Us2.ca processes acquired transthoracic cardiac ultrasound images to support qualified cardiologists, sonographers, or other licensed professional healthcare practitioners in their diagnosis of cardiac amyloidosis. Us2.ca is intended for use only in adult patients with increased left ventricular wall thickness, defined as an interventricular septal thickness (IVSd) or left ventricular posterior wall thickness (LVPWd) ≥ 12mm. Us2.ca is not intended to provide a diagnosis and does not replace current standards of care. The results from Us2.ca are not intended to exclude the need for further follow-up on cardiac amyloidosis.

The Us2.ai platform is a clinical decision support tool that analyzes echocardiogram images in order to generate a series of AI-derived measurements. Fully automated, functional reporting with disease indications is also provided, in line with ASE & ESC guidelines. Echo images are sent to the Us2.ai platform where they are processed, analyzed and measured. Results that meet the confidence threshold for both image quality and measurement accuracy are passed through to a report for review by the clinical users. Report text is also generated and presented with the measurements, providing functional reporting and disease indications. The ultimate clinical decision and interpretation reside solely with the clinician. Us2.ca is an enhancement to Us2.ai existing Us2.v2 software, adding the capability to detect cardiac amyloidosis. It is an image post-processing analysis software device used for viewing and quantifying cardiovascular ultrasound images in DICOM format. The device is intended to aid diagnostic review and analysis of echocardiographic data, patient record management and reporting. The primary intended function of Us2.ca is to automatically identify patients who require additional follow-up for cardiac amyloidosis. In doing so, the primary benefit is to improve clinical echocardiographic workflow, enabling clinicians to generate and edit reports faster, with precision and with full control. The final clinical decision of the results still remains with the clinicians.

Here's a breakdown of the acceptance criteria and the study proving Us2.ca meets them, based on the provided FDA 510(k) Clearance Letter:

Us2.ca Device Performance Study Summary

Us2.ca is an AI-powered software designed to analyze transthoracic cardiac ultrasound images to support healthcare practitioners in the diagnosis of cardiac amyloidosis in adult patients with increased left ventricular wall thickness (IVSd or LVPWd ≥ 12mm). The device is not intended as a standalone diagnostic tool but as an adjunctive clinical decision support system.

1. Acceptance Criteria and Reported Device Performance

The primary performance metrics for Us2.ca were sensitivity and specificity for the detection of cardiac amyloidosis. The benchmarks for acceptance criteria were established with reference to current standards of care and existing relevant publications.

Table of Acceptance Criteria and Reported Device Performance

Note: The document states "The benchmark used in deriving the acceptance criteria of Us2.ca was made with reference to current standards of care and existing relevant publications." However, explicit numerical acceptance thresholds for sensitivity and specificity are not provided in the excerpt. The reported performance metrics are presented as the results that met the unstated acceptance criteria.

2. Sample Sizes and Data Provenance

• Training Set Sample Size: 4,371 patients (2,241 CA Cases, 2,130 Control Cases)
• Test Set (External Validation) Sample Size: 1,647 patients (664 CA Cases, 983 Control Cases)
• Data Provenance:
  • Country of Origin: The external validation cohort was sourced from six clinical sites across the United States (USA) and Japan. The training data came from "entirely separate data providers," implying diverse origins as well.
  • Retrospective or Prospective: All echocardiographic studies were retrospectively obtained from routine clinical evaluations.

3. Number of Experts and Qualifications for Ground Truth

The document does not explicitly state the number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience") used to establish the ground truth for the test set. However, it indicates that the device "supports qualified cardiologists, sonographers, or other licensed professional healthcare practitioners in their diagnosis of cardiac amyloidosis," implying that the ground truth would have been established by such qualified professionals.

4. Adjudication Method for the Test Set

The document does not describe the specific adjudication method (e.g., 2+1, 3+1) used for establishing the ground truth of the test set. It mentions the "testing data involved two cohorts: Cardiac Amyloidosis Group (CA Group) and Control Group," but not the process for classifying patients into these groups.

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

The document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to assess how human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the Us2.ca algorithm.

6. Standalone (Algorithm Only) Performance

Yes, a standalone performance study was conducted. The reported sensitivity of 86.9% and specificity of 87.4% are results of the Us2.ca algorithm's performance on the test set, without human intervention or assistance during the evaluation phase. The overall yield of 87.1% also reflects the algorithm's ability to generate confident predictions.

7. Type of Ground Truth Used

The type of ground truth used was expert consensus / clinical diagnosis implicitly. Patients were categorized into a "Cardiac Amyloidosis Group (CA Group)" and "Control Group," indicating that established clinical diagnoses of cardiac amyloidosis (or lack thereof) were used as the reference standard. The "diagnosis of cardiac amyloidosis" is the target of the device's support to "qualified cardiologists, sonographers, or other licensed professional healthcare practitioners."

8. Sample Size for the Training Set

The sample size for the training set was 4,371 patients.

9. How Ground Truth for the Training Set Was Established

The document states that the training and external validation datasets were "sourced from entirely separate data providers." While it doesn't explicitly detail the methodology for establishing ground truth for the training set, it can be inferred that it followed similar clinical diagnostic processes as the test set, leading to the classification of "CA Cases" and "Control Cases." This would typically involve clinical evaluation, imaging interpretation by experts, and potentially confirmatory tests as standard clinical practice for cardiac amyloidosis diagnosis.

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InVision Precision Cardiac Amyloid is an automated machine learning-based decision support system, indicated as a screening tool for adult patients aged 65 years and over undergoing cardiovascular assessment using echocardiography.

When utilized by an interpreting physician, this device provides information alerting the physician for referral to confirmatory investigations.

InVision Precision Cardiac Amyloid is indicated in adult populations over 65 years of age. Patient management decisions should not be made solely on the results of the InVision Precision Cardiac Amyloid.

The InVision Precision Cardiac Amyloid (InVision PCA) is a Software as a Medical Device (SaMD) machine-learning screening algorithm to identify high suspicion of cardiac amyloidosis from routinely obtained echocardiogram videos. The device assists clinicians in the diagnosis of cardiac amyloidosis.

The InVision PCA algorithm uses a machine learning process to identify the presence of cardiac amyloidosis. The device inputs images and videos from echocardiogram studies, and it outputs a report suggestive or not suggestive of cardiac amyloidosis.

