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AVIEW LCS is intended for the review and analysis and reporting of thoracic CT images for the purpose of characterizing nodules in the lung in a single study, or over the time course of several thoracic studies. Characterizations include nodule type, location of the nodule and measurements such as size (major axis), estimated effective diameter from the volume of the volume of the nodule, Mean HU (the average value of the CT pixel inside the nodule in HU), Minimum HU, Max HU, mass (mass calculated from the CT pixel value), and volumetric measures (Solid Major, length of the longest diameter measured in 3D for a solid portion of the nodule. Solid 2nd Major: The length of the longest diameter of the solid part, measured in sections perpendicular to the Major axis of the nodule), VDT (Volume doubling time), Lung-RADS (classification proposed to aid with findings) and CAC score and LAA analysis. The system automatically performs the measurement, allowing lung nodules and measurements to be displayed and, also integrate with FDA certified Mevis CAD (Computer-aided detection) (K043617).

AVIEW LCS is intended for use as diagnostic patient imaging which is intended for the review and analysis of thoracic CT images. Provides following features as semi-automatic nodule measurement (segmentation), maximal plane measure, 3D measure and volumetric measures, automatic nodules detection by integration with 3th party CAD. Also provides cancer risk based on PANCAN risk model which calculates the malignancy score based on numerical or Boolean inputs. Follow up support with automated nodule matching and automatically categorize Lung-RADS score which is a quality assurance tool designed to standardize lung cancer screening CT reporting and management recommendations that is based on type, size, size change and other findings that is reported.

The provided text does not contain detailed acceptance criteria for specific performance metrics of the AVIEW LCS device, nor does it describe a study proving the device meets particular acceptance criteria with quantitative results.

The document is a 510(k) premarket notification summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed performance study like a clinical trial.

However, based on the information provided, here's what can be extracted and inferred regarding performance and testing:

1. Table of Acceptance Criteria and Reported Device Performance

As specific quantitative acceptance criteria and detailed performance metrics are not explicitly stated in the provided text for AVIEW LCS, I cannot create a table of acceptance criteria and reported device performance. The document generally states that "the modified device passed all of the tests based on pre-determined Pass/Fail criteria" for software validation.

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

The document does not specify the sample size used for any test set or the data provenance (e.g., country of origin, retrospective/prospective). The described "Unit Test" and "System Test" are internal software validation tests rather than clinical performance studies involving patient data.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not mention using experts to establish ground truth for a test set. This type of information would typically be found in a clinical performance study.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method for a test set. This is relevant for clinical studies where multiple readers assess cases.

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

The document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was performed. Therefore, no effect size of human readers improving with AI vs. without AI assistance is mentioned.

6. Standalone (Algorithm Only) Performance Study

The document does not explicitly state that a standalone (algorithm only without human-in-the-loop performance) study was conducted. The "Performance Test" section refers to DICOM, integration, and thin client server compatibility reports, which are software performance tests, not clinical efficacy or diagnostic accuracy studies for the algorithm itself. The device description mentions "automatic nodules detection by integration with 3rd party CAD (Mevis Visia FDA 510k Cleared)", suggesting it leverages an already cleared CAD system for detection rather than having a new, independently evaluated detection algorithm as part of this submission.

7. Type of Ground Truth Used

The document does not specify the type of ground truth used for any performance evaluation. Again, this would be characteristic of a clinical performance study.

8. Sample Size for the Training Set

The document does not provide the sample size for any training set. This is typically relevant for AI/ML-based algorithms. The mention of "deep-learning algorithms" for lung and lobe segmentation suggests a training set was used, but its size is not disclosed.

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

The document does not explain how ground truth for any potential training set was established.

Summary of available information regarding testing:

The "Performance Data" section (8) of the 510(k) summary focuses on nonclinical performance testing and software verification and validation activities.

• Nonclinical Performance Testing: The document states, "This Medical device is not new; therefore, a clinical study was not considered necessary prior to release. Additionally, there was no clinical testing required to support the medical device as the indications for use is equivalent to the predicate device. The substantial equivalence of the device is supported by the nonclinical testing." This indicates the submission relies on the substantial equivalence argument and internal software testing, not new clinical performance data for efficacy.
• Software Verification and Validation:
  • Unit Test: Conducted using Google C++ Unit Test Framework on major software components for functional, performance, and algorithm analysis.
  • System Test: Conducted based on "integration Test Cases" and "Exploratory Test" to identify defects.
    • Acceptance Criteria for System Test: "Success standard of System Test is not finding 'Major', 'Moderate' defect."
    • Defect Classification:
      • Major: Impacting intended use, no workaround.
      • Moderate: UI/general quality, workaround available.
      • Minor: Not impacting intended use, not significant.
  • Performance Test Reports: DICOM Test Report, Performance Test Report, Integration Test Report, Thin Client Server Compatibility Test Report.

