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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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