The device has no physical form and is installed as a third-party application to an institution's PACS system.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) clearance letter:

InVision Precision Cardiac Amyloid: Acceptance Criteria and Performance Study

InVision Precision Cardiac Amyloid (InVision PCA) is a Software as a Medical Device (SaMD) machine-learning screening algorithm developed to identify a high suspicion of cardiac amyloidosis from routinely obtained echocardiogram videos. It acts as a decision support system, alerting interpreting physicians for referral to confirmatory investigations for adult patients aged 65 years and over undergoing cardiovascular assessment using echocardiography.

The device's performance was validated through a comprehensive study, demonstrating its substantial equivalence to the predicate device.

1. Acceptance Criteria and Reported Device Performance

The primary acceptance criteria for the InVision PCA device were established based on its ability to reliably screen for cardiac amyloidosis. The reported performance metrics from the validation study are as follows:

Note: While specific numerical acceptance thresholds are not explicitly stated as "passing" values (e.g., "must achieve >X% sensitivity"), these reported values are presented as the results that successfully met the predefined endpoints of the validation study, implying they satisfied the implicit acceptance criteria deemed necessary for clearance.

2. Sample Size and Data Provenance for Test Set

• Sample Size: 1221 unique echocardiogram studies.
• Data Provenance: The data were selected from three geographically different U.S. sites. The study was conducted on "previously acquired" images, indicating it was a retrospective study.

3. Number of Experts and Qualifications for Ground Truth

The provided document does not explicitly state the number of experts used to establish the ground truth nor their specific qualifications. It mentions "confirmatory reference data," which could imply a consensus of expert opinion but does not detail the process.

4. Adjudication Method for Test Set

The document does not explicitly state the adjudication method used (e.g., 2+1, 3+1). It refers to the ground truth being established by "confirmatory reference data, such as diagnostic imaging or pathology," suggesting a definitive diagnostic pathway rather than a multi-reader visual interpretation adjudication.

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

No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was explicitly described in the provided text, meaning there is no information on how much human readers improve with AI vs. without AI assistance. The study focused on the standalone performance of the AI model.

6. Standalone (Algorithm Only) Performance

Yes, a standalone (algorithm only) performance study was conducted. The reported sensitivity of 0.607 and specificity of 0.990 are directly attributable to the InVision PCA algorithm's performance in analyzing echocardiogram studies against confirmed ground truth.

7. Type of Ground Truth Used

The ground truth for the test set was established using confirmatory reference data, such as diagnostic imaging or pathology. This indicates a high-fidelity ground truth derived from definitive diagnostic procedures rather than solely expert consensus on images.

8. Sample Size for Training Set

The document does not specify the sample size used for the training set. It only details the sample size for the validation/test set.

9. How Ground Truth for Training Set Was Established

The document does not explicitly state how the ground truth for the training set was established. It is assumed that similar rigorous methods involving confirmatory diagnostic imaging or pathology would have been used for the training data, consistent with the approach for the test set, but this is not directly mentioned.

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The Etiometry Platform™ software features the Data Aggregation software module version 5.4 and the Risk Analytics Engine software module version 9.2.

The Data Aggregation & Visualization software module is intended to record and display multiple physiological parameters of adult, pediatric, and neonatal patients from supported bedside devices. The software module is not intended for alarm notification, nor is it intended to control any of the independent bedside devices to which it is connected. The software module is intended to be used by healthcare professionals for the following purposes:

To remotely consult regarding a patient's status and

To remotely review other standard or critical near real-time patient data in order to aid in clinical decisions and deliver patient care in a timely manner.

The Data Aggregation & Visualization software module can display numeric physiologic data and waveforms captured by other medical devices:

Airway flow, volume, and pressure Arterial blood pressure (invasive and non-invasive, systolic, diastolic, and mean) Bispectral index (BIS, signal quality index, suppression ratio) Cardiac Index Cardiac output Central venous pressure Cerebral perfusion pressure End-tidal CO2 Heart rate Heart rate variability Intracranial pressure Left atrium pressure Oxygen saturation (intravascular, regional, SpO2) Premature ventricular counted beats Pulmonary artery pressure (systolic, diastolic, and mean) Pulse pressure variation Pulse Rate Respiratory rate Right atrium pressure Temperature (rectal, esophageal, tympanic, blood, core, nasopharyngeal, skin) Umbilical arterial pressure (systolic, diastolic, and mean) Electrocardiogram Plethysmograph

The Data Aggregation & Visualization software module can display laboratory measurements including arterial and venous blood gases, complete blood count, and lactic acid.

The Data Aggregation & Visualization software module can display information captured by the Risk Analytics Engine software module.

The Risk Analytics Engine software module calculates four indices: the IDO2 Index™ for inadequate delivery of oxygen, the IVCO2 Index™ for inadequate ventilation of carbon dioxide, the ACD Index™ for acidemia, and the HLA Index™ for hyperlactatemia.

The IDO2 Index is indicated for use by health care professionals with post-surgical patients 0 to 12 years of age and weighing 2 kg or more under intensive care and patients 18 years of age or older under intensive care and not on Mechanical Circulatory Support. The IDO2 Index is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the Data Aggregation & Visualization software module. When the IDO2 Index is increasing, it means that there is an increasing risk of inadequate oxygen delivery, and attention should be brought to the patient. The IDO2 Index presents partial quantitative information about the patient's cardiovascular condition, and no therapy or drugs can be administered based solely on the interpretation statements.

The IVCO2 Index is indicated for use by healthcare professionals with invasively ventilated patients 0 to 12 years of age under intensive care. The IVCO2 Index is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the Data Aggregation and Visualization software module. When the IVCO2 Index is increasing, it means that there is an increasing risk of inadequate carbon dioxide ventilation, and attention should be brought to the patient. The IVCO2 Index presents partial quantitative information about the patient's respiratory condition, and no therapy or drugs can be administered based solely on the interpretation statements.

The ACD Index is indicated for use by health care professionals with invasively ventilated patients 0 to 12 years of age and weighing 2 kg or more under intensive care. The ACD Index is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the Data Aggregation and Visualization software module. When the ACD Index is increasing, it means that there is an increasing risk of acidemia, and attention should be brought to the patient. The ACD Index presents partial quantitative information about the patient's respiratory condition, and no therapy or drugs can be administered based solely on the interpretation statements.

