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Fully Automatic Electronic Blood Pressure Monitor is for use by medical professionals or at home and is a non-invasive blood pressure measurement system intended to measure the diastolic and systolic blood pressures and pulse rate of an adult individual by using a non-invasive technique in which an inflatable cuff is wrapped around the upper arm. The cuff circumference is limited to 15cm-48cm (approx. 5.9"-18.9").

Fully Automatic Electronic Blood Pressure Monitor (BP-300C, BP-300CV, BP-300V, BPM1, BPX1, KD-338N, KD-553, KD-557BR, KD-558, KD-558BR, KD-595, KD-5031N, KD-5810, KD-5810B, KD-5811, KD-5811A, KD-5811V, KD-5815, KD-5920, KD-5920L, KD-5920TL, KD-5923, KN-550LT) is designed and manufactured according to IEC 80601-2-30.

The operational principle is based on Oscillo-metric and silicon integrates pressure sensor technology. It can calculate the systolic and diastolic blood pressure and display the result. The measurements results can also be classified by the function of blood pressure classification indicator. If any irregular heartbeat is detected, it can be shown to the user.

The provided document is a 510(k) clearance letter for various blood pressure monitors. It outlines the regulatory approval process and compares the new devices to a predicate device. However, it does not contain the detailed acceptance criteria and study results in the format typically used for AI/software devices.

Specifically, this document describes validation against standards for medical electrical equipment (IEC 60601-1, IEC 60601-1-2, IEC 60601-1-11) and automated non-invasive sphygmomanometers (IEC 80601-2-30, ISO 81060-2). It focuses on the substantial equivalence of the physical blood pressure monitors and their underlying oscillometric and pressure sensor technology, rather than the performance of an AI algorithm based on a test set, ground truth, and expert interpretations.

Therefore, many of the specific questions about AI/software device validation (e.g., sample size for the test set, data provenance, number of experts for ground truth, MRMC studies, standalone performance, training set details) cannot be answered from this document.

However, I can extract information related to the performance of the blood pressure monitors themselves, based on the included standards.

Acceptance Criteria and Device Performance (for Blood Pressure Monitor functionality, not AI):

Since this is a blood pressure monitor, the primary performance criteria relate to its accuracy in measuring blood pressure and pulse rate, and compliance with relevant safety and performance standards for automated non-invasive sphygmomanometers.

• IEC 60601-1:2005+AMD1: 2012+AMD2: 2020
• IEC 60601-1-2:2014+AMD1: 2020
• IEC 60601-1-11: 2015+AMD1: 2020
• IEC 80601-2-30: 2018 | All listed standards were met, demonstrating basic safety, essential performance, EMC, and home healthcare environment compliance. |

Unable to Answer from Document (Common for AI/Software Device Submissions, but not for this type of device):

The following questions are not applicable or cannot be answered from this 510(k) summary because the device described is a physical blood pressure monitor, not an AI/software device that interprets medical images or other complex data requiring expert adjudication, training sets, or MRMC studies.

• Sample size used for the test set and the data provenance:
  • Test Set Size: "A total of 231 patients (107 males and 124 females) were enrolled in the study." This is the clinical study population for blood pressure measurement accuracy.
  • Data Provenance: Not explicitly stated (e.g., country of origin). The study is described as a "clinical study," which implies prospective data collection for the purpose of the study.
• 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. Ground truth for blood pressure measurement is established through a standard auscultation method (manual measurement by medical professionals using a stethoscope and sphygmomanometer), not by interpretation of images by experts.
• Adjudication method (e.g. 2+1, 3+1, none) for the test set:
  • Not applicable. Ground truth is direct measurement by a reference method.
• 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-assisted diagnostic device.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
  • The device is a standalone blood pressure monitor. No human-in-the-loop interaction for interpretation (as in AI devices) is relevant. Its performance is its direct measurement accuracy.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
  • Ground Truth: "Standard auscultation method was used as the reference blood pressure monitor measuring." This is the established clinical standard for direct comparison.
• The sample size for the training set:
  • Not applicable. This is not an AI/machine learning device requiring a training set.
• How the ground truth for the training set was established:
  • Not applicable.

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The iHealth® Early Pregnancy Test is an OTC in vitro diagnostic test used to assess early pregnancy by detecting the presence of the hCG (human chorionic gonadotropin) hormone in urine. The test device is intended for use as an aid in early detection of pregnancy, in some cases as early as six (6) days before the missed period, i.e., as early as five (5) days before the day of the expected period.

The iHealth® Early Pregnancy Test Strip is an OTC in vitro diagnostic test used to assess early pregnancy by detecting the presence of the hCG (human chorionic gonadotropin) hormone in urine. The test device is intended for use as an aid in early detection of pregnancy, in some cases as early as six (6) days before the missed period, i.e., as early as five (5) days before the day of the expected period.

