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The BodyGuardian™ Remote Monitoring System detects and monitors cardiac arrhythmias in ambulatory patients, when prescribed by a physician or other qualified healthcare professional.

The BodyGuardian Remote Monitoring System is intended for use with adult and pediatric patients who are at least 29 days old in clinical and non-clinical settings to collect and transmit electrocardiogram (ECG) and other health parameters to healthcare professionals for monitoring and evaluation. Health parameters, such as heart rate and ECG data, are collected from external devices such as ECG sensors.

The BodyGuardian Remote Monitoring System does not provide any diagnosis.

The BodyGuardian Remote Monitoring System (BGRMS) is a system for recording and analyzing ECG data for cardiac arrhythmias to assist healthcare professionals, including ECG technicians at 24/7 attended analysis centers in evaluating a patient's cardiac health. Reports are generated for clinician review, that provide analysis and summary of the ECG data collected during a patient's monitoring study. Both the predicate and proposed devices, feature a modular design inclusive of outpatient cardiac telemetry (commonly called mobile cardiac telemetry (MCT)), cardiac event monitor and connected/non-connected Holter modalities. Components in the system external to the software include ECG monitors, electrodes, mobile phones and apps.

The BGRMS System includes the following main components:

• ECG monitor – a patient worn device for ECG waveform data collection and transmission, utilized with compatible electrodes
• Mobile App – applications that execute on an off-the-shelf (OTS) smartphone to communicate with the ECG monitor and the PatientCare Server for collection and transmission of data
• PatientCare – server software responsible for receiving, storing, analyzing, and displaying and reporting data gathered from the ECG monitors; includes the ECG analysis algorithm BeatLogic™
• AI-Based Device Software Functionality (AI-DSF) – Automated classification of continuous
  ECG based on the proprietary BeatLogic™ AI algorithm. BeatLogic consists of an ensemble of deep neural networks (DNNs), trained on real-world patient data and post-processing logic that combines the DNN output to produce individual beat, rhythm, and waveform classifications. This output is intended to be reviewed and confirmed by healthcare professionals to assist in diagnosis.

The provided FDA 510(k) clearance letter and summary for the BodyGuardian Remote Monitoring System (BGRMS v3.0) contains information on the device's acceptance criteria and study to prove it.

Acceptance Criteria and Reported Device Performance

The clinical validation results met all predefined acceptance criteria, though the specific criteria are not explicitly detailed in the provided document beyond "substantially equivalent performance for BeatLogic." The performance was assessed by evaluating the Sensitivity and Positive Predictive Value (PPV) for key rhythms. While specific numerical values for the acceptance criteria are not given, the reported device performance is stated as meeting these unspecified criteria.

Details of the Study Proving Device Meets Acceptance Criteria

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

• Sample Size: Not explicitly stated, but described as "real-world, randomly selected ECG records" with a demographic breakdown of 48.6% Female, 39.2% Male, 12.2% unknown gender, 50.1% < 65 years of age, 49.8% ≥ 65 years of age, and 0.1% unknown age.
• Data Provenance: "Real-world patient data" with representation across "US geographic region," indicating data from the United States. The data is retrospective as it was used to train and validate the algorithm, selected to reflect various algorithm outputs, compatible ECG device configurations, accessory types, and demographic factors.

2. 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 document states that the BeatLogic™ AI algorithm's "output is intended to be reviewed and confirmed by healthcare professionals to assist in diagnosis," but it does not specify how the ground truth for the test set was established or the number/qualifications of experts involved in this process.

3. Adjudication Method for the Test Set

This information is not provided in the document.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• Was it done?: No, an MRMC comparative effectiveness study is not explicitly mentioned. The study focuses on the standalone performance of the AI algorithm (BeatLogic™).
• Effect size of human readers improvement: Not applicable, as an MRMC study was not described.

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

• Was it done?: Yes. The performance measurement of the BeatLogic™ algorithm involved evaluating Sensitivity and PPV, which are metrics typically used for standalone algorithm performance against a ground truth. The document explicitly states, "Performance of the algorithm was assessed by evaluating the Sensitivity and Positive Predictive value (PPV) for key rhythms across different patient subgroups." Furthermore, it mentions that the algorithm's "output is intended to be reviewed and confirmed by healthcare professionals to assist in diagnosis," implying that the performance reported is that of the algorithm prior to human review.

