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The Tag of the RFLS is intended for percutaneous placement in the breast to mark (>30 days) a lesion intended for surgical removal. Using image guidance (such as ultrasound or radiography) or aided by non-imaging guidance (RFLS), the RFID Tag is located and surgically removed with the target tissue. The RFLS is intended only for the non-imaging detection and localization of the Tag that has been implanted in a lesion intended for surgical removal.

The proposed RFID Localization System is a marker-with-detector localization device that employs miniature RFID tags as markers and a hand-held reader that can measure distance to the tag. The RFLS is comprised of a Tag, Tag Applicator/Tag Applicator S, LOCalizer Reader) and LOCalizer Surgical Probe (Surgical Probe). The Tag, when used in conjunction with the Reader and Surgical Probe, can be used as a guide for the surgeon to refer to in the excision of tissue. The RFLS is a prescription device meant only for use by trained professionals.

Since the provided text is a 510(k) summary for a medical device (RFID Localization System), and not a study report for a diagnostic AI/ML device, much of the requested information regarding acceptance criteria for AI/ML performance, sample sizes for AI test/training sets, expert ground truth establishment, and MRMC studies is not directly applicable or present in this document.

The document discusses performance data related to the physical device's function, safety, and compatibility, rather than the performance of an AI model in a diagnostic context. The "RFID Localization System" is a marker-with-detector device, not an AI-powered diagnostic system.

However, I can extract the acceptance criteria and performance data as described for this physical device, along with other relevant information that is present.

Here's the closest possible interpretation of your request based on the provided text, focusing on the device validation rather than AI performance:

Device: RFID Localization System (RFLS)
Intended Use: Percutaneous placement in the breast to mark a lesion intended for surgical removal. Aids in non-imaging detection and localization of the implanted Tag for surgical removal.

1. Table of Acceptance Criteria and Reported Device Performance

For a physical device like the RFLS, "acceptance criteria" relate more to meeting design specifications and safety/performance standards. The document states that the RFLS met the applicable design and performance requirements. The "performance" is demonstrated by passing the various tests listed, affirming its functionality and safety.

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

The document does not specify a "test set sample size" in the context of an AI/ML model. Instead, it refers to various engineering and biological validation tests. For many tests, it explicitly states that the performance was "previously established" through prior 510(k) clearances (e.g., K190932, K181692, K163667, DEN040007/K033440). This indicates that the current submission relies on historical test data and validations, with some additional testing (e.g., multiple tags, updated packaging/shipping in K190932, and the explicitly mentioned electrosurgery and MRI compatibility for K163667, and the current submission's specific software and electrical safety/EMC testing).

• Data Provenance: The document doesn't specify the country of origin for the underlying test data, but it refers to FDA-cleared predicate and reference devices, implying compliance with US regulatory standards. All validations appear to be retrospective or performed as part of device development and previous submissions.

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

This information is not applicable. This is a physical medical device, not an AI/ML diagnostic algorithm requiring expert "ground truth" for interpretations of medical images or data. The "ground truth" for the device's performance would be established by objective measurements against engineering specifications and validated medical standards.

4. Adjudication Method for the Test Set:

Not applicable. This concept applies to human reader studies often used for AI/ML validation, not the direct performance testing of a physical medical device.

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

Not applicable. This type of study is for evaluating observer performance with or without AI assistance, which is not the subject of this 510(k). The device is a physical marker and localization system, not a diagnostic AI.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance:

Not applicable. This device is not an algorithm; it's a marker-with-detector system that a human surgeon uses. Its performance is inherent in its physical properties and functionality.

7. Type of Ground Truth Used:

The "ground truth" for this device's validation is based on:

• Engineering specifications and design requirements.
• Objective measurements (e.g., distance readings, physical stability, electrical safety parameters).
• Compliance with established international and ASTM standards (e.g., ISO 11607-1, ASTM D4169-09, ISO 10993-1, IEC 60601-1).
• Biocompatibility assessments of materials.
• Validation of sterilization processes.

8. Sample Size for the Training Set:

Not applicable. This device does not use an AI/ML model that requires training data in the conventional sense.

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

Not applicable, as there is no AI/ML training set. The "ground truth" for the device's design and manufacturing is established through scientific principles, engineering validated methods, and adherence to medical device regulatory standards and best practices.