The iHealth® Early Pregnancy Test is a lateral flow immunoassay technique device designed for the qualitative determination of human chorionic gonadotropin (hCG) concentration in human urine samples, and is indicated for use as an aid in early detection of pregnancy. The iHealth® Early Pregnancy Test is designed to detect as early as six (6) days before the day of the missed period, i.e., as early as five (5) days before the day of the expected period. Test result is displayed to the user as two lines for a 'Pregnant' result and one line for a 'Not Pregnant' result.

The iHealth® Early Pregnancy Test are in two different formats: Stick and Strip format. Two test formats use identical strips and each test strip in the device consists of: Sample pad, colloidal gold binding pad, nitrocellulose membrane, absorbent paper and PVC board, colloidal gold binding pad coated with hCG monoclonal antibody 1, test line(T Line) coated with hCG-monoclonal antibody 2 and control line(C Line) coated with goat anti-mouse IgG antibody.

Two formats have the same performance specifications. The difference is that Stick format is designed to be tested in dip or midstream method, and Strip format is tested in dip method only.

The provided document describes the iHealth® Early Pregnancy Test and iHealth® Early Pregnancy Test Strip, which are over-the-counter in vitro diagnostic devices used to detect human chorionic gonadotropin (hCG) in urine for early pregnancy detection.

Here's a breakdown of the acceptance criteria and the studies performed:

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

The document doesn't explicitly state "acceptance criteria" in a separate table. However, based on the performance characteristics presented, the key acceptance criteria for this type of device would typically revolve around its analytical sensitivity (detection limit) and its ability to accurately classify pregnant and non-pregnant states. The sensitivity of hCG detection is a critical performance metric for early pregnancy tests.

Table 1: Key Performance Metrics for iHealth® Early Pregnancy Tests

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

• Analytical Sensitivity (Precision):

  • Test Set Sample Size: 405 individual tests for each hCG concentration (0, 3, 5, 7.5, 10, 12.5, 15, 25 mIU/mL) for each device format (Strip, Stick-dip, Stick-midstream), totaling 8 concentrations x 405 tests = 3240 tests per device type. The total number of individual readings is significantly higher due to multiple lots and operators. For the 5 mIU/mL the total samples are 170 negative and 235 positive in the Strip format.
  • Data Provenance: Not explicitly stated, but the study was performed "at three sites" by "nine operators," suggesting a multi-site internal study. The samples were "Not-pregnant female urine was spiked with hCG traceable to the 5th WHO international standard".

• Specificity Study:

  • Test Set Sample Size: 300 urine samples (100 from premenopausal, 100 from peri-menopausal, 100 from postmenopausal women).
  • Data Provenance: Not explicitly stated, but implies collected urine samples.

• Hook Effect Test:

  • Test Set Sample Size: Not explicitly quantified, but performed on urine samples spiked with hCG at 0, 10, 1,000, 10,000, 100,000, and 1,000,000 mIU/mL.

• Cross-Reactivity & Interfering Substances:

  • Test Set Sample Size: Not explicitly quantified. Performed on negative and 10 mIU/mL hCG positive urine samples spiked with various substances.

• Method Comparison Study:

  • Test Set Sample Size: 204 pregnant and non-pregnant subjects. 109 positive samples and 95 negative samples in total across both formats. iHealth® Early Pregnancy Test Strip: 103 samples (54 positive, 49 negative). iHealth® Early Pregnancy Test Stick: 101 samples (55 positive, 46 negative).
  • Data Provenance: Not explicitly stated, but implies collected samples from subjects.

• Lay User Study (Conformity with Professional):

  • Test Set Sample Size: 204 pregnant and not pregnant women subjects. iHealth® Early Pregnancy Test Strip: 103 subjects (54 positive, 49 negative). iHealth® Early Pregnancy Test Stick: 101 subjects (55 positive, 46 negative).
  • Data Provenance: Women subjects with diverse educational and professional backgrounds and ages between 18 and 55 years old who tested their own urine samples.

• Lay User Spiked Standard Study:

  • Test Set Sample Size: 100 women.
  • Data Provenance: Women with diverse educational and professional backgrounds (18-55 years old) who performed testing using spiked samples.

• Detection of hCG in Early Pregnancy Clinical Samples:

  • Test Set Sample Size: 83 subjects for each day relative to expected missed period (-8 to +1).
  • Data Provenance: Early pregnancy urine samples collected from subjects.

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

• Analytical Sensitivity (Precision): The results were generated by "nine operators." Their specific qualifications are not detailed, but their involvement across three sites suggests trained personnel in a laboratory or clinical setting.
• Method Comparison and Lay User Studies: The "professional testing" serves as the reference standard against which the lay user and candidate devices were compared. The number and qualifications of these "professionals" are not specified in the document.
• Detection of hCG in Early Pregnancy Clinical Samples: The "early pregnancy urine samples" themselves imply a clinical diagnosis of pregnancy, which would typically be established by medical professionals. However, the number and qualifications of these professionals are not explicitly stated.

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

The document does not describe an explicit adjudication method for reconciling conflicting results, such as 2+1 or 3+1. For the analytical studies, the results are presented as counts of positive/negative outcomes, implying a direct outcome rather than expert adjudication. For the comparison studies, results are compared to a "professional" result, which acts as a gold standard, so an adjudication process for discrepancies would implicitly fall under how the "professional" result was determined.