6. The Type of Ground Truth Used

The ground truth annotations were established based on "ground truth annotations on real-world ECG data." The method of establishing these annotations (e.g., expert consensus, pathology, outcomes data) is not explicitly stated. However, the context of cardiac arrhythmia detection strongly suggests ground truth would be established by qualified cardiologists or electrophysiologists.

7. The Sample Size for the Training Set

• Sample Size: Not explicitly stated, but described as "real-world, randomly selected ECG records" that ensured "representation across algorithm outputs, compatible ECG device configurations and accessory types and demographic factors encompassing patient age, gender, geographic location, and indication for monitoring."

8. How the Ground Truth for the Training Set was Established

The document states that the BeatLogic™ AI algorithm consists of "deep neural networks (DNNs), trained on real-world patient data." However, the specific method for establishing the ground truth for this training data is not explicitly provided. It is implied that this involved annotations on "real-world patient data," but the process for generating these annotations (e.g., expert review, automated processes) is not detailed.

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The Assert-IQ™ ICM is indicated for the monitoring and diagnostic evaluation of patients who experience unexplained symptoms that may be cardiac-related such as: dizziness, palpitations, chest pain, syncope, and shortness of breath, as well as patients who are at risk for cardiac arrhythmias such as bradycardia, tachycardia, and sinus pauses. The Assert-IQ™ ICM is also indicated for patients who have been previously diagnosed with atrial fibrillation (AF) or who are susceptible to developing AF. The Assert-IQ™ ICM is intended to be inserted subcutaneously in the left pectoral region, also described as the left anterior chest wall. The Assert-IQ™ ICM has not been specifically tested for pediatric use.

The Assert-IQ™ ICM system is intended to help physicians monitor, diagnose, and document the rhythm in patients who are susceptible to cardiac arrhythmias and unexplained symptoms, as indicated. The Assert-IQ™ Insertable Cardiac Monitor (ICM) family of Insertable Cardiac Monitor devices includes cleared models DM5000, DM5300, and DM5500. A fourth model is being included as the subject device within this 510(k)—the Assert-IQ™ 4 ICM, model DM5100.

Overview of Technological features relative to predicate (K251221) Assert-IQ™ ICM devices:

• Patient-initiated triggering of EGM storage using the myMerlin™ mobile application. This includes capability for the patient to identify symptoms, which are stored with the EGM for physician review which is identical in Assert-IQ™ ICM models DM5500 and DM5000.

• Automated triggering of EGM storage when tachycardia, bradycardia, or pauses are detected; with physician-programmable values for pause duration, bradycardia rate, tachycardia rate, and number of tachycardia intervals, which is identical in Assert-IQ™ ICM models DM5500 and DM5000.

• Automated triggering of EGM storage when atrial fibrillation (AF) is detected, with physician programmable values for AF duration. The ability to inhibit EGM storage due to noise and allow for detection and storage of AF and non-AF (pause, bradycardia, and tachycardia) arrhythmias after noise exit, which is identical Assert-IQ™ ICM models DM5500 and DM5000.

• Collection and display of diagnostic trends, including AF burden, which is identical in Assert-IQ™ ICM models DM5500 and DM5000 and PVC burden, available in the subject device DM5100 and in model DM5500

• Remote monitoring capability, which is identical in Assert-IQ™ ICM models DM5500 and DM5000

• Remote Programming capability, which is available in the subject device DM5100 and in model DM5500.

• The subject device model DM5100 has a 4-year battery longevity, positioned between the longevity of) model DM5500 (6 years) and model DM5000 (3 years). This design change does not raise new or different questions of safety or effectiveness.

• Accelerometer in subject device model DM5100 is 1D configuration which is identical to that in model DM5000 and differs from model DM5500 (3D configuration). This configuration does not raise new or different questions of safety or effectiveness.

The provided FDA 510(k) Clearance Letter for the Assert-IQ™ Insertable Cardiac Monitor System (K253516) does not contain the detailed information necessary to fully answer all aspects of your request.

