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EchoPAC Software Only / EchoPAC Plug-in is intended for diagnostic review and analysis of ultrasound images, patient record management and reporting, for use by, or on the order of a licensed physician. EchoPAC Software Only / EchoPAC Plug-in allows post-processing of raw data images from GE ultrasound scanners and DICOM ultrasound images.

Ultrasound images are acquired via B (2D), M, Color M modes, Color, Power, Pulsed and CW Doppler modes, Coded Pulse, Harmonic, 3D, and Real time (RT) 3D Mode (4D).

Clinical applications include: Fetal/Obstetrics; Abdominal (including renal and GYN); Urology (including prostate); Pediatric; Small organs (breast, testes, thyroid); Neonatal and Adult Cephalic; Cardiac (adult and pediatric); Peripheral Vascular; Transesophageal (TEE); Musculo-skeletal Conventional; Musculo-skeletal Superficial; Transrectal (TR); Transvaginal (TV); Intraoperative (vascular); Intra-Cardiac; Thoracic/Pleural and Intra-Luminal.

EchoPAC Software Only / EchoPAC Plug-in provides image processing, annotation, analysis, measurement, report generation, communication, storage and retrieval functionality to ultrasound images that are acquired via the GE Healthcare Vivid family of ultrasound systems, as well as DICOM images from other ultrasound systems. EchoPAC Software Only will be offered as SW only to be installed directly on customer PC hardware and EchoPAC Plug-in is intended to be hosted by a generalized PACS host workstation. EchoPAC Software Only / EchoPAC Plug-in is DICOM compliant, transferring images and data via LAN between systems, hard copy devices, file servers and other workstations.

The provided 510(k) clearance letter and summary discuss the EchoPAC Software Only / EchoPAC Plug-in, including a new "AI Cardiac Auto Doppler" feature. The acceptance criteria and the study proving the device meets these criteria are primarily detailed for this AI-driven feature.

Here's an organized breakdown of the information:

1. Acceptance Criteria and Reported Device Performance (AI Cardiac Auto Doppler)

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EchoConfidence is Software as a Medical Device (SaMD) that displays images from a Transthoracic Echocardiogram, and assists the user in reviewing the images, making measurements and writing a report.

The intended medical indication is for patients requiring review or analysis of their echocardiographic images acquired for their cardiac anatomy, structure and function. This includes automatic view classification; segmentation of cardiac structures including the left and right ventricle, chamber walls, left and right atria and great vessels; measures of cardiac function; and Doppler assessments.

The intended patient population is both healthy individuals and patients in whom an underlying cardiac disease is known or suspected; the intended patient age range is for adults (>= 22 years old) and adolescent in the age range 18 – 21 years old.

EchoConfidence is Software as a Medical Device (SaMD) that displays images from a Transthoracic Echocardiogram, and assists the user in reviewing the images, making measurements and writing a report.

Here's an analysis of the provided FDA 510(k) clearance letter for EchoConfidence (USA), incorporating all the requested information:

Acceptance Criteria and Device Performance Study for EchoConfidence (USA)

The EchoConfidence (USA) device, a Software as a Medical Device (SaMD) for reviewing, measuring, and reporting on Transthoracic Echocardiogram images, underwent a clinical evaluation to demonstrate its performance against predefined acceptance criteria.

1. Acceptance Criteria and Reported Device Performance

The primary acceptance criteria for EchoConfidence were based on the "mean absolute error" (MAE) of the AI's measurements compared to three human experts. The reported performance details indicate that the device met these criteria.

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The VeriSight/VeriSight Pro ICE catheter is intended for intra-cardiac and intra-luminal visualization of cardiac and great vessel anatomy and physiology as well as visualization of other devices in the heart of adult and pediatric patients. The catheter is intended for imaging guidance only, not treatment delivery, during cardiac interventional percutaneous procedures.