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 is not applicable to the device described. The iHealth® Early Pregnancy Test is an in vitro diagnostic (IVD) device that provides a visual result (lines on a stick/strip) for human interpretation. It is not an AI-assisted diagnostic tool for human readers interpreting complex medical images or data. Therefore, an MRMC study assessing AI assistance is not relevant here.

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

This is not applicable. The device provides a visual output that is then interpreted by the user (human-in-the-loop). Even the "professional testing" involves human interpretation of the device's output. The device itself is not a standalone algorithm that provides a diagnosis without human interaction/interpretation of the visual signal.

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

• Analytical Sensitivity (Precision): The ground truth was established by spiking not-pregnant female urine with known concentrations of hCG traceable to the 5th WHO international standard. This is a scientific, controlled ground truth.
• Specificity: The ground truth was based on urine samples from normal, non-pregnant women. Clinical status (non-pregnant) served as the ground truth.
• Method Comparison and Lay User Studies: The ground truth for these studies was the result obtained by professional testing using the candidate device or the predicate device. This implies a reference method result, likely also interpreted by professionals.
• Detection of hCG in Early Pregnancy Clinical Samples: The ground truth for "early pregnancy urine samples" implies a clinical diagnosis of pregnancy based on medical assessment, which is outcomes data (known pregnancy status) rather than just a subjective expert consensus on the device's result.

8. The sample size for the training set:

The document describes performance studies (analytical and clinical comparisons) but does not mention a training set sample size. This is expected as the iHealth® Early Pregnancy Test is a lateral flow immunoassay, which is a chemical/biological test, not a machine learning model that requires a "training set."

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

Since there is no "training set" in the context of a machine learning model for this device, this question is not applicable. The device's performance is intrinsically linked to the chemical properties of the antibodies and reagents used, validated through the performance studies described.

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Fully Automatic Electronic Blood Pressure Monitor is for use by medical professionals or at home and is a non-invasive blood pressure measurement system intended to measure the diastolic and systolic blood pressures and pulse rate of an adult individual by using a non-invasive technique in which an inflatable cuff is wrapped around the upper arm. The cuff circumference is limited to 17cm-48cm (approx, 6 11/16"- 18 29/32").

Fully Automatic Electronic Blood Pressure Monitor is designed and manufactured according to IEC 80601-2-30. The operational principle is based on oscillometric and silicon integrates pressure sensor technology. It can calculate the systolic and diastolic blood pressure, the measurements results can also be classified. If any irregular heartbeat is detected, it can be shown to the user.

The provided FDA 510(k) summary for the "Fully Automatic Electronic Blood Pressure Monitor" pertains to a Class II medical device, specifically a non-invasive blood pressure measurement system. The acceptance criteria and the study proving the device meets these criteria are outlined, primarily focusing on the device's accuracy in measuring blood pressure and pulse rate.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for blood pressure monitors are typically defined by international standards like ISO 81060-2. While the exact numerical criteria for "Criteria 1" and "Criteria 2" are not explicitly stated in this document (e.g., mean difference and standard deviation), the document states that the device "met criteria 1 and criteria 2 of ISO 81060-2."

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

• Sample Size: 231 patients (107 males and 124 females) were invited for the study.
• Data Provenance: The document does not specify the country of origin for the data. The study appears to be prospective, as patients were "invited for the study" and a "standard auscultation method was used as the reference blood pressure monitor measuring," indicating a controlled clinical trial setting.

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

• Number of Experts: Not specified.
• Qualifications of Experts: The ground truth was established using the "standard auscultation method." This method typically involves trained medical professionals (e.g., doctors, nurses) using a stethoscope and sphygmomanometer. The qualifications of the individuals performing the auscultation are not detailed in this document.

4. Adjudication Method for the Test Set

• The document states that the "standard auscultation method was used as the reference blood pressure monitor measuring, and same sequential method was chosen." This suggests that the reference measurements were obtained sequentially. There is no mention of a multi-reader/adjudication method for resolving discrepancies in ground truth establishment, as the auscultation method itself is the direct reference measurement.

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 document describes the validation of an automated blood pressure monitor, not an AI-assisted diagnostic tool where human readers interact with AI. Therefore, an MRMC study and effect size of human reader improvement with AI assistance are not relevant to this device's validation.

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

• Yes. The study evaluates the "Fully Automatic Electronic Blood Pressure Monitor" directly against a "standard auscultation method." This is an assessment of the device's standalone performance, as it operates automatically to provide blood pressure readings without a human actively interpreting or modifying its output during the measurement process.

7. The Type of Ground Truth Used

• Expert Consensus/Reference Standard: The ground truth was established using the "standard auscultation method." This is considered the clinical gold standard reference for non-invasive blood pressure measurement.

