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Auto CPAP System is a CPAP (Continuous Positive Airway Pressure) device designed for the treatment of adult Obstructive Sleep Apnea (OSA) only, either in the hospital or at home.
Auto CPAP System is for prescription use only. It is a travel CPAP device intended for single-patient use.
LightTrip App is a mobile application for patients to remotely operate BMC Mini serial devices, and transmit, store and display usage and treatment information. It can also receive parameters and device firmware upgrade data from the cloud, and then transmit these data to the device. The LightTrip app also allows healthcare professionals to remotely configure compatible OSA therapy devices. LightTrip App is for prescription use only.

Auto CPAP System is a microprocessor controlled, blower-based system that generates continuous positive airway pressure (CPAP) to support treatment of obstructive sleep apnea. The system provides fixed or auto-adjust pressure from 4 to 20 cmH2O above the ambient atmospheric pressure to a patient's oral/nasal airway.
The Auto CPAP System is designed and developed on the basis of the predicate device Luna CPAP and Auto CPAP System (K153387). The parameters and algorithms of the two devices, such as the treatment mode (CPAP and AutoCPAP), the pressure range (4 to 20 cm H2O), and the Respiratory Event Detection, are consistent. Compared with the predicate device, the subject device does not include a humidifier and an LCD screen. The parameters of subject device are displayed and set via the LightTrip App.

The provided document is a 510(k) summary for the BMC Medical Co., Ltd. Auto CPAP System. Unlike typical AI/ML device submissions, this document focuses on the substantial equivalence of a CPAP device to existing predicate devices, primarily through bench testing for various physical and electrical performance metrics. It does not describe a study involving an AI algorithm that diagnoses or assists in the diagnosis of a medical condition in the way that would typically involve acceptance criteria related to diagnostic performance.

Therefore, many of the requested elements for describing AI/ML device performance, such as sample size for test sets, data provenance, expert ground truth, adjudication methods, MRMC studies, standalone performance, and training set details, are not applicable to this specific submission.

The "Performance Data" section primarily details bench testing and standards compliance, which are the acceptance criteria for a device like a CPAP system.

Here's an interpretation of the provided document in the context of your request, noting where information is not applicable:

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

For a CPAP device, the acceptance criteria are generally defined by performance standards that ensure the device delivers therapy accurately and safely. The document states that "bench testing were conducted according to ISO 80601-2-70:2015" and lists specific tests. The implication is that the device met the requirements of these standards.

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The Luna® CPAP and Auto CPAP System are intended to deliver pressure for the treatment of Obstructive Sleep Apnea. The optional integrated hunidifier is indicated for the humidification and warming of air from the flow generator. These devices are intended for single patient use by prescription in the home or hospital/institutional environment on adult patients.

The materials of water chamber of Luna® CPAF and Auto CPAP device were changed. The suppliers of Polycarbonate and silicone were changed. The basic functional and performance characteristics of the Luna® CPAP and Auto CPAF devices are unchanged from the predicate devices (K141770).

This document (K153387) is a 510(k) premarket notification for a medical device (Luna® CPAP and Auto CPAP System) to the U.S. Food and Drug Administration (FDA). It is not a study report that describes the acceptance criteria and performance of an AI/ML device in the way requested. Instead, it is a submission seeking clearance to market a modified version of an existing device (K141770) based on demonstrating "substantial equivalence" to the predicate device.

Therefore, the specific information requested about acceptance criteria, study details, sample sizes, ground truth establishment, expert involvement, and MRMC studies for an AI/ML device is not present in this document.

The document primarily focuses on:

• Device Modification: The only modification is a change in the materials used for the water chamber of the Luna® CPAP and Auto CPAP system. The suppliers of Polycarbonate and silicone were changed.
• Demonstration of Substantial Equivalence: The submission asserts that the modified device is substantially equivalent to the predicate device (K141770) because it has the "same intended use, same operating principle, same technology, and same manufacturing process."
• Non-Clinical Testing for Material Change: To address the material change, biological and mechanical tests were conducted on the new materials and the water chamber.

