 25 drugs in the various drug class colors and RFID payload."
• Data Provenance: The document states that both laboratory (non-clinical environment) and surgical (clinical) tests were performed. There is no specific mention of the country of origin for the data, but the regulatory compliance refers to US (FCC, ANSI, ASTM, Joint Commission, ASA) and international (ISO, IEC, TUV, CISPR) standards, suggesting a general applicability or data collected within these regulatory frameworks. The description implies prospective testing as part of product development and verification before market release.

3. Number of Experts and Qualifications for Ground Truth

• The document does not specify the number of experts used to establish ground truth for the test set.
• Qualifications of experts: While not explicitly stated for the test set ground truth, the "Typical users of this system are trained professionals, including but not limited to physicians, nurses, pharmacists, and technicians" who ultimately control and review the device's output. The system's output is based on "site managed formulary lookup database" which is managed by "DOP pharmacy, anesthesiology, Dir Medical Staff, etc." These clinicians and department heads would collectively define the "source of truth" for the formulary.

4. Adjudication Method for the Test Set

• The document does not explicitly describe an adjudication method for the test set in the conventional sense (e.g., 2+1, 3+1 for image interpretation).
• The system uses a "source of truth" which is the "site managed formulary lookup database." The device's performance is verified against this established database for drug identification, concentration, and associated labeling information. "Input codes and output read content was confirmed," indicating a direct comparison against expected outputs derived from this ground truth.

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

• A MRMC comparative effectiveness study was not explicitly mentioned or described in the provided text. The device is described as a "scanning/RFID & printing system" and not an AI-assisted diagnostic tool that humans interpret. Its function is to automate and verify drug labeling, not to assist human readers in making diagnostic decisions where an "effect size of how much human readers improve with AI vs without AI assistance" would be relevant.

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

• The device's core function is an automated process of scanning, verifying against a formulary, and printing/encoding. The non-clinical lab tests verifying "error free NDC/UDI vial reading and labeling of prepared drugs to ASA/ISO standards" and "error free accuracy and precision" for RFID processing are effectively standalone performance evaluations of the algorithm and hardware.
• The document states, "No automated decision processes tied to patient care are involved with SLS operation. ... Medical personnel review the results and inputs processed by the Codonics SLS and offers ample opportunity for competent human intervention in the case of a malfunction or other failure." This confirms that while the device performs its functions autonomously, it operates within a workflow that includes human oversight.

7. Type of Ground Truth Used

• The primary ground truth used is the site-managed formulary lookup database. This database contains verified drug names, concentrations, expiration data, and site-specific rules.
• For barcode and RFID performance, the ground truth is the known, correctly coded/tagged information on the original drug vials and the intended output for the generated labels. This is based on established national and international standards (NDC, UDI, ASA, ISO, ASTM, TJC).

8. Sample Size for the Training Set

• The document does not specify a sample size for a training set. This is generally a concept applicable to machine learning algorithms. The SLS system appears to operate based on established rules, databases, and barcode/RFID standards rather than learning from a training dataset in the typical AI/ML sense. It is a deterministic system that performs verification and printing based on pre-defined information.

9. How Ground Truth for the Training Set Was Established

• As the system does not appear to use a training set for machine learning, the concept of establishing ground truth for a training set is not applicable in this context. The core "knowledge" for the device, the drug formulary, is established and managed by "DOP pharmacy, anesthesiology, Dir Medical Staff, etc.," and is a human-curated and verified database that the system utilizes.

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Intended use

This device is intended for use in anesthetizing adults, pediatric patients, and neonates. The device can be used for mechanical ventilation, manual ventilation, pressure-supported spontaneous breathing, and spontaneous breathing. The device is equipped with the following basic functions:

• Ventilation monitoring
• Inspiratory 02 measurement
• Device monitoring
• Anesthetic gas receiving system

The following options are additionally available:

• Patient-gas measurement module for 02, CO2, N2O, and anesthetic gases
• 02 insufflation

Anesthesia is achieved through a mixture of pure oxygen and Air (medical compressed air) or pure oxygen and nitrous oxide, with the addition of volatile anesthetic agents.

Ventilation is accomplished on the patient through a laryngeal mask, a breathing mask, or an endotracheal tube.

The integrated breathing system can be used with partial rebreathing (low-flow or minimum-flow).

Indications

The device is specified for inhalational anesthesia and/or patient ventilation in accordance with the intended use during surgical or diagnostic interventions.

The Atlan anesthesia workstation was developed and is manufactured by Dräger in Lübeck, Germany. The anesthesia workstation is specified for inhalational anesthesia using volatile anesthetic agents and/or patient ventilation, including the delivery of oxygen and the monitoring of device functions as well as the patient's and/or anesthetic parameters. Atlan is available in different device variants and can be upgraded by software and hardware options as well as attachable accessories.

The Atlan anesthesia workstation consists of four major subsystems, each of which operates on its own specific principle while interacting with the other subsystems to achieve the intended use. These major subsystems include:

• Gas reception and delivery, i.e., gas mixer o
• o Anesthetic breathing system
• o Anesthetic ventilator
• o Anesthetic gas scavenger

The Atlan anesthesia workstation receives medical gases from a cylinder or central gas supply, creates a gas mixture, or composition, and delivers this mixture at a determined flow rate to the anesthetic breathing system.