The clearance is for a Special 510(k), which indicates that the changes made to the device (model DM5100) are minor and do not introduce new indications for use or alter the fundamental scientific technology compared to a previously cleared predicate device (K251221). As such, the submission primarily leverages testing and validation from the predicate device and focuses on demonstrating that the new model performs equivalently and does not raise new questions of safety or effectiveness.

Therefore, much of the information you requested regarding new acceptance criteria, performance studies, sample sizes, ground truth establishment, or MRMC studies for this specific submission (K253516) is not present. The document explicitly states "No new clinical functionality, user needs, or intended use introduced," and "Existing validation activities from predicate (K251221) models (e.g., usability, algorithm performance, cybersecurity, compliance) remain applicable and sufficient."

However, I can extract the available information and highlight what is missing based on your prompts.

Acceptance Criteria and Device Performance (Based on available information)

The document refers to verification activities to confirm the DM5100 meets design specifications and performs equivalently to predicate models. It also mentions "algorithm performance" being leveraged from the predicate. Without access to the predicate device's 510(k) submission (K251221), specific performance criteria for the AI algorithms are not explicitly stated in this document.

Study Details (Based on available information)

This 510(k) submission is a Special 510(k), and therefore, a primary performance study on the AI algorithm was not conducted for this specific submission (K253516). The document explicitly states that "Existing validation activities from predicate (K251221) models (e.g., usability, algorithm performance, cybersecurity, compliance) remain applicable and sufficient."

To get answers to many of the following questions, one would need to review the original 510(k) submission for the predicate device (K251221).

1. Sample size used for the test set for AI algorithm performance: Not provided in this document. It leverages previous validation from K251221.

   • Data provenance (e.g., country of origin of the data, retrospective or prospective): Not provided in this document.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided in this document. It leverages previous validation from K251221.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not provided in this document. It leverages previous validation from K251221.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Not provided in this document. It is unlikely for a Special 510(k) which primarily relies on equivalence to a predicate. The document implies the AI is for automated detection and presumably works in conjunction with a physician review, but doesn't detail reader studies.

   • If so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not provided in this document.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: The document mentions "Automated triggering of EGM storage when tachycardia, bradycardia, or pauses are detected," and "Automated triggering of EGM storage when atrial fibrillation (AF) is detected." This implies a standalone algorithm for detection. However, specific standalone performance metrics (e.g., sensitivity, specificity, PPV for these detections) are not provided in this document for this specific K253516 submission, as they are leveraged from the predicate K251221.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not provided in this document. It leverages previous validation from K251221.

7. The sample size for the training set: Not provided in this document. It leverages previous validation from K251221.

8. How the ground truth for the training set was established: Not provided in this document. It leverages previous validation from K251221.

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Intended Use:
The devices are intended to be used for monitoring and recording of, and to generate alarms for multiple physiological parameters of adults, pediatrics, and neonates.

Indications for Use
The monitors are indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients.

The monitors are only for use on one patient at a time.

The monitors are not therapeutic devices.

The monitors are for prescription use only.

The ECG measurement is intended to be used for diagnostic recording of rhythm and detailed morphology of complex cardiac complexes (according to AAMI EC 11).

ST segment monitoring is intended for use with adult patients only and is not clinically validated for use with neonatal and pediatric patients.

The transcutaneous gas measurement (tcGas) with the M1018A plug-in module is restricted to neonatal patients only.

BIS is intended for use under the direct supervision of a licensed health care practitioner or by personnel trained in its proper use. It is intended for use on adult and pediatric patients within a hospital or medical facility providing patient care to monitor the state of the brain by data acquisition of EEG signals. The BIS may be used as an aid in monitoring the effects of certain anesthetic agents. Use of BIS monitoring to help guide anesthetic administration may be associated with the reduction of the incidence of awareness with recall in adults during general anesthesia and sedation.

The SSC Sepsis Protocol, in the ProtocolWatch clinical decision support tool, is intended for use with adult patients only.

The Integrated Pulmonary Index (IPI) is intended for use with adult and pediatric (1 to 12 years) patients only. The IPI is an adjunct to and not intended to replace vital sign monitoring.

The derived measurement Pulse Pressure Variation (PPV) is intended for use with sedated patients receiving controlled mechanical ventilation and mainly free from cardiac arrhythmia. The PPV measurement has been validated only for adult patients.