Here's how the information provided in the document relates to your request, highlighting what can be extracted and what cannot:

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

• Not Applicable in the AI/ML context. This document is about a material change in a CPAP device, not an AI/ML diagnostic or prognostic tool.
• What is shown: The document refers to "appropriate biological tests conducted and passed for these components, in accordance with FDA guidance #G95-1" and "Series mechanical tests were also conducted to demonstrate the mechanical performance of the water chamber, including: drop test, leak test, and humidity performance test." The specific acceptance criteria for these tests (e.g., maximum allowable leakage, specific drop test pass/fail conditions) are not detailed in this summary, nor are the quantitative results of these tests, beyond a statement that they "demonstrated substantial equivalence."

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

• Not Applicable in the AI/ML context. This is not an AI/ML study.
• What is shown: The document does not specify sample sizes (e.g., number of water chambers tested for biocompatibility or mechanical performance). It also does not mention data provenance in the context of clinical studies, as this is a non-clinical material change submission.

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)

• Not Applicable in the AI/ML context. There is no "ground truth" establishment in the sense of expert review of images or clinical data for this type of submission. The "ground truth" here is the biological and mechanical safety/performance of the new materials, assessed through standardized lab tests.

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

• Not Applicable in the AI/ML context. No adjudication method is described as there's no diagnostic or interpretative task involved.

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 is not an AI/ML device, and no MRMC study was performed or is relevant to this submission.

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

• Not Applicable. This is not an AI/ML algorithm.

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

• What is shown: The "ground truth" for this submission are the results of standardized biocompatibility tests (ISO 10993 series, including Genotoxicity, Cytotoxicity, Implantation, Sensitization, and Irritation tests, plus Intracutaneous Reactivity, Ames test, In vitro Mammalian Cell Gene Mutation Test, Skin Sensitization Mammalian Test, In vitro Chromosome Aberration In Vitro Cytotoxicity Test and Muscle Implantation Test) and mechanical performance tests (drop test, leak test, and humidity performance test). The acceptance is that the new materials and components pass these established safety and performance standards.

8. The sample size for the training set

• Not Applicable. This is not an AI/ML device where a training set is relevant.

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

• Not Applicable. See point 8.

In summary, this document is a regulatory submission for a minor device modification (material change) of a CPAP system and does not contain the information typically found in an AI/ML device study report.

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The 3B Luna CPAP and Auto CPAP Systems are intended to deliver positive pressure for the treatment of Obstructive Sleep Apnea. The optional integrated humidifier is indicated for the humidification and warming of air from the flow generator. These devices are intended for single patient use by prescription in the home or hospital / institutional environment on adult patients.

The Luna CPAP and Auto CPAP System is a microprocessor-controlled, blower-based system that generates positive airway pressure from 4 to 20 cm H20. The device is intended for use with a patient interface (mask). The device has been modified to include a color LCD, menu driven user interface, and a redesigned enclosure. The electrical circuit was redesigned to incorporate the color LCD. The basic functionality and performance characteristics of the Luna CPAP and Auto CPAP are unchanged from the predicate device RESmart CPAP and Auto CPAP (K132967).

This document is a 510(k) premarket notification for the 3B™ Luna CPAP and Auto-CPAP System, which includes an integrated heated humidifier. It compares the proposed device to a predicate device, the RESmart® CPAP and Auto-CPAP Systems (K132967). The main purpose of the notification is to demonstrate substantial equivalence to the predicate device, not necessarily to independently prove the device meets specific performance criteria beyond those of the predicate.

Here's an analysis of the acceptance criteria and study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state "acceptance criteria" in the traditional sense of a new technology proving its efficacy against established benchmarks. Instead, it aims to demonstrate that the proposed device is substantially equivalent to a predicate device. Therefore, the "acceptance criteria" can be inferred as matching or performing similarly to the predicate device based on various technical specifications and non-clinical testing.

Device Performance Evidence Summary: The study essentially asserts that the proposed device performs equivalently to the predicate device. The performance data presented is a side-by-side comparison of specifications for both devices, highlighting that the proposed device shares the same intended use, operating principle, technology, materials, and manufacturing process as the predicate, or has comparable or slightly improved characteristics.

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

This document describes a premarket notification (510(k)) that relies heavily on bench testing and comparison to a predicate device, not clinical trials with human subjects.

