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The Comarch e-Care Platform is intended to connect with physiological measurement devices (weight scales, blood pressure meters, pulse oximeters, peak flow meters, thermometers, glucometers) intended to use at home and send the measurement results to central server. Comarch e-Care Platform serves as Software as a Medical Device and can be used only with FDA cleared measurement devices.

Comarch e-Care Platform displays the collected measurements on the Web application and securely stores them in a database server, where the caregiver can view them, leave comments and contact patient if necessary. Caregivers are able to set thresholds individually for each patient. Measurement results sent to e-Care Platform from connected devices are analyzed and if result is beyond the threshold, caregiver gets the notification.

The Comarch e-Care Platform is not interpretive, nor is it intended for diagnosis or as a substitute for medical care, and it is not intended to provide real time data. It is made available to patients when time-critical care is not required.

The Comarch e-Care Platform is contraindicated for patients requiring direct - medical supervision or emergency intervention. It is intended for patients who are willing and capable of managing its use. Clinical judgment and experience by a caregiver are required to check and interpret the information delivered.

Comarch e-Care Platform is a software intended for use in remote patient monitoring outside of traditional healthcare settings (e.g. at home). Components of Comarch e-Care Platform are: Comarch SMA application, Comarch e-Care application, application server, database server.

Comarch SMA is a software application intended to use by patients. It is designed to collect, display and transmit vital sign measurements captured from commercially available home monitoring devices.

The following vital signs are collected: temperature, glucose, noninvasive blood pressure, pulse oximetry, weight and spirometry.

The Comarch e-Care Platform is a software intended for use in remote patient monitoring outside of traditional healthcare settings (e.g. at home). It connects with physiological measurement devices (weight scales, blood pressure meters, pulse oximeters, peak flow meters, thermometers, spirometers, glucometers) and sends the measurement results to a central server. The platform displays collected measurements on a web application, securely stores them, and allows caregivers to view and analyze them, leave comments, and contact patients. Caregivers can set individual thresholds for each patient, and the platform notifies the caregiver if a measurement result is beyond the set threshold. The device is not interpretive, not intended for diagnosis or as a substitute for medical care, and does not provide real-time data. It is for patients who are able and willing to manage its use, and clinical judgment by a caregiver is required to interpret the information.

Here's an analysis of the acceptance criteria and supporting study information:

1. Table of Acceptance Criteria and Reported Device Performance:

The provided document does not explicitly present a table of quantitative acceptance criteria for device performance. Instead, the substantial equivalence determination for the Comarch e-Care Platform is based on similarities to predicate devices in terms of:

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

The document states that "no clinical tests were conducted." Therefore, there is no specific test set or data provenance mentioned for clinical performance evaluation. The evaluation was based on non-clinical verification and validation.

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)

Since no clinical tests were conducted and no specific test set-based ground truth was established, this information is not applicable and not provided in the document.

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

Since no clinical tests were conducted and no test set ground truth was established by experts, this information is not applicable and not provided.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

The document explicitly states "no clinical tests were conducted." Therefore, no MRMC comparative effectiveness study was done, and no effect size regarding human reader improvement with/without AI assistance is provided.

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

The substantial equivalence is based on the device's functional similarities to predicate devices in remote patient monitoring, data transmission, storage, and caregiver notification, rather than a standalone performance evaluation of a diagnostic algorithm. The device itself is described as "not interpretive" and "not intended for diagnosis." The non-clinical verification and validation focused on the software's functionality and adherence to design specifications. Therefore, a standalone performance study in the context of diagnostic accuracy was not performed for this device.

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

As no clinical tests were performed, there was no "ground truth" derived from patient data, expert consensus, pathology, or outcomes data for the purpose of validating diagnostic or interpretive accuracy. The "ground truth" for the non-clinical verification and validation would have been the design specifications and requirements of the software.

8. The sample size for the training set

The document states "no clinical tests were conducted." This implies that the device did not undergo a process involving a "training set" for machine learning model development in the context of clinical performance. The platform's functionality is about data handling and alerts based on pre-set thresholds, not on learning from a dataset to perform interpretations. Therefore, this information is not applicable and not provided.

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

As no training set was used for clinical performance evaluation, this information is not applicable and not provided.