In conclusion, the provided 510(k) summary primarily addresses software validation and verification to demonstrate substantial equivalence, rather than a clinical performance study with specific acceptance criteria related to diagnostic accuracy, reader performance, or a detailed description of ground truth establishment.

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The A Plus Internal Fixture System is intended to be placed in the upper or lower jaw to support prosthetic devices, such as artificial teeth, and to restore a patient's chewing function. This may be accomplished using either a two stage surgical procedure or a single stage surgical procedure. It is intended for delayed loading.

The A Plus internal fixtures are made with Grade 4 titanium. The systems consist of one-stage and two-stage root form dental implants, associated with abutment systems, which provide the dentist with screw and cement retained restoration options.

The devices covered by this submission are A Plus internal fixture, screw, abutment and some accessories. The diameters of A Plus internal fixtures are 3.4 mm, 3.8 mm, 4.3 mm, 4.8 mm, and 5.3 mm, and the lengths are 8 mm, 10 mm, 12 mm, and 14 mm.

This document describes a 510(k) premarket notification for a medical device called the "A Plus Internal Fixture System," which is an endosseous dental implant. The core of this submission is to demonstrate substantial equivalence to legally marketed predicate devices, not to prove clinical effectiveness through extensive studies as might be required for a novel device. Therefore, the information provided focuses on non-clinical performance and design similarity rather than complex clinical trial data or AI performance metrics.

Based on the provided text, the device itself is an endosseous dental implant, not an AI-powered diagnostic or assistive tool. Consequently, questions related to AI performance, human reader improvement with AI assistance, expert consensus for ground truth establishment in a diagnostic context, and training/test set sample sizes for algorithms are not applicable to this submission.

Here's an analysis based on the information provided, addressing the relevant points and noting the non-applicability of others:

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

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

• Sample Size for Test Set: Not specified for any of the non-clinical tests. For mechanical and material tests like fatigue and surface analysis, samples of the physical device components are tested according to the relevant ISO standards, but a "test set" in the context of clinical or AI validation is not applicable here.
• Data Provenance: The device manufacturer is T-Plus Implant Tech. Co., Ltd. in New Taipei City, Taiwan. The non-clinical testing was performed either directly by the manufacturer or by a contracted lab, with the results provided as part of the 510(k) submission. No information on the country of origin of "data" in a patient or imaging context is relevant or provided. The studies are not clinical human studies; they are bench tests.

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

• This question is not applicable. "Ground truth" established by experts is relevant for diagnostic devices, especially those using AI, where a human expert's interpretation (e.g., radiologist's reading) is compared to the device's output. For an endosseous dental implant, "ground truth" is established by engineering specifications, material science standards, and the results of physical and chemical tests (e.g., measuring forces, observing material degradation, analyzing surface composition).

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

• This question is not applicable. Adjudication methods like 2+1 or 3+1 are used in clinical studies or multi-reader studies to resolve discrepancies among expert readers when establishing a "ground truth" for a diagnostic task. The tests performed for this dental implant are non-clinical bench tests (e.g., fatigue, biocompatibility), where results are determined by objective measurements against predefined standards.

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

• This question is not applicable. The "A Plus Internal Fixture System" is a physical dental implant, not an AI-powered diagnostic or assistive device. Therefore, no MRMC comparative effectiveness study involving human readers or AI assistance was performed or is relevant to its substantial equivalence determination.

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

• This question is not applicable. The device is an endosseous dental implant, not an algorithm. There is no "standalone algorithm" performance to report.

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

• The "ground truth" for this device's performance is based on established engineering standards, material specifications, and regulatory requirements. For example, for the fatigue test, the "ground truth" is that the implant must withstand a certain number of load cycles at a specified force as per ISO 14801. For biocompatibility, the "ground truth" is that the materials must not elicit adverse biological reactions as defined by ISO 10993 series for medical devices. For surface coating, the "ground truth" is the absence of residual particles.

8. The sample size for the training set

• This question is not applicable. There is no "training set" for this type of device, as it is not a machine learning or AI-based system.

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

• This question is not applicable. As there is no training set, there is no ground truth to be established for it.

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Patient monitors are intended to be used for monitoring, displaying, reviewing, storing and alarming of multiple physiological parameters including ECG (3-lead or 5-lead or 12-lead selectable), arrhythmia detection, ST segment analysis, Heart Rate (HR), Respiration Rate (RESP), Temperature (TEMP), Pulse Oxygen Saturation (SpO2), Pulse Rate (PR), Non-invasive Blood Pressure (NIBP), Invasive Blood Pressure (IBP), Carbon dioxide (CO2), Anesthetic Gas (AG), Impedance Cardiograph (ICG), Cerebral State Index (CSI), Bispectral Index (BIS), Total Hemoglobin(SpHb), Carboxyhemoglobin (SpCO), and Methemoglobin(SpMet).

