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The DataCaptor™ System is indicated for use in data collection and clinical information management either directly or through networks with independent bedside devices. DataCaptor™ is not intended for monitoring purposes, nor is the software intended to control any of the clinical devices (independent bedside devices / information systems) to which it is connected.

The DataCaptor™ System consists of DataCaptor™ Connectivity Software a data acquisition system designed to retrieve and deliver near-real-time data from multiple vendors' bedside medical devices and send it to clinical or hospital information systems in HL7 standard format; Capsule Neuron™ with Docking Station (for high acuity environments) or Mini-Dock (for low acuity environments), a bedside hardware platform for device connectivity, and the Datack (1017 Terminal Server, a Serial-to-Ethernet concentrator for the medical environment that connects RS-232 equipped bedside medical devices to the hospital network for safe transmission of the clinical or hospital information system.

This appears to be a 510(k) summary for a medical device called the DataCaptor™ System. It's important to note that a 510(k) summary focuses on demonstrating "substantial equivalence" to a predicate device, rather than proving novel clinical effectiveness through extensive performance studies like those required for a PMA (Premarket Approval).

Therefore, the type of detailed "study that proves the device meets the acceptance criteria" as typically understood for AI/ML devices (with metrics, sensitivity/specificity, reader studies, etc.) is not present in this document. This document primarily describes design changes and verification/validation activities to ensure the modified device performs comparably to its predicate.

Here's an analysis of the provided text in response to your questions:

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

The document states: "Design verification and validation activities to pre-determined pass/fail criteria were based on results of risk an alres va confirm system performance, functionality and reliability to be commensurate to the predicate device."

However, no specific quantitative acceptance criteria or reported device performance data (e.g., in terms of accuracy, precision, or error rates for data collection) are provided in a table format in this public summary. The focus is on demonstrating that the modified DataCaptor™ System performs commensurately to the predicate device. This is typical for a Special 510(k), which deals with modifications to a previously cleared device.

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

The document mentions "Design verification and validation activities," implying testing was conducted. However, no specific sample sizes for a test set, data provenance (country of origin), or whether the data was retrospective or prospective are mentioned. This level of detail is typically not included in a 510(k) summary, as the emphasis is on confirming that the system itself functions as intended, not necessarily on its performance with a specific dataset of clinical outcomes.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

Not applicable. This device is a data collection system, not an AI/ML diagnostic or assistive tool that would require expert-established ground truth on clinical images or patient conditions. Its "performance" refers to its ability to accurately retrieve and transmit data from medical devices. The verification and validation activities would focus on the technical integrity of data transfer, not on clinical interpretations.

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

Not applicable. As above, the device's function does not involve clinical interpretation requiring adjudication.

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

No. This device is a data connectivity solution, not an AI-powered assistive tool for human readers. Therefore, an MRMC study is irrelevant to its function.

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

The DataCaptor™ system is described as "a data acquisition system designed to retrieve and deliver near-real-time data from multiple vendors' bedside medical devices and send it to clinical or hospital information systems." Its performance is inherent in its ability to correctly acquire and transmit data. While it operates "standalone" in capturing data, its "performance" is about technical accuracy and reliability of data transfer, not about a standalone clinical interpretation task. The document states it is "not intended for monitoring purposes, nor is the software intended to control any of the clinical devices." This reinforces that its performance is not a clinical "standalone" assessment in the way AI diagnostics are evaluated.

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

For a data collection system, the "ground truth" would likely involve:

• Verification of data integrity: Comparing the data captured by DataCaptor™ to the raw data output directly from the bedside medical devices (e.g., via a controlled test environment or direct measurement).
• Verification of data transmission: Ensuring the data is correctly formatted and transmitted to the clinical information systems (HL7 standard).
• System functionality testing: Ensuring all software and hardware components operate as designed.

No specific methodology for establishing this "ground truth" (e.g., expert consensus) is outlined in the summary, as it's a technical verification process rather than a clinical one.

8. The sample size for the training set

The DataCaptor™ System is a software and hardware system for data collection, not an AI/ML model that would be "trained" on a dataset in the typical sense. Therefore, there is no "training set" or corresponding sample size.

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

Not applicable, as there is no training set for this type of device.