Atlan's anesthetic breathing system is the interface between the anesthesia workstation and the patient. Its purpose is to deliver the gas composition to the patient. While doing so, the anesthetic breathing system converts the continuous gas flow to the patient's intermittent respiratory flow, supports controlled or assisted ventilation, and allows for gas sampling and pressure measurements. Furthermore, the anesthetic breathing system conditions the inspiratory gas by means of a heater and removes carbon dioxides from the patient's expired qas.

The anesthetic ventilator drives fresh gas from the anesthetic breathing system to the patient and expired gas to the anesthetic gas scavenger.

Atlan's integrated anesthetic gas scavenger collects all waste anesthetic gases received from the breathing circuit and passes it on to a hospital disposal system.

The anesthesia workstation is also comprised of several minor subsystems whose interactions with the main subsystems help to address considerations of patient safety and system integrity. The minor subsystems include:

• o Gas monitoring
• o Ventilation and airway monitoring
• Device monitoring, including system self test o
• Embedded control display o
• RFID capabilities o

The provided text is a 510(k) Premarket Notification Summary for the "Atlan" anesthesia workstation. It describes the device, its intended use, and compares it to a predicate device (Perseus A500, K133886) and several reference devices.

However, the document does not contain specific acceptance criteria tables, reported device performance metrics, sample sizes for test sets, data provenance, information about expert ground truth establishment, adjudication methods, details of comparative effectiveness studies (MRMC), standalone performance data, or details about training set ground truth establishment.

Instead, it states that "The Atlan anesthesia workstation is a new device and has undergone extensive testing to qualify it with e.g., national and international consensus standards, technical system requirements and other requirements." It then lists the types of verification and validation activities performed, such as:

• Sterilization
• Biocompatibility
• Software, including cybersecurity
• Electrical safety
• Electromagnetic compatibility (EMC)
• Compliance with various IEC and ISO standards (e.g., IEC 60601-1-8 for alarm systems, ISO 80601-2-13 for anesthetic workstations, ISO 80601-2-55 for respiratory gas monitors)
• Waveforms, including comparisons to the predicate device and performance as per ASTM-F1101
• Technical System Requirements (risk control measures, technical data, essential safety and performance)
• Accessories compatibility
• Human factors engineering (IEC 60601-1-6 for Usability, IEC 62366-1 for the application of usability engineering to medical devices)

The document concludes that "The conclusions drawn from non-clinical tests and the comparison of intended use and technological characteristics with its predicate demonstrate that the new product Atlan is as safe, as effective and performs as well as or better than the legally marketed device Perseus K133886 as identified in this section of the submission."

Therefore, I cannot provide the requested table and detailed study information because it is not present in the provided text. The document summarizes the types of testing performed and the conclusion of those tests but does not offer the specific data points requested in your prompt.

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The Carestation750/750c anesthesia systems are intended to provide monitored anesthesia care, general inhalation anesthesia and/ or ventilatory support to a wide range of patients (neonatal, pediatric, and adult). The anesthesia systems are suitable for use in a patient environment, such as hospitals, surgical centers, or clinics. The systems are intended to be operated by a clinician qualified in the administration of general anesthesia.

The GE Carestation 750/750c anesthesia machines (Carestation 750 series) are intended to provide general inhalation anesthesia and ventilatory support to a wide range of patients (neonate, pediatric, and adult). The anesthesia systems are suitable for use in a patient environment such as hospitals, surgical centers, or clinics. They represent one of the systems in a long line of products based on the Datex-Ohmeda Aestiva (K000706), Aespire View (K122445), Aisys CS2 (K170872), Avance CS2 (K131945), Carestation 620/650/650c (Carestation 600 series) (K151570) Anesthesia Systems. The Carestation 750 series anesthesia systems are intended to be operated by a clinician qualified in the administration of general anesthesia.

The Carestation™ 750/750c anesthesia systems combine advanced anesthesia delivery, patient monitoring. and care information management. The contemporary, compact design allows for easy mobility and addresses many ergonomic considerations including an effective cable management solution, aesthetic covers, and an expandable work surface area. Optional integrated features include auxiliary common gas outlet, auxiliary 02 outlet, auxiliary 02+Air outlet, suction control, and respiratory gas monitoring. The system provides integration of ventilation and gas delivery on a 15-inch color graphical touchscreen interface. This system also features electronic gas mixing of oxygen and a balance gas of either N2O or Air. The Carestation 750 series represents one of the systems in a long line of products based on the Datex-Ohmeda Aestiva (K000706), AespireView (K122445), Aisys CS2 (K170872), Avance CS2 (K131945), and Carestation 600 Series (K151570) Anesthesia Systems.

This anesthesia system is designed for mixing and delivering inhalation anesthetics (Isoflurane, Sevoflurane, or Desflurane), Air, O2, and N2O. This anesthesia system has Recruitment maneuvers, a feature to perform automated lung recruitment maneuver in a single step or in multi steps.