The arrhythmia detection, ST segment analysis only applied to a single adult patient.

The monitors are to be used in healthcare facilities by clinical physicians or appropriate medical staff under the direction of physician.

It is not intended for helicopter transport, hospital ambulance, or home use.

The Patient Monitors consist of two parts, which are host units and function modules.

The host units of Any View A Series Patient Monitors are available in four modules, which are Any View A3, AnyView A5, Any View A6 and AnyView A8, The units, themselves, did NOT collect any physiological data from the patient, which are collected by function modules and transmitted to the host unit. They shall be worked with the basic function module, EMS or MPS.

The host units of Q Series Patient Monitors are available in six modules, which are Q2, Q3, Q6 and Q7. These host units could complete the measurement of ECG, RESP, TEMP, SpO2, NIBP and IBP.

In addition, there are several extended function modules, which could be connected with the host units to complete the measurement functions, including TEMP, CO2 Mainstream, CO2 Sidestream, SpO2 Nellcor, SpO2 Masimo, AG Mainstream, AG Sidestream, ICG and CSM (CSI).

Here's a breakdown of the acceptance criteria and the study information based on the provided document.

Acceptance Criteria and Device Performance

This 510(k) submission focuses on demonstrating substantial equivalence to a predicate device, rather than providing specific performance metrics against pre-defined numerical clinical acceptance criteria. The acceptance criteria are primarily based on compliance with recognized medical device standards and qualitative comparison to a predicate device.

"Study" Information (Non-Clinical Test Conclusion)

The document primarily describes non-clinical tests to verify compliance with design specifications and demonstrate substantial equivalence, rather than a clinical study with a test set of patient data and expert ground truth.

1. Sample size used for the test set and the data provenance: Not applicable in the context of a clinical study. The document refers to "non clinical tests" and compliance with standards. It does not mention a test set of patient data.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for a test set of patient data is not mentioned as the study is non-clinical.
3. Adjudication method for the test set: Not applicable. There is no mention of a test set requiring adjudication.
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 device is a patient monitor, not an AI-assisted diagnostic tool for human readers.
5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable in the traditional sense of an AI algorithm. The performance evaluation is based on the device's ability to accurately measure physiological parameters and comply with industry standards, which inherently means its "standalone" performance for collecting and displaying data.
6. The type of ground truth used:
   • For safety and EMC: Compliance with international and national standards (IEC 60601-1, IEC 60601-1-2).
   • For performance of specific physiological measurements: Compliance with specific standards like AAMI EC13 (cardiac monitors), AAMI SP10 (sphygmomanometers), and ISO 9919 (pulse oximeters). These standards typically define accuracy requirements against known calibration sources or reference methods.
7. The sample size for the training set: Not applicable. This document does not describe a machine learning model requiring a training set.
8. How the ground truth for the training set was established: Not applicable.

Overall Conclusion from the Document:

The proposed device (A Series / Q Series Patient Monitors) was determined to be Substantially Equivalent (SE) to the predicate devices based on non-clinical tests demonstrating compliance with recognized safety and performance standards. The comparison focused on intended use, technological characteristics, and principles of operation.

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The 'ABaer System with ABaer I/O Function' is indicated for use when it is necessary for a trained health care professional to measure or determine cochlear function. The device can be used for patients of all ages, from newborn infants through adults, to include geriatric patients. The otoacoustic emissions test is especially indicated for use in testing individuals for whom behavioral results are deemed unreliable, such as infants, young children, and cognitively impaired adults.

The ABaer I/O Function is a Windows® based software application for use with the ABaer Hearing Screening System. The ABaer I/O software option enables the ABaer device user to perform DPOAE Input/Output (I/O) testing at different test frequencies, frequency ratios and intensity levels in addition to the ABR and OAE based hearing screening functions. The graphical representation of the test results in the form of stimulus level presented versus measured DPOAE level provides an effective way for the user to view and evaluate stimulus level-sensitive information about DPOAE responses.

The ABaer Hearing Screening System (HSS) performs an automated auditory evoked response (ABaer) screening and/or an automated otoacoustic emissions (AOAE) screening. The automated auditory brainstem response test (ABaer) involves placement of three recording electrodes on an infant's head. The electrodes record electrical activity generated by the auditory nervous system that results from the presentation of a click stimulus into the patient's ear via acoustic transducers (i.e. insert earphones, headphones, OAE probe, acoustic ear couplers). The system collects and averages evoked potential data in order to perform ABR based screening, recording and analysis functions, provides one channel of data recording, and includes the Point Optimized Variance Ratio (POVR) algorithm for optimizing signal quality, implementing the automated screening function and enhancing speed of test completion in the same manner as in the predicate device (K021801). The device presents the resulting POVR score and a Pass/Refer recommendation to the user.

