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The Mac-Lab system is indicated for use on patients of all ages when a physician determines that a patient would benefit from a hemodynamic procedure. Mac-Lab may be used in a variety of hospital and clinical settings to record hemodynamic data and measurements, which may then be displayed, filtered, digitized, amplified, measured, and calculated and/or transmitted for storage, analysis and viewing at distributed locations.

The CardioLab system is indicated for use on patients of all ages when a physician determines that a patient would benefft from an electrophysiology procedure. CardioLab may be used in a variety of hospital and clinical settings to record electrophysiology data and measurements, which may then be displayed filtered, amplified, measured, and calculated and/or transmitted for storage, analysis and viewing at distributed locations.

The ComboLab system is indicated for use on patients of all ages when a physician determines that a patient would benefit from either a hemodynamic or electrophysiology procedure. ComboLab may be used in a variety of hospital and clinical settings to record hemodynamic and electrophysiology data and measurements, which may then be displayed, filtered, digitized, amplified, measured, calculated and/or transmitted for storage, analysis and viewing at distributed locations.

The MLCL Client Software is indicated for use on patients of all ages when a physician determines that a patient would benefit from either a hemodynamic or electrophysiology procedure. MLCL Client Software may be used in a variety of hospital and clinical settings to record, document and/or review hemodynamic and electrophysiology data and measurements, which may then be displayed, filtered, digitized, amplified, measured, calculated and/or transmitted for storage, analysis and viewing at distributed locations.

Mac-Lab and CardioLab are hemodynamic and electrophysiology (EP) recording systems, respectively. A third configuration, ComboLab, allows the user to access both CardioLab and Mac-Lab functions, though only one application may be accessed at a time.

These devices are used during interventional and related procedures to process, display and record hemodynamic and electrophysiology (EP) data depending on the type of procedure performed. The data is acquired and displayed real-time for multiple physiological parameters to allow the user to view the data. The data may be entered manually through the use of a dedicated keyboard/mouse/barcode scanner or acquired via procedural information devices, imaging devices and interfaced data devices, and may then be displayed, filtered, digitized, amplified, measured, and calculated.

A fourth configuration, called the MLCL Client Software, is the core Mac-Lab and CardioLab application software which is available for installation on a stand-alone workstation (i.e. outside of the Mac-Lab/CardioLab/ComboLab acquisition systems described above). The MLCL Review Software may be used to record, document, analyze, store and transmit data, including data from supported patient monitors.

Mac-Lab, CardioLab, ComboLab and the MLCL Client Software provide the ability to transmit patient data for storage, analysis and viewing at distributed locations within a clinical facility via network connectivity but may also be used stand-alone (not connected to a network).

Mac-Lab, CardioLab, ComboLab and the MLCL Client Software are not intended to be used as a patient monitor and are not intended to alert the licensed health care practitioner of a change in patient status.

The provided text describes the acceptance criteria and the study that proves the device meets the acceptance criteria for the Mac-Lab Recording Systems, CardioLab Recording Systems, ComboLab Recording Systems, and MLCL Client Software.

Here's a breakdown of the requested information:

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

The document does not explicitly state quantitative acceptance criteria in a table format for performance metrics. Instead, it focuses on demonstrating substantial equivalence to a predicate device through non-clinical testing and comparison of technological characteristics. The overall reported device performance is that the device is "as safe, as effective, and performs as well as the legally marketed predicate device."

Summary of Reported Device Performance and Equivalence Claims:

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

The document explicitly states: "The subject of this premarket submission, Mac-Lab, CardioLab and ComboLab AltiX, did not require clinical studies to support comparability to the predicate device." Therefore, there is no sample size for an external "test set" and no direct data provenance related to a clinical study. The evaluation primarily relied on non-clinical testing and comparison to the predicate device.

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)

Since no clinical studies were performed to establish a "test set" with ground truth from experts, this information is not applicable and not provided in the document.

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

As no clinical test set requiring expert ground truth or adjudication was conducted, this information is 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

No MRMC comparative effectiveness study was done. The device is a recording system for hemodynamic and electrophysiology data and measurements, not an AI-assisted diagnostic tool for human readers.

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

The document describes "Performance testing (Verification)" and "System performance testing (Verification)" as part of the non-clinical tests. These would likely involve evaluating the algorithm's performance in isolation or as part of the system. However, specific standalone performance metrics or a detailed description of "algorithm-only" performance is not provided beyond the general statement of performing "as well as" the predicate. The device's primary function is data acquisition, display, and processing, not automated diagnosis that would typically warrant a standalone algorithmic performance study distinct from the system's overall function.

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

For the non-clinical testing, the "ground truth" for verification and validation would be established by reference to engineering specifications, established standards (e.g., IEC 60601 series), and the performance of the legally marketed predicate device. This is primarily technical or functional ground truth, rather than clinical ground truth (like pathology or outcomes data).

8. The sample size for the training set

The document does not mention any "training set" as part of an AI/machine learning model. The device is described as a recording system, and the changes primarily involve hardware updates (new EP amplifier) and inclusion of existing technology (DFR technology). There is no indication of machine learning components that would require a distinct training set.

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

As there is no mention of a training set for an AI/machine learning component, this information is not applicable.

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The IVUS modality of the iLab™ Polaris Multi-Modality Guidance System is intended for ultrasound examinations of intravascular pathology. Intravascular ultrasound is indicated in patients who are candidates for transluminal interventional procedures such as angioplasty and atherectomy.