This anesthesia system uses electronic flow valve ventilation technology offering Volume Control Ventilation with tidal volume compensation and electronic PEEP. This technology also features Pressure Control Ventilation, optional Pressure Support Ventilation with an Apnea Backup (PSVPro™) that is used for spontaneously breathing patients, Synchronized Intermittent Mandatory Ventilation (SIMV) modes, Pressure Control Ventilation-Volume Guarantee (PCVG), Continuous Positive Airway Pressure + Pressure Support Ventilation (CPAP + PSV), and VCV Cardiac Bypass. In Volume Control Ventilation, a patient can be ventilated using a minimal tidal volume of 20 ml. In Pressure Control Ventilation, volumes as low as 5 ml can be measured. These advanced features allow for the ventilation of a broad patient range. The device includes the following basic components:

The Carestation 750 series anesthesia systems supply set flows of medical gases to the breathing system using an electronic gas mixer (O2 with Air or O2 with N2O). Gas flows are adjusted by the user on the touchscreen, the flows are displayed on the system graphical user interface assembly as numerical digits and as electronic representations of flow meters. The system provides an option for auxiliary mixed Oxygen + Air flow delivery where 02 with Air are blended and delivered to an auxiliary port used to support spontaneously breathing patients using a nasal cannula. An optional auxiliary O2 supply includes a separate O2 flow tube and needle valve flow control that delivers O2 flow to an auxiliary port used to support spontaneously breathing patients using a nasal cannula. The gas flow from the optional auxiliary O2 subsystem does not flow through the electronic gas mixer.

The Carestation 750 series models include up to 3 breathing gases with O2 and Air as standard, and N20 as an optional breathing gas. The systems include two vaporizer positions compatible with, Isoflurane, Sevotlurane, and Desflurane vaporizers. The Carestation 750 is available with up to three back-up gas cylinder connections. The Carestation 750 series systems are also available in pendant (Carestation 750c) models.

The system uses touchscreen technology, hard keys, and a Comwheel to access system functions, menus, and settings on a 15'' color graphical user interface assembly (aka display). The graphical user interface assembly is mounted on an arm on the left side of the machine. It can be rotated via the arm toward, or away from, the system to adjust the horizontal position. The arm is available allowing the display to be tilted up or down to adjust the vertical viewing angle or be tilted left or right to adjust the horizontal position of the display. An optional arm can be raised/lowered and rotated 360 degrees. A split screen field can be set to show gas trends, Spirometry loops, a Paw gauge, airway compliance, and optional ecoFlow information. If none is selected, the waveforms expand to fill the split screen area.

The Carestation 750 series systems accept Tec 7, and Tec 820/850 series vaporizers on a 2position Selectatec manifold. Safety features and devices within the systems are designed to decrease the risk of hypoxic mixtures, multiple anesthetic agent mixtures, complete power failure, or sudden gas supply The Carestation 750 series systems are available with optional integrated respiratory gas failures. monitoring which can be physically integrated into the system, receive electronic power from the Carestation 750/750c, and communicate measured values to the Carestation 750/750c for display on the system graphical user interface assembly. When supplied as an option, integrated respiratory gas monitoring is provided via the GE CARESCAPE series (EsCAiO or E-sCAiOV) respiratory airway modules (GE Healthcare Finland Oy, CE 0537) which is identical to the module used on Avance CS2.

The Anesthesia Ventilator used in the Carestation 750 series is a microprocessor based, electronically controlled, pneumatically driven ventilator that provides patient ventilation during surgical procedures. This version of the GE 7900 ventilator is equipped with a built-in system for monitoring inspired oxygen (using an optional 02 cell or optional integrated gas module), patient airway pressure, and exhaled volume. Flow sensors in the breathing circuit are used to monitor and control patient ventilation.

This allows for the compensation of gas and tubing compression losses, fresh gas contribution, and small gas leakage from the breathing absorber, bellows, and pneumatic system connections. User settings and microprocessor calculations control breathing patterns. The user interface keeps ventilation settings in memory. The user may change settings with a simple ventilation parameter setting sequence. A bellows contains breathing gasses to be delivered to the patient and provides a barrier keeping patient gas separate from the ventilatory drive gas. Positive End Expiratory Pressure (PEEP) is regulated electronically. Positive pressure is maintained in the breathing system so that occurs is outward from the patent breathing circuit.

This ventilator comes with a standard ventilation mode as well as optional ventilation modes.

Standard ventilation modes:

• VCV (Time Cycled, Volume Controlled Ventilation) .
• . PCV (Time Cycled, Pressure Control Ventilation)

Optional ventilation modes:

• VCV-SIMV (Synchronized Intermittent Mandatory Ventilation Volume Control)
• . PCV-SIMV (Synchronized Intermittent Mandatory Ventilation Pressure Control)
• PSVPro (Pressure supported ventilation with apnea backup)
• . PCV-VG (Pressure Controlled ventilation - Volume Guaranteed)
• . PCV-VG-SIMV (Synchronized Intermittent Mandatory Ventilation, Pressure Controlled ventilation - Volume Guaranteed)
• . CPAP+PSV (Continuous Positive Airway Pressure/Pressure Support)

The system can include an internal, factory installed, suction regulator and control visible from the front of the machine. It can mount different monitors using an arm or shelf mounts. The mounting is achieved through a combination of GE Healthcare adapters and other third-party mounts, including one that allows for the physical integration of the GE Monitor Series B650 (K102239). The Carestation 750 system also includes an optional cable management solution, which can help user to manage the various cables attached to the system.

The provided document is a 510(k) Premarket Notification for the Carestation 750/750c anesthesia system. It primarily focuses on demonstrating substantial equivalence to a predicate device (Carestation 620/650/650c) through technological characteristic comparison and bench testing against recognized standards.