8. The Sample Size for the Training Set

• Not applicable / Not specified. This document describes a validation study for a medical device's performance, not the development or training of an AI algorithm. Blood pressure monitors with oscillometric principles are based on established engineering principles and typically do not involve a "training set" in the context of machine learning model development. The document refers to "non-clinical tests" and a "clinical test," which relate to verification and validation of the device's design and performance against standards.

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

• Not applicable. As noted above, this section is not relevant to the type of device and validation described.

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Fully Automatic Electronic Blood Pressure Monitor is for use by medical professionals or at home and is a non-invasive blood pressure measurement system intended to measure the diastolic and systolic blood pressures and pulse rate of an adult individual by using a non-invasive technique in which an inflatable cuff is wrapped around the upper arm. The cuff circumference is limited to 22cm-48cm.

Fully Automatic Electronic Blood Pressure Monitor ( KD-5811A, KD-5811V and KD-5815) is designed and manufactured according to IEC 80601-2-30.

The operational principle is based on oscillometric and silicon integrates pressure sensor technology. It can calculate the systolic and diastolic blood pressure, and display the result on the LCD. The measurements results can also be classified by the function of blood pressure classification indicator. If any irregular heartbeat is detected, it can be shown to the user.

Here's a breakdown of the acceptance criteria and the study information for the "Fully Automatic Electronic Blood Pressure Monitor," based on the provided FDA 510(k) summary:

Acceptance Criteria and Reported Device Performance

Note: The provided document states that the new device models (KD-5811A, KD-5811V, and KD-5815) have the same pressure and pulse rate accuracy as the predicate device (KD-5811, K210770). The predicate device's performance aligned with these criteria. The specific methods used to measure and verify these performances against the acceptance criteria are detailed in the study section.

Study Information

The document refers to a "clinical test report" for a previously cleared device (KD-5961, K083246) as a reference, stating that the subject devices (KD-5811A, KD-5811V, KD-5815) share the same algorithm and design principle. The acceptance criteria for accuracy were verified by meeting ISO 81060-2.

Here's the breakdown of the study details based on the provided text, recognizing that details for KD-5961 are referenced rather than presented explicitly for the current submission:

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

• Test Set Sample Size: Not explicitly stated for the referenced clinical test report of KD-5961 or for the current submission. The document only mentions that accuracy was verified by meeting ISO 81060-2 criteria.
• Data Provenance: Not specified in the provided document (e.g., country of origin, retrospective or prospective).

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

• Not specified. The ISO 81060-2 standard typically involves trained observers for reference measurements, but the exact number and qualifications are not detailed in this document.

4. Adjudication Method for the Test Set

• Not specified. ISO 81060-2 outlines procedures for reference measurements and statistical analysis for accuracy, but specific adjudication methods like 2+1 or 3+1 are not mentioned.

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

• No, an MRMC comparative effectiveness study is not mentioned or implied. The device is a "Fully Automatic Electronic Blood Pressure Monitor," which typically implies standalone operation without human-in-the-loop assistance in the measurement itself. The comparison is between automated devices and a reference standard (likely auscultation) as per ISO 81060-2.

6. Standalone Performance Study

• Yes, a standalone performance study was done for the reference device KD-5961, as implied by the statement: "Accuracy of the blood pressure monitors for the clinical test report was verified by meeting criteria 1 and criteria 2 of ISO 81060-2." This standard specifically assesses the performance of automated non-invasive sphygmomanometers without human assistance.

7. Type of Ground Truth Used

• The ground truth used would be the reference measurements obtained through a validated method, as prescribed by ISO 81060-2. For blood pressure monitors, this typically involves simultaneously comparing the device's readings to those obtained by skilled observers using a standardized auscultatory method (e.g., mercury sphygmomanometer) on the same subjects.

8. Sample Size for the Training Set

• Not applicable. This device is an electronic blood pressure monitor based on oscillometric technology, not an AI/ML algorithm that requires a "training set" in the conventional sense of machine learning. The "algorithm" mentioned (amplitude) refers to the computational method used to derive blood pressure values from oscillometric pulses.

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

• Not applicable, as there is no training set in the AI/ML context for this type of device. The device's "algorithm" (amplitude) is based on established oscillometric principles and likely calibrated during manufacturing based on engineering specifications and potentially clinical validation data.

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The AD-2126 Transcutaneous Electrical Nerve Stimulators TENS device is intended for temporary relief of pain associated with sore and aching muscles due to strain from exercise or normal household and work activities, It is also intended for symptomatic relief and management of chronic, intractable pain associated with arthritis.

The AD-2126 Transcutaneous Electrical Nerve Stimulators (TENS) is transcutaneous electrical nerve stimulator for relief of muscular pain and sold without prescription.

The device consists of a microprocessor, buttons, electrical pads, and display. Keys can control the device to choose the operation modes, adjust pulse output strength, then the channel that effectively transfers your desired choice of programmed electrical pulses directly through electrode adhesive pads to the suggested area of the body where the electrodes are placed, causing minimal muscle stimulation. The LCD display can show user the mode and strength chosen and other information.