The automated otoacoustic emissions (AOAE) screening functionality of the ABaer system involves producing controlled acoustic signals in the ear canal and measures the resulting evoked otoacoustic emissions that are generated by the inner ear as a result of normal hearing processes. The ABaer device performs both distortion product otoacoustic emissions (DPOAE) tests and transient evoked otoacoustic emissions (TEOAE) tests.

The OAE stimuli are generated via miniature receivers and the sounds in the external ear canal are recorded via a miniature microphone, all embedded in the Bio-logic OAE probe. The system collects, averages and analyzes data samples until specified measurement and test parameters are achieved. For transient evoked otoacoustic emissions (TEOAEs), the reproducibility and the difference value between the TEOAE and the noise floor amplitudes are calculated and presented to the user. For distortion product otoacoustic emissions (DPOAEs), the DP and noise floor amplitudes are calculated and presented to the user. A pass or refer recommendation is assigned at the end of the test automatically based on the test protocol parameters and measured OAE parameters.

The ABaer I/O is a software option to be used in conjunction with the ABaer system. The standard DPOAE test measures otoacoustic response to a series of frequency-pairs of tones, varving the frequency while keeping the level or intensity of the stimulus tones at a constant level. The ABaer I/O software option enables the ABaer device user to perform DPOAE testing at different stimulus intensities in order to obtain the 'DPOAE Input / Output (I/O) function' for user defined test frequencies, frequency ratios and intensity levels. The graphical representation of the test results in the form of stimulus level vs. DPOAE level provides an effective way for the user to view and evaluate stimulus levelsensitive information about DPOAE response.

Here's an analysis of the provided text regarding the acceptance criteria and study for the ABaer with ABaer I/O Function device:

1. Acceptance Criteria and Reported Device Performance

The submission for the ABaer with ABaer I/O Function is a 510(k) premarket notification. This type of submission primarily focuses on establishing "substantial equivalence" to legally marketed predicate devices, rather than establishing specific quantitative performance acceptance criteria in the same way a de novo or PMA submission might. The document repeatedly states equivalence to predicate devices, which implies that the device's performance is acceptable if it is comparable to the established performance of those predicates.

Therefore, the "acceptance criteria" here are implicitly tied to the performance characteristics of the predicate devices. The "reported device performance" is the assertion of equivalence to these predicates.

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

The document does not provide any details about:

• The sample size used for any test set (e.g., number of subjects or recordings).
• The data provenance (e.g., country of origin, retrospective or prospective nature).

The submission relies on "Design verification and validation" and "risk analysis documentation" to support its claims of equivalence, but the specifics of these tests (including sample sizes and data characteristics) are not disclosed in this summary.

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

The document does not provide any details about:

• The number of experts, if any, used to establish ground truth.
• The qualifications of such experts.

The nature of the device (an audiometer and evoked response stimulator) suggests that ground truth would typically be established through established audiology protocols and objective physiological measurements, rather than subjective expert consensus on interpretations of complex images or signals requiring multiple expert reads. However, the document does not elaborate on how ground truth was established for any testing.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method. Given the lack of information on expert involvement and specific test sets, an adjudication method is not mentioned.

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 reported. This type of study is more common for diagnostic imaging AI devices where human reader performance is a key metric. This device is an automated physiological measurement tool; therefore, an MRMC study comparing human reader improvement with AI assistance is not applicable to its stated function or the information provided.

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

The device itself is described as performing "automated auditory evoked response (ABaer) screening" and "automated otoacoustic emissions (AOAE) screening," and it provides a "Pass/Refer recommendation." This inherently implies standalone algorithm performance. While a trained healthcare professional uses the device and interprets the DPOAE I/O function (which provides "stimulus level-sensitive information"), the core screening functions (ABR, TEOAE, DPOAE with Pass/Refer) are driven by the algorithm itself.

The claim of "equivalence" to predicate devices, which also perform automated screening, suggests that the standalone performance of the ABaer system's algorithms is being validated against the performance of those established, FDA-cleared devices. However, no specific standalone performance metrics or study details are provided.

7. The Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for any testing. For devices measuring physiological responses like ABR and OAE, ground truth is typically established through:

• Established audiological diagnostic criteria: Based on a combination of different audiological tests (e.g., behavioral audiometry, tympanometry, acoustic reflex testing) and clinical assessment, often considered the "gold standard."
• Pathology/Clinical Outcomes: In some cases, confirmed diagnoses or long-term outcomes could serve as ground truth, but this is less common for screening devices that identify risk.

Given the device's function and the focus on "equivalence," it is highly probable that the ground truth for "verification and validation" would have involved comparison against established clinical assessments or existing, validated audiometric devices.