The VeriSight/VeriSight Pro Intracardiac Echocardiography (ICE) catheters are sterile, disposable, and for single use only. The catheter's distal end has an ultrasound transducer providing 2D and/or 3D imaging capabilities. A handle, located at the proximal end of the catheter, has two steering wheels that can be manually operated to control four-way articulation of the distal segment in anterior, posterior, left and right directions. The catheter has a 9 French (F) shaft and a usable length of 90 cm.
The VeriSight and VeriSight Pro ICE Catheters are identical in all regards (material, processing, assembly and packaging). The VeriSight ICE catheter provides 2D ultrasound imaging capabilities. The VeriSight Pro ICE catheter provides 2D and/or 3D ultrasound imaging capabilities, depending on the model and configuration of the EPIQ ultrasound system it connects to. The catheters are compatible with ancillary equipment such as sheaths and introducers. The catheters are sterilized via Ethylene Oxide.
The catheters connect to the Philips EPIQ Diagnostic Ultrasound System via a patient interface module (PIM); the connection between the catheter and the PIM is located outside the sterile field. The catheters will not operate if connected to any other imaging system. These catheters are for exclusive use with Philips EPIQ 7C, CVx, and CVxi series of ultrasound systems, cleared under K202216.

Based on the provided FDA 510(k) clearance letter for the Philips VeriSight Intracardiac Echocardiography Catheters, the device is not an AI/ML-based device. The submission focuses on adding pediatric indications to an existing, cleared device, and thus, the information requested about acceptance criteria and a study proving an AI device meets those criteria is not applicable to this specific submission.

The document explicitly states:

• "The purpose of this submission is solely to add pediatric indications to the currently cleared VeriSight/VeriSight Pro ICE catheters (cleared under K200812)." (Page 6)
• "No additional non-clinical performance testing was executed for this change." (Page 6)
• "The VeriSight/VeriSight Pro ICE catheters use the same scientific technology, operating principles and shares similar indications for use as the predicate device. Therefore, clinical data is not required to establish substantial equivalence." (Page 6)

Therefore, the document does not contain details regarding acceptance criteria, performance data, test sets, ground truth establishment, or any other aspects related to the validation of an AI/ML device. The clearance is based on the substantial equivalence to a predicate device for the expanded indications, rather than a new technological performance validation.

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Fetal EchoScan is a machine learning-based computer-assisted diagnosis (CADx) software device indicated as an adjunct to fetal heart ultrasound examination in pregnant women aged 18 or older undergoing second-trimester anatomic ultrasound exams.

When utilized by an interpreting physician, Fetal EchoScan provides information regarding the presence of any of the following suspicious radiographic findings:

• overriding artery
• septal defect at the cardiac crux
• abnormal relationship of the outflow tracts
• enlarged cardiothoracic ratio
• right ventricular to left ventricular size discrepancy
• tricuspid valve to mitral valve annular size discrepancy
• pulmonary valve to aortic valve annular size discrepancy
• cardiac axis deviation

Fetal EchoScan is to be used with cardiac fetal ultrasound video clips containing interpretable 4-chamber, left ventricular outflow tract, right ventricular outflow tract standard views.

Fetal EchoScan is intended for use as a concurrent reading aid for interpreting physicians (OB-GYN, MFM). It does not replace the role of the physician or of other diagnostic testing in the standard of care. When utilized by an interpreting physician, this device provides information that may be useful in rendering an accurate diagnosis regarding the potential presence of morphological abnormalities that might be suggestive of fetal congenital heart defects that may be useful in determining the need for additional exams.

Fetal EchoScan is not intended for use in multiple pregnancies, cases of heterotaxy and postnatal ultrasound exams.

Fetal EchoScan is a cloud-based software-only device which uses neural networks to detect suspicious cardiac radiographic findings for further review by trained and qualified physicians. Fetal EchoScan is intended to be used as an adjunct to the interpretation of the second-trimester fetal anatomic ultrasound exam performed between 18 and 24 weeks of gestation, for pregnant women aged 18 or more.

Here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) Clearance Letter for Fetal EchoScan v1.1:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" but rather presents the performance metrics achieved by the device in both standalone and reader studies. The implication is that these performance levels were deemed acceptable for clearance.

Table 1. Standalone Performance of Fetal EchoScan v1.1 for all suspicious radiographic findings Combined

Table 2. Reader Study Performance of Fetal EchoScan v1.1 for all suspicious radiographic findings Combined

Note: The numerical acceptance criteria for "high sensitivity" and "high specificity" are not explicitly defined in the provided document, but the reported performance values surpassed what was considered acceptable by the FDA for substantial equivalence.

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

• Test Set Sample Size (Standalone Testing): 877 clinically acquired fetal ultrasound exams.
• Test Set Sample Size (Reader Study): 200 exams.
• Data Provenance:
  • Country of Origin: U.S.A. and France.
  • Retrospective or Prospective: The document doesn't explicitly state whether the data was retrospective or prospective, but it mentions "clinically acquired" exams, which often implies retrospective use of existing data.

