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The ClearCount Medical Solutions SmartWand-DTX™ System is indicated for use in counting and displaying the number of RFID-tagged surgical sponges, laparatomy sponge, and towels detected by the device and providing a noninvasive means of locating retained RFID-tagged surgical sponges, towels, and other tagged items within a surgical site.

The SmartWand-DTX™ System is based on radio frequency identification (RFID) tags. The RFID tags are provided to manufacturers of surgical disposables for inclusion into their surgical sponges, laparotomy pads and surgical towels. The disposable manufacturer permanently attaches the RFID tags to the gauze or fabric of the disposables. The tags are then programmed to contain information about the type and number of disposables in the package. This allows the sponges, pads, and towels to be individually recognized by an RFID reader. The RFID tag function is the same as that for the SmartSponge Plus System.

The SmartWand-DTX is a device comprised of a handheld scanning antenna that is attached to an electronics box that contains an RFID reader and supporting electronics. Integrated RFID technology allows the capture of the information coded on the unique RFID tags in the sponges, pads and towels. When the tagged sponges, pads, and towels are detected by the scanning wand, the device displays the type and number of each type of item that is detected. The system recognizes RFID-tagged items that may be inside the surgical site.

Here's an analysis of the acceptance criteria and study information for the ClearCount Medical Solutions SmartWand-DTX System, based on the provided text:

Important Note: The provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than providing a detailed clinical study report with specific performance metrics and statistical analysis. Therefore, much of the requested information (like specific sample sizes for test/training sets, detailed expert qualifications, MRMC data, and quantitative ground truth establishment) is not explicitly stated in this type of regulatory submission. The answers below are derived directly from the available text.

1. Table of Acceptance Criteria and Reported Device Performance

Study Information:

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

• Sample Size: Not explicitly stated. The text mentions "simulated use" and "verification and validation testing," but no specific number of cases or items tested is provided.
• Data Provenance: The testing was "Non-Clinical," conducted in a "laboratory setting." There is no mention of human subject data, retrospective, or prospective studies in a clinical environment.

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

• Not explicitly stated. Given the non-clinical, laboratory setting, ground truth would likely refer to the known presence and location of RFID tags placed by the testing personnel rather than expert clinical consensus.

4. Adjudication Method for the Test Set

• Not applicable/Not stated. Since the testing was non-clinical and involved detecting pre-placed tags, a formal adjudication method by experts (like 2+1 or 3+1) is not relevant in this context. The "ground truth" would be the known, objective presence/absence/location of the tags.

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, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This device is a standalone detection system, not an AI-assisted diagnostic tool for human readers. It's intended to replace manual counting and provide localization, not enhance human interpretation of images. Consequently, there's no mention of "human readers" or "AI assistance."

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

• Yes, a standalone performance assessment was conducted. The device (SmartWand-DTX System) is described as "algorithm only without human-in-the-loop performance" in terms of its core detection and counting function. It directly detects RFID tags and displays information. The testing involved the device's ability to "properly locating all tags" in simulated use.

7. The Type of Ground Truth Used

• The ground truth in the non-clinical testing was based on the known, objective presence and location of RFID-tagged items placed by the testing personnel in a simulated environment. It was not expert consensus, pathology, or outcomes data, as those are relevant to clinical diagnostic devices.

8. The Sample Size for the Training Set

• Not specified. The document primarily discusses verification and validation testing, not the training of a machine learning algorithm. While the device contains a "proprietary software operating on a microcontroller unit," specific "training set" data for an AI model is not detailed. The system is based on RFID technology and reading unique identifying numbers, which implies a more deterministic operating principle rather than a continuously learning AI model that requires a discrete training set.

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

• Not applicable/Not specified as a distinct "training set" in the context of an AI algorithm. The device relies on reading pre-programmed RFID tags. If there was any internal calibration or refinement, it's not described in terms of a formal training set.

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The ClearCount Medical Solutions SmartSponge™ PLUS System is indicated for use in counting and recording the number of RFID-tagged surgical sponges, laparatomy sponges and towels used during surgical procedures, as well as for providing a non-invasive means of locating retained RFID-tagged surgical sponges, towels, and other tagged items within a surgical site.

The SmartSponge™ PLUS System includes surgical sponges, laparatomy pads and surgical towels, each of which contains a unique radio frequency identification (RFID) tag permanently attached to the gauze or fabric. The tags allow the sponges and towels to be individually recognized by an RFID reader.

The SmartBucket is a specially designed cart containing a microcontroller unit with specialized software designed for mobile data collection. Integrated RFID technology allows capture of the information coded on the unique RFID tag on the sponges, pads and towels. The microcontroller unit counts the initial number of sponges introduced into a surgical case, and using the custom software program, reports the total sponges discarded at the end of the procedure, and compares that number to the original. By providing a count of the items entered into surgery, and a count of those discarded and removed permanently from the surgical field, personnel can be alerted to sponges that may still remain in the surgical field prior to closing the patient.

A Detection Wand is an additional antenna that is tethered by a cable to the SmartBucket. It is powered and controlled by the SmartBucket. The antenna functions as an additional RFID antenna to the system, functioning in an identical manner to the internal SmartBucket antennas. By using a keypad the user may select activate the Detection Wand antenna. When in Detection Wand mode, the system uses the Wand antenna to recognize RFID-tagged items that may be inside the surgical site.

A Detection Mat is a disposable or reusable element with multiple RFID tags embedded inside, along with several passive printed circuit traces. Like the RFID-tagged sponges, the Detection Mat tags contain unique identifying numbers and are distinguishable by the system software. The Detection Mat is placed on the operating room table before the patient is brought into the room and is covered by the standard sheets or drapes used in surgery, thus not making contact with the patient. The RFID tags in the Matt provide feedback to the user that the Detection Wand is being held close enough to the patient to ensure proper reading. The tags in the Detection Mat also ensure that the Detection Wand scan has covered the appropriate areas of the patient. The passive circuit traces help to enhance the readability of the RFID tags in the Detection Mat.