Summary of what can be gleaned about "acceptance criteria" and "study" from this document:

• Acceptance Criteria (Implied): The central acceptance criterion for this Special 510(k) was that the modified DataCaptor™ System would continue to demonstrate "substantial equivalence" to its predicate device. This means its performance, functionality, and reliability should be "commensurate" with the previously cleared device, and it should meet its intended use of data collection and clinical information management without being used for monitoring or control of devices.
• Study (Verification/Validation Activities): The "study" mentioned is not a clinical trial or performance study in the sense of comparing diagnostic accuracy. Instead, it refers to:
  • Software verification and validation: Ensuring the software changes meet specifications and function correctly.
  • Environmental and safety testing: For hardware changes.
  • System validation: Confirming the overall system performance, functionality, and reliability.
    These activities were designed to ensure the device met "pre-determined pass/fail criteria" derived from a risk analysis, confirming its equivalence to the predicate. Specific details of these technical tests (e.g., number of test cases, types of data simulated) are not provided in this public summary.

In essence, this 510(k) summary focuses on demonstrating that modifications to an existing device do not alter its safety or effectiveness, rather than performing a new clinical efficacy study.

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The DataCaptor™ System is indicated for use in data collection and clinical information management either directly or through networks with independent bedside devices. DataCaptor™ is not intended for monitoring purposes, nor is the software intended to control any of the clinical devices (independent bedside devices / information systems) it is connected to.

Based on an open-architecture design, DataCaptor is a data acquisition and distribution software, using ActiveX and the Distributed Component Object Model. This tool retrieves data from serial, network or analog devices and, via an ActiveX control, makes this data available over network or any other type of communication for use in software applications. We do not supply any hardware - our customers can buy cable and connect the devices directly to the COM port (we provide a wiring diagram that shows them pin configurations) or they can use a multiport box or card, an RS-232 to Ethernet converter is used if several devices need to be connected to the network and there are not necessarily computers next to each one. We don't recommend hardware suppliers. The change enables interface to additional models of connected medical devices. Also the DataCaptor Solution now includes the DMM Server module and the DataPortal module. The DMM Server module allows one to process the data originating from the devices and decoded by DataCaptor, i-e, it allows users to create averages, to suppress some data, to streamline the frequency of data that come out of DataCaptor. The DataPortal module converts a dataflow from a Microsoft COM (Component Object Model) container into a TCP/IP Sockets container

The provided text describes a 510(k) premarket notification for a medical device called DataCaptor™. This submission is for an upgrade to an existing product, K013019 and K020197, adding compatibility with additional medical devices and upgrading basic capabilities.

However, the 510(k) summary does not contain information about acceptance criteria or a study that proves the device meets specific performance criteria. Instead, it focuses on demonstrating substantial equivalence to a predicate device. This type of submission generally relies on demonstrating that changes made to the device do not alter its fundamental functionality, safety, or effectiveness, thus not requiring new clinical performance studies.

Therefore, many of the requested sections regarding acceptance criteria and study details cannot be extracted from this document.

Here's a breakdown of what can and cannot be answered based on the provided text:

1. Table of Acceptance Criteria and 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)

• Not provided. The document does not describe any specific test set or data used to evaluate the upgraded DataCaptor™ system's performance beyond internal verification that the new interfaces function as intended.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable. No expert review or ground truth establishment is described in this submission.

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

• Not applicable. No adjudication method is mentioned as there is no specific test set of data presented.

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

• Not applicable. This device is data collection software and is explicitly stated not to be for "monitoring purposes" or for "controlling any of the clinical devices." It is not an AI-assisted diagnostic or interpretive tool that would involve human readers or MRMC studies.

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

• Not applicable in the context of diagnostic performance. While the software operates without human-in-the-loop for data collection, a standalone performance study in the sense of diagnostic accuracy (which is often what this question implies) was not conducted or reported. Its performance is related to its ability to acquire and distribute data, not to make clinical interpretations.

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

• Not applicable. No ground truth in the context of clinical or diagnostic performance is mentioned. The "truth" here would likely be the accurate and reliable transfer of data from medical devices, which is verified through engineering tests rather than clinical expert consensus or pathology.

8. The sample size for the training set

• Not applicable. This software is described as data acquisition and distribution software using ActiveX and Distributed Component Object Model. It does not appear to be an AI/ML device that requires a training set in the conventional sense for learning patterns from data.

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

• Not applicable. As no training set is described (see point 8), no method for establishing its ground truth is provided.

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The DataCaptor™ System is indicated for use in data collection and clinical information management either directly or through networks with independent bedside devices. DataCaptor™ is not intended for monitoring purposes, nor is the software intended to control any of the clinical devices (independent bedside devices / information systems) it is connected to.