In summary, the Comarch e-Care Platform's acceptance was based on a demonstration of substantial equivalence to existing predicate devices through comprehensive non-clinical verification and validation testing, ensuring the software's functionality, adherence to design specifications, and addressing identified risks. No clinical studies or performance evaluations requiring test sets, ground truth establishment, or expert adjudication were conducted, as the device is not interpretive and does not provide diagnostic insights.

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The U-RIGHT TD-4280 Blood Glucose Monitoring System is intended for use in the quantitative measurement of glucose in fresh capillary whole blood samples from the finger. It is intended to be used by a single person and should not be shared.

The U-RIGHT TD-4280 Blood Glucose Monitoring System is intended for self testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. It is not intended for the diagnosis of or screening for diabetes mellitus or be used on neonates. The meter contains speaking functions but is not intended for use by visually impaired users.

The U-RIGHT TD-4280 Blood Glucose Test Strips are for use with the U-RIGHT TD-4280 Blood Glucose Meter to quantitatively measure glucose (sugar) in fresh capillary whole blood samples.

The system consists of three main products: the meter, test strips, and control solutions. These products have been designed, tested, and proven to work together as a system to produce accurate blood glucose test results.

Here's an analysis of the provided text regarding the U-RIGHT TD-4280 Blood Glucose Monitoring System:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document doesn't explicitly state specific numerical acceptance criteria for the U-RIGHT TD-4280 Blood Glucose Monitoring System. Instead, it relies on demonstrating substantial equivalence to a predicate device (TD-4277 Blood Glucose Monitoring System). The performance characteristic considered for equivalence is "system accuracy performance."

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

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). This type of detail is typically found in the full study report, which is not included in this 510(k) summary. The summary only mentions "A comparison of system accuracy performance" and "Software verification and validation testing."

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

This information is not provided in the given text. For a blood glucose monitoring system, the "ground truth" for accuracy studies is typically established by comparing the device's readings to laboratory reference methods (e.g., YSI analyzer) performed by trained clinical laboratory personnel, not by medical experts in the sense of radiologists or pathologists.

4. Adjudication Method for the Test Set

This information is not provided in the given text. Adjudication methods are typically relevant for studies where subjective expert review is involved. For a blood glucose meter, the comparison is usually against an objective reference laboratory method, making a traditional adjudication process (like 2+1) less applicable.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

An MRMC comparative effectiveness study is not applicable to this device. This type of study is used for diagnostic imaging devices where human readers interpret images, often with and without AI assistance, to assess the AI's impact on human performance. The U-RIGHT TD-4280 is a blood glucose monitoring system, directly measuring glucose levels, not assisting human interpretation of complex data.

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

The context of "standalone" performance (algorithm only) is usually discussed for AI/ML-driven diagnostic systems. For a blood glucose meter, the device itself provides the measurement, and its "standalone performance" is what is being evaluated (i.e., how accurately it measures glucose). The entire system (meter + strip) is designed to operate without continuous human-in-the-loop interpretation beyond the user taking the reading. The summary implies the device's accuracy was tested independently.

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

The exact type of ground truth is not explicitly stated, but for blood glucose monitoring systems, the ground truth is universally established through laboratory reference methods. This would involve comparing the glucose readings from the U-RIGHT TD-4280 system to results obtained from a highly accurate laboratory instrument, such as a YSI glucose analyzer, which is considered the gold standard for glucose measurement in blood samples.

8. The Sample Size for the Training Set

The concept of a "training set" is primarily relevant for machine learning algorithms. While the device contains software, the 510(k) summary does not indicate that it uses a machine learning algorithm that requires a separate "training set" in the conventional sense. The device's electrochemical biosensor technology likely relies on pre-defined algorithms and calibration, rather than a learned model from a large training dataset. Therefore, information about a training set sample size is not provided and likely not applicable in the AI/ML context.

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

As noted in point 8, the concept of a "training set" as it relates to AI/ML is likely not applicable here. If the device's internal algorithms required calibration, this would be done against laboratory reference methods to ensure accuracy across the measurement range, similar to how ground truth for performance evaluation is established. However, the document does not detail these developmental and calibration processes.

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