Here's an analysis of the provided text regarding the ClearCount Medical SmartSponge™ PLUS System, focusing on acceptance criteria and supporting study details:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document mentions "simulated use in patient models that represented worst case biological situations." However, no specific sample size (number of simulated cases, number of sponges, or number of tests) for the test set is provided.

The data provenance is from non-clinical testing, using simulated patient models. The document does not specify the country of origin, but given the FDA submission, it's presumably conducted under U.S. regulatory standards or by a manufacturer seeking to market in the U.S. It explicitly states "Non-Clinical testing," confirming it's not a prospective or retrospective study involving actual patients.

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

This information is not provided in the document. The "ground truth" for the simulated use in patient models would likely have been established by the study designers or engineers setting up the scenarios of "worst case biological situations" and knowing the expected location and quantity of tagged items. There is no mention of independent experts establishing this ground truth.

4. Adjudication Method for the Test Set

This information is not provided. Without details on who assessed the device's performance in the "simulated use," an adjudication method cannot be 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

No, an MRMC comparative effectiveness study was not done. The device's primary function is to automate sponge counting and aid in detection, not to assist human readers in interpreting medical images in the traditional sense of an MRMC study. The "Detection Wand" is a tool for locating tagged items, not for improving human interpretation of visual data.

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

Yes, a form of standalone performance was implicitly evaluated for the detection component. The statement "The validated software functioned as intended under simulated use, properly locating all tags" suggests the algorithm's ability to locate tags independently within the simulated environment. While a human operates the Detection Wand, the core detection by the RFID system and its interpretation by the software is a standalone algorithm function. The counting function is also purely algorithmic.

7. The Type of Ground Truth Used

The ground truth used was based on pre-defined scenarios within simulated patient models where the exact quantity and location of RFID-tagged items would be known to the experimenters. This is an engineered or experimental ground truth, not derived from expert consensus, pathology, or outcomes data from real patients.

8. The Sample Size for the Training Set

The document does not provide information on the sample size for a training set. The device appears to rely on established RFID technology and programmed logic, rather than a machine learning model that would typically require a training set in the conventional sense. If there was any "training" (e.g., for system calibration), its details are not mentioned.

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

Since no training set is mentioned or implied for a typical machine learning model, this information is not applicable / not provided. The functionality seems to be based on direct sensing and programming, not a learned model from data.

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The ClearCount Medical Solutions SmartSponge™ System is indicated for use in counting and recording the number of RFID-tagged surgical sponges, laparatomy sponges and towels used during surgical procedures.

The SmartSponge™ System includes surgical sponges, laparatomy pads and surgical towels, each unit of which contains a unique radio frequency identification (RFID) tag permanently attached to the gauze or fabric. The tags allow the sponges and towels to be individually recognized by an RFID reader.

The SmartBucket is a specially designed cart containing a microcontroller unit with specialized software designed for mobile data collection. Integrated RFID technology allows capture of the information coded on the unique RFID tag on the sponges, pads and towels. The microcontroller unit counts the initial number of sponges introduced into a surgical case, and using the custom software program, reports the total sponges discarded at the end of the procedure, and compares that number to the original. By providing a count of the items entered into surgery, and a count of those discarded and removed permanently from the surgical field, personnel can be alerted to sponges that may still remain in the surgical field prior to closing the patient.

The ClearCount Medical Solutions SmartSponge™ System is designed to count and record RFID-tagged surgical sponges, laparotomy sponges, and surgical towels during surgical procedures. The provided text outlines non-clinical performance data and results.

Here's a breakdown of the requested information:

1. Table of acceptance criteria and reported device performance:

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

The document states "simulated finished product testing of the total system" and "properly counting sponges in simulated body fluids." However, it does not specify the sample size for this test set nor the data provenance (e.g., country of origin, retrospective or prospective).

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 of experts used or their qualifications for establishing ground truth for the test set. The ground truth for counting accuracy was established through "simulated use, properly counting sponges in simulated body fluids," implying a controlled experimental setup rather than expert interpretation of medical images or conditions.

4. Adjudication method for the test set:

The document does not specify an adjudication method (such as 2+1 or 3+1) for the test set. Given the nature of the device (counting sponges), the ground truth for counting accuracy would likely be directly observable and verifiable rather than requiring expert adjudication.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, and the effect size:

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned or described in the provided text. The document compares the device's accuracy and effectiveness to "hand counting sponges" but does not provide specific effect sizes for human readers with or without AI assistance in an MRMC study.

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

Yes, a standalone performance study was done implicitly. The "software validation of the SmartBucket scanning device" and "simulated finished product testing of the total system" describe the device's ability to "properly count sponges" without human intervention for the counting process itself. The system is designed to provide a count that personnel then use to determine if sponges remain.

7. The type of ground truth used:

The ground truth used for the counting accuracy of the SmartSponge System was directly observable and verifiable counts in simulated use conditions, specifically "properly counting sponges in simulated body fluids." This is an objective measurement of the device's functionality rather than expert consensus, pathology, or outcomes data.

8. The sample size for the training set:

The document does not specify the sample size for any training set. Given the context of a 2007 510(k) submission for a device using RFID for counting, it's possible that traditional "training sets" in the modern machine learning sense were not explicitly defined or reported in the same way. The software validation suggests a designed and tested algorithm rather than a continuously learning AI model.

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

The document does not describe how ground truth for a training set (if one was formally used) was established. The software validation likely involved testing against known, correct counts of sponges in controlled environments.

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