• Test Set: No independent human subject test set is described. The "test set" consists of bench performance tests comparing the proposed device to the predicate.
• Sample Size: Not applicable in the context of human data. The sample size for non-clinical testing (e.g., number of devices tested for pressure accuracy) is not specified, but it's implied that sufficient units were tested to demonstrate performance.
• Data Provenance: The document does not provide information about the country of origin for any human data (as there isn't any mentioned). The testing itself is non-clinical, likely conducted by the manufacturer (BMC Medical CO., LTD, CHINA). The study is retrospective in the sense that it refers to the performance and testing of the predicate device (K132967) for many aspects, and current bench testing for the proposed device.

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

This type of information is not applicable to this 510(k) submission.

• Ground Truth: The "ground truth" for this submission is established through engineering and performance specifications and compliance with international standards (e.g., ISO, IEC), rather than expert consensus on medical outcomes or pathology.
• Experts: The document does not describe the involvement of medical experts to establish a "ground truth" for a test set. Engineering and regulatory specialists would be involved in designing and evaluating the bench tests.

4. Adjudication Method for the Test Set

This is not applicable. There is no "test set" in the sense of a set of cases requiring adjudication by multiple experts. The evaluation is based on objective measurements from bench testing against predefined engineering specifications.

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, an MRMC comparative effectiveness study was not done. This device is a CPAP/Auto-CPAP system for delivering positive pressure, not an AI-assisted diagnostic or interpretative tool that would involve human "readers" or AI assistance in a clinical setting.

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

This question is not applicable as the device is not an algorithm that functions in a standalone diagnostic capacity. It is a medical device for therapeutic delivery of positive pressure.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The "ground truth" used for this submission is based on:

• Engineering Specifications and Performance Standards: Defined by international standards (ISO 17510-1:2007, IEC 60601 series) and the specifications of the predicate device.
• Performance Bench Testing: Objective measurements conducted in a laboratory setting to verify that the device meets its design specifications (e.g., pressure accuracy, sound level, humidification, electrical safety).
• Biocompatibility Testing: Conducted in the predicate device against ISO 10993 standards, and carried over due to identical materials.

8. The Sample Size for the Training Set

This is not applicable. The device is not based on machine learning or AI that requires a "training set" of data.

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

This is not applicable as there is no training set for this device.

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The 3B and BMC RESmart CPAP and Auto CPAP Systems are intended to deliver positive pressure for the treatment of Obstructive Sleep Apnea. The optional integrated humidifier is indicated for the humidification and warming of air from the flow generator device. These devices are intended for single patient use by prescription in the home or hospital/institutional environment on adult patients.

The RESmart CPAP and Auto CPAP System is a microprocessorcontrolled, blower-based system that generates positive airway pressure from 4 to 20 cm H2O. The device is intended for use with a patient circuit that is used to connect the device to the patient interface (mask). The RESmart CPAP and Auto CPAP device has been modified to include porting the iCode software to a web-based application. The basic functional and performance characteristic of the RESmart CPAP and Auto CPAP is unchanged from the predicate device RESmart CPAP and Auto CPAP (K110629).

The provided text is a 510(k) Summary for the RESmart CPAP/Auto CPAP device (K131707). It describes modifications made to the iCode software feature of an existing device (K110629), porting it to a web-based application and developing a smartphone application for it.

The key takeaway is that this submission K131707 is NOT about the performance of the CPAP device itself in treating sleep apnea, but rather about the software modifications to the iCode feature, which is used for compliance reporting. The document explicitly states: "The basic functional and performance characteristic of the RESmart CPAP and Auto CPAP is unchanged from the predicate device RESmart CPAP and Auto CPAP (K110629)." and "There are no changes in logic, Rather the extent of the device algorithm, or function. modification is a simple change from a desktop environment to a web server based environment."

Therefore, the acceptance criteria and study information requested in the prompt, particularly those pertaining to medical device performance (e.g., effect size of human readers improving with AI, standalone algorithm performance, ground truth for medical outcomes), are not applicable to this specific 510(k) submission.

Here's the breakdown based on the provided text, focusing on the software modifications:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: Not applicable. The "test set" here refers to software testing, not clinical data for device performance. The evaluation was focused on the functionality and integrity of the software changes.
• Data Provenance: Not applicable, as no clinical data for device performance was generated or used for this specific submission's modifications. The modifications relate to the iCode feature, which reports compliance data from the device, not clinical data about the device's efficacy.