The HLA Index is indicated for use by health care professionals with post-surgical patients 0 to 12 years of age and weighing 2 kg or more under intensive care. The HLA Index is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the Data Aggregation software module. When the HLA Index is increasing, it means that there is an increasing risk of hyperlactatemia, and attention should be brought to the patient. The HLA Index presents partial quantitative information about the patient's cardiovascular condition, and no therapy or drugs can be administered based solely on the interpretation statements.

The Etiometry Platform allows ICU clinicians and quality improvement teams to aggregate data from multiple sources, store it in a database for analysis, and view the streaming data. The platform features include:

• Adjunctive status indicators
• Customizable display of physiologic parameters over the entire patient stay
• Configurable annotation
• . Web-based visualization that may be used on any standard browser
• Minimal IT footprint
• . Software-only solution – no new bedside hardware required
• . Highly reliable and robust operation
• . Auditable data storage

Here's an analysis of the acceptance criteria and study information for the Etiometry Platform (DAV 5.4 RAE 9.2), based on the provided text:

Based on the provided text, the Etiometry Platform (DAV 5.4 RAE 9.2) was evaluated against "the same acceptance criteria as the predicate device," which are explicitly listed as: discriminatory power, range utilization, resolution/limitation, and robustness.

Unfortunately, the document does not provide specific quantitative acceptance criteria values (e.g., "discriminatory power > X, range utilization between Y and Z") nor does it provide the reported device performance values for each of these criteria. It only states that "All results met the same acceptance criteria as the predicate device."

Therefore, the table below will list the acceptance criteria as described, but the "Reported Device Performance" column cannot be filled with specific numbers from this document.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: 4721 data points (referred to as "points") from 779 patients.
• Data Provenance: "data from different clinical sites in the US." The study was retrospective, as stated by "The adjunctive status indicators were retrospectively computed on all de-idents."

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

The document does not specify the number of experts used or their qualifications for establishing ground truth for the test set. It mentions that the "adjunctive status indicators" were "designed based on principles of physiology, with parameters chosen to reflect those specified in the medical literature," implying a foundation in expert knowledge, but not a direct expert review of the test set for ground truth.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method for the test set.

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

The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study.

6. Standalone (Algorithm Only) Performance

The study primarily describes the performance of the integrated "Risk Analytics Engine software module" which calculates the indices based on manipulated physiological data and laboratory measurements. This indicates a standalone (algorithm only) performance evaluation as it assessed the indices themselves against predetermined criteria. The "Data Aggregation & Visualization software module" also has standalone functions of displaying data.

7. Type of Ground Truth Used

The ground truth for evaluating the adjunctive status indicators (IDO2, IVCO2, ACD, HLA Indices) was based on their ability to predict "risk of inadequate oxygen delivery," "risk of inadequate carbon dioxide ventilation," "increasing risk of acidemia," and "increasing risk of hyperlactatemia." The indices were "derived by mathematical manipulations of the physiologic data and laboratory measurements" and validated against acceptance criteria related to discriminatory power, range utilization, resolution/limitation, and robustness.

While not explicitly stating a direct "ground truth" label like "expert consensus" or "pathology," the nature of the indices suggests they are evaluated against an expected physiological response or correlation based on established medical understanding and literature, rather than a definitive "true positive/negative" diagnosis from a gold standard. The validation against "discriminatory power" implies an ability to differentiate between states, which would require some form of reference for those states.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set. It only mentions "Test datasets were used to evaluate the impact of the changes during the development process. Validation datasets were used after development was complete to validate performance using independent data." The 4721 data points / 779 patients explicitly refer to the validation dataset.

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

The document does not explicitly describe how the ground truth for the training set was established. It states that the models (indices) were "designed based on principles of physiology, with parameters chosen to reflect those specified in the medical literature." This implies an expert-driven design process informed by medical knowledge rather than a label-based "ground truth" derived for a training set in a traditional supervised machine learning context. Without details on specific training data, further information on its ground truth establishment cannot be provided.

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EchoGo Amyloidosis 1.0 is an automated machine learning-based decision support system, indicated as a screening tool for adult patients aged 65 years and over with heart failure undergoing cardiovascular assessment using echocardiography.

When utilised by an interpreting physician, this device provides information alerting the physician for referral to confirmatory investigations.

EchoGo Amyloidosis 1.0 is indicated in adult patients aged 65 years and over with heart failure. Patient management decisions should not be made solely on the results of the EchoGo Amyloidosis 1.0 analysis.

EchoGo Amyloidosis 1.0 takes a 2D echocardiogram of an apical four chamber (A4C) as its input and reports as an output a binary classification decision suggestive of the presence of Cardiac Amyloidosis (CA).

The binary classification decision is derived from an AI algorithm developed using a convolutional neural network that was pre-trained on a large dataset of cases and controls.

The A4C echocardiogram should be acquired without contrast and contain at least one full cardiac cycle. Independent training, tune and test datasets were used for training and performance assessment of the device.

EchoGo Amyloidosis 1.0 is fully automated without a graphical user interface.

The ultimate diagnostic decision remains the responsibility of the interpreting clinician using patient presentation, medical history, and the results of available diagnostic tests, one of which may be EchoGo Amyloidosis 1.0.

EchoGo Amyloidosis 1.0 is a prescription only device.

The provided text describes the acceptance criteria and a study proving that the EchoGo Amyloidosis 1.0 device meets these criteria.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated as clear, quantitative thresholds in a "table" format within the provided text. Instead, the document describes the study that was conducted to demonstrate performance against generally accepted metrics for such devices (e.g., sensitivity, specificity, PPP, NPV, repeatability, reproducibility).

However, based on the results presented in the "10.2 Essential Performance" and "10.4 Precision" sections, we can infer the achieved performance metrics. The text states: "All measurements produced by EchoGo Amyloidosis 1.0 were deemed to be substantially equivalent to the predicate device and met pre-specified levels of performance." It does not, however, explicitly list those "pre-specified levels."

Here's a table summarizing the reported device performance:

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

• Test Set Sample Size: 1,164 patients
  • 749 controls
  • 415 cases
• Data Provenance: Retrospective case:control study, collected from multiple sites spanning nine states in the USA. The data also included some "Non-USA" origin (as seen in the subgroup analysis table, but the overall testing data seems to be primarily US-based based on the description).

