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Indicated for displaying images of the tracheobronchial tree to aid the physician in guiding endoscopic tools or catheters in the pulmonary tract and to enable marker placement within soft lung tissue. It does not make a diagnosis and is not an endoscopic tool. Not for pediatric use.

The LungPoint Software is a device that guides a bronchoscope and commercially available endoscopic tools to a prespecified target in or adjacent to the bronchial tree by providing a path, which is displayed on a 3D reconstruction of a CT scan. The Software allows visualization of the interior of the bronchial tree; placement of catheters in the bronchial tree; visualization of a prespecified target in lung tissue; and placement of markers into soft lung tissue to guide radiosurgery and thoracic surgery. The FlexNeedle is an aspiration needle. When used together with the LungPoint Software, the needle can be guided to a prespecified targeted area within the respiratory organs.

The software is installed on an off-the-shelf PC computer system, and is intended to be used with commercially-available flexible bronchoscopes with CT scans that are saved in DICOM format.

The provided text is a 510(k) summary for the LungPoint™ Planning and Virtual Bronchoscopic Navigation (VBN) Software. It primarily focuses on demonstrating substantial equivalence to predicate devices rather than presenting detailed performance studies with acceptance criteria and specific statistical results.

Therefore, many of the requested details about acceptance criteria, performance data, test set characteristics, expert qualifications, and ground truth establishment are not explicitly available in the provided text.

Here's an analysis based on the information that is present:

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

The document states: "No new risks were identified as the use of endoscopic tools, like the FlexNeedle, is inherent to the design and intended use of the existing LungPoint Software. Additionally, no new verification and validation testing was performed as the software was not modified in any way to allow for the use of the FlexNeedle."

This indicates that the primary "acceptance criterion" for this specific 510(k) submission was the absence of new risks or modifications to the software that would necessitate new performance testing. Therefore, there is no direct table of acceptance criteria and reported device performance for this submission. The performance of the underlying LungPoint Software would have been established in previous 510(k) clearances (K091160).

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in the text, as no new performance testing was conducted for this specific submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided in the text, as no new performance testing was conducted for this specific submission.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This information is not provided in the text, as no new performance testing was conducted for this specific submission.

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

There is no mention of an MRMC comparative effectiveness study or AI assistance in the provided text. The device is described as "guid[ing] a bronchoscope... by providing a path, which is displayed on a 3D reconstruction of a CT scan." This suggests a visualization and navigation tool rather than an AI-assisted diagnostic tool.

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

No standalone performance study is mentioned in the text for this submission. The device is explicitly intended to "aid the physician in guiding endoscopic tools," indicating a human-in-the-loop design.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

This information is not provided in the text, as no new performance testing was conducted for this specific submission.

8. The sample size for the training set

This information is not provided in the text, as no new performance testing was conducted for this specific submission. The device is not described as being based on machine learning or requiring a "training set" in the context of this 510(k).

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

This information is not provided in the text, as no new performance testing was conducted for this specific submission and the device's mechanism isn't described as requiring a training set in this context.

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Indicated for displaying images of the tracheobronchial tree to aid the physician in guiding endoscopic tools or catheters in the pulmonary tract and to enable marker placement within soft lung tissue. It does not make a diagnosis and is not an endoscopic tool. Not for pediatric use.

LungPoint Procedure Planning, a software only device. As with the predicate, it provides the physician with 3D reconstruction of the patient's lungs, derived from the CT images and thus provides a more realistic view of the lungs. The physician can use the 3D virtual animation and associated images to view and explore pre-selected targets in the lung tissue before conducting a procedure. Like the predicate, the software allows for printing the procedure plan as a map, which consists of a bifurcation-by-bifurcation description of the route to the selected target. The LungPoint Procedure Planning software is installed on an off-the-shelf PC computer, and is intended to be used in conjunction with commercially-available CT scan images that are saved in DICOM format.

The provided text does not contain the detailed performance data, acceptance criteria, or study specifics typically found in a scientific study or detailed regulatory submission for proving a device meets acceptance criteria.

The document is a 510(k) summary for a software-only medical device (LungPoint™ Procedure Planning Software) and primarily focuses on establishing substantial equivalence to a predicate device. It highlights that the modified device has the same intended use, technological characteristics, and hardware as the planning phase of the predicate, using the exact same software and core algorithms.

Here's a breakdown of what can be extracted and what is missing based on your request:

Acceptance Criteria and Device Performance (Not explicit in the document)

The document does not explicitly state specific acceptance criteria in terms of quantitative metrics (e.g., accuracy, precision, sensitivity, specificity) for the LungPoint Procedure Planning Software. Therefore, a table of acceptance criteria and reported device performance cannot be generated from the provided text.