Self-adhesive electrodes are used in this device, and they are designed with conductive adhesive interface between the patient's skin and the Electrical Stimulator which help electrical signals transferred from the TENS device to the body and complete its function. The electrodes to be used with the device have been cleared under submission number K130987 with the trade name of ValuTrode® Neurostimulation Electrodes; model number: 50X50.

This document refers to a 510(k) premarket notification for the AD-2126 Transcutaneous Electrical Nerve Stimulators (TENS). There is no acceptance criteria or study information for an AI/ML device in this document. The document describes non-clinical testing performed to establish substantial equivalence to a predicate TENS device, not an AI/ML device.

Therefore, I cannot provide the requested information.

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The Infrared Ear thermometer is intended for the intermittent measurement of body temperature from the ear canal on people of all ages except for babies under 3 months. It is suitable for home use and healthcare facilities use.

The iHealth PT5 Infrared Ear Thermometer is a hand-held, reusable, battery operated device, which can measure human body temperature on one's ear canal. Its operation is based on measuring the natural thermal radiation from the ear canal with a built-in correction algorithm to compensate the influence of ambient temperature using a heated tip. Put the probe of the thermometer into a patient's ear canal, after a self-check, pressing the activation button to start the measurement of proposed infrared radiation. The electrical signal read out from the detector is fed to the circuit for amplification and calculation. The final measured temperature will be appeared on a LED display. It is recommended that adult take the measurement instead when infants and children cannot use the ear thermometer themselves.

The iHealth PT5 Infrared Ear Thermometer measures body temperature from the ear canal. The device's performance was evaluated through non-clinical and clinical tests to demonstrate substantial equivalence to a predicate device.

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state the country of origin for the clinical data or whether it was retrospective or prospective. However, based on the testing being conducted to international standards (ASTM and ISO), it is likely to be a prospective clinical study specifically designed for regulatory submission.

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

This information is not explicitly provided in the document. The document states "Clinical accuracy validation test report included temperature readings," implying that a reference standard temperature measurement (often oral or rectal temperature by trained clinicians) was used as the ground truth, but the number or qualifications of experts involved in establishing this ground truth are not detailed.

4. Adjudication Method for the Test Set:

This information is not explicitly provided in the document.

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

This type of study is not applicable here as the device is an infrared ear thermometer, which measures a physical parameter (temperature) and does not involve human readers interpreting images or data to make a diagnosis that would be assisted by AI. The device operates in a standalone manner.

6. Standalone Performance:

Yes, a standalone performance study was done. The "Clinical Accuracy Validation Test" performed according to ASTM E1965-98 directly evaluated the iHealth PT5 Infrared Ear Thermometer's performance in measuring body temperature in a clinical setting without human interpretation or intervention in the temperature measurement itself. The non-clinical tests also evaluated the device's technical standalone performance.

7. Type of Ground Truth Used:

Based on the nature of a clinical accuracy validation test for thermometers, the ground truth would most likely be established by a reference standard temperature measurement (e.g., core body temperature measurements from oral or rectal thermometers) taken by trained medical personnel. This is implied by the reference to ASTM E1965-98, which outlines methods for clinical performance evaluation of intermittent thermometers.

8. Sample Size for the Training Set:

The document does not mention a training set in the context of this device. Thermometers typically rely on established physical principles and calibration, not machine learning algorithms that require a "training set" in the conventional sense. The "correction algorithm to compensate the influence of ambient temperature using a heated tip" mentioned in the device description refers to a predefined algorithm, not one trained on a dataset.

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

As there is no mention of a training set for a machine learning algorithm, this question is not applicable based on the provided text.

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Fully Automatic Electronic Blood Pressure Monitor is for use by medical professionals or at home and is a non-invasive blood pressure measurement system intended to measure the diastolic and systolic blood pressures and pulse rate of an adult individual by using a non-invasive technique in which an inflatable cuff is wrapped around the wrist. The cuff circumference is limited to 14cm-25cm.

Fully Automatic Electronic Blood Pressure Monitor (KD-743V, KD-743B, KD-752) is designed and manufactured according to IEC 80601-2-30.

The operational principle is based on oscillometric and silicon integrates pressure sensor technology. It can calculate the systolic and diastolic blood pressure, and display the result on the LCD. The measurements results can also be classified by the function of blood pressure classification indicator. If any irregular heartbeat is detected, it can be shown to the user.

This document is a 510(k) summary for a blood pressure monitor, not an AI/ML device. Therefore, the detailed information typically required for describing an AI/ML model's acceptance criteria and validation study (such as training/test set sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth establishment for AI models) is not provided in this document.

The document discusses the substantial equivalence of the "Fully Automatic Electronic Blood Pressure Monitor" (models KD-743V, KD-743B, KD-752) to predicate devices (KD-753, K183535 and KD-721, K163276). The "acceptance criteria" here refer to meeting established performance standards for non-invasive blood pressure monitors, rather than the performance metrics of an AI algorithm.