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

• Number of Experts: Three (3) pediatric cardiologists.
• Qualifications of Experts: Pediatric cardiologists. No further details on years of experience or board certification are provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Majority voting among the three pediatric cardiologists.

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

• Was an MRMC study done? Yes.
• Effect Size of Human Readers' Improvement with AI vs. without AI assistance:
  • ROC AUC: Humans improved by +14.9% (from 0.825 unaided to 0.974 aided), with a p-value of 0.002.
  • Mean Sensitivity: Humans improved by +15.3% (from 0.782 unaided to 0.935 aided).
  • Mean Specificity: Humans improved by +21.1% (from 0.759 unaided to 0.970 aided).

6. Standalone Performance Study

• Was a standalone study done? Yes.
• Performance Metrics: Refer to Table 1 above. The AI system had a conclusive output rate of 98.8%. Sensitivity ranged from 0.977 to 0.987, and Specificity ranged from 0.963 to 0.977 for the detection of any suspicious findings, depending on how inconclusive outputs were treated.

7. Type of Ground Truth Used

• Ground Truth Type: Expert consensus. Specifically, it was derived from a "truthing process in which three pediatric cardiologists assessed the presence or absence of each of the eight findings, and majority voting was used." This constitutes expert consensus.

8. Sample Size for the Training Set

• The document states: "The ultrasound examinations used for training and validation are entirely distinct from the examinations used in standalone testing." However, the specific sample size for the training set is not provided in the clearance letter. It only mentions that the data used for standalone testing (877 exams) and the reader study (200 exams) were distinct from the training and validation data.

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

• The document states: "The ultrasound examinations used for training and validation are entirely distinct from the examinations used in standalone testing." However, the methodology for establishing ground truth for the training set is not explicitly detailed in the provided text. It can be inferred that a similar expert review process would have been used, but no specific details are given.

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EchoGuide is a vascular ultrasound imaging device meant to aid in identification of the cannulation site on the skin of mature arteriovenous fistulas/grafts (AVFs/AVGs) in adult patients by appropriately trained healthcare providers in clinical settings. This device is not meant to replace the current standard of care cannulation methods.

EchoGuide is a 3D automated ultrasound solution designed to provide the benefits of ultrasound for arteriovenous fistula/graft cannulation without the need for extensive training. EchoGuide uses a three-dimensional probe to acquire live coronal plane images, in addition to automating imaging settings, to allow users to quickly assess the position, trajectory, and size of an arteriovenous fistula/graft. Users can then mark the position and trajectory of the access on the patient's skin before removing the probe and proceeding with cannulation.

The EchoGuide probe houses a 2D array on a track. The piezoelectric material in the transducer is used as an ultrasound source to transmit sound waves into the body. Sound waves are reflected back to the transducer and converted to electrical signals that are processed. The transducer is a 52 mm linear (192 element) motorized probe capable of capturing a volume of data. The motorized probe collects a series of 2D images to capture a volume. Through image analysis and processing, the volumes are sliced to create live coronal plane renderings.

The ultrasound system has a laptop form factor, with a bottom touch screen for user interaction and an additional top screen for display. The ultrasound system includes a transmitter and receiver, are all self-contained within the case. The ultrasound system interfaces with the probe through a port on the right side of the system.

The EchoGuide user interface defaults to a conventional 2D ultrasound image when the system powers on. Users can switch between this view, and the live coronal imaging via controls on the bottom screen of the ultrasound. Users can freeze the imaging and capture a snapshot of the fistula/graft as well. The snapshot displays a static view of the fistula/graft in the coronal and transverse planes.

EchoGuide is intended to be used in a clinical setting at the point of hemodialysis care.

This document primarily focuses on the FDA 510(k) clearance process for "EchoGuide (Version 1)" and its substantial equivalence to predicate devices, rather than a detailed report of a study proving the device meets specific performance acceptance criteria for an AI algorithm. The provided text touches on non-clinical and clinical testing but does not provide the granular details required to answer all parts of your request, particularly regarding specific performance metrics (e.g., sensitivity, specificity, accuracy), expert qualification for ground truth, and the specifics of AI-assisted human reader studies.