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

The document does not explicitly state the number of experts or their specific qualifications (e.g., radiologists with X years of experience) used to establish the ground truth for the test set. It mentions that clinical validation was conducted to "assess agreement with reference ground truth" but does not detail how this ground truth was derived or by whom.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1, none) used for the test set's ground truth establishment.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, the document does not describe an MRMC comparative effectiveness study where human readers improve with AI vs. without AI assistance. The study described is a standalone performance validation of the algorithm against a defined ground truth.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

Yes, a standalone performance study was done. The results presented (sensitivity, specificity, PPV, NPV) are for the algorithm's performance without a human-in-the-loop. The device is described as "fully automated without a graphical user interface" and is a "decision support system" that "provides information alerting the physician for referral." The performance metrics provided are directly from the algorithm's output compared to ground truth.

7. The Type of Ground Truth Used

The document states: "The clinical validation study was used to demonstrate consistency of the device output as well as to assess agreement with reference ground truth." However, it does not specify the nature of this "reference ground truth" (e.g., expert consensus, pathology, outcomes data).

8. The Sample Size for the Training Set

The training data characteristics table shows the following sample sizes:

• Controls: 1,262 (sum of age categories: 118+197+337+388+222)
• Cases: 1,302 (sum of age categories: 122+206+356+389+229)
• Total Training Set Sample Size: 2,564 patients

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

The document states: "The binary classification decision is derived from an AI algorithm developed using a convolutional neural network that was pre-trained on a large dataset of cases and controls." It mentions that "Algorithm training data was collected from collaborating centres." However, it does not explicitly describe how the ground truth labels (cases/controls) for the training set were established. It is implied that these were clinically confirmed diagnoses of cardiac amyloidosis (cases) and non-amyloidosis (controls), but the method (e.g., biopsy, clinical diagnosis based on multiple tests, expert review) is not detailed.

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EchoGo Heart Failure 2.0 is an automated machine learning-based decision support system, indicated as a diagnostic aid for patients undergoing routine functional cardiovascular assessment using echocardiography. When utilised by an interpreting clinician, this device provides information that may be useful in detecting heart failure with preserved ejection fraction (HFpEF).

EchoGo Heart Failure 2.0 is indicated in adult populations over 25 years of age. Patient management decisions should not be made solely on the results of the EchoGo Heart Failure 2.0 analysis.

EchoGo Heart Failure 2.0 takes as input an apical 4-chamber view of the heart that has been captured and assessed to have an ejection fraction ≥50%.

EchoGo Heart Failure 2.0 takes as input a 2D echocardiogram of an apical four chamber tomographic view and reports as output a binary classification suggestive of the presence, or absence of heart failure with preserved ejection fraction (HFpEF). EchoGo Heart Failure 2.0 also provides users with an EchoGo Score ranging from 0 to 100% to support the binary classification. The EchoGo Score informs the binary classification when referenced against the pre-determined decision threshold (50%).

To aid in the interpretation of the EchoGo Score, a comparative visual analysis is provided. A histogram format displays the reported EchoGo Score output against a population of patients with known disease status (Independent Testing Dataset). This allows the user to interpret the EchoGo Score relative to the decision threshold of 50%.

EchoGo Heart Failure 2.0 should receive an input echocardiogram acquired without contrast and contain at least one full cardiac cycle.

EchoGo Heart Failure 2.0 is fully automated and does not comprise a graphical user interface.

EchoGo Heart Failure 2.0 is intended to be used by an interpreting clinician as an aid to diagnosis for HFpEF. The ultimate diagnostic decision remains the responsibility of the interpreting clinician using patient presentation, medical history, and the results of available diagnostic tests, one of which may be EchoGo Heart Failure 2.0.

EchoGo Heart Failure 2.0 is a prescription only device.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Reported Device Performance

Study Details

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

   • Test Set Sample Size: 1,578 patients (785 controls and 793 cases).
   • Data Provenance: Retrospective case:control study. The data was collected from multiple independent clinical sites spanning five states in the US.

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

   • The document states that the ground truth was established by "ground truth classifications of cases (HFpEF) or controls," but it does not specify the number or qualifications of experts who established this ground truth for the test set.

3. Adjudication method for the test set:

   • The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1). It only refers to "ground truth classifications," implying these were already established.

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 (MRMC) comparative effectiveness study evaluating human readers with and without AI assistance was not done. The study focuses on the standalone performance of the device. The device is intended as a "diagnostic aid" for use "by an interpreting clinician," but its performance evaluation presented here is not an MRMC study.

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

   • Yes, a standalone performance study was done. The reported sensitivity, specificity, AUROC, and precision values are for the device (algorithm) itself without human intervention in the classification output for the test set. The device provides a "binary classification suggestive of the presence, or absence of heart failure with preserved ejection fraction (HFpEF)" and an "EchoGo Score."

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

   • The ground truth was based on "ground truth classifications of cases (HFpEF) or controls," and "known and expected proportions of HFpEF." While not explicitly stated as "expert consensus," this terminology strongly implies clinical diagnoses were used to establish the HFpEF status for each patient in the dataset. It does not mention pathology or outcomes data specifically for ground truth.

7. The sample size for the training set:

   • The sample size for the training set is not explicitly stated in the provided text. It mentions that the "Subject device AI model was trained on more data and with additional preprocessing steps and data augmentations" compared to the predicate device, and the testing data cohort was a "22.9% increase in data beyond the testing data cohort utilized for the 510k submission of EchoGo Heart Failure 1.0." However, the exact size of the training set is not provided.

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

   • The document does not explicitly describe how the ground truth for the training set was established. It only states that the AI model was "trained on more data" with "additional preprocessing steps and data augmentations." It is highly probable it was established similarly to the test set ground truth (i.e., using clinical diagnoses or expert classifications), given the nature of the diagnostic task.

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The Vivio® System is indicated to non-invasively estimate left ventricular end-diastolic pressure (LVEDP) is above or below 18mmHg. This measurement can aid in the diagnosis of heart failure when used by qualified healthcare professionals as an adjunct alongside other clinically relevant information. For use in adults only.