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

• Number of Experts: Not applicable. Ground truth in a clinical sense (e.g., expert consensus on medical images) is not relevant for this software modification. Software verification and validation are typically performed by software engineers and quality assurance personnel.
• Qualifications of Experts: The document refers to "internal procedures" for code review and verification, implying software development and QA professionals were involved.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable in the context of clinical expert adjudication. Software testing methodologies (e.g., unit testing, integration testing, system testing, user acceptance testing) would have been used to verify the new software features.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size of Human Readers Improve with AI vs. Without AI Assistance

• MRMC Study: No.
• Effect Size of Human Readers with AI: Not applicable. This device is a CPAP machine, not an AI-assisted diagnostic tool that humans read. The software modifications are for compliance reporting, not clinical interpretation or assistance.

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

• Standalone Performance: While the "iCode Report Generator" could be considered an "algorithm" (processing the iCode string to generate reports), its standalone performance in a medical evaluative sense (e.g., diagnostic accuracy) is not applicable. Its function is to accurately process an input string and generate a report, verified through software testing. The smartphone app uses OCR, which is an algorithm, but its performance is in accurately recognizing the characters, not making medical diagnoses.

7. The Type of Ground Truth Used

• Type of Ground Truth: For the software modifications, the "ground truth" would be the expected output and behavior based on the software requirements and design specifications. For the OCR component of the smartphone app, the ground truth would be the actual characters displayed on the RESmart LCD.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable. There is no mention of a training set in the context of machine learning for medical outcomes. For the OCR component, the vendor would have used training data to develop the OCR model, but its specifics are not provided in this regulatory summary.

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

• Ground Truth for Training Set: Not applicable in a medical device performance context. If referring to the OCR component, the ground truth would have been established by human verification of scanned characters. This information is not provided in the 510(k) summary.

In summary: This 510(k) pertains to software modifications for a compliance reporting feature of an already cleared CPAP device. The evaluation criteria and studies are focused on software verification and validation, ensuring the new web-based and smartphone applications correctly handle the iCode data without altering the device's fundamental medical function or performance. Therefore, many of the typical questions asked about AI/medical device clinical studies are not applicable here.

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The REMstar Auto CPAP System delivers positive airway pressure therapy for the treatment of Obstructive Sleep Apnea.

The REMstar Auto Continuous Positive Airway Pressure (CPAP) System is a microprocessorcontrolled, blower-based system that generates positive airway pressures from 4 to 20 cmH₂O. The device is intended for use with a patient circuit that is used to connect the device to the patient interface (mask). The previously cleared REMstar Auto has been modified to include a Split-Night with Auto-CPAP mode to simplify spilt night studies and to add a Non Responsive Apnea Hypopnea (NRAH) flag for clinical use. The basic functional and performance characteristics of the REMstar Auto CPAP System is unchanged from its device predicate (REMstar Auto CPAP System K012554).

The provided text describes a 510(k) summary for the Respironics REMstar Auto CPAP System (K031460). This submission focuses on modifications to an existing device and aims to demonstrate substantial equivalence to its predicates. The information required to fulfill the request, particularly regarding specific acceptance criteria, device performance, and details of a clinical study, is NOT present in the provided document.

The document states:

• "Design verification tests were performed on the REMstar Auto CPAP System in accordance with of the risk analysis and product requirements. All tests were verified to meet the required acceptance criteria."
• "The basic functional and performance characteristics of the REMstar Auto CPAP System is unchanged from its device predicate (REMstar Auto CPAP System K012554)."

This indicates that internal design verification tests were conducted, but the specifics of what those acceptance criteria were and what the reported performance results were are not detailed. It also does not describe a clinical study in the format requested.

Therefore, many of the requested sections cannot be filled from the provided text.

Here's what can be extracted and what cannot:

1. Table of Acceptance Criteria and Reported Device Performance:

Reasoning: The document states that "All tests were verified to meet the required acceptance criteria," but it does not list these criteria or the specific results. The focus of this 510(k) is on demonstrating substantial equivalence primarily through design modifications and compliance with standards, rather than presenting novel performance data from a clinical trial.

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

• Sample Size for Test Set: Not specified. (The document mentions "Design verification tests" but no specific clinical "test set" with patient data).
• Data Provenance: Not specified.

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

• Not applicable. There is no mention of a human-reviewed "ground truth" test set.