Therefore, the document does not contain the kind of detailed information typically found in a study proving a device meets acceptance criteria related to a specific performance metric or clinical outcome, especially for AI or algorithmic performance. There is no information about:

• Specific acceptance criteria for device performance in terms of diagnostic accuracy or clinical effectiveness. The acceptance criteria mentioned are related to compliance with quality assurance measures and recognized safety standards.
• A study that proves the device meets acceptance criteria in a clinical setting with patient data, experts, or specific performance metrics like sensitivity, specificity, or effect sizes.
• Sample sizes for test sets, data provenance, number of experts, adjudication methods, MRMC studies, or standalone algorithm performance.
• Ground truth types or sample sizes for training sets in the context of AI/algorithms.

Based on the provided document, here's what can be extracted regarding acceptance criteria and performance, albeit in a different context than what might be expected for an AI-driven device:

1. Table of Acceptance Criteria and Reported Device Performance (based on compliance criteria):

The study that proves the device meets the acceptance criteria is described as:

• Bench testing: "Bench testing was performed to establish substantial equivalence of the Carestation 750/750c."
• Verification and validation testing: "Verification and validation testing was performed according to predetermined acceptance criteria, which concluded that the Carestation 750/750c is substantially equivalent to the predicate Carestation 620/650/650c."
• Non-clinical design verification and validation tests: "The Carestation 750/750c anesthesia machines incorporate modifications to the predicate Carestation 620/650/650c. These modifications did not require clinical testing. The changes made were completely evaluated by non-clinical design verification and validation tests to verify and validate the safety and functionality of the anesthesia machines."

Regarding the specific questions about AI/algorithmic studies, the document provides no relevant information:

2. Sample size used for the test set and the data provenance: Not applicable/Not provided. The testing focused on engineering validation and adherence to standards, not on a clinical test set with patient data for algorithmic performance.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable/Not provided. Ground truth in the context of clinical expert review is not mentioned.
4. Adjudication method: Not applicable/Not provided.
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 device is an anesthesia machine, not an AI-assisted diagnostic tool for human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. The device itself is an anesthesia machine, not a standalone algorithm.
7. The type of ground truth used: For the engineering and standards compliance, the "ground truth" would be the specifications and requirements of the standards themselves, as well as validated engineering measurements against design specifications.
8. The sample size for the training set: Not applicable/Not provided. This is not an AI/ML submission that would typically involve training sets of data for model development.
9. How the ground truth for the training set was established: Not applicable/Not provided.

In summary, this document is a regulatory submission for an anesthesia machine, which relies on demonstrating equivalence to an existing device through rigorous engineering testing and compliance with established medical device standards. It does not describe an AI or algorithm evaluation study.

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The A8, A9 Anesthesia System is a device used to administer to a patient, continuously or intermittently, a general inhalation anesthetic and to maintain a patient's ventilation.

The A8, A9 is intended for use by licensed clinicians in the administration of general anesthesia, for patients requiring anesthesia within a health care facility, and can be used in adult, pediatric and neonate populations.

High Flow Nasal Cannula (HFNC) is indicated for delivery of nasal high flow oxygen to spontaneously breathing adult patients. It can be used for pre-oxygenation and short-term supplemental oxygenation (up to 10 minutes) during intubation in operating rooms. It is not intended for apneic ventilation. HFNC is indicated for use in adults only.

The A8, A9 Anesthesia System is a continuous flow inhalation gas anesthesia system that delivers anesthetic vapor and provides for automatic and manual modes of ventilation. The A8, A9 Anesthesia System incorporates O2, CO2, N2O and Agent concentration monitoring (Desflurane, Isoflurane, Halothane, and Sevoflurane). The A8, A9 Anesthesia System is a modified version the previously cleared Mindray A7 Anesthesia System cleared in K171292.

The provided text describes the 510(k) premarket notification for the Mindray A8, A9 Anesthesia System, focusing on demonstrating substantial equivalence to predicate devices rather than proving the device meets specific acceptance criteria based on studies involving human readers or AI performance metrics.

Therefore, most of the information requested in your prompt (acceptance criteria table with performance, sample size for test set, data provenance, number of experts for ground truth, adjudication method, MRMC study, standalone performance, training set size, and ground truth establishment for training set) is not available in this document.

The document details engineering tests and conformance to standards, which are different from clinical performance studies for AI/radiology devices.

Here's a breakdown of what is available and what is not:

Information Found in the Document:

• Device Name: A8, A9 Anesthesia System
• Predicate Devices: K171292 (A7 Anesthesia System), K192972 (BeneVision N Series Patient Monitors). Reference devices also listed.
• Technological Differences from Predicate:
  • Change the Vaporizer Type and the addition of Electronic Vaporizers (A9)
  • Change certain parameters of the ventilator modes
  • Addition of the High Flow Nasal Cannula Oxygen (HFNC)
  • Change the Anesthetic Gas Module and Accessories
  • Addition of the Sealed Lead Acid Battery
• Performance Data (Type of Studies Conducted):
  • Functional and System Level Testing (bench testing) to validate performance and ensure specifications are met.
  • Biocompatibility Testing (conformance to ISO standards: 10993-1, -5, -10, -18, 18562-1, -2, -3)
  • Software Verification and Validation Testing (following FDA's "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices")
  • Electromagnetic Compatibility and Electrical Safety (conformance to IEC and ANSI/AAMI standards: ES60601-1, IEC 60601-1-6, -1-8, ISO 80601-2-13, -2-55, IEC 60601-1-2)
  • Bench Testing (conformance to ASTM and ISO standards: F1101-90, IEC 60601-1-6, -1-8, ISO 5360, 10079-3, 80601-2-13, -2-55)

Information NOT Found in the Document (and why):

This document is for an Anesthesia System, which is a hardware medical device with integrated software for control and monitoring. It is not an AI-driven image analysis or diagnostic device that would typically involve acceptance criteria related to human reader performance, expert ground truth, or MRMC studies. The "performance data" section focuses on testing the device's functional specifications, safety, and compliance with general medical device standards.