The closest statement regarding "performance" is in Section 8: "The planned modifications were subjected to the Broncus design control process. Appropriate labeling changes, risk analysis, and design verification were performed to assure that the Procedure Planning software continues to meet its intended use." This indicates that verification activities were performed, but the specific metrics used and the results are not detailed.

Study Details (Largely Not Reported)

Since specific performance metrics and a formal study are not detailed, many of your requested items cannot be definitively answered from this 510(k) summary.

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

• Not reported. The document states "design verification were performed" but does not specify the size or characteristics of any test set used.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not reported. No information about expert involvement in establishing ground truth for testing is provided.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

• Not reported. Adjudication methods are not mentioned.

4. 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 comparative effectiveness study is not mentioned or implied. The device is described as "aiding the physician" but no comparative effectiveness study results are provided.

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

• It's implied that standalone verification was done ("design verification were performed"), but no specific performance metrics or study details for standalone performance are provided. The device is "software only" and "aids the physician," suggesting its primary function is standalone image processing for planning.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• Not reported. With no detailed study information, the type of ground truth used for any verification is not specified.

7. The sample size for the training set:

• Not reported. This document pertains to a modification of an already cleared device, and it states the "exact same software (including core algorithms)" are used as the predicate. Information about the training set for the original predicate device or any retraining for this version is not provided.

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

• Not reported. As above, no details on the training set or its ground truth establishment are given.

Summary of what the document does indicate regarding "performance" and "studies":

• Reliance on Predicate Device: The core argument for safety and effectiveness is that the LungPoint Procedure Planning Software version 2.0 is substantially equivalent to the planning phase of its predicate device (K091160). It explicitly states: "Both products provide guidance to the physician and use the exact same software (including core algorithms) for planning. The key features: 3D animation and printable plan/map; are identical to those of the planning phase of the predicate device and the same software algorithms are used." This implies that the performance characteristics of the planning component were established with the predicate.
• Design Control Process: "The planned modifications were subjected to the Broncus design control process. Appropriate labeling changes, risk analysis, and design verification were performed to assure that the Procedure Planning software continues to meet its intended use." This is a general statement indicating internal verification processes were followed, but without specific data.
• Risk Management: "Risk management is ensured via a hazard analysis and FMECA, which are used to identify potential hazards. These potential hazards are controlled via software development, verification testing and/or validation testing." This confirms that testing was done to mitigate risks, but again, without specific performance outcomes.

To reiterate, this 510(k) summary provides a high-level overview for regulatory clearance based on substantial equivalence, rather than a detailed scientific publication outlining specific performance studies with quantitative results and acceptance criteria.

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Indicated for displaying images of the tracheobronchial tree to aid the physician in guiding endoscopic tools or catheters in the pulmonary tract and to enable marker placement within soft lung tissue. It does not make a diagnosis and is not an endoscopic tool. Not for pediatric use.

This premarket notification covers Broncus' LungPoint VBN System. The VBN System is a software only device, providing a navigation system to help the bronchoscopist plan and proceed to a predefined target site (also referred to as region of interest (ROI) in the tracheobronchial tree. Specifically, the VBN system provides guidance to targets preselected by the bronchoscopist in lung tissue. In doing so, the VBN can provide guidance to lymph nodes to enable tissue sampling. It can also facilitate the return to an exact location in the lungs that had previously been treated for assessment of or continued therapy, or enable marker placement.

The provided document is a 510(k) summary for the Broncus LungPoint™ Virtual Bronchoscopic Navigation (VBN) Software. It primarily focuses on demonstrating substantial equivalence to a predicate device after software modifications, rather than presenting a detailed study with specific acceptance criteria and performance metrics for the device itself.

Therefore, much of the requested information cannot be extracted from this document because the submission does not detail a study conducted to establish acceptance criteria and prove the device meets them in the way clinical performance studies typically do for diagnostic or therapeutic devices. This 510(k) is for a software modification, and the performance data section mentions "design control process," "labeling changes, risk analysis, and design verification," rather than a clinical performance study.

Here's a breakdown of what can and cannot be answered based on the provided text:

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

This information is not available in the provided document. The 510(k) summary refers to design verification and risk analysis as evidence of performance, but it does not specify quantitative acceptance criteria or corresponding reported device performance metrics in a clinical context.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

This information is not available in the provided document. No specific test set or clinical study data is detailed for the performance validation.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not available in the provided document. There is no mention of a test set requiring ground truth established by experts.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

This information is not available in the provided document. No test set requiring ground truth adjudication is described.