The Vivio System consists of an Arm Cuff system (BP cuff electronics, connection tubinq), EKG patch, and a software application that runs on an off-the-shelf computer tablet. Data is collected non-invasively at the brachial artery and via a single-lead EKG Patch. An off-the-shelf blood pressure arm cuff is attached to the patient's upper arm as if taking a blood pressure measurement. The Arm Cuff is attached to the blood pressure cuff with the quick connect end connected to the pneumatic pump within the Arm Cuff Electronics Enclosure. Two off-the-shelf electrodes are attached to the snap connections on the bottom of the EKG Patch and then connected on the left side of the patient's chest. Both the pneumatic pump attached to the Arm Cuff and EKG Patch are then powered on by pressing and releasing their respective power buttons. The Vivio App is opened on an off-the shelf computer tablet, and the on-screen prompts are followed to connect the Arm Cuff and the EKG Patch. The user is prompted to enter required patient and user data, initiating a patient recording session. Data from the recording session is processed by a proprietary algorithm and results reported on the computer tablet.

The provided document focuses on the 510(k) summary for the Vivio® System, which is an "Adjunctive Cardiovascular Status Indicator" device. It outlines the regulatory classification, device description, indications for use, technological characteristics, and summaries of non-clinical and clinical tests.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Indications for Use: The Vivio® System is indicated to non-invasively estimate left ventricular end-diastolic pressure (LVEDP) as above or below 18mmHg. This measurement aids in the diagnosis of heart failure when used by qualified healthcare professionals as an adjunct alongside other clinically relevant information, for use in adults only.

1. Table of Acceptance Criteria and Reported Device Performance

The document states "The primary efficacy objectives for the study were met." The acceptance criteria for efficacy are directly linked to diagnostic performance metrics:

Note: The document does not explicitly state these as acceptance criteria but rather as "primary efficacy objectives for the study." In the context of a 510(k) submission, meeting these pre-specified performance thresholds demonstrates the device's ability to achieve its intended use. The LVEDP threshold for diagnosis is 18mmHg.

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

• Sample Size (Test Set):

  • Cath Lab Cohort: 195 subjects scheduled for non-emergent left heart catheterization.
  • Non-Aged Healthy Cohort: 101 subjects.
  • Aged Healthy Cohort: 40 subjects.
  • Total Enrolled: 195 + 101 + 40 = 336 subjects.
  • However, the statistical analysis was performed on a "subset of validation data that had passed the completeness and quality requirements outlined in the data management plan and procedure." The exact size of this final validation subset, specifically used for the reported sensitivity and specificity, is not explicitly stated, but it would be derived from the 195 subjects in the "Cath Lab Cohort" used for comparison to invasive LVEDP. The sensitivity and specificity numbers are specifically tied to the LVEDP estimate, which would rely on the cohort with ground truth LVEDP measurements (Cath Lab Cohort).

• Data Provenance:

  • Geographic Origin: The Cath Lab Cohort was enrolled "across 9 sites" (locations not specified, assuming within the US for FDA clearance), and the two Healthy Cohorts were enrolled "at a single site" (location not specified).
  • Nature of Data: Prospective validation study.

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

• The document states that the ground truth for the Cath Lab Cohort was "gold-standard invasive LVEDP obtained via the Millar Mikro-Cath." This is a direct physiological measurement, not established by human experts in a subjective reading context.
• For the "reference-standard non-invasive echocardiographic indices" used for the Aged Healthy Cohort, the document does not specify the number or qualifications of experts involved in establishing these indices. However, echocardiographic indices are objective measurements and calculations, typically performed by trained sonographers and interpreted by cardiologists/echocardiographers.

4. Adjudication Method for the Test Set

• Since the primary ground truth for LVEDP was an invasive, objective measurement (Millar Mikro-Cath), no expert adjudication method (like 2+1 or 3+1) would have been necessary for this part of the ground truth establishment.
• For the echocardiographic indices, the document does not provide details on adjudication, but as noted above, these are typically objective measurements.

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

• The document does not indicate that an MRMC comparative effectiveness study was done to show how human readers improve with AI vs. without AI assistance.
• The Vivio System is described as a device that "estimates LVEDP" and "aids in the diagnosis of heart failure when used by qualified healthcare professionals as an adjunct." It provides a calculated clinical parameter, not necessarily an AI-driven image interpretation system that assists human readers.

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

• Yes, the reported "sensitivity of 80% (64-91), and specificity of 80% (73-86)" appear to be a measure of the Vivio System's (algorithm's) standalone performance in classifying LVEDP as above or below 18mmHg against the invasive gold standard. The device generates the estimate, and these metrics directly assess its accuracy.

7. Type of Ground Truth Used

• Primary Ground Truth: "Gold-standard invasive LVEDP obtained via the Millar Mikro-Cath" for the Cath Lab Cohort. This is direct physiological outcomes data.
• Reference Standard: "Reference-standard non-invasive echocardiographic indices" for the Aged Healthy Cohort. This relies on established clinical measurements, not subjective expert consensus.

8. Sample Size for the Training Set

• The document does not specify the sample size used for the training set. It mentions a "proprietary algorithm" and a "fixed parameter model" (as opposed to AI/machine learning), but no details on its development data are provided.

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

• The document does not specify how the ground truth for the training set was established. Given the device uses a "fixed parameter model" rather than a machine learning model, it implies that the algorithm's parameters were likely determined based on physiological principles and pre-existing clinical data/models, rather than a large-scale data-driven training process with established ground truth labels in the same way as an AI model.

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Cleerly ISCHEMIA analysis software is an automated machine learning-based decision support tool, indicated as a diagnostic aid for patients undergoing CT analysis using Cleerly Labs software. When utilized by an interpreting healthcare provider, this software tool provides information that may be useful in detecting likely ischemia associated with coronary artery disease. Patient management decisions should not be made solely on the results of the Cleerly ISCHEMIA analysis.

Cleerly ISCHEMIA is an add-on software module to Cleerly Labs (K202280, K190868) that determines the likely presence or absence of coronal vessel ischemia based on quantitative measures of atherosclerosis, stenosis, and significant vascular morphology from typically-acquired Coronary Computed Tomography Angiography images (CCTA). Cleerly ISCHEMIA, in conjunction with Cleerly Labs, outputs a Cleerly ISCHEMIA Index (CII), a binary indication of negative CII (likely absence of ischemia) or positive CII (likely presence of ischemia) with its threshold equivalent to invasive FFR >0.80 vs. ≤0.80, respectively, as identified in professional societal practice guidelines.