1. A table of acceptance criteria and the reported device performance: Not provided in the format of performance metrics against specific acceptance thresholds for diagnostic accuracy, sensitivity, specificity, etc. The document generally states that "the devices continue to meet specifications and the performance of the device is equivalent to the predicate" based on functional and system-level testing, and compliance with standards. Key technical characteristics are compared in a large table, but this is a comparison to the predicate, not a list of acceptance criteria with measured performance against them.
2. Sample sized used for the test set and the data provenance: Not applicable in the context of this type of device submission. The "test set" here refers to the actual physical devices undergoing bench and functional testing, not a dataset of patient images or clinical cases.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth in this context would be engineering specifications and validated measurement techniques, not expert clinical interpretation.
4. Adjudication method: Not applicable.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No. This type of study is for evaluating diagnostic performance, typically for imaging devices or AI algorithms assisting human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This device is an anesthesia system, not a standalone AI algorithm for diagnosis.
7. The type of ground truth used: For this device, ground truth is established by engineering design specifications, international and national consensus standards (e.g., ISO, IEC, ASTM), and validated measurement instruments.
8. The sample size for the training set: Not applicable for this type of device. There is no "training set" in the machine learning sense described. Software validation ensures the embedded software performs as designed and specified for controlling the anesthesia system.
9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document describes a regulatory submission for an anesthesia system, which relies on demonstrating safety and efficacy through engineering testing and adherence to established performance standards for medical devices, rather than AI model validation studies common for diagnostic algorithms.

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The indication for Flow-c/Flow-e Anesthesia System is administering inhalation Anesthesia while controlling the entire ventilation of patients with no ability to breathe, as well as in supporting patients with a limited ability to breathe. The system is intended for use on neonatal to adult patient populations. The system is intended for use in hospital environments, except MRI environment, by healthcare professionals trained in inhalation Anesthesia administration.

The indication for the Flow-i/Flow-c/Flow-e Anesthesia system is administering inhalation Anesthesia while controlling the entire ventilation of patients with no ability to breathe, as well as in supporting patients with a limited ability to breathe. The system is intended for use on neonatal to adult patient populations. The system is intended for use in hospital environments, except MRI environment, by healthcare professionals trained in inhalation Anesthesia administration.

Flow-i, Flow-c and Flow-e Anesthesia systems within the Flow Anesthesia family 4.7 are high-performance Anesthesia systems designed to meet the many ventilatory challenges within Anesthesia, as well as to provide inhalation Anesthesia. It is intended to serve a wide range of patients from neonatal to adult.

Flow Anesthesia family is a software-controlled semi-closed system for inhalation Anesthesia (Sevoflurane, Desflurane, Isoflurane and/or nitrous oxide).

The Flow-i/-c/-e 4.7 consists of a core, where gases are mixed and administered, and a User Interface where the settings are made and ventilation and anesthesia are monitored.

The Flow-i/-c/-e 4.7 is based on the cleared predicate device FLOW-i 4.2 (K160665) with some improvements.

The provided text is a 510(k) Summary for an anesthesia system. It outlines the device description, indications for use, comparison to a predicate device, and non-clinical testing. However, it does not contain the specific information required to answer your request about acceptance criteria and a study proving the device meets those criteria for an AI/algorithm-based device.

This document describes a medical device (anesthesia system) which is hardware and software controlled, but there is no mention of an AI/algorithm that performs diagnostic or prognostic functions, or that assists human readers in an interpretive task. The "MAC Brain" mentioned is a display indicator based on a calculated MAC value, not an AI model requiring a separate validation study with human experts, MRMC studies, or specific performance metrics like sensitivity/specificity against ground truth.

Therefore, I cannot extract the requested information regarding:

1. A table of acceptance criteria and the reported device performance for an AI/algorithm.
2. Sample size used for the test set and data provenance for an AI/algorithm.
3. Number of experts and their qualifications for establishing ground truth for an AI/algorithm.
4. Adjudication method for an AI/algorithm's test set.
5. MRMC comparative effectiveness study results for AI assistance.
6. Standalone performance of an AI algorithm.
7. Type of ground truth used for an AI/algorithm.
8. Training set sample size for an AI algorithm.
9. How ground truth for the training set was established for an AI algorithm.

The document focuses on demonstrating substantial equivalence of an anesthesia machine to a predicate device, based on changes that "do not affect the overall performance or technology of the device" or "raise different questions about safety and effectiveness." The testing mentioned (Software: Code review, Static code analysis, System testing; Performance: System testing, Regression, Free User testing, Waveform testing, Comparative testing for MAC Brain, Comparative testing for Recruitment Maneuver) is typical for hardware and software validation of a medical device, not specifically for an AI/ML algorithm requiring clinical performance studies against human experts or a gold standard.