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

• Not applicable. There is no mention of a clinical "test set" requiring adjudication.

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:

• MRMC Study Done: No. This document describes a CPAP device, not an AI-assisted diagnostic tool that would typically involve human readers.
• Effect Size of AI Improvement: Not applicable.

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

• This is not an AI algorithm. It's a medical device. The "standalone performance" refers to the device's functional and performance characteristics, which are stated to be "unchanged from its device predicate."

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

• Not applicable in the context of typical AI device ground truth. For a CPAP device, performance is typically verified against engineering specifications, simulated physiological conditions, and potentially sleep lab data if new therapeutic efficacy claims were made (which they are not in this submission, as it's a modification to an already cleared device).

8. The sample size for the training set:

• Not applicable. This is not an AI/Machine Learning device that undergoes a "training set" process.

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

• Not applicable.

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The REMstar Auto CPAP System delivers positive airway pressure therapy for the treatment of Obstructive Sleep Apnea.

The REMstar Auto Continuous Positive Airway Pressure (CPAP) System is a microprocessorcontrolled, blower-based system that generates positive airway pressures from 4 to 20 cmH₂O. The device is intended for use with a patient circuit that is used to connect the device to the patient interface (mask). The Respironics Model 7410 Voyager has been modified to detect hypopneas and to add an interface to adjust the settings of the Remstar Heated Humidifier. The design implementation of the humidifier and the humidifier interface is the same as the Remstar Plus CPAP System (K010263). The basic functional and performance characteristic of the REMstar Auto CPAP System is unchanged from the predicate device (Model 7410 Voyager K974879).

This document is a 510(k) premarket notification for the Respironics REMstar Auto CPAP System. It focuses on demonstrating substantial equivalence to predicate devices and does not contain detailed information about specific acceptance criteria, performance studies, or clinical trial data as typically found in a comprehensive clinical study report. Therefore, I can only extract limited information based on the provided text.

Here's an attempt to answer your questions based on the available text:

1. Table of acceptance criteria and the reported device performance

   The document states: "Design verification tests were performed on the REMstar Auto CPAP System because of the risk analysis and product requirements. All tests were verified to meet the required acceptance criteria."

   However, no specific acceptance criteria or detailed reported device performance metrics are provided in the text. The submission highlights that the device has the "Same operating principle," "Same technology," and "Same manufacturing process" as the predicate, and that its "basic functional and performance characteristic... is unchanged from the predicate device (Model 7410 Voyager K974879)." The key modification is the ability to "detect hypopneas."

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 mentions "design verification tests" but does not specify the sample size used for any test set or the data provenance. There is no indication of clinical trial data or patient data being used for these tests; "design verification tests" typically refer to engineering and bench testing.

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 submission focuses on substantial equivalence based on design and technical characteristics rather than a clinical evaluation requiring expert ground truth assessment.

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

   This information is not provided in the document.

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 is mentioned. The device is a CPAP system, not an AI-assisted diagnostic tool that would typically involve human readers. The new feature is the automatic detection of hypopneas, which is an algorithmic function, but its comparative effectiveness in improving human reader performance is not discussed.

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

   The document mentions that the modified device "has been modified to detect hypopneas." This suggests an algorithm for detecting hypopneas is part of the device's functionality. The "design verification tests" would have evaluated the performance of this detection algorithm in a standalone manner (without a human in the loop for the detection itself), but details of these tests and their results are not provided.

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

   The document refers to "design verification tests" but does not specify the type of ground truth used for evaluating the hypopnea detection, or any other functional aspect. For a feature like hypopnea detection, ground truth would typically come from polysomnography data scored by sleep experts, but this is not mentioned.

8. The sample size for the training set

   The document does not mention any training set or machine learning development process, nor a sample size for it. While the device detects hypopneas, it doesn't explicitly state that this detection uses a modern machine learning model that would require a distinct training set. It could be based on rule-based algorithms.

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

   Since no training set is mentioned, information on how its ground truth was established is not provided.

In summary, this 510(k) submission is a regulatory document focused on demonstrating substantial equivalence, not a detailed technical or clinical study report. It states that design verification tests were conducted and met acceptance criteria, but it does not provide the specifics of these tests, including sample sizes, ground truth establishment, or detailed performance metrics.

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