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

This information is not available in the provided document. The submission details software modifications for a navigation system, not a diagnostic AI system that would typically undergo an MRMC study to compare human reader performance with and without AI assistance.

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

This information is not applicable/not available in the provided document in the context of typical standalone performance studies for AI software. The device is described as a "Virtual Bronchoscopic Navigation (VBN) Software" that "provides guidance to targets preselected by the bronchoscopist." This implies a human-in-the-loop system where the software aids the physician, rather than acting as a standalone diagnostic algorithm. No standalone performance metrics are provided.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

This information is not available in the provided document. There is no mention of ground truth as no formal clinical performance study is detailed.

8. The sample size for the training set

This information is not available in the provided document. The document refers to software modifications and design verification, not to the training of a machine learning model, which would involve a training set.

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

This information is not available in the provided document. As there is no mention of a training set, the method for establishing its ground truth is also not provided.

Summary of what the document does state about performance:

The document states under "8. Performance Data":
"The planned modifications were subjected to the Broncus design control process. Appropriate labeling changes, risk analysis, and design verification were performed to assure that the VBN software continues to meet its intended use."

And under "9. Safety and Effectiveness":
"Risk management is ensured via a hazard analysis and FMECA, which are used to identify potential hazards. These potential hazards are controlled via software development, verification testing and/or validation testing."

This indicates that the performance verification for this 510(k) submission was based on internal design control processes, risk analysis, and software verification/validation testing, rather than a clinical trial or performance study against predefined clinical acceptance criteria. The submission is focused on demonstrating substantial equivalence of the modified software to its predicate, particularly regarding an "enhanced graphical user interface (GUI)" and streamlined planning/procedure processes.

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The Yield Tissue Sampler is indicated for use in aspiration in carinal, paratracheal and hilar lesions of the bronchial tree where biopsy forceps cannot obtain a submucosal sample.

This premarket notification covers Broncus' Yield Tissue Sampler. The Yield Tissue Sampler is a transbronchial aspiration needle used for transbronchial retrieval of tissue samples. It is compatible with flexible 2-mm working channel bronchoscopes and is available in two sizes, 18 and 21 gauge.

The provided text describes a 510(k) premarket notification for the Broncus Technologies, Inc. Yield™ Tissue Sampler. The document focuses on demonstrating substantial equivalence to predicate devices rather than presenting a study proving the device meets specific acceptance criteria related to AI or algorithm performance. Therefore, many of the requested categories are not applicable.

Here's an analysis of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document only states that the device "passed pre-established acceptance criteria" for strength and aspiration, and that "all items tested were within specification tolerances" for dimensional testing. Specific numerical values for acceptance criteria or performance are not provided.

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

• Not applicable. This document describes performance testing for a medical device (transbronchial aspiration needle), not a study involving a test set of data for an AI algorithm. The performance tests mentioned (dimensional, strength, aspiration, biocompatibility) are laboratory-based and device-specific, not data-driven in the context of AI.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience):

• Not applicable. As above, this is not an AI algorithm study requiring expert-established ground truth.

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 device is a physical medical instrument, not an AI system designed to assist human readers or perform diagnostic tasks.

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

• Not applicable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• Not applicable. For the physical device testing, "ground truth" would equate to established engineering standards, material properties, and manufacturing specifications.

8. The sample size for the training set:

• Not applicable. There is no AI training set mentioned or implied for this physical device.

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

• Not applicable. There is no AI training set.

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The Yield Mini Doppler Probe with Broncus® Monitor is intended for use through a bronchoscope to detect blood flow behind airway walls in the tracheobronchial tree.

This premarket notification covers Broncus' Yield Mini Doppler System, which comprises the Yield Mini Doppler Probe and Broncus Monitor. The Mini Doppler System will enable the bronchoscopist to identify vessel-free areas prior to performing transbronchial procedures such as needle aspiration or biopsy.

Acceptance Criteria and Study for Yield™ Mini Doppler System (K090743)

The provided documentation for the Yield™ Mini Doppler System (K090743) is a 510(k) Summary and an FDA clearance letter. These documents attest to the device's substantial equivalence to predicate devices rather than providing detailed acceptance criteria and a study demonstrating the device meets those criteria in the context of a clinical performance trial with specific metrics.

Therefore, much of the requested information regarding specific acceptance criteria, performance metrics, sample sizes, ground truth establishment, and multi-reader studies is not explicitly available in the provided text. The submission focuses on demonstrating safety and equivalence through engineering testing and comparison to existing cleared devices.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The provided document does not specify quantitative clinical acceptance criteria or performance metrics such as sensitivity, specificity, or diagnostic accuracy that would be typically found in a clinical performance study for an AI/algorithm-based device. The "acceptance criteria" discussed are primarily related to manufacturing, safety, and functionality.