Here's a breakdown of what can be extracted and what information is not available from the provided text, in the context of your specific questions:

Acceptance Criteria and Reported Device Performance

The acceptance criteria for blood pressure monitors are generally based on international standards like ISO 81060-2, which specifies requirements for the accuracy of automated non-invasive sphygmomanometers. The document states:

"Accuracy of the blood pressure monitors for the clinical test report was verified by meeting criteria 1 and criteria 2 of ISO 81060-2."

While the specific numeric criteria (e.g., mean difference and standard deviation of differences between device and reference measurements) are not explicitly listed in a table within this document, meeting ISO 81060-2 criteria 1 and 2 is the acceptance benchmark.

Detailed Information as Requested for AI/ML Studies:

1. A table of acceptance criteria and the reported device performance: See table above. This is based on NIBP standards, not AI performance metrics.

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

   • The document refers to "clinical test report of KD-7961" and "clinical test report of KD-972" being used as reference for the subject devices. These reports verified accuracy by meeting ISO 81060-2 criteria.
   • ISO 81060-2 typically recommends a minimum of 85 subjects for validation. While not explicitly stated here for the specific test, it's implied that the reference clinical tests adhered to this.
   • Data Provenance: Not specified (e.g., country of origin). The document states the company is based in Tianjin, China.
   • Retrospective or Prospective: Not specified. Clinical validation studies for medical devices are typically prospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable in the context of this device study. Ground truth for blood pressure monitors is established by simultaneously measuring blood pressure using a calibrated reference method (e.g., auscultation by trained observers) and the device under test. It's not a consensus-based reading like for imaging. ISO 81060-2 requires a minimum of three trained observers for reference measurements. Their specific qualifications (e.g., years of experience) are not detailed in this summary, beyond "trained observers."

4. Adjudication method for the test set: Not applicable in the AI/ML sense. For NIBP validation, the "adjudication" is the comparison of device readings against the mean of the reference observers' readings, as per ISO 81060-2. There's no consensus or 2+1/3+1 adjudication of images.

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 a blood pressure monitor, not an AI-assisted diagnostic imaging device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable in the AI/ML sense. The device itself is "standalone" in that it performs the measurement. The "algorithm" here refers to the oscillometric method of blood pressure determination, not a separate AI algorithm that provides diagnostic interpretations.

7. The type of ground truth used: Reference blood pressure measurements obtained by trained observers using a reference method (e.g., auscultation), according to ISO 81060-2.

8. The sample size for the training set: Not applicable. This is a conventional medical device, not an AI/ML device that requires a distinct "training set" for model development. The "algorithm" for blood pressure measurement is based on established oscillometric principles and does not "learn" from a training set in the way an AI model does.

9. How the ground truth for the training set was established: Not applicable, as there's no training set for this type of device.

In summary: The provided document is a regulatory submission for a conventional medical device (blood pressure monitor), not an AI/ML product. Therefore, many of the detailed questions regarding AI/ML model validation are not addressed, as they are not relevant to this type of device. The "acceptance criteria" revolve around established performance standards for blood pressure measurement accuracy (ISO 81060-2).

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Fully Automatic Electronic Blood Pressure Monitor is for use by medical professionals or at home and is a non-invasive blood pressure measurement system intended to measure the diastolic and systolic blood pressures and pulse rate of an adult individual by using a non-invasive technique in which an inflatable cuff is wrapped around the upper arm. The cuff circumference is limited to 22cm-48cm.

Fully Automatic Electronic Blood Pressure Monitor (KD-5810, KD5810B, KD-5811, KD-5920TL and KD-552) is designed and manufactured according to IEC 80601-2-30.

The operational principle is based on oscillometric and silicon integrates pressure sensor technology. It can calculate the systolic and diastolic blood pressure, and display the result on the LCD. The measurements results can also be classified by the function of blood pressure classification indicator. If any irregular heartbeat is detected, it can be shown to the user.

The provided text describes a 510(k) premarket notification for Andon Health Co., Ltd.'s Fully Automatic Electronic Blood Pressure Monitor. The document indicates that the device's accuracy was verified by meeting criteria 1 and 2 of ISO 81060-2. However, the text does not explicitly provide a table of acceptance criteria and reported device performance for the subject device itself. Instead, it refers to prior clinical test reports for predicate devices (KD-5961, BP3, and BP5) as references.

Therefore, not all requested information can be extracted directly for the subject device from the provided text. I will answer based on the information available and will highlight where specific details are not provided for the subject device but are referenced through predicate devices.

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

The provided text states that the accuracy of the blood pressure monitors in the clinical test reports was verified by meeting Criteria 1 and Criteria 2 of ISO 81060-2. However, it does not provide a specific table with numerical acceptance criteria and the reported performance for the subject device models (KD-5810, KD-5810B, KD-5811, KD-5920TL, and KD-552). It refers to the clinical test reports of predicate devices.

