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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 RF Surgical Systems Inc. Detection System is intended to provide a non-invasive means of locating retained surgical sponges, gauze and other tagged items within a surgical site. It is to be employed as an adjunctive detection method to current surgical sponge and gauze counting systems and methods

The RF Surgical Systems Inc. Detection System consists of:

1. The Power/Control Console contains the electronics that power and control the detector/scanner. The console also includes the user interface for system operation and communication of the system status, operation and alarms to the user.
2. The Transponder/Tag is a single use electrically passive device that is designed to radiate a magnetic signature when stimulated by magnetic impulses from the detector/scanner. The tag does not store or communicate any information or unique code and is to be mechanically attached to guaze sponges at the manufacturing site and processed as part of the item
3. The Detection Wand is a transceiver type antenna designed to stimulate the transponder tag assembly with magnetic impulses and then detect the resultant magnetic signature from the tag. The Power/Control Console provides the power and control to the detection scanner. The detection scanner is intended and designed to be a single-use disposable device and will be supplied in a sterile condition as it will enter the surgical field.

Here's an analysis of the acceptance criteria and study information for the RF Surgical Systems Inc. Detection System, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly present a table of quantitative acceptance criteria with corresponding performance metrics in the format typically seen for algorithm performance (e.g., sensitivity, specificity, AUC). Instead, it describes functional requirements and then states that the device "performed as intended."

Based on the "Technological Characteristics" and "Non-Clinical Performance Data" sections, here's an interpretation of the implied acceptance criteria and the reported performance:

2. Sample Size and Data Provenance for the Test Set

• Sample Size: The document does not provide a specific numerical sample size for the test set used in non-clinical testing. It mentions "simulated use in patient models" and "Animal studies using large swine species."
• Data Provenance:
  • Simulated Use in Patient Models: These are likely laboratory or in-house simulations.
  • Animal Studies: "large swine species" were used. The country of origin for these animal studies is not specified, but it's implied to be part of the manufacturer's testing process for FDA submission.
• Retrospective or Prospective: Both "simulated use" and "animal studies" would typically be considered prospective in nature, as they were conducted specifically to test the device.

3. Number of Experts and Qualifications for Ground Truth

The document does not explicitly state that experts were used to establish ground truth in the traditional sense of consensus reading for image analysis. Rather, the ground truth for "locating all tags" in the simulated and animal studies would have been the known physical presence and location of the tagged sponges or items.

4. Adjudication Method for the Test Set

Given the nature of the device (detection of physical tags) and the described testing (simulations, animal studies where tags are physically present), an adjudication method like 2+1 or 3+1 for expert consensus is not applicable. The "ground truth" is directly observable.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. The document describes a standalone performance study of the device itself (detecting tags), not a comparative effectiveness study involving human readers with and without AI assistance (which is not an AI device in the typical sense, but rather a detection system). Therefore, no effect size of human reader improvement is reported.

6. Standalone (Algorithm Only) Performance

Yes, a standalone performance assessment was conducted. The "Non-Clinical Performance Data" section describes testing where the "RF Surgical Detection System performed as intended" and "the validated software functioned as intended under simulated use, properly locating all tags." This indicates an evaluation of the device's ability to detect tags independently.

7. Type of Ground Truth Used

The ground truth used was:

• Known physical presence and location of tagged items: In simulated patient models and animal studies, the researchers would have known precisely where the tagged sponges were placed.
• Biocompatibility data: For biocompatibility, the ground truth would be established through standard biological testing methods and comparison to known safe materials (predicates), likely through laboratory analysis rather than expert consensus on images.

8. Sample Size for the Training Set

The document does not provide information about a "training set" for the device. This device is described as an RF detection system using transponder tags and a detection wand, not an AI/machine learning system that typically requires a large training dataset. The "software" mentioned likely refers to control and processing logic rather than a trainable AI algorithm.

9. How Ground Truth for the Training Set Was Established

As no training set is indicated, this question is not applicable based on the provided text. The device's operation is based on physical principles of radio frequency detection, not on learning from data.

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