Based on an open-architecture design, DataCaptor is a data acquisition and distribution software, using ActiveX and the Distributed Component Object Model. This tool retrieves data from serial, network or analog devices and, via an ActiveX control, makes this data available over network or any other type of communication for use in software applications. We do not supply any hardware - our customers can buy cable and connect the devices directly to the COM port (we provide a wiring diagram that shows them pin configurations) or they can use a multiport box or card, an RS-232 to Ethernet converter is used if several devices need to be connected to the network and there are not necessarily computers next to each one. We don't recommend hardware suppliers.

This submission is a 510(k) summary for the DataCaptor™ Data Acquisition and Distribution Software. The purpose of this 510(k) is to demonstrate substantial equivalence to a previously cleared device (K013019) with the primary change being added compatibility with additional medical devices.

Therefore, the submission does not include any studies related to acceptance criteria or device performance because it relies on the predicate device's established safety and effectiveness. The FDA's letter (Exhibit 3) also confirms that the device is substantially equivalent to legally marketed predicate devices without requiring a PMA.

As a result, I cannot provide the requested information regarding acceptance criteria, study details, sample sizes, expert qualifications, adjudication methods, or MRMC studies, as these were not part of this 510(k) submission.

Ask a specific question about this device

The DataCaptor™ System is indicated for use in data collection and clinical information management either directly or through networks with independent bedside devices. DataCaptor™ is not intended for monitoring purposes, nor is the software intended to control any of the clinical devices (independent bedside devices / information systems) it is connected to.

Based on an open-architecture design, DataCaptor is a data acquisition and distribution software, using ActiveX and the Distributed Component Object Model. This tool retrieves data from serial, network or analog devices and, via an ActiveX control, makes this data available over network or any other type of communication for use in software applications. We do not supply any hardware - our customers can buy cable and connect the devices directly to the COM port (we provide a wiring diagram that shows them pin configurations) or they can use a multiport box or card, an RS-232 to Ethernet converter is used if several devices need to be connected to the network and there are not necessarily computers next to each one. We don't recommend hardware suppliers.

This submission is a 510(k) premarket notification for the DataCaptor™ Data Acquisition and Distribution Software. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than providing extensive studies with acceptance criteria and performance metrics typically seen for novel or higher-risk devices.

Therefore, the provided text does not contain the acceptance criteria or a study directly proving the device meets acceptance criteria in the format requested. Instead, it focuses on demonstrating substantial equivalence to a predicate device.

However, I can extract the relevant information regarding the equivalence comparison, which serves as the basis for the FDA's decision to allow the device to market.

Summary of Device Equivalence and FDA Determination:

The DataCaptor™ Data Acquisition and Distribution Software's acceptance is based on its substantial equivalence to a legally marketed predicate device, the Hewlett-Packard M2376A DeviceLink System (K000635). The FDA determined that the device is substantially equivalent for the stated indications for use.

1. Table of Acceptance Criteria and Reported Device Performance

As this is a 510(k) based on substantial equivalence, there are no explicit "acceptance criteria" in the sense of quantitative performance thresholds that the device had to meet through a specific study. Instead, the "acceptance criteria" can be inferred from the comparison areas used to demonstrate equivalence to the predicate device. The device "performance" is implicitly deemed acceptable because it is "substantially equivalent" to a device already deemed safe and effective.

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

• Not Applicable. This submission does not describe a test set or data provenance in the context of performance testing for acceptance criteria. The equivalence demonstration relies on a comparison of intended use, technological characteristics, and operational environment with a predicate device.

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

• Not Applicable. There was no "ground truth" to establish for a test set in this type of submission. The ground truth for regulatory acceptance is the established safety and effectiveness of the predicate device.

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

• Not Applicable. No test set or expert adjudication process is described in this submission.

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

• Not Applicable. This is not an AI/CAD device. It is data collection software. No MRMC study was conducted or described.

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

• Not Applicable. This is data collection software, not an algorithm with performance metrics measured in a standalone fashion. Its function is to acquire and distribute data, not to make diagnostic or prognostic predictions.

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

• For the purpose of this 510(k) submission, the "ground truth" is the established safety and effectiveness of the predicate device, the Hewlett-Packard M2376A DeviceLink System, K000635, as accepted by the FDA. The new device demonstrates substantial equivalence to this established "truth."

8. The sample size for the training set:

• Not Applicable. This submission does not describe a training set as it is not a machine learning or AI-driven device requiring model training.

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

• Not Applicable. As there is no training set, there is no ground truth established for it.

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