The provided document describes the Cleerly ISCHEMIA device and its clinical validation. Here's a breakdown of the requested information based on the text:

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

The document does not explicitly state pre-defined acceptance criteria (e.g., minimum sensitivity of X% and specificity of Y%). Instead, it presents the results of the primary endpoint analysis from the CREDENCE Trial and then pooled results from additional studies. Therefore, the reported device performance serves as the basis for demonstrating acceptable clinical performance.

Additional performance data from pooled US and OUS cohorts are also provided:

The conclusion states, "Cumulatively, these data demonstrate acceptable clinical performance," implying that the presented performance values met the internal acceptance standards for regulatory submission.

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

• Test Set (CREDENCE Trial Validation Set): 305 patients.
• Data Provenance: The CREDENCE Trial was a prospective, multicenter trial conducted across 17 centers (later mentioned as 23 centers in the pooled data description, implying evolution or different reporting) between 2014 and 2017. It recruited patients with stable symptoms and without a prior diagnosis of CAD, referred for non-emergent ICA. The primary endpoint analysis was based on the validation set from this trial. The document states a "US/OUS cohort population" was used for pooled data, and then breaks down the pooled data into "Pooled US" (N=149 subjects) and "Pooled OUS" (N=433 subjects). The CREDENCE trial, being a large multi-center study, likely spanned multiple countries, but the specific breakdown of US vs. OUS for the initial CREDENCE derivation/validation sets isn't explicitly detailed; however, subsequent pooled data clearly delineate US and OUS categories.

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

The document states, "Clinical validation testing was done to validate the diagnostic performance of Cleerly ISCHEMIA for non-invasive determination of the functional significance of CAD, as referenced to direct invasive measurement of FFR as the reference standard." It also mentions, "All index tests were interpreted blindly by core laboratories."

• Number of Experts: Not explicitly stated for the interpretation of FFR.
• Qualifications of Experts: Not explicitly stated, though "core laboratories" implies a standard of expertise in cardiology and interventional procedures necessary for FFR measurement and interpretation.

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

The document mentions that FFR was the "reference standard." Invasive FFR measurement is an objective physiological assessment, rather than a subjective interpretation requiring adjudication. For the interpretation of the CCTA images that serve as input to Cleerly Labs (and subsequently Cleerly ISCHEMIA), it states, "All index tests were interpreted blindly by core laboratories." The specific adjudication method (e.g., consensual read vs. single reader) by these core laboratories for CCTA interpretation is not detailed. However, the ground truth for Cleerly ISCHEMIA is directly linked to the quantitative invasive FFR values.

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 involving human readers with and without AI assistance is described in this document. The study focuses on the standalone diagnostic performance of the Cleerly ISCHEMIA algorithm against an invasive reference standard (FFR). It is presented as a "diagnostic aid" for use by an interpreting healthcare provider, implying it provides information to the provider, but the study doesn't quantify interaction or improvement.

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

Yes, a standalone performance evaluation was clearly done. The clinical validation section explicitly describes the performance of the "Cleerly ISCHEMIA" device in detecting likely ischemia as referenced to invasive FFR. The results (sensitivity, specificity, etc.) are reported for the algorithm's output.

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

The ground truth used was invasive fractional flow reserve (FFR), described as the "reference standard" for determining the functional significance of coronary artery disease. A Cleerly ISCHEMIA Index (CII) of positive (likely ischemia) corresponds to invasive FFR ≤0.80, and negative CII corresponds to FFR >0.80.

8. The sample size for the training set

The CREDENCE Trial cohort was divided into two subsets: "the first half of enrollees at each site assigned to the derivation (n = 307) and the second half to the validation (n = 305) data set." The derivation set (n=307) would typically serve as the training/development set for the algorithm. The document doesn't explicitly refer to it as the "training set," but "derivation" implies its use in developing/optimizing the algorithm.

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

For the derivation set, the ground truth would have been established in the same manner as for the validation set: direct invasive measurement of FFR. The CREDENCE trial collected FFR data for all enrollees, which were then allocated to either the derivation or validation sets.

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The T3 Platform™ software features the T3 Data Aggregation & Visualization software module version 5.0 and the T3 Risk Analytics Engine software module version 9.0.

The T3 Data Aggregation & Visualization software module is intended for the recording and display of multiple physiological parameters of the adult, pediatric, and neonatal patients from supported bedside devices. The software module is not intended for alarm notification or waveform display, nor is it intended to control any of the independent bedside devices to which it is connected. The software module is intended to be used by healthcare professionals for the following purposes:

• To remotely consult regarding a patient's status, and
• To remotely review other standard or critical near real-time patient data in order to aid in clinical decisions and deliver patient care in a timely manner.

The T3 Data Aggregation & Visualization software module can display numeric physiologic data captured by other medical devices:

• · Airway flow, volume, and pressure
• · Arterial blood pressure (invasive and non-invasive, systolic, diastolic, and mean)
• Bispectral index (BIS, signal quality index, suppression ratio) .
• Cardiac Index
• Cardiac output .
• Central venous pressure .
• . Cerebral perfusion pressure
• End-tidal CO2 .
• · Heart rate
• Heart rate variability .
• Intracranial pressure .
• . Left atrium pressure
• Oxygen saturation (intravascular, regional, SpO2) .
• Premature ventricular counted beats .
• Pulmonary artery pressure (systolic, diastolic, and mean) .
• Pulse pressure variation
• · Pulse Rate
• · Respiratory rate
• Right atrium pressure .
• Temperature (rectal, esophageal, tympanic, blood, core, nasopharyngeal, skin)
• · Umbilical arterial pressure (systolic, diastolic, and mean)

The T3 Data Aggregation & Visualization software module can display laboratory measurements including arterial and venous blood gases, complete blood count, and lactic acid. T3 Data Aggregation & Visualization software module can display information captured by the T3 Risk Analytics Engine software module.

The T3 Risk Analytics Engine software module calculates four indices: the IDO2 Index™ for inadequate delivery of oxygen, the IVCO2 Index™ for inadequate ventilation of carbon dioxide, the ACD Index™ for acidemia, and the HLA Index™ for hyperlactatemia.

The IDO2 Index™ is indicated for use by health care professionals with post-surgical patients 0 to 12 years of age and weighing 2 kg or more under intensive care. The IDO2 Index™ is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the T3 Data Aggregation & Visualization software module. When the IDO2 Index™ is increasing, it means that there is an increasing risk of inadequate oxygen delivery and attention should be brought to the patient. The IDO2 Index™ presents partial quantitative information about the patient's cardiovascular condition, and no therapy or drugs can be administered based solely on the interpretation statements.