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Indications for use for ELYSION diode laser hair removal system with 755nm and 810nm applicators include: · Hair Removal with Static and Dynamic modes intended for permanent reduction in hair regrowth, defmed as a long term, stable reduction in the number of hairs re-growing when measured at 6, 9, and 12 months after the completion of a treatment regime. · Treatment of Pseudofolliculitis barbae (PFB) · Use on all skin types (Fitzpatrick I-VI).

ELYSION diode laser hair removal system is a medical electrical equipment intended for hair removal treatment, the basic principle of which is selective photothermolysis, which consist in the specific destruction of a follicle due to an increase of the temperature induced by a high-powered beam of light which is selectively absorbed by the melanin. The ELYSION equipment consists of a central unit and a set of 3 removable applicators. The ELYSION equipment emits laser radiation (near-infrared light with a wavelength range of 755 nm & 810 nm), pulsed through the laser aperture situated at the tip of the applicator. The device applicator contains the diode which emits the laser energy whereas the power delivered, and the working frequency being controlled by the machine's central unit. The applicator emits the energy through a sapphire window, in contact with the skin throughout the treatment, aimed at damaging the hair follicle. The device has three types of applicators differentiated by the emitted wavelength and the area of emission. Two of the applicators emitting radiation at 810 nm wavelength for two areas 10×10 mm² and 18×10 mm² and another one emitting radiation at 755 nm for an area of 10×10 mm². Two different operation modes are available: static mode and dynamic mode, which are differentiated basically by the frequency range defined for each mode (1-2-3 Hz for static and 5-10-15 Hz for dynamic). The emission of energy is activated in the form of continuous pulses when pressing the applicator button. The applicator sapphire tip is cooled to a constant temperature to cool the skin, so that it partially anaesthetizes the tissue reducing the risk of damage to the epidermis during treatment.

The provided document is a 510(k) summary for the ELYSION diode laser hair removal system. It describes the device, its indications for use, and a comparison to legally marketed equivalent devices to demonstrate substantial equivalence.

However, it explicitly states:

"No clinical study is included in this submission"

Therefore, the document does not contain the information required to answer your questions about acceptance criteria, device performance, sample sizes, expert involvement, adjudication methods, MRMC studies, standalone performance, or ground truth establishment. The approval for this device appears to be based on non-clinical testing and substantial equivalence to predicate devices, rather than a clinical study demonstrating specific performance metrics against pre-defined acceptance criteria.

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Indications for use for PRIMELASE diode laser hair removal system with 755mm, 810mm and 810 - 1060mm applicators include:

· Hair Removal with Static and Dynamic modes intended for permanent reduction in hair regrowth, defined as a long term, stable reduction in the number of hairs re-growing when measured at 6, 9, and 12 months after the completion of a treatment regime.

• Treatment of Pseudofolliculitis barbae (PFB).

· Use on all skin types (Fitzpatrick I-VI).

PRIMELASE diode laser hair removal system is a medical electrical equipment intended for hair removal treatment, the basic principle of which is selective photothermolysis, which consist in the specific destruction of a follicle due to an increase of the temperature induced by a high-powered beam of light which is selectively absorbed by the melanin.

The PRIMELASE machine consists of a central unit and a set of 5 removable applicators. The PRIMELASE equipment emits laser radiation (beam infrared light with a wavelength range of 755 mm to 1060 nm, typically and most used 810 nm.), pulsed through the laser aperture situated at the tip of the applicator.

The device applicator contains the diode which emits the laser energy whereas the power delivered, and the working frequency being controlled by the machine's central unit. The applicator emits the energy through a sapphire window, in contact with the skin throughout the treatment, aimed at damaging the hair follicle. The treatment can be provided in two modes, dynamic and static. Dynamic mode uses the low fluence & high frequency and is used for initial treatments. Static mode uses high fluence & low frequency and is used for residual hair removal.

The emission of energy is activated in the form of continuous pulses when pressing the applicator button. The applicator sapphire tip is cooled to a constant temperature to cool the skin, so that it partially anaesthetizes the tissue reducing the risk of damage to the epidermis during treatment

This document is an FDA 510(k) clearance letter and related submission materials for the PRIMELASE diode laser hair removal system. The information provided in it does not describe a study that proves the device meets specific performance acceptance criteria for an AI/ML powered device, especially not those involving diagnostic accuracy or human interpretation. Instead, it demonstrates substantial equivalence to predicate laser hair removal devices based on technical specifications and safety standards.

Therefore, I cannot extract the information required for your request, as the provided text does not contain:

1. A table of acceptance criteria and reported device performance (in the context of an AI/ML study).
2. Sample size used for a test set or data provenance for an AI/ML study.
3. Number of experts used to establish ground truth or their qualifications for an AI/ML study.
4. Adjudication method for a test set for an AI/ML study.
5. Information about a multi-reader multi-case (MRMC) comparative effectiveness study for an AI/ML device.
6. Information about standalone performance of an algorithm without human-in-the-loop.
7. Type of ground truth used (expert consensus, pathology, outcomes data, etc.) for an AI/ML study.
8. Sample size for a training set for an AI/ML study.
9. How the ground truth for a training set was established for an AI/ML study.