8. The Sample Size for the Training Set

The document does not provide any information about a training set or its sample size. This is common for 510(k) submissions of this type, especially for devices developed through traditional engineering and signal processing methods rather than deep learning or machine learning, where distinct training and test sets are fundamental. The "ABaer I/O Function" is described as a "software option," implying feature extension rather than a new AI model requiring extensive new training.

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

Since no training set is mentioned, this information is not provided.

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The A&D Medical UA-1000 family digital blood pressure monitors are intended for used by adults with 12 years older to measure the systolic and diastolic blood pressure and pulse rate.

Measure blood pressure (systolic and diastolic) and pulse rate.

UA-1000 family uses an inflatable cuff which is wrapped around the patient's upper arm. The cuff is inflated automatically by an internal pump in the device. The systolic and diastolic blood pressures are determined by oscillometric method. The deflation rate is controlled by the internal electronic-controlled exhaust valve. There is a quick exhaust mechanism so that the pressure of the cuff can be completely released. There is a maximum pressure safety setting at 300mmHg. The device will not inflate the cuff higher than 300mmHg. The UA-1000 family will turn on an irregular heartbeat indicator if an irregular heartbeat was detected during the measurement process. At the end of the measurement, the systolic and diastolic pressures with pulse rate are shown on the LCD and stored in the device memory. For UA-1030T, the results is also annoucment in voice. The cuff is also deflated automatically at the same time. There are two memory banks to separate the measurements in the morning time and in the evening time. UA-1020 and UA-1030T can conduct average function for 3 consecutive measurmenets automatically.

Here's an analysis of the provided text regarding the A&D Medical UA-1000 Family Digital Blood Pressure Monitors, structured to answer your questions:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

The provided text focuses on demonstrating substantial equivalence to predicate devices rather than detailing specific acceptance criteria and their direct fulfillment through a standalone study. The key "acceptance criteria" inferred here are the Accuracy specifications, which are stated as unchanged from the predicate devices.

Note: The document explicitly states "No change - the same" for these critical performance parameters, indicating that the UA-1000 family is expected to perform identically to the previously cleared predicate devices for which these criteria were established and met. No new performance data for these specific criteria are provided in this summary.

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

The provided 510(k) summary does not contain information about the sample size used for a test set or the data provenance (e.g., country of origin, retrospective/prospective) for proving device performance. The submission relies on establishing substantial equivalence to previously cleared devices rather than providing new clinical or performance study data for the UA-1000 family.

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

The 510(k) summary does not mention the use of experts or the establishment of ground truth for a test set. As noted above, the submission focuses on substantial equivalence. For blood pressure monitors, ground truth is typically established using a reference method (e.g., auscultatory readings by trained observers) rather than expert consensus on images or data.

4. Adjudication Method for the Test Set

The 510(k) summary does not describe any adjudication method for a test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is typically used for diagnostic imaging devices where human readers interpret medical images with and without AI assistance to assess the AI's impact on diagnostic accuracy. This is not applicable to a blood pressure monitor.

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

The concept of "standalone performance" as typically applied to AI algorithms (without human-in-the-loop) does not directly apply in this context for a blood pressure monitor. The device's primary function is to measure blood pressure using an oscillometric method. Its "standalone performance" is inherently its ability to accurately measure blood pressure on its own. The accuracy specifications (BP: +/- 3mmHg or +/- 2%; Pulse: +/- 5%) are the standalone performance metrics. The document states that these are "No change - the same" as the predicate devices, implying prior validation of these metrics.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

For blood pressure monitors, the ground truth for accuracy studies is typically established by simultaneous measurements using a recognized reference standard, such as:

• Auscultation by trained observers: This involves two or more trained observers simultaneously taking blood pressure readings using a mercury sphygmomanometer (or an equivalent validated device) while being blinded to each other's readings and the test device's readings.
• Intra-arterial measurements: In highly controlled research settings, direct intra-arterial measurements can serve as the gold standard.

The provided 510(k) summary does not detail how the ground truth was established for the original predicate devices or for any implied re-validation of the UA-1000 family. It simply states that the "Accuracy" is "No change - the same" as the predicates, meaning the original ground truth methods used for the predicates are presumed to apply.

8. The Sample Size for the Training Set

The 510(k) summary does not provide any information regarding a training set sample size. This type of information would be relevant for devices employing machine learning or AI algorithms that require training data. While the device uses an oscillometric algorithm, it is a well-established method not typically referred to as an "AI algorithm" in this context that undergoes iterative training with a distinct "training set" in the modern sense of deep learning.

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

As there is no mention of a training set in the context of an AI/ML algorithm requiring separate training and testing phases, this question is not applicable based on the provided document. The oscillometric method is a deterministic algorithm rather than a learned one in the contemporary AI sense.