Here's an analysis based on the information provided, highlighting what can be discerned and what is missing:

Acceptance Criteria and Device Performance for EchoGuide (Version 1)

The provided FDA 510(k) clearance letter and summary primarily discuss the substantial equivalence of the EchoGuide device to existing predicate devices, focusing on its intended use, technological characteristics, and conformance to general safety and performance standards for imaging devices. It does not explicitly state acceptance criteria in terms of specific performance metrics (e.g., sensitivity, specificity, accuracy for identifying cannulation sites) for an AI component.

The document mentions "imaging accuracy and quality" as confirmed by a clinical study, but without providing the quantitative acceptance criteria or the reported performance values against these criteria.

Missing Information:

• A specific table of acceptance criteria for AI-driven performance metrics (e.g., a specific target sensitivity or accuracy).
• Reported device performance values against these specific AI-driven criteria. The text only vaguely states "confirms the imaging accuracy and quality of EchoGuide for in vivo use."

Study Details (Based on available information):

1. Table of Acceptance Criteria and Reported Device Performance:

As noted above, this level of detail is not provided in the given FDA 510(k) document. The document focuses on showing substantial equivalence and conformance to general device standards.

2. Sample Size and Data Provenance for Test Set:

• Sample Size: The document states, "Data from a non-significant risk observational study was used to confirm in vivo imaging adequacy of EchoGuide for the exam of hemodialysis accesses." It further specifies that "Images were collected on upper arm arteriovenous fistulae." However, the exact sample size (number of patients or images) for this "test set" is not specified.
• Data Provenance: The study was described as a "prospective, single arm, non-randomized, observational study." The country of origin is not explicitly stated, but given the FDA clearance, it's highly likely to be US data or data suitable for US regulatory submission.

3. Number and Qualifications of Experts for Ground Truth:

• The document does not provide any information on the number of experts used to establish ground truth or their specific qualifications (e.g., "Radiologist with X years of experience").
• It mentions the device is "meant to aid in identification of the cannulation site...by appropriately trained healthcare providers in clinical settings," but doesn't detail how the 'true' cannulation sites were established for the study.

4. Adjudication Method:

• The document does not provide any information on the adjudication method used for establishing ground truth (e.g., 2+1, 3+1 consensus).

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

• The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance. The focus is on the device's imaging quality for in vivo use.
• Therefore, there is no information on the effect size of how much human readers improve with AI vs. without AI assistance.

6. Standalone (Algorithm-Only) Performance:

• The device is described as an "ultrasound solution designed to provide the benefits of ultrasound for arteriovenous fistula/graft cannulation" and automates "imaging settings" to allow users to "assess the position, trajectory, and size." It also states, "EchoGuide is a vascular ultrasound imaging device meant to aid in identification of the cannulation site...by appropriately trained healthcare providers."
• This phrasing suggests that the device (likely including algorithmic processing for image presentation and perhaps automated measurements/guidance, but without explicit AI claims) is intended to be used by a human operator to assist in a task. It is not presented as a standalone diagnostic algorithm that outputs a decision on its own.
• The document does not explicitly describe a standalone ("algorithm-only") performance study in terms of metrics like sensitivity, specificity, or AUC as one might see for a diagnostic AI. The "imaging accuracy and quality" mentioned is likely related to the visual representation and utility for a human user.

7. Type of Ground Truth Used:

• The document refers to a "non-significant risk observational study" where "Images were collected on upper arm arteriovenous fistulae." The study's primary objective was "data collection." It confirms "in vivo imaging adequacy."
• The nature of the ground truth is not explicitly stated beyond being related to "in vivo imaging adequacy" for "hemodialysis accesses." For specific "identification of the cannulation site," ground truth could potentially involve:
  • Expert consensus (e.g., radiologists/sonographers outlining the vessel)
  • Pathology (unlikely for this application)
  • Outcomes data (e.g., successful cannulation rates after using the device, but this is usually a separate clinical utility study)
  • Perhaps direct measurements or annotations from images performed by clinicians in the study.
• The specific method for establishing the 'true' cannulation site or vessel parameters is not detailed.

8. Sample Size for Training Set:

• The document describes a clinical evaluation/validation study, but it does not provide information on the sample size of any training set used for the development of the EchoGuide's algorithms (if it uses machine learning/AI models). This information is typically found in design validation documentation, not necessarily in the public 510(k) summary focused on substantial equivalence.

9. How Ground Truth for Training Set Was Established:

• Since information on a training set is not provided, there is no information on how ground truth for such a set would have been established.