The IVCO2 Index™ is indicated for use by health care professionals with invasively ventilated patients 0 to 12 years of age under intensive care. The IVCO2 Index™ is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the T3 Data Aggregation and Visualization software module. When the IVCO2 Index™ is increasing, it means that there is an increasing risk of inadequate carbon dioxide ventilation and attention should be brought to the patient. The IVCO2 Index™ presents partial quantitative information about the patient's respiratory condition, and no therapy or drugs can be administered based solely on the interpretation statements.

The ACD Index™ is indicated for use by health care professionals with invasively ventilated patients 0 to 12 years of age and weighing 2 kg or more under intensive care. The ACD Index™ is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the T3 Data Aggregation and Visualization software module. When the ACD Index™ is increasing, it means that there is an increasing risk of acidemia and attention should be brought to the patient. The ACD Index™ presents partial quantitative information about the patient's respiratory condition, and no therapy or drugs can be administered based solely on the interpretation statements.

The HLA Index™ is indicated for use by health care professionals with post-surgical patients 0 to 12 years of age and weighing 2 kg or more under intensive care. The HLA Index™ is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the T3 Data Aggregation & Visualization software module. When the HLA Index™ is increasing, it means that there is an increasing risk of hyperlactatemia and attention should be brought to the patient. The HLA Index™ presents partial quantitative information about the patient's cardiovascular condition, and no therapy or drugs can be administered based solely on the interpretation statements.

The Tracking, Trajectory, Trigger (73) intensive care unit software solution allows clinicians and quality improvement teams in the ICU to aggregate data from multiple sources, store it in a database for analysis, and view the streaming data. System features include:

• Adjunctive status indicators ●
• Customizable display of physiologic parameters over the entire patient stay
• . Configurable annotation
• Web-based visualization that may be used on any standard browser
• Minimal IT footprint ●
• Software-only solution no new bedside hardware required ●
• Highly reliable and robust operation
• . Auditable data storage

The T3 Platform™ software calculates four indices: the IDO2 Index™ for inadequate delivery of oxygen, the IVCO2 Index™ for inadequate ventilation of carbon dioxide, the ACD Index™ for acidemia, and the HLA Index™ for hyperlactatemia. The document specifies that studies were performed to validate the IVCO2 Index.

Here's an analysis of the acceptance criteria and study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document explicitly states that the acceptance criteria for the IVCO2 Index™ are: discriminatory power, range utilization, resolution/limitation, and robustness. It is stated that "All results met the same acceptance criteria as the predicate device".

However, specific quantitative values for these criteria (e.g., a specific threshold for discriminatory power, or a defined range utilization percentage) and the exact performance metrics obtained are not provided in the given text. The document confirms that the device met these criteria without detailing the quantitative results of the performance.

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

• Test Set 1 (Neonatal ICU patients):
  • Sample Size: 180 patients, contributing 1108 PaCO2 measurements from arterial blood gases.
  • Data Provenance: Retrospective, obtained from two Level IV regional NICUs in the US.
• Test Set 2 (Non-NICU patients):
  • Sample Size: 2090 patients, contributing 29,841 PaCO2 measurements from arterial blood gases.
  • Data Provenance: Retrospective, from clinical sites in the US.
  • Demographics: 42% neonates, 32% infants, and 26% children. 46% female and 54% male.

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

The document does not specify the number of experts or their qualifications used to establish the ground truth for the test set. It mentions "PaCO2 measurements from arterial blood gases," which implies that the ground truth was based on laboratory measurements rather than expert review.

4. Adjudication Method for the Test Set

The document does not mention an adjudication method for the test set. Given that the ground truth appears to be objective laboratory measurements (PaCO2 from arterial blood gases) and not subjective expert interpretations, a traditional adjudication process (like 2+1 or 3+1) would not be applicable or necessary.

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

No MRMC comparative effectiveness study was mentioned. The study described focuses on the device's performance against objective physiological measurements, not on its impact on human reader performance.

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

Yes, a standalone performance study was done. The IVCO2 Index™ was "retrospectively computed on all de-identified patients" and "evaluated against the same acceptance criteria as the predicate device." This indicates an algorithm-only evaluation, without human-in-the-loop assistance.

7. Type of Ground Truth Used

The ground truth used was objective physiological data/laboratory measurements, specifically PaCO2 measurements from arterial blood gases.

8. Sample Size for the Training Set

The document mentions "Development test sets are used to evaluate the impact of the development changes during the development process." However, it does not specify the sample size for the training set(s) used to develop the IVCO2 Index™ model. It only provides details for the validation sets.

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

The document states that the "adjunctive status indicators are produced by a model-based approach. The model-based approach is designed based on principles of physiology with parameters chosen to reflect those specified in the medical literature." It does not explicitly detail how ground truth was established for the training data beyond implying it was based on physiological principles and medical literature, and presumably also included patient data similar to the validation sets.

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The T3 Platform™ software features the T3 Data Aggregation & Visualization software module version 5.0 and the T3 Adult Risk Analytics Engine software module version 1.0.

The T3 Data Aggregation & Visualization software module is intended for the recording and display of multiple physiological parameters of the adult, pediatric, and neonatal patients from supported bedside devices. The software module is not intended for alarm notification or waveform display, nor is it intended to control any of the independent bedside devices to which it is connected. The software module is intended to be used by healthcare professionals for the following purposes:

• . To remotely consult regarding a patient's status, and
• To remotely review other standard or critical near real-time patient data in order to aid in . clinical decisions and deliver patient care in a timely manner.

The T3 Data Aggregation & Visualization software module can display numeric physiologic data captured by other medical devices:

• · Airway flow, volume, and pressure
• · Arterial blood pressure (invasive and non-invasive, systolic, diastolic, and mean)
• · Bispectral index (BIS, signal quality index, suppression ratio)
• · Cardiac Index
• . Cardiac output
• Central venous pressure .
• . Cerebral perfusion pressure
• · End-tidal CO2
• · Heart rate
• · Heart rate variability
• Intracranial pressure .
• . Left atrium pressure
• Oxygen saturation (intravascular, regional, SpO2) .
• · Premature ventricular counted beats
• · Pulmonary artery pressure (systolic, diastolic, and mean)
• · Pulse pressure variation
• · Pulse Rate
• · Respiratory rate
• · Right atrium pressure
• · Temperature (rectal, esophageal, tympanic, blood, core, nasopharyngeal, skin)
• · Umbilical arterial pressure (systolic, diastolic, and mean)

The T3 Data Aggregation & Visualization software module can display laboratory measurements including arterial and venous blood gases, complete blood count, and lactic acid.