The IntelliVue NMT is intended to be used as an objective neuromuscular transmission monitor that measures the muscle response to electrical stimulation of a peripheral nerve. The NMT Module is intended to be used with adult and pediatric patients.

The Masimo rainbow SET measurement is indicated for the noninvasive monitoring of functional oxygen saturation of arterial hemoglobin (SpO2), pulse rate, carboxyhemoglobin saturation (SpCO), methemoglobin saturation (SpMet), total hemoglobin concentration (SpHb), and/or respiratory rate (RRac). The Masimo rainbow SET measurement is indicated for use during both no motion and motion conditions, and for patients who are well or poorly perfused.

The non-invasive Masimo O3 Regional Oximeter System and accessories are indicated for use as an adjunct monitor of absolute and trended regional hemoglobin oxygen saturation of blood (rSO2) in the cerebral region under the sensors. The Masimo O3 Regional Oximeter System and accessories are indicated for use on adults ≥40 kg and on pediatrics ≥5 kg and <40 kg, in healthcare environments.

The SedLine Sedation Monitor is intended to monitor the state of the brain by real-time data acquisition and processing of EEG signals. The system includes the Patient State Index (PSI), a proprietary computed EEG variable that is related to the effect of anesthetic agents. The agents include: Alfentanil, Desflurane, Fentanyl, Isoflurane, Nitrous Oxide, Propofol, Remifentanil, and Sevoflurane. The SedLine Sedation Monitor is intended for use with adult patients (18 years of age and older) in the operating room (OR), intensive care unit (ICU), and clinical research laboratory.

The Edwards FloTrac solution offers continuous assessment of hemodynamic parameters. It is indicated to be used by qualified personnel or trained clinicians in a critical care environment in a hospital setting. It is indicated for use in adult critical care patients in which the balance between cardiac function, fluid status, vascular resistance and pressure needs continuous assessment. It may be used for monitoring hemodynamic parameters in conjunction with a perioperative goal directed therapy protocol in a hospital environment. The Edwards FloTrac solution is indicated to be used in the operating room, intensive care unit, and emergency room.

The monitors are intended for use by trained healthcare professionals in a hospital environment.
They are not intended for home use.
The monitors are additionally intended for use in transport situations within hospital environments.

The IntelliVue Patient Monitors MX400, MX450, MX500 and MX550 acquire multiple physiological patient signals, display measurement values, waves and trends, generate physiological and technical alarms, provide data recording and support patient data management.

The monitors support multiple non-invasive and invasive measurements such as ECG, arrhythmia, ST, QT, SpO2, respiration rate, pulse rate, heart rate, invasive and non-invasive blood pressure, temperature, CO2, tcpO2/ tcpCO2, C.O., CCO, intravascular SO2, SvO2, ScvO2, EEG, BIS, NMT, and gas analysis.

The monitors offer a monitoring solution optimized for the surgical, cardiac, medical and neonatal care environments. They are located at the patient bedside vicinity and can also be used during patient transport inside hospitals. The monitors have a color display with touchscreen as a primary input device. They also support a specialized remote control, keyboard and pointing devices such as a mouse. External displays can be connected to a built-in video port to provide an adaptive duplicate image of the primary display. The monitors can interact with several compatible external measuring and auxiliary devices locally at the bedside or in transport situations and with the Central Station via LAN or wireless link.

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The intended use of TigerConnect Alarm Management is to provide an interface with clinical systems to forward information associated to the particular event to the designated display device(s).

For medical alarms, TigerConnect Alarm Management is intended to serve as a parallel, redundant, forwarding mechanism to inform healthcare professionals of particular medical related events TigerConnect Alarm Management does not alter the behavior of the primary medical devices and associated alarm annunciations. The display device provides a visual, and/or audible and/or vibrating mechanism upon receipt of the alert.

TigerConnect Alarm Management is intended for use as a secondary alert. It does not replace the alarm function on the primary device.

TigerConnect (TC) Alarm Management is a software as a medical device (SaMD) installed on the Amazon Web Services (AWS) TigerConnect cloud capable of acquiring alarms, events, and parameters from healthcare systems, and intelligently forwarding this information as secondary notifications to designated commercial endpoint devices. The endpoint devices act as non-regulated medical device data system (MDDS) devices that display the information from TC Alarm Management.