Summary of Missing Information Critical for Full Response:

The provided document, being a 510(k) clearance letter and summary, primarily focuses on demonstrating substantial equivalence to predicate devices and adherence to general performance standards, rather than providing a detailed clinical study report for an AI-driven device with specific performance metrics against pre-defined acceptance criteria. Therefore, many of the specific details requested regarding AI performance studies, sample sizes, expert qualifications, and ground truth establishment are not present in this particular type of FDA document.

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The Echo Intracranial Base Catheter is indicated for the introduction of interventional devices into the neurovasculature.

The Echo Intracranial Base Catheter is a single lumen, flexible, variable stiffness catheter with a 0.100 inch inner diameter, designed for use in facilitating the insertion and guidance of appropriately sized interventional devices into the neurovascular system. It has a radiopaque marker band on the distal end a luer hub at the proximal end. The distal catheter shaft has a 14 cm lubricious coating to reduce friction during use. It is packaged with a dilator and two rotating hemostatic valves. The Echo Intracranial Base Catheter is compatible with introducer sheaths with an inner diameter of 9F or greater. The Echo Intracranial Base Catheter is supplied sterile, non-pyrogenic, and intended for single use only.

The provided text describes a medical device called the "Echo™ Intracranial Base Catheter", but it does not contain the acceptance criteria or a study proving that an AI-driven device meets acceptance criteria.

Instead, the document is a 510(k) premarket notification for a traditional medical device (a catheter) and discusses its substantial equivalence to a predicate device. It details bench testing, animal safety testing, biocompatibility, sterilization, and shelf life, which are standard for such device submissions. It does not mention any AI component or software, nor does it refer to acceptance criteria in the context of device performance metrics like sensitivity, specificity, or any other statistical measure typically used for AI/software-driven diagnostic or assistive tools.

Therefore, I cannot extract the requested information regarding AI acceptance criteria or studies from this document.

If you have a document describing an AI-driven device and its performance studies, please provide that text.

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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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Fetal EchoScan is a machine learning-based computer-assisted diagnosis (CADx) software device indicated as an adjunct to fetal heart ultrasound examination in pregnant women aged 18 or older undergoing second-trimester anatomic ultrasound exams.

When utilized by an interpreting physician, Fetal EchoScan provides information regarding the presence of any of the following suspicious radiographic findings:

• overriding artery
• septal defect at the cardiac crux
• abnormal relationship of the outflow tracts
• enlarged cardiothoracic ratio
• right ventricular to left ventricular size discrepancy
• tricuspid valve to mitral valve annular size discrepancy
• pulmonary valve to aortic valve annular size discrepancy
• cardiac axis deviation

Fetal EchoScan is to be used with cardiac fetal ultrasound video clips containing interpretable 4-chamber, left ventricular outflow tract, right ventricular outflow tract standard views.

Fetal EchoScan is intended for use as a concurrent reading aid for interpreting physicians (OB-GYN, MFM). It does not replace the role of the physician or of other diagnostic testing in the standard of care. When utilized by an interpreting physician, this device provides information that may be useful in rendering an accurate diagnosis regarding the potential presence of morphological abnormalities that might be suggestive of fetal congenital heart defects that may be useful in determining the need for additional exams.

Fetal EchoScan is not intended for use in multiple pregnancies, cases of heterotaxy, and postnatal ultrasound exams.

Fetal EchoScan is a cloud-based software-only device which uses neural networks to detect suspicious cardiac radiographic findings for further review by trained and qualified physicians. Fetal EchoScan is intended to be used as an adjunct to the interpretation of the second-trimester fetal anatomic ultrasound exam performed between 18 and 24 weeks of gestation, for pregnant women aged 18 or more.

Here's a breakdown of the acceptance criteria and study details for the Fetal EchoScan device, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" as a set of predefined thresholds. Instead, it presents the performance of the device in various metrics and then concludes that these results demonstrate substantial equivalence. For the purpose of this request, I will infer the implied acceptance criteria from the reported performance and the conclusion of substantial equivalence.

Inferred Acceptance Criteria & Reported Device Performance

2. Sample Size and Data Provenance for the Test Set

• Sample Size for Standalone Test Set: 877 clinically acquired fetal ultrasound exams.
• Sample Size for MRMC Test Set: 200 exams.
• Data Provenance: The data was collected from 11 centers in the U.S.A. and France. It was retrospectively collected as it refers to "clinically acquired fetal ultrasound exams".