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

• Sample Size for Test Set: Not specified. The document refers to "performance testing" but does not detail the nature or sample size of any clinical or in-vitro test set for evaluating the device's ability to detect blood flow. The clearance is based on equivalence to predicate devices and engineering testing.
• Data Provenance: Not applicable, as there's no clinical test set data from patients described. The testing described (mechanical, thermal, electrical safety, biocompatibility) are engineering and laboratory tests.

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

• Number of Experts: Not applicable. There is no mention of a human-read test set or expert ground truth establishment for a clinical performance evaluation.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. No clinical test set requiring adjudication is described.

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

• MRMC Study: No. The document does not describe any MRMC study comparing human reader performance with or without AI assistance. This device is a diagnostic tool, not an AI-assisted interpretation product.

6. Standalone Performance Study

• Standalone Performance Study: The document describes "Performance testing of the Yield Mini Doppler System included mechanical, thermal and electrical safety testing." These are standalone engineering tests. However, a standalone clinical performance study (e.g., sensitivity, specificity for detecting blood flow compared to a definitive reference standard in a patient population) that would typically be associated with an AI algorithm's standalone performance is not described. The clearance relies on substantial equivalence to predicate devices.

7. Type of Ground Truth Used

• Type of Ground Truth: For the "Performance Data" mentioned (mechanical, thermal, electrical safety, biocompatibility), the ground truth would be engineering specifications, regulatory standards (e.g., ISO 10993), and safety requirements. For the device's core function of detecting blood flow, while not explicitly detailed, the inherent design and operation are deemed equivalent to previously cleared devices which would have established their ability to detect blood flow. There's no mention of pathology, outcomes data, or expert consensus as ground truth for a clinical efficacy study within this submission.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This device is a Doppler system, not an AI/machine learning algorithm that requires a training set.

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

• Ground Truth for Training Set Establishment: Not applicable, as there is no training set for an AI/machine learning algorithm.

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Indicated for displaying images of the tracheobronchial tree to aid the physician in guiding endoscopic tools or catheters in the pulmonary tract and to enable marker placement within soft lung tissue. It does not make a diagnosis and is not an endoscopic tool. Not for pediatric use.

This premarket notification covers Broncus' LungPoint VBN System. The VBN System is a software only device, providing a navigation system to help the bronchoscopist plan and proceed to a predefined target site in the tracheobronchial tree. Specifically, the VBN system provides global quidance to targets preselected by the bronchoscopist in peripheral airways. In doing so, the VBN can provide local guidance to lymph nodes to enable tissue sampling. It can also facilitate the return to an exact location in the lungs that had previously been treated for assessment of or continued therapy. The VBN software is installed on an off-the-shelf PC computer system, and is intended to be used with commercially-available flexible bronchoscopes with HRCT scans that are saved in DICOM format.

Here's a breakdown of the acceptance criteria and study information for the LungPoint™ Virtual Bronchoscopic Navigation (VBN) System, based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The 510(k) summary does not explicitly state "acceptance criteria" but rather presents performance data from studies. The interpretation is that the demonstrated accuracy values were deemed acceptable by the FDA for clearance.

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

• Animal Study: The document mentions "an animal study" in a "canine model" but does not specify the exact number of animals or trials conducted.
• Phantom Study: The document refers to "an earlier phantom study performed by Merritt et al" but does not specify the sample size (number of phantom cases/measurements).
• Data Provenance:
  • Animal Study: Canine model (prospective, as it was conducted to evaluate the system).
  • Phantom Study: Not explicitly stated, but phantom studies are typically controlled and designed prospectively.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The provided 510(k) summary does not include information on the number or qualifications of experts used to establish ground truth for either the animal or phantom studies. The ground truth for accuracy was likely established through direct measurement of physical distances, rather than expert consensus on subjective interpretations.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method for the test set. Given the nature of measuring distance error in physical or virtual environments, it's unlikely that adjudications by multiple readers were required in the same way they would be for subjective image interpretations.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done or reported in this 510(k) summary. The studies focused on the standalone accuracy of the navigation system rather than its impact on human reader performance.

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

Yes, the reported studies primarily assess the standalone performance of the LungPoint VBN system. The accuracy measurements (distance error) evaluate the system's ability to precisely align virtual targets with actual targets, which is an intrinsic performance characteristic of the algorithm/system rather than its direct impact on a human user's diagnostic ability.