FFR and DFR™ are intended for use in catheterization and related cardiovascular specialty laboratories to compute, and display various physiological parameters based on the output from one or more electrodes, transducers, or measuring devices.

FFR and DFR are indicated to provide hemodynamic information for use in the diagnosis and treatment of patients that undergo measurement of physiological parameters.

The Imaging Catheters generate ultrasound images and are intended for ultrasound examination of vascular and cardiac pathology. Boston Scientific manufactures a wide variety of catheters for different applications. The recommended use of each of these catheters may vary depending on the size and type of the catheter. Please refer to the Imaging Catheter Directions for Use, packaged with each catheter.

Indications for Auto Pullback Use (IVUS Only)

Automatic Pullback is indicated when the following occurs:

• The physician/operator wants to standardize the method in which intravascular ultrasound images are obtained and documented: procedure-to-procedure, operator-to-operator.

· The physician/operator wants to make linear determinations post-procedurally, which requires the imaging core of a catheter to be pulled back at a known uniform speed.

· Two-dimensional, longitudinal reconstruction of the anatomy is desired.

The iLab™ Polaris Multi-Modality Guidance System is a non-patient contacting, diagnostic device designed to provide three (3) primary modalities: IVUS (Intravascular Ultrasound), FFR (Fractional Flow Reserve) and Diastolic hyperemia-Free Ratio (DFR).

The Polaris 2.12 (iLab 3.12) software update supports the iLab™ Polaris Multi-Modality Guidance System and supports existing device IVUS, FFR and DFR™ functionality as well as includes scaling updates to DFR™ equalization values, enhanced imaging modes and OS patches for additional security.

Diastolic hyperemia-Free Ratio™ (DFR) is a resting index that measures multiple diastolic portions during the cardiac cycle. DFR™ calculates the diastolic portion of the cardiac cycle averaged over five beats, using two criteria for the measurement windows: 1) Pa less than mean Pa and 2) down sloping Pa values. No hyperemic agent is required for DFR™ calculation. The Polaris 2.12 software update adds an automatic scaling feature for DFR™ equalization.

This looks like an FDA 510(k) clearance letter and summary for a software update to an existing medical device, the iLab Polaris Multi-Modality Guidance System. Based on the provided text, the submission focuses on non-clinical performance data for a software update, not a new AI/ML-based diagnostic algorithm requiring extensive clinical validation.

Therefore, many of the requested points regarding the study that proves the device meets acceptance criteria (especially those related to clinical performance, ground truth, expert adjudication, and MRMC studies) are explicitly stated as "Not applicable" in the document.

Here's a breakdown of the available information:

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

The document does not provide a specific table of acceptance criteria with corresponding performance metrics (e.g., sensitivity, specificity, accuracy) for a diagnostic AI/ML algorithm. Instead, it states that
"Determination of substantial equivalence is based on an assessment of non-clinical performance data which includes software verification and validation carried out on Polaris 2.12 (iLab 3.12) software. Testing was conducted according to applicable international standards, FDA recognized consensus standards, and the same well-established test methods and criteria applied to the predicate device."

Device Performance Reported: The core performance claim is "substantial equivalence" to the predicate device (iLab Polaris Multi-Modality Guidance System K191008). This is based on:

• Non-clinical performance data: software verification and validation.
• Compliance with standards: IEC 62304 and FDA Guidance for Premarket Submissions for Software Contained in Medical Devices.

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

• Sample Size for Test Set: This information is not provided in the document. As the submission is based on non-clinical software verification and validation, it's unlikely to involve traditional clinical "test sets" with patient data in the same way an AI diagnostic algorithm would.
• Data Provenance: This information is not provided and is likely not relevant given the non-clinical nature of the submission (software V&V).

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

• Not Applicable. The submission explicitly states "Clinical Performance Data: Not applicable. A determination of Substantial Equivalence for this modification is not based on clinical data." Therefore, there was no ground truth derived from expert review of patient cases in the context of this 510(k).

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

• Not Applicable. As no clinical "test set" with expert review was used for performance evaluation, no adjudication method was employed.

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. The document clearly states: "Clinical Performance Data: Not applicable." Therefore, no MRMC study was conducted.

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

• No, not in the context of a new diagnostic algorithm. The device is a "Multi-Modality Guidance System" with IVUS, FFR, and DFR functionalities, intended for use by clinicians. The software update (Polaris 2.12 / iLab 3.12) supports these existing functionalities and includes "scaling updates to DFR™ equalization values, enhanced imaging modes and OS patches for additional security." This is a software update to a guidance system, not a standalone diagnostic algorithm that makes a diagnosis.

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

• Not Applicable. As no clinical performance data was submitted, there was no ground truth established from patient data. The "ground truth" for software verification and validation would be adherence to specified software requirements and functionality, confirmed through various engineering tests.

8. The sample size for the training set

• Not Applicable. This submission is for a software update to an existing device, not a de novo AI/ML algorithm that requires a "training set" of data.

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

• Not Applicable. Since there was no "training set" of data for an AI/ML algorithm, no ground truth was established for it.

In summary: This FDA 510(k) submission primarily relies on non-clinical software verification and validation to demonstrate substantial equivalence for a software update to an existing medical device. It does not introduce a new AI/ML diagnostic algorithm that would typically require the extensive clinical study data (including test sets, ground truth, expert review, and MRMC studies) you've inquired about.

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