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The Active Signal A SCOPE (or noise-immune stethoscope) is intended for medical diagnostic purposes only. It may be used for the detection and amplification of acoustic signals generated by physiologic activity in the body. In the presence of relatively mild ambient noise it is used in Acoustic Mode and functions as a passive electronic stethoscope to receive sounds produced by the heart, lungs, bowel and other internal organs. To retain audibility at higher sound levels it is switched to Doppler Mode where an audible tone is produced by the ultrasound frequency-shift caused by motion of the heart and lungs.

It can be used on any person undergoing a physical assessment. It is not intended to be used for diagnosis and treatment by unlicensed, untrained, or unqualified medical persons.

Active Signal's stethoscope combats noise intrusion through use of two modes of operation depending on the intensity of background noise:

1. In the presence of relatively benign ambient noise (loud accident scenes, ambulances, emergency rooms, civilian Medevac helicopters, etc.) the device is configured as an amplified electronic stethoscope employing a passive piezoelectric sensor. Noise rejection is imparted by design of the piezoelectric element and mass of the housing.

2. When the ambient sound levels exceed the passive sensing limit, an active Doppler mode is engaged. This transposes the detection of vital physiological sounds from the audio frequency range (used by conventional or electronic stethoscopes) where physiological sounds typically overlap the background noise and hence are swamped out, to ultrasound which puts the measurement into an entirely different frequency band.

DEVICE CONFIGURATION: The top section of the device is the battery compartment, which contains two 1.5V AA-cells.

The device is held between the index and middle fingers, with the thumb free to operate a 4-button control panel shown at here. The bottom section contains the stethoscope sensors and signal-processing electronics. For operation as a passive amplified electronic stethoscope (Mode 1, above), a tall column of piezoelectric ceramic material is used as the sensing element contacting the center of the front face. At the top, this column is pressed against the stethoscope's casing. For the active ultrasound-Doppler mode of operation (Mode 2, above), two semicircle-shaped disks, made of piezoelectric material, are embedded in the sensor head, where one functions as a transmitting and the other as a receiving transducer. Details of the mounting geometry, the gap size between the discs and the gap orientation, and also the carrier frequency, determine the width of the sound beam and its penetration depth.

A thumb-operated 4-button control panel allows the device to be turned on (press any mode button), the signal volume to be set (+ and - buttons in the horizontal plane), and the operating mode to be selected.

Here's an analysis of the provided text regarding the A SCOPE™ electronic stethoscope, focusing on acceptance criteria and the study details:

1. Table of Acceptance Criteria and Reported Device Performance:

The document primarily focuses on establishing "substantial equivalence" to predicate devices rather than defining specific quantitative acceptance criteria or performance metrics for the A SCOPE™ itself. The acceptance criteria described are largely related to meeting regulatory standards and demonstrating
that the device performs "as intended" and is comparable to existing devices for its stated use.

Summary of Device Performance (as reported and implied):

The document broadly states that the A SCOPE™ successfully completed all required testing with "positive end points" and "performs as intended." It explicitly claims the ability to detect heart and lung sounds in high-noise environments using Doppler technology. However, it does not provide specific quantifiable performance metrics such as sensitivity, specificity, accuracy, signal-to-noise ratio measurements, or comparative audibility studies. The performance is primarily asserted through its adherence to standards and substantial equivalence to predicate devices, focusing on its operational modes and intended use.

Detailed Study Information:

The provided text describes a submission for a 510(k) premarket notification, which typically focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than conducting a full-scale clinical efficacy trial with specific, granular performance metrics. As such, the information you're requesting for a "study" in the traditional sense (e.g., test set, training set, ground truth) is largely not present in the provided document.

Here's what can be extracted, and what is missing:

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

• Sample Size for Test Set: Not specified. The document mentions "bench testing" and "U.S. Army qualification testing" but does not detail the number of subjects, cases, or recordings used for evaluation.
• Data Provenance (country of origin, retrospective/prospective): Not specified. While "U.S. Army qualification testing" is mentioned, details about the origin, nature, or type of data collected during this testing are absent.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.
  The document does not detail any expert-driven ground truth establishment for a specific test set.

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

• Adjudication Method: Not specified.

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: Not indicated. The product is an electronic stethoscope that assists in detecting sounds, not an AI-driven image analysis tool for human readers. No MRMC study or AI assistance effect size is mentioned.

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

• Standalone Performance: Not explicitly detailed. The device is an electronic stethoscope intended for human use ("human-in-the-loop"). The "testing" mentioned is for the device's functional performance, but not in the context of a standalone AI algorithm's performance.

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

• Type of Ground Truth: Not specified for performance validation. The document implies that the device's ability to detect physiological sounds (heart, lungs, bowel) is the ground truth for its function, but it doesn't detail how this was independently verified or quantified during testing. The primary ground truth for its regulatory acceptance is its substantial equivalence to predicate devices.