7. Type of Ground Truth Used

• Animal Study: The ground truth was based on the "actual target in the real bronchoscope video" which implies physical, measurable locations marked or identified in a live setting, against which the virtual targets were compared. This would be a form of direct measurement/physical truth.
• Phantom Study: Similarly, the ground truth for the phantom study would have been based on physical precision measurements within the phantom model.

8. Sample Size for the Training Set

The 510(k) summary does not mention or specify a sample size for the training set for the VBN software. As a navigation system, its core function is to process existing HRCT scans (DICOM format) to create virtual pathways. While software development involves testing and calibration, the summary does not detail a separate "training set" in the context of machine learning model development. This submission precedes the widespread emphasis on AI/ML training data reporting in regulatory submissions.

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

Since no training set is mentioned in the provided document, there is no information on how its ground truth was established. For a navigation system of this type, the "training" (if applicable in a more traditional software sense) would likely involve adherence to anatomical models and engineering specifications for calculating virtual paths and registering images.

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The Exhale Probe is intended for blood-flow detection and electrosurgery procedures (i.e. coagulation/cauterization, hemostasis, etc.) in the upper airways and tracheobronchial tree through a bronchoscope.

The Exhale Probe is a multi-function catheter that provides: (1) Doppler audio output in the presence of pulmonary vessel blood-flow; and (2) radiofrequency (RF) energy to a target site within the upper airway or tracheobronchial tree. The probe is inserted into through the 2 mm working channel of a standard bronchoscope and connects to the Exhale Doppler Processing Unit and a standard, commercially available, electrocautery radiofrequency generator.

This document is a 510(k) summary for the "Exhale Probe," a medical device cleared by the FDA in 2001. It describes the device and its intended use, but it does not contain information about acceptance criteria or a study proving the device meets those criteria, as typically found in clinical trials for new medical devices or AI algorithms.

Instead, this document focuses on demonstrating substantial equivalence to previously cleared predicate devices. Substantial equivalence is a pathway for market clearance where a new device is shown to be as safe and effective as a legally marketed device (the predicate). This usually involves demonstrating similar intended use, technological characteristics, and performance, often through non-clinical testing.

Therefore, I cannot provide the requested information for this specific device based on the provided text. The document is primarily a regulatory submission demonstrating equivalence, not a performance study against predefined acceptance criteria.

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The Broncus™ Coagulation Electrode System is intended for coagulation or hemostasis in the tracheobronchial tree.

The Broncus Coagulation Electrode System consists of a catheter, a RF generator, and a commercially available patient return electrode. The RF generator produces RF power in a monopolar mode. The catheter delivers RF energy to the desired target site and relays temperature and other feedback to the RF generator. The patient return electrode is used to complete the return path for the electrical current.

The provided document is a 510(k) summary for the Broncus™ Coagulation Electrode System, dated January 17, 2000. It describes the device, its intended use, and its comparison to predicate devices, but it does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and the comprehensive study that proves the device meets those criteria.

Specifically, this document focuses on establishing substantial equivalence to predicate devices based on intended use and non-clinical performance and biocompatibility testing. It lacks the specifics of clinical study design, sample sizes for training or test sets, expert qualifications, ground truth establishment, or specific performance metrics that would be expected for a detailed AI/ML device submission.

Here's a breakdown of what can and cannot be answered from the provided text:

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

• Note: The document states that the "Coagulation Electrode System has undergone and passed functional and electrical testing designed to assess the performance of the catheter and the RF generator" and "The materials used in the Coagulation Electrode have proven biocompatibility." However, it does not specify the quantitative acceptance criteria (e.g., specific voltage ranges, tissue temperature limits, or mechanical strength thresholds) for these tests, nor does it provide detailed numerical results beyond "passed."

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not Available: The document does not mention any clinical test sets, human subject data, or data provenance. The testing described is non-clinical (functional, electrical, and biocompatibility).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not Applicable/Available: As no clinical test set or human subject data is mentioned, there is no discussion of experts establishing ground truth for such data.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not Applicable/Available: No clinical test set is discussed.

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 device is a medical instrument (coagulation electrode system), not an AI/ML diagnostic or assistive tool. Therefore, an MRMC study related to human reader performance with or without AI assistance is not relevant to this submission.

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

• Not Applicable: This is a physical medical device, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Not Applicable/Available: For the non-clinical tests described, the "ground truth" would be engineering specifications and standards for functional and electrical performance, and established biocompatibility standards for materials. These are not explicitly detailed in the summary.

8. The sample size for the training set

• Not Applicable/Available: This is a hardware device, not an AI/ML algorithm that requires a training set.

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

• Not Applicable/Available: No training set is relevant for this device.

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