TC Alarm Management is intended for use as a secondary alarm; it does not replace the alarm function on the primary device.

Users receive interactive, time-critical information from clinical systems directly via their endpoint devices as text (visual) or alarms (audible) or data. Received attributes related to the presentation of alerts include color and quantity of tones (beeps) in addition to, and in coordination with, event priorities. TC Alarm Management allows users to be aware of their patients' status and alarm conditions when they are away from the patient and patient monitoring system.

A portion of the software, the Health Services Agent, is deployed to the customers' premises using ECS Anywhere, an extension of Amazon ECS that allows customers to run native ECS tasks on customer-managed infrastructure without compromising on the simplicity and control of the cloud. ECS Anywhere must be deployed in a customer provided virtual machine prior to implementing the Health Systems Agent. This allows TC Alarm Management to securely send, receive, process, and appropriately encrypt data from systems that are typically unencrypted.

TC Alarm Management connects to the information sources through wired ethernet connections which are part of the customer's infrastructure and acquires patient data from primary medical device sources and clinical systems. TigerConnect implementation engineers configure TC Alarm Management, in collaboration with the customer, to determine which information, including alarm notifications, is delivered to which users. TC Alarm Management then formats the data for wireless delivery to the endpoint devices through TigerConnect's proprietary Clinical Collaboration Platform (CCP) non-regulated MDDS messaging service.

TC Alarm Management is designed to accept inputs from a variety of healthcare systems utilizing the HL7 Minimum Lower Layer Protocol (MLLP) communication protocol. These clinical systems include the following:

• Medical device gateways providing HL7 MLLP communications, which includes, but is not limited to, the following parameters and events surrounding them, e.g. alarms:
  • Patient monitors such as electrocardiograms (ECGs), respiratory rate, pulse oximetry (SpO2 and heart rate), end-tidal carbon dioxide (EtCO2), non-invasive blood pressure, cardiac output, temperature and associated derived outputs.
  • Lung ventilators.
  • Respiratory gas monitors.
  • IV infusion (including patient-controlled analgesia) pumps.
• Nurse call systems.
• Electronic health record (EHRs) systems.

TC Alarm Management provides a wireless communications system platform for delivery of secondary notifications to endpoint devices with Android or iOS operating systems (OSs) through the CCP messaging service.

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The Assert-IQ™ ICM is indicated for the monitoring and diagnostic evaluation of patients who experience unexplained symptoms that may be cardiac-related such as: dizziness, palpitations, chest pain, syncope, and shortness of breath, as well as patients who are at risk for cardiac arrhythmias such as bradycardia, tachycardia, and sinus pauses. The Assert-IQ ICM is also indicated for patients who have been previously diagnosed with atrial fibrillation (AF) or who are susceptible to developing AF. The Assert-IQ ICM is intended to be inserted subcutaneously in the left pectoral region, also described as the left anterior chest wall. The Assert-IQ ICM has not been specifically tested for pediatric use.

The Assert-IQ™ ICM is designed to help physicians and clinicians monitor, diagnose and document the heart rhythm in patients who are susceptible to cardiac arrhythmias and unexplained symptoms by detecting arrhythmias and transmitting data for review. The Assert-IQ ICM system, cleared under K230286 on May 17, 2023, includes implantable and remote care components. The implantable components include the Assert-IQ™ ICM device models DM5000, DM5300, or DM5500. The remote care portion consists of the Merlin.net™ Software model MN7000 and myMerlin™ mobile apps (Android (APP1000) and iOS (APP1001)).

The subject of this premarket notification is the integration of two new artificial intelligence (AI) algorithms utilizing machine learning (ML) techniques for the evaluation of atrial fibrillation (AF) and Pause episodes within the Assert-IQ™ ICM remote care component, Merlin.net MN7000. The goal of the AI-enabled function in Merlin.net is to reduce non-actionable clinical review burden due to false Pause and false AF episodes presented for clinician review. Specifically, this premarket submission pertains to the addition of the proposed deep neural network AI models as integrated sub-components of the Merlin.net software, MN7000, resulting in MN7000 version v2.0. There are no other proposed changes to the Assert-IQ device hardware, device firmware, device detection algorithms or other components of the system cleared in K230286.