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Three (3) pediatric cardiologists.
• Qualifications of Experts: The document specifies "pediatric cardiologists" but does not provide details on their years of experience or other specific qualifications beyond their specialty.

4. Adjudication Method for the Test Set

• Adjudication Method: Majority voting. This means that if at least two out of the three pediatric cardiologists agreed on the presence or absence of a finding, that was established as the ground truth.

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

• Was an MRMC study done? Yes.
• Effect size of human readers improvement with AI vs. without AI assistance:
  • ROC AUC for any suspicious finding: +14.9% increase (from 0.825 unaided to 0.974 aided, p=0.002).
  • Mean Sensitivity for any suspicious finding: +15.3% increase (from 0.782 unaided to 0.935 aided).
  • Mean Specificity for any suspicious finding: +21.1% increase (from 0.759 unaided to 0.970 aided).

6. Standalone (Algorithm Only) Performance Study

• Was a standalone study done? Yes.
• The results are presented in Table 1, showing sensitivity and specificity for "Any suspicious findings" and each individual finding, calculated under two scenarios for inconclusive outputs.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. Specifically, it was derived from a truthing process by three pediatric cardiologists using majority voting.

8. Sample Size for the Training Set

• The document states that "The ultrasound examinations used for training and validation are entirely distinct from the examinations used in standalone testing," but it does not explicitly provide the sample size for the training set.

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

• The document states that the "ultrasound examinations used for training and validation are entirely distinct from the examinations used in standalone testing." However, similar to the training set sample size, it does not explicitly describe how the ground truth for the training set was established. It only details the ground truth establishment for the test sets (standalone and MRMC).

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iCardio.ai EchoMeasure is software that is used to process previously acquired DICOM-compliant cardiac ultrasound images, and to make measurements on these images in order to provide automated estimation of several cardiac measurements. The data produced by this software is intended to be used to support qualified cardiologists, sonographers, or other licensed professional healthcare practitioners for clinical decision-decision-making.

iCardio.ai EchoMeasure is indicated for use in adult patients.

iCardio.ai EchoMeasure is a software device used to process previously acquired DICOM-compliant transthoracic cardiac ultrasound images. The software provides automated view classification and quality check of images to then provide several automated estimation of cardiac anatomical measurements and quantities.

iCardio.ai EchoMeasure is a comprehensive software application that seamlessly integrates image pre-processing and quality check of standard cardiac ultrasound views and provides automated measurements of standard cardiac parameters and measurements.

iCardio.ai EchoMeasure is designed to sort through and determine the eligibility criteria for downstream processing, including image quality, and appropriate cardiac view. The following pre-processing steps are considered in making a determination about image eligibility for processing:

• Echocardiographic view classification
• Echocardiographic view overall image quality -
• -End-Diastolic and End-Systolic frame identification

iCardio.ai EchoMeasure automatically sorts through and recognizes these key parameters to then allow an image to pass for automated processing for measurement of several cardiac parameters, including:

• 1. Left Ventricular Volume (A2C, A4C, and Biplane; Systole and Diastole)
• 2. Left Ventricular Diameter (Systole and Diastole)
• 3. Right Ventricular Diameter
• 4. Posterior Wall Thickness
• 5. Aortic Annulus Diameter
• 6. Left Ventricular Outflow Tract Diameter
• 7. Sinus of Valsalva Diameter
• 8. Sinotubular Junction Diameter
• 9. Left Atrium Dimension
• 10. Interventricular Septal Thickness

Machine learning based view detection, quality grading, key frame selection, automated keypoint detection and segmentation form the basis of the software's automated analysis.

iCardio.ai EchoMeasure output is intended for consumption by 3rd party software and hardware vendors. Additionally iCardio.ai has a native browser interface for reviewing the report summary as well as a functionality to download the available report in PDF format. The iCardio.ai EchoMeasure browser interface allows the end user to view both 2D image and cine loops determined by the software and to review the automated measurements produced. It is the option of the reviewing clinician to accept, reject, edit, or ignore the output provided by iCardio.ai EchoMeasure.

A report, automatically generated from the calculated parameters, is returned to the interpreting clinician. This software device aims to aid diagnostic review and analysis of echocardiographic data, patient record management, and reporting. It also features tools for organizing and displaying