The document explicitly states: "No clinical study is included in this submission." (Page 6, Section 5.7 Clinical Test Conclusion). The "Non-Clinical Test Conclusion" (Page 6, Section 5.6) refers to compliance with general electrical, safety, software, and biological standards (e.g., IEC 60601-1, IEC 62304, ISO 14971, ISO 10993) relevant to medical devices, not performance acceptance criteria for an AI/ML algorithm or its impact on human performance. The "Substantially Equivalence (SE) Comparison" (Page 7) compares technical specifications to predicate devices to demonstrate equivalence for FDA clearance.

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The A5 Anesthesia System is a device used to administer to a patient, continuously or intermittently, a general inhalation anesthetic and to maintain a patient's ventilation.

The A5 is intended for use by licensed clinicians, for patients requiring anesthesia within a health care facility, and can be used in adult and pediatric (including neonate, infant, child and adolescent) populations.

The A5 Anesthesia System is a continuous flow inhalation gas anesthesia system that delivers anesthetic vapor and provides for automatic and manual modes of ventilation. The A5 incorporates 02, CO2, N2O and Agent concentration monitoring (Desflurane, Isoflurane, Enflurane, Sevoflurane and Halothane).

This document is a 510(k) summary for the A5 Anesthesia System. It describes the device, its intended use, and compares it to a predicate device and several reference devices to demonstrate substantial equivalence.

Here's a breakdown of the requested information based on the provided text:

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

The document does not explicitly present acceptance criteria in a table format with specific quantitative thresholds. Instead, it lists various "Nonclinical testing and Performance" areas that were tested and states that "The functional and system level testing showed that the device continues to meet specifications and the performance of the device is equivalent to the predicate."

However, the "Device Comparison Table" on pages 7 and 8 provides a comparison of technical characteristics between the subject device (A5 Anesthesia System) and the predicate device (A5 Anesthesia Delivery System (K123211)). This table implies that the performance of the subject device in these listed characteristics meets or is equivalent to the predicate device, which can be interpreted as the acceptance criteria being parity with the predicate.

Here's an attempt to construct a table based on the provided information, interpreting the predicate device's specifications as the acceptance criteria for the subject device:

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

The document lists "Nonclinical testing and Performance" areas like:

• Software (Unit testing, Integration testing, System testing)
• Performance
• Power Supply
• Thermal
• Cleaning and Disinfection
• Fresh Flow Optimizer
• AG Module
• Heating Module
• Waveform Comparison
• Biocompatibility (Volatile Organic Compounds, Particulate Testing, Cytotoxicity, Sensitization, Irritation / intracutaneous reactivity, Extractables and leachables (E&L) testing, Inorganic gases testing)
• Human Factors Validation Testing
• Testing as per consensus standards (AAMI/ANSI ES60601-1, IEC 60601-1-2, ISO 80601-2-13, ISO 80601-2-55, ASTM F1101-90, AIM 7351731)

However, the document does not specify:

• The sample size used for any of these tests.
• The data provenance (e.g., country of origin, retrospective or prospective nature of the data if any clinical data was implied).

The testing appears to be mostly focused on hardware and software functionality, performance, and safety against specified standards, rather than clinical trial data involving patient samples or expert interpretations.

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)

This information is not provided in the document. The testing described is primarily non-clinical (engineering, software, biocompatibility, standards compliance). There is no mention of "ground truth" being established by experts in the context of clinical interpretation, as might be found in studies for diagnostic AI devices.

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

This information is not provided in the document. Given the nature of the tests (non-clinical performance, safety, and standards compliance), an adjudication method as typically used for clinical endpoints or image interpretation is not applicable or described.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not conducted or described. This type of study is relevant for AI-powered diagnostic or decision support tools that assist human readers, which is not the primary function of this anesthesia system.

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

The device is an "Anesthesia System" which delivers anesthetic vapor and provides ventilation. It includes an "Anesthetic Gas Module" for monitoring, but it is not an "algorithm only" device operating without human-in-the-loop. Its performance, as described by the parameters in the comparison table, would be standalone in terms of its ability to measure and deliver gases accurately, but it functions as part of a system used by clinicians. There is no mention of a standalone algorithm's performance in isolation from the hardware components.

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

For the non-clinical tests (software, performance, electrical safety, biocompatibility, etc.), the "ground truth" would be defined by engineering specifications, international consensus standards, and predicate device performance. For example, the accuracy of gas measurement (e.g., CO2 accuracy 0 to 1%: +/-.1%) would be tested against calibrated references, where the reference measurement itself serves as the ground truth. There is no mention of clinical ground truth types like pathology or outcomes data.

8. The sample size for the training set

This information is not applicable and not provided. The A5 Anesthesia System is a traditional medical device (hardware and software for life support functions and monitoring), not an AI/Machine Learning model that requires a "training set" in the computational sense. The software testing mentioned (Unit, Integration, System) refers to traditional software development and verification, not machine learning model training.

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

This information is not applicable and not provided for the same reasons as point 8.

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The A7 Anesthesia System is a device used to administer to a patient, continuously or intermittently, a general inhalation anesthetic and to maintain a patient's ventilation.

The A7 is intended for use by licensed clinicians, for patients requiring anesthesia within a health care facility, and can be used in adult and pediatric (including neonate, infant, child and adolescent) populations.