The two new AI algorithms (CARE: Classification using AI for Rhythm Evaluation) classify AF and pause EGM episodes detected by Assert-IQ ICM devices as either true or false detection. Episodes classified as "true" will be retained in the transmission data and displayed to clinicians for review in Merlin.net web application, whereas episodes classified as "false" will be removed and not displayed to the user. These two AI algorithms, CARE-AF and CARE-Pause, are designed to significantly reduce false episodes, while maintaining true arrhythmic episodes detected by the Assert-IQ devices.

Here's a summary of the acceptance criteria and study details for the Assert-IQ ICM System with AI, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance

The core purpose of the AI algorithms (CARE-AF and CARE-Pause) is to reduce non-actionable clinical review burden due to false pause and false AF episodes while maintaining true arrhythmic episodes. The acceptance criteria are therefore focused on "relative sensitivity" and "false positive reduction."

Study Details

The document describes two primary studies for assessing the performance of the AI algorithms:

1. Retrospective Observational Cohort Study (for independent AI algorithm performance)

• Sample Size (Test Set):
  • CARE-Pause: 1498 Assert-IQ ICM patients
  • CARE-AF: 911 Assert-IQ ICM patients
• Data Provenance: Retrospective, observational cohort study. Patients were from 504 clinics across the United States (for CARE-Pause) and 360 clinics across the United States (for CARE-AF). Data was from Assert-IQ ICM patients who had AF or Pause detection over 30 days of remote monitoring post device implant.
• Number of Experts & Qualifications: Not explicitly stated. The document refers to "the overall system performance of Assert IQ with CARE-AF is assessed using data collected from the Assert-IQ post-market study (NCT06172699) comparing device detection against a Holter monitor." For the retrospective study, the ground truth establishment method implies expert review, but the number and qualifications of these experts are not provided.
• Adjudication Method: Not explicitly stated. The description mentions "AF and Pause EGM episodes detected by Assert-IQ ICM devices as either true or false detection," implying expert review to establish ground truth for these episodes. The method of achieving consensus among experts for this ground truth is not detailed (e.g., 2+1, 3+1).
• Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: No. This study focuses on standalone AI algorithm performance relative to the predicate device's detections, and then overall system performance without explicit human-in-the-loop comparison.
• Standalone Performance: Yes. The "relative sensitivity" and "false positive reduction" metrics directly assess the independent performance of the AI algorithms (CARE-AF and CARE-Pause) in classifying episodes as true or false, relative to the existing device detection. The "overall system performance" metrics also reflect the algorithm's influence on the final output presented to clinicians.
• Type of Ground Truth: The ground truth for individual episodes was established by classifying EGM episodes as "true" or "false." This likely refers to expert consensus interpretation of the EGM data, but this is not explicitly detailed.
• Sample Size (Training Set): Not provided in the text. The document only states that "Patients whose ICM data have been utilized in algorithm training and preliminary performance evaluation were completely excluded from this study" (referring to the test set).
• Ground Truth for Training Set: Not provided in the text. It can be inferred that ground truth was established for training data in a similar manner to the test set, likely through expert review of EGM episodes.

2. Assert-IQ Prospective, Multicenter Post-Market Study (NCT06172699) - for Overall System Performance of CARE-AF

• Sample Size (Test Set): 151 patients enrolled, with 135 patients having analyzable data.
• Data Provenance: Prospective, multicenter post-market study (NCT06172699). Patients had symptomatic, drug-refractory paroxysmal or persistent AF.
• Number of Experts & Qualifications: Not explicitly stated.
• Adjudication Method: Not explicitly stated. The study compared Assert-IQ ICM AF detection against Holter assessment (up to 7 days per patient). This indicates that the Holter assessment served as a primary reference for ground truth for AF detection, likely interpreted by experts.
• Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: No, not explicitly described as such. The study compares the Assert-IQ system with CARE-AF against Holter assessment, not human readers with and without AI.
• Standalone Performance: The "overall system performance" metrics for Assert-IQ with CARE-AF from this study represents the performance of the algorithm-enhanced system.
• Type of Ground Truth: Holter assessment (likely interpreted by experts) served as the ground truth comparator for AF detection.
• Sample Size (Training Set): Not provided.
• Ground Truth for Training Set: Not provided.

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