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The ORLocate™ system is indicated for use in recording and counting the number of RFID-tagged surgical sponges, laparotomy sponges, towels and other tagged items used during surgical procedures in which counting is required. In addition, the product is indicated for providing a non-invasive means of detecting retained RFID-tagged surgical sponges, towels and other tagged items within a surgical site, as an adjunctive detection method to current surgical counting systems and methods.

Haldor ORLocate™ system is an RFID system providing a solution that enables the enumeration of sponges and surgical manual instruments, utilizing passive tags for keeping track of the items during surgery and to identify counting problems. In addition, the system provides a non-invasive means of locating retained surgical items within a surgical site. The submission consists of the ORLocate™ system which includes: cart and antennas. Additionaly the submission includes accessories which are: associated single use surgical sponges, gauzes, pads and surgical towels each fitted with a uniquely coded RFID tag and uniquely coded RFID tag used for surgical instruments. The RF frequency the system uses is 13.56 MHz according to ISO 15693. The system supplies also a semi-automatic application to help in counting untagged items, the count information is first entered manually and the calculations are automatic.

The ORLocate™ System is an RFID-based system for counting and detecting surgical items. The provided document, a 510(k) Summary, details the device's technical characteristics, intended use, and non-clinical performance data to demonstrate substantial equivalence to predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" with numerical thresholds for performance. However, based on the non-clinical performance data section, the criteria can be inferred from the tests performed and the conclusion that the device functions as intended and is as safe and effective as predicate devices.

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

The document states "Non-clinical testing included demonstrating performance of system and tagged items in laboratory tests." However, it does not specify the sample sizes used for any of the tests (e.g., how many sponges were tested for counting accuracy, how many instruments for tag pull test).

The data provenance is described as "laboratory tests," implying controlled settings rather than real-world clinical data. The document does not mention the country of origin of the data explicitly, but the 510(k) owner is based in Israel, suggesting the testing likely occurred there or in collaboration with international labs. The study is retrospective in the sense that the testing was performed and then reported for the 510(k submission.

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

The document does not provide information on the number of experts used or their qualifications for establishing ground truth for the test set. Given the nature of the non-clinical tests (e.g., biocompatibility, tag attachment, software counting accuracy), ground truth would likely be established through objective measurements and validated procedures rather than solely expert consensus.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method for the test set. Given that the non-clinical tests are largely objective performance evaluations (e.g., measuring count accuracy, pull force, EMC compliance), an adjudication method in the context of expert review is unlikely to be relevant.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The document focuses on non-clinical performance data to establish substantial equivalence, not on human reader performance with or without AI assistance.

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

Yes, the non-clinical tests described are essentially standalone performance evaluations of the algorithm and device. The "counting accuracy test" and validation that the "software functioned as intended under simulated use, properly counting sponges in body fluids" demonstrate the algorithm's performance without direct human-in-the-loop assistance for the core counting mechanism. The system is designed to perform automatic counts and provide alerts if items are missing, which is a standalone algorithmic function.

7. The Type of Ground Truth Used

The ground truth used for the non-clinical tests would have been established through:

• Objective measurement/validation: For counting accuracy, the actual number of sponges present would be the ground truth. For tag pull tests, the measured force would be compared against a standard.
• Established standards: Compliance tests like IEC 60601-1-2:2007 and IEC 60601-1:1988 + A1:1991 + A2:1995 refer to external ground truths established by international standards bodies.
• Simulated environment: For the software, simulated body fluids were used to test performance, implying a controlled and known environment against which the device's output was compared.

8. The Sample Size for the Training Set

The document does not mention a training set sample size. The system described is an RFID detection and counting system, which typically relies on pre-programmed logic for tag identification and counting, rather than a machine learning model that requires a "training set" in the conventional sense. If there are any adaptive or learning components, they are not detailed in this summary.

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

Since a training set is not mentioned for machine learning purposes, the method for establishing its ground truth is not applicable/not provided. The system's operational parameters (e.g., RFID tag protocols, counting logic) would be established through engineering design and validation, not model training.

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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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