The A7 Anesthesia System is a continuous flow inhalation gas anesthesia system that delivers anesthetic vapor and provides for automatic and manual modes of ventilation. The A7 incorporates 02, CO2, N2O and Agent concentration monitoring (Desflurane, Isoflurane, Enflurane, Sevoflurane and Halothane).

This document refers to the Mindray A7 Anesthesia System (K171292). It outlines its indications for use, device description, and a comparison to a predicate device (K151954), along with reference devices, to demonstrate substantial equivalence.

Here's an analysis of the acceptance criteria and study information based on the provided text:

Summary of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the Mindray A7 Anesthesia System are implicitly derived from its comparison to a predicate device (also an A7 Anesthesia System, K151954) and several reference devices. The core principle is "substantial equivalence," meaning the new device performs at least as well as, or comparably to, the established devices.

The table below summarizes the key technical characteristics and their reported performance/specifications for both the subject device and its direct predicate. Since the document states "The functional and system level testing showed that the device continues to meet specifications and the performance of the device is equivalent to the predicate," the performance of the subject device is presented as matching the predicate.

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The GE Healthcare anesthesia machines are intended to provide general inhalation anesthesia and ventilatory support to a wide range of patients (neonatal, pediatric, adult). The GE Healthcare anesthesia machines are to be used only by medical professionals trained and qualified in the administration of general anesthesia.

The GE Healthcare anesthesia machines are intended to provide general inhalation anesthesia and ventilatory support to a wide range of patients (neonatal, pediatric, adult). The GE Healthcare anesthesia machines are to be used only by medical professionals trained and qualified in the administration of general anesthesia.

The GE Healthcare anesthesia systems supply set flows of medical gases to the breathing system. Gas flows are selected by the user and displayed on the display unit or through pneumatic flow meters. A large selection of options may be available to configure the system, including frames, brake style, gases, and anesthetic agents.

The GE anesthesia machines include a microprocessor based, electronically controlled, pneumatically driven ventilator that provides patient ventilation during surgical procedures. The ventilator is equipped with a built-in monitoring system for inspired oxygen, airway pressure, and inhaled and exhaled volume. Flow, gas, and pressure sensors in the breathing circuit are used to control and monitor patient ventilation as well as measure inspired oxygen concentration. This allows for the compensation of compression losses, fresh gas contribution and small leakage in the breathing absorber, bellows and system. User setting and microprocessor calculations control breathing patterns. User interface keeps settings in memory. The user may change settings with a simple and secure setting sequence. A bellows contains breathing gasses to be delivered to the patient. Positive End Expiratory Pressure (PEEP) is regulated electronically. Positive pressure is maintained in the breathing system so that any leakage that occurs is outward. An RS-232 serial digital communications port connects to and communicates with external devices. Ventilatory modes for the device, include Volume Mode, Pressure Control Mode, Synchronous Intermittent Mandatory Ventilation (optional), Pressure Support with Apnea Backup Ventilation (optional).

This is a 510(k) premarket notification for a medical device family (GE Healthcare anesthesia machines) and not a study describing a new algorithm or AI. Therefore, much of the requested information regarding AI-specific acceptance criteria and study details (like sample sizes for test/training sets, expert ground truth, MRMC studies, standalone performance) is not applicable or available in this document.

However, based on the provided text, I can infer the acceptance criteria for substantial equivalence and summarize the study that proves the device meets those criteria.

The primary purpose of this 510(k) submission is to demonstrate that the modified GE Healthcare anesthesia machines, incorporating two alternate flow sensors, are substantially equivalent to their previously cleared predicate devices. The "study" here refers to the non-clinical testing performed to establish this substantial equivalence.

Here's a breakdown of the available information:

Acceptance Criteria and Reported Device Performance

The acceptance criteria for substantial equivalence are implicitly tied to the performance requirements of the predicate devices. The goal is to show that the modified devices perform equivalently and raise no new questions of safety or effectiveness.

Study Details (Non-AI Specific)

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

   • The document describes a series of non-clinical tests (component-level and system-level testing, biocompatibility testing, reprocessing validation). It does not specify a "test set" in the context of patient data or algorithm performance. Instead, it refers to tests on the device's components and the complete system.
   • Data provenance: Not explicitly stated as country of origin, but the submission is from GE Healthcare, Datex-Ohmeda, Inc., located in Madison, Wisconsin, USA. The testing is described as occurring prior to the submission date (September 2017). This is a retrospective analysis of engineering, functional, and safety tests performed on the device.

2. 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. This submission does not involve clinical data that would require expert ground truth labeling in the context of an AI/algorithm study. The "ground truth" for these tests are engineering specifications, validated test methods, and compliance with industry standards.

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

   • Not applicable. Adjudication methods are relevant for subjective interpretations (e.g., image review), not for objective engineering tests on a physical device.

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. This is not an AI/software device that assists human readers. It is a modification to an anesthesia gas machine.

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

   • Not applicable. This is not an AI/algorithm. Performance tests were conducted on the modified physical device.

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

   • The ground truth for these tests are established engineering specifications, validated test methods, and compliance with relevant voluntary industry standards (e.g., ISO 10993 for biocompatibility) that define the expected performance and safety characteristics of an anesthesia gas machine.

7. The sample size for the training set:

   • Not applicable. This is not an AI/machine learning model that undergoes training with a dataset.

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

   • Not applicable. See #7.

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