The T3 Data Aggregation & Visualization software module can display information captured by the T3 Adult Risk Analytics Engine software module.

The T3 Adult Risk Analytics Engine software module calculates the Adult IDO2 Index for inadequate delivery of oxygen. The Adult IDO2 Index is indicated for use by health care professionals with post-surgical patients 18 years of age or older under intensive care and not on Mechanical Circulatory Support. The Adult IDO2 Index is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the T3 Data Aggregation & Visualization software module. When the Adult IDO2 Index is increasing, it means that there is an increasing risk of madequate oxygen delivery and attention should be brought to the patient. The Adult IDO2 Index presents partial quantitative information about the patient's cardiovascular condition, and no therapy or drugs can be administered based solely on the interpretation statements.

The Tracking, Trajectory, Trigger (73) intensive care unit software solution allows clinicians and quality improvement teams in the ICU to aggregate data from multiple sources, store it in a database for analysis, and view the streaming data. System features include:

• · Customizable display of physiologic parameters over entire patient stay
• Configurable annotation
• Web-based visualization that may be used on any standard browser
• Minimal IT footprint
• Software-only solution no new bedside hardware required
• Highly reliable and robust operation
• Auditable data storage

Here's a breakdown of the acceptance criteria and study details for the T3 Platform™ software:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for Test Set

• Sample Size: 4251 mixed venous oxygen saturation measurements from 634 patients.
• Data Provenance: Clinical data from different clinical sites in the US (retrospective). The data were obtained by the T3 Platform software and were de-identified.

3. Number of Experts and Qualifications for Ground Truth Establishment (Test Set)

This information is not explicitly provided in the document. The document states that the Adult IDO2 Index was "retrospectively computed on all de-identified patients" and "evaluated against the same acceptance criteria as the supportive predicate device," implying an objective ground truth related to mixed venous oxygen saturation, but not explicitly stating an expert consensus process for the test set.

4. Adjudication Method for Test Set

This information is not explicitly provided in the document. The evaluation was against objective acceptance criteria and not described as involving human adjudication.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned for this device. The study focused on the standalone performance of the Adult IDO2 Index against established acceptance criteria.

6. Standalone Performance Study

Yes, a standalone study was done. The Adult IDO2 Index algorithm's performance was validated using clinical datasets.

7. Type of Ground Truth Used

The ground truth used was based on mixed venous oxygen saturation measurements, a physiological parameter that the Adult IDO2 Index aims to reflect (likelihood of inadequate delivery of oxygen, specifically defined as mixed venous oxygen saturation below a threshold of 50%).

8. Sample Size for Training Set

The document does not explicitly state the sample size used for the training set. It mentions that the Adult Risk Analytics Engine version 1.0 employs the "same model of human physiology as the one utilized in Risk Analytics Engine version 8.0 cleared under K213230 for the computation of the IDO2 index in pediatric patients (0 to 12 years of age), however, the physiology model has been modified to extend the age-based parameterization." This suggests that a previous model was adapted, but details of the original or updated training data size are not provided.

9. How Ground Truth for Training Set Was Established

The document does not explicitly describe how the ground truth for the training set was established. It states that the Adult Risk Analytics Engine version 1.0 "employs the same model of human physiology" as its predecessor, which was "modified to extend the age-based parameterization." This implies the model was developed based on physiological principles and likely validated against clinical data that included mixed venous oxygen saturation measurements. However, the specific process for establishing ground truth during the training phase is not detailed.

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EchoGo Heart Failure 1.0 is an automated machine learning-based decision support system, indicated as a diagnostic aid for patients undergoing routine functional cardiovascular assessment using echocardiography. When utilised by an interpreting clinician, this device provides information that may be useful in detecting heart failure with preserved ejection fraction (HFpEF).

EchoGo Heart Failure 1.0 is indicated in adult populations over 25 years of age. Patient management decisions should not be made solely on the results of the EchoGo Heart Failure 1.0 analysis.

EchoGo Heart Failure 1.0 takes as input an apical 4-chamber view of the heart that has been captured and assessed to have an ejection fraction ≥50%.

EchoGo Heart Failure 1.0 is a software-only medical device manufactured by Ultromics Limited and granted breakthrough status by the FDA under Q212613.

EchoGo Heart Failure 1.0 takes as input a DICOM file containing an echocardiogram and reports a classification decision suggestive of the presence or absence of heart failure with preserved ejection fraction (HFpEF). The output of this device is based on an artificial intelligence (Al) model developed using a convolutional neural network that produces the classification result. The model takes as input a 2D echocardiogram in which an apical 4-chamber view of the heart has been captured and assessed to have an ejection fraction ≥50% (this would normally be computed using a medical device for the assessment of cardiac function of the left ventricle, for example K213275). The echocardiogram should be acquired without contrast and contain at least one full cardiac cycle.

Independent training, validation and test datasets were used for training and performance assessment of the device. EchoGo Heart Failure 1.0 is fully automated and does not comprise a user interface.

EchoGo Heart Failure 1.0 produces a report containing the result of the classification, and this report is intended to be used by an interpreting clinician as an aid to diagnosis for HFpEF. The results are intended as an additional input to standard diagnostic pathways and should only be used by an interpreting clinician. The device is a diagnostic aid and thus according to common medical sense and the principles of differential diagnosis any diagnostic finding derived from usage of this product must be confirmed by additional diagnostic investigations, if in doubt. The ultimate diagnostic decision remains the responsibility of the interpreting clinician using patient presentation, medical history, and the results of available diagnostic tests, one of which may be EchoGo Heart Failure 1.0.

EchoGo Heart Failure 1.0 is a prescription only device.

Here's a breakdown of the acceptance criteria and the study proving the device meets those criteria, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text only explicitly states the reported device performance and the p-values for one-sided binomial exact tests against a priori acceptance criteria. The specific numerical acceptance criteria (e.g., minimum sensitivity and specificity thresholds) are not directly stated in the document. However, since the p-values are reported as p

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