Based on general knowledge of ISO 81060-2, the criteria typically involve:

ISO 81060-2 Criteria (General Reference, not explicitly stated in document for subject device):

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

For the subject devices (KD-5810, KD-5810B, KD-5811, KD-5920TL, and KD-552), the text does not explicitly state the sample size used for the test set or the data provenance directly for these devices.
It states:

• "The proposed devices KD-5810, KD-5810B, KD-5811 and KD-5920TL has the same algorithm and design principle with cleared device KD-5961 (K083246), so the clinical test report of KD-5961can be used as a reference when considering the clinical effect of KD-5810, KD-5810B, KD-5811 and KD-5920TL."
• "The proposed devices KD-552 has the same algorithm and design principle with cleared device BP3 (K102939) and BP5 (K120672), so the clinical test report of BP3 (K102939) and BP5 (K120672) can be used as a reference when considering the clinical effect of KD-552."

To determine the sample sizes and data provenance, one would need to access the clinical test reports for K083246 (KD-5961), K102939 (BP3), and K120672 (BP5). The provided document does not contain this information.

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

The text does not provide details on the number or qualifications of experts used to establish the ground truth for the clinical studies mentioned (those for KD-5961, BP3, and BP5). For blood pressure measurement validation studies, ground truth is typically established by trained observers (e.g., physicians or nurses) using a sphygmomanometer following a standardized protocol (such as auscultation with a mercury sphygmomanometer or an equivalent reference device).

4. Adjudication method for the test set

The text does not specify an adjudication method for the test set. In blood pressure validation studies, ground truth is usually established by highly trained technicians or physicians taking multiple reference readings, and often, paired readings are averaged or discrepancies between two observers are reconciled, but these specific methods are not described in this 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

The device described is a Noninvasive Blood Pressure Measurement System. This type of device is not typically evaluated using MRMC studies as it is a standalone measurement device, not an interpretive imaging or diagnostic system where human readers interpret results. Therefore, this section is not applicable. The document does not describe any MRMC studies or human reader improvement with AI assistance.

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

Yes, the clinical validation referenced through the predicate devices (KD-5961, BP3, BP5) would assess the standalone performance of the algorithm in measuring blood pressure against a reference standard. The text states: "Accuracy of the blood pressure monitors for the clinical test report was verified by meeting criteria 1 and criteria 2 of ISO 81060-2." This implies a standalone performance assessment.

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

For blood pressure monitors validated against ISO 81060-2, the "ground truth" (or reference measurement) is typically established by simultaneous or sequential measurements taken by trained observers using a calibrated reference sphygmomanometer (e.g., mercury or an equivalent validated device) using the auscultatory method. This is a direct expert measurement, not pathology or outcomes data.

8. The sample size for the training set

The provided text does not provide any information regarding a "training set" or its sample size. Blood pressure monitors like this typically use established algorithms (oscillometric method) and are validated against clinical standards rather than being "trained" in the machine learning sense with a distinct training set. If the algorithm involved machine learning, this information would be critical, but the description ("operational principle is based on oscillometric and silicon integrates pressure sensor technology") suggests a traditional signal processing approach.

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

As there is no mention of a "training set" in the context of machine learning, the question of how its ground truth was established is not applicable based on the provided document. The ground truth for the validation (test) set would be established as described in point 7.

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The Wireless No-Touch Forehead Thermometer is intended for the intermittent measurement of body temperature from the forehead on people of all ages. It can transmit the temperature to a smart device with Bluetooth and can be used by consumers in the household environment and by healthcare providers.

The iHealth PT3SBT Wireless No-Touch Forehead Thermometer is hand-held, reusable, battery operated device, which can measure human body forehead skin temperature without touching patient's skin. Its operation is based on measuring the natural thermal radiation from the central forehead. Put the probe of the thermometer at a measurement distance less than or equal to 1.18 inch (3cm) without touching the skin at the center of the patient's forehead. It uses a thermopile sensor to measure the patient's forehead temperature and convert it to the oral equivalent. Pressing the activation button to start the measurement of proposed infrared radiation. The electrical signal read out from the detector is fed to the circuit for amplification and calculation. The final measured temperature will be appeared on a LED display of the device. The thermometer can also be connected to a smart device through Bluetooth and display the measured temperature result on the smart device.

The provided text describes the regulatory clearance of a medical device, the "iHealth Wireless No-Touch Forehead Thermometer (model PT3SBT)", by the FDA. It details the device's characteristics, comparison to a predicate device, and the non-clinical and clinical tests performed to support its substantial equivalence.

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

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device can be inferred from the standards it claims to comply with and the performance characteristics stated. The primary acceptance criteria relate to accuracy, safety, and functionality.

2. Sample Size for the Test Set and Data Provenance

• Sample Size: The clinical investigation used a total of 130 subjects. This was broken down into:
  • Infants: 40 subjects (0-1 year)
  • Children: 40 subjects (1-5 years)
  • Adults: 50 subjects (>5 years)
• Data Provenance: The document does not explicitly state the country of origin for the clinical test data. Given the manufacturer's address in Tianjin, China, it is highly probable the study was conducted there. The study appears to be prospective as it's described as a "clinical investigation" and "clinical test report".