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The Tag of the RFLS is intended for percutaneous placement in the breast to mark (>30 days) a lesion intended for surgical removal. Using image guidance (such as ultrasound or radiography) or aided by non-imaging guidance (RFLS), the RFID Tag is located and surgically removed with the target tissue. The RFLS is intended only for the non-imaging detection and localization of the Tag that has been implanted in a lesion intended for surgical removal.

The Tag, Tag Applicator, LOCalizer Reader, and LOCalizer Surgical Probe are components of the Health Beacons RFID Localization System (RFLS). The proposed device is a marker-with-detector localization device that employs miniature RFID tags as markers and a hand-held reader that can measure distance to the Tag, when used in conjunction with the Reader and Surgical Probe, can be used as a guide for the surgeon during the excision of tissue. The RFLS is a prescription device meant only for use by trained professionals, specifically breast surgeons and diagnostic radiologists.

This document is a 510(k) Summary for the RFID Localization System (RFLS). It describes a medical device and provides information on its performance data, but it does not contain the results of a clinical study with acceptance criteria and a detailed comparison against it. The document primarily focuses on technical performance testing and equivalence to predicate devices, rather than a clinical effectiveness study measuring human reader improvement or standalone algorithm performance against a clinical ground truth.

Therefore, many of the requested fields cannot be filled from the provided text.

Here's a breakdown of what can be extracted and what is missing:

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

The document lists "Performance Testing" categories, but it does not provide specific acceptance criteria or quantitative performance results for clinical endpoints. Instead, it states that tests were performed to demonstrate that the RFLS "met the applicable design and performance requirements."

• Reader/Probe Design Verification performed.
• Localizer System and Component Accuracy, Repeatability Evaluation performed.
• Test Media Evaluation performed.
• Reader and Surgical Probe Operating Environment Evaluation performed.
• Tissue Marker Migration Evaluation performed.
• Usability Verification and Validation performed.
• Electrosurgical tool compatibility performed.
• Packaging Validation performed.
• Sterilization Validation performed. |
  | Biocompatibility requirements per ISO 10993-1 | Met requirements; previously established for reference device (K163667) using identical patient-contacting materials. |
  | Electrical Safety and EMC per IEC 60601-1 and IEC 60601-1-2 | Passed testing for both standards. |
  | Software Verification and Validation testing per FDA Guidances | Conducted and documentation provided. |

2. Sample size used for the test set and the data provenance: Not mentioned. The performance data refers to device-level technical testing rather than a clinical test set.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable, as this document does not describe a study involving expert-established ground truth for a clinical test set.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.

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 document does not describe an MRMC study or AI assistance. The device is a localization system, not an AI diagnostic tool for image interpretation.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable in the context of an "algorithm only" performance. The device is a "marker-with-detector localization device" used by trained professionals. The performance tests mentioned are for the physical device system.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable, as this document does not describe a study involving clinical ground truth. The "ground truth" for the device's technical performance would be engineering specifications and measurements.

8. The sample size for the training set: Not applicable. This is not an AI/ML device that requires a training set in the conventional sense.

9. How the ground truth for the training set was established: Not applicable.

Summary of what the document does provide:

• Device Name: RFID Localization System (RFLS)
• Intended Use: Percutaneous placement of a Tag in the breast to mark a lesion for surgical removal. The RFLS (hand-held reader and surgical probe) assists in non-imaging detection and localization of the Tag.
• Predicate Device: Cianna Medical SAVI Scout Reflector and SAVI Scout System (K171767)
• Comparison: The RFLS has the "same indications for use, principles of operation and similar fundamental scientific technology and materials" as the predicate.
• Performance Data Types: System verification, design verification, accuracy/repeatability, test media evaluation, operating environment, tissue marker migration, usability, electrosurgical tool compatibility, packaging, sterilization, biocompatibility, electrical safety, and software V&V.
• Biocompatibility: Established based on an identical reference device (K163667) and adherence to ISO 10993-1.
• Electrical Safety & EMC: Passed IEC 60601-1 and IEC 60601-1-2 standards.
• Software V&V: Conducted per FDA guidances.

The document concludes that the RFLS is substantially equivalent to its predicate device based on these performance and technical characteristic comparisons.

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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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