8. The sample size for the training set:

• Sample Size for Training Set: Not applicable/specified. This device predates widespread AI/ML applications in medical devices requiring distinct training sets as described. The device is a hardware/firmware product, not a trainable AI model.

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

• Ground Truth for Training Set: Not applicable/specified. See point 8.

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The A&D Medical UM-101 digital blood pressure monitor is intended for use by medical professionals for measuring the systolic and diastolic blood pressure and pulse rate.

Indications For Use:
Measure blood pressure (systolic and diastolic) and pulse rate.

The A&D Medical UM-101 digital blood pressure monitor is intended for use by medical professionals for measuring the systolic and diastolic blood pressure and pulse rate. UM-101 uses an inflated cuff which is wrapped around the upper arm. The cuff is inflated manually by the medical professionals. The systolic and diastolic blood pressures are determined by auscultatory method. The deflation rate is regulated by a mechanical valve controlled by the medical professionals. There is a quick release valve so that the pressure of the cuff can be completely released at any time during the measurement. During the deflation, the systolic and diastolic can be marked by the medical professionals through a "MARK" button. UM-101 has an internal counter so it remembers the number of measurements. The information can be retrieved by holding the "MARK" button.

The provided text is a 510(k) summary for the A&D Medical UM-101 Digital Blood Pressure Monitor. It outlines the device's technical characteristics and the standards it meets. However, it does not contain the detailed study information regarding acceptance criteria, reported device performance, sample sizes, expert ground truth establishment, adjudication methods, MRMC studies, or standalone performance that you requested.

The document primarily focuses on demonstrating substantial equivalence to a predicate device by listing the standards the device meets. These standards effectively serve as the acceptance criteria for this particular device submission rather than specific performance metrics from a clinical study.

Here's a breakdown of the information that can be extracted or inferred, and a clear statement about what is missing:

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

Missing Information: The document states the device meets these standards, but it does not provide the specific performance metrics (e.g., mean difference, standard deviation of differences for blood pressure readings) that would typically be reported from a clinical validation study against a reference standard to demonstrate compliance with these standards. For devices like blood pressure monitors, ANSI/AAMI SP-10 would have specific accuracy requirements that the device would need to meet.

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

Missing Information: The document does not provide any information about a specific clinical test set, sample size, or data provenance. The assessment appears to be based on compliance with general and specific medical device standards rather than a detailed clinical validation study described in the summary.

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)

Missing Information: This information is not provided as no clinical test set or ground truth establishment process is described beyond adherence to general standards. For a blood pressure monitor, the "ground truth" during testing typically involves comparison against measurements taken by trained observers using a reference method (e.g., auscultation with a manometer) rather than expert consensus on images.

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

Missing Information: This information is not provided as no clinical test set or adjudication process is 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

Not Applicable/Missing Information: This is a blood pressure monitor, not an AI-assisted diagnostic imaging device for human readers. Therefore, an MRMC study is not relevant in this context, and no such study is mentioned.

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

Applicable (in a different sense): The device itself is a "standalone" blood pressure monitor that provides a reading. It's not an algorithm in the sense of AI performing a diagnostic task. The performance would be its inherent accuracy in measuring blood pressure. However, the document does not provide standalone accuracy metrics from a clinical study against a reference standard. It only states compliance with standards like ANSI/AAMI SP-10, which imply acceptable standalone performance.

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

Missing Information (beyond inference): The document does not explicitly state the type of ground truth used for compliance with the listed standards. For blood pressure monitors, the "ground truth" in standard validation protocols (like AAMI SP-10) typically involves simultaneous auscultatory measurements by trained observers or other reference methods.

8. The sample size for the training set

Not Applicable/Missing Information: This device is a traditional blood pressure monitor, not an AI/ML-based algorithm that requires a "training set." Therefore, this concept is not relevant, and no such information is provided.

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

Not Applicable/Missing Information: As mentioned above, this is not an AI/ML device, so a training set and its associated ground truth establishment are not applicable.

In summary: The provided 510(k) summary focuses on demonstrating compliance with recognized standards (ANSI/AAMI SP-10, European Directives, EN standards for safety and EMC) to establish substantial equivalence. It does not provide the specific detailed clinical validation study results (like sample sizes, expert qualifications, exact performance metrics) that would typically be contained within a more comprehensive clinical data section of a 510(k) submission, even though such studies are generally performed to meet these standards.

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The A&I Pediatric Wheelchairs are intended to provide mobility to persons, primarily children, who may be limited to a sitting position.
The A&I Bariatric Wheelchairs are intended to provide mobility to persons, primarily larger adults, who may be limited to a sitting position.

Not Found

I am sorry, but the provided text is a 510(k) premarket notification letter from the FDA to a medical device manufacturer for wheelchairs. It confirms the substantial equivalence of the wheelchairs to previously marketed devices and outlines regulatory requirements.

This document does not contain information about the acceptance criteria or a study proving a device meets acceptance criteria. It is a regulatory approval letter, not a scientific study report. Therefore, I cannot extract the requested information.

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The UA-767BT is designed to be used by end users who are eighteen (18) years and older at home to monitor their blood pressure (systolic and diastolic) and pulse rate. At the end of each measurement, the results will be stored in the UA-767BT memory. UA-767BT through its Bluetooth wireless communication port can also transfer the measurements stored in memory to other electronic devices, such as an Access Point, PC, a modem, or a printer. The end user should not have common arrhythmias, such as atrial or ventricular premature beats or atrial fibrillation. UA-767BT uses the oscillometric method to conduct the measurement. It is not designed for ambulatory use. The arm circumference range shall be between 5.1 inches (13.0 cm) to 17.7 inches (45.0 cm).

The A&D Medical UA-767BT digital blood pressure monitor is intended for use by adults for measuring the systolic and diastolic blood pressure and pulse rate. UA-767BT uses an inflated cuff which is wrapped around the upper arm. The cuff is inflated by an electrical air pump. The systolic and diastolic blood pressures are determined by oscillometric method. The deflation rate is controlled by a preset mechanical valve at a constant rate. At any moment of measurement, the user can deflate the cuff by pressing the blue "START" button. The measurement results are displayed on the LCD and transmitted to a Bluetooth enabled devices, such as a PC, a PDA, a printer, or and access point.

Here's a breakdown of the acceptance criteria and study information for the A&D Medical UA-767BT Digital Blood Pressure Monitor, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document explicitly states, "UA-767BT is not clinically tested." Instead, it relies on demonstrating conformance to the NIST/AAMI SP-10 standard and FDA guidance "Non-invasive Blood Pressure (NIBP) Monitor Guidance." The performance reported is "Conformed" for each section, indicating the device met the requirements outlined in the standard for non-invasive blood pressure monitors.

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

• Sample Size for Test Set: Not explicitly stated in the provided document. The document refers to conformance to AAMI SP-10 tests, which typically involve a specified number of subjects and measurements, but these details are not provided here.
• Data Provenance: Not explicitly stated. The tests are described as conforming to the AAMI SP-10 standard, which implies a standardized testing methodology. However, details about the location or specifics of the test data (e.g., country of origin, retrospective/prospective) are not included.

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

• Number of Experts: Not explicitly stated. For the "Overall system efficacy" (4.4.2) and "Auscultatory method as the reference standard" (4.4.2.1) criteria, the AAMI SP-10 standard typically requires measurements to be taken by trained observers (experts) using a reference method. However, the exact number and qualifications of these observers are not detailed in this submission.
• Qualifications of Experts: Not explicitly stated. For the auscultatory method, the experts would typically be trained clinicians (e.g., physicians, nurses) proficient in taking manual blood pressure measurements.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. In AAMI SP-10 testing, when multiple observers are used for the auscultatory reference, there are usually methods for reconciling differences in their readings (e.g., averaging, discarding outliers, or requiring consensus). However, this document does not describe the specific adjudication method used.

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: No, an MRMC comparative effectiveness study was not conducted. This device is a standalone blood pressure monitor and does not involve AI assistance for human readers or interpretation of medical images/data in a way that would necessitate such a study. The product is a direct measurement device rather than an interpretive aid.

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

• Standalone Performance: Yes, the device's performance was evaluated in a standalone manner. The entire testing listed in the table under "DEVICE TESTING" refers to the performance of the UA-767BT device itself, measuring its accuracy and conformance to the AAMI SP-10 standard without human intervention in the measurement process (other than operating the device as intended for a non-invasive blood pressure monitor). The key statements are: "UA-767BT is not clinically tested" and "It uses the identical software codes and pressure detection related hardware as the predicate devices to determine systolic, diastolic, and pulse rate." This indicates technical performance validation against a standard, not a human-in-the-loop study.

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

• Type of Ground Truth: The ground truth for the device's accuracy (specifically for blood pressure measurements) was established through the auscultatory method as the reference standard (SP-10 Section 4.4.2.1). This method involves trained human observers using a stethoscope and sphygmomanometer to manually determine systolic and diastolic blood pressure, which is considered the gold standard for non-invasive blood pressure measurement against which automated devices are compared.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This device does not use machine learning or AI that would require a "training set" in the conventional sense. Its function is based on oscillometric principles and pre-programmed algorithms, not a learning model developed from a data set. The document states it uses "identical software codes and pressure detection related hardware as the predicate devices."

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

• Ground Truth for Training Set: Not applicable, as there is no mention of a training set for machine learning. The device's underlying algorithms are based on established oscillometric principles and inherited from a predicate device, which would have undergone its own validation based on the auscultatory method as ground truth.

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