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

The document does not provide details on the number or qualifications of experts used to establish the ground truth for human body temperature measurements in the clinical study. It refers to compliance with ASTM E1965-98 (2016), which is a standard specification for infrared thermometers, implying that the ground truth methodology would follow the requirements of this standard for comparison thermometers (e.g., rectal thermometers) and trained personnel for measurement.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1) for establishing the ground truth measurements. The ASTM E1965-98 standard typically involves comparing the test thermometer's readings against a reference (usually invasive, like rectal or oral) temperature obtained by trained personnel, rather than multiple expert adjudicators in the way an AI diagnostic image study might.

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

• Was it done? No, an MRMC study comparing human readers with and without AI assistance was not done. This device is a thermometer, not an AI-powered diagnostic imaging tool. Its primary function is direct measurement, not interpretation by human readers.

• Effect Size of Human Reader Improvement: Not applicable, as no MRMC study was conducted.

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

• The study primarily focuses on the device's direct measurement performance, which is inherently "standalone" in the sense that the thermometer provides a numerical output directly. While it can connect to a smart device via Bluetooth, the core temperature measurement capability is in the device itself, making its clinical accuracy evaluation a standalone performance assessment. The "algorithm" here is the embedded software that converts infrared radiation into a temperature reading.

7. Type of Ground Truth Used

The ground truth used for the clinical accuracy study was established by comparison to a reference method (likely an invasive, highly accurate thermometer like a rectal or oral thermometer) as specified by ASTM E1965-98 (2016). This standard outlines procedures for clinical accuracy assessment for body temperature measurement. The document states "clinical bias and uncertainties met the acceptance criteria of the clinical study protocol" in relation to this standard.

8. Sample Size for the Training Set

The document does not explicitly mention a "training set" sample size. For a medical device like a thermometer, the "training" usually refers to the internal calibration and development data rather than a separate dataset for training a machine learning algorithm in the way an AI diagnostic tool would have. The performance evaluation is based on the validation (test) set described in point 2.

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

As no specific "training set" for AI algorithm training is mentioned (as this is a thermometer, not an AI diagnostic model), the method of establishing ground truth for such a set is not detailed. For the internal development and calibration of the thermometer, standardized temperature sources and laboratory reference thermometers would be used to establish the ground truth for programming the device's measurement algorithm.

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The Clinical Digital Thermometer is intended to measure the body temperature oral or axillaries (under the arm) and to be used by consumers in household environments. It is intended for use on adults and children ages 4 and up.

The iHealth PT1 Clinical Digital Thermometer is hand-held, predictive, thermistor-based, stick thermometer capable of measuring temperature in about 30 seconds. The thermometer uses a negative temperature coefficient thermistor embedded in a measurement tip that is in contact with the measurement site. As the thermistor changes temperature, the resistance of the thermistor also changes, which is measured by the thermometer and converted to a measurement of the temperature of the tip of the thermometer. This temperature, following the use of the predictive algorithm, is then displayed to the end user. Because the thermometer displays the measurement for the physiological site at which it is used, it does not need to convert this temperature via clinical offset.

The provided text is for a Clinical Digital Thermometer (iHealth PT1). Here's a breakdown of the acceptance criteria and the study that proves the device meets those criteria:

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

2. Sample sized used for the test set and the data provenance

• Sample Size: 110 subjects
  • 40 subjects in the children's group (4-5 years old)
  • 70 subjects in the adults' group (> 5 years old)
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective or prospective), but the study is described as a "Clinical investigation," which typically implies a prospective design.

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

The document does not specify the number or qualifications of experts used to establish ground truth for the clinical accuracy validation test. It only mentions that the "Clinical investigation and data analysis have performed according to ISO 80601-2-56." This standard outlines requirements for clinical accuracy, which would involve comparison to a reference thermometer reading.

4. Adjudication method for the test set

The document does not explicitly state the adjudication method (e.g., 2+1, 3+1). The clinical accuracy test was conducted according to ISO 80601-2-56, which specifies protocols for such measurements, likely involving a skilled operator taking readings against a reference.

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 is not applicable as the device is a clinical digital thermometer, not an AI-assisted diagnostic tool involving human readers.

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

This is a standalone device in the sense that it provides a temperature reading directly to the user. The "predictive algorithm" mentioned in the device description is integral to its function of displaying a rapid temperature measurement, so its performance (as part of the device) was evaluated.

7. The type of ground truth used

For the clinical accuracy validation test, the ground truth was established by comparison to a reference temperature measurement, as dictated by the ISO 80601-2-56 standard for clinical thermometers.

8. The sample size for the training set

The document does not explicitly mention a separate "training set" in the context of machine learning model training. The predictive algorithm used by the thermometer would likely have been developed and validated during the device's design phase, prior to this specific submission. The listed clinical accuracy validation test dataset (110 subjects) is for the evaluation of the device's performance, not necessarily for training the predictive algorithm itself.

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

As there's no explicit mention of a training set for an AI model in the context of this submission, the method for establishing its ground truth is not described. The predictive algorithm's development would likely have used a robust set of physiological temperature data paired with reference measurements, conforming to relevant scientific and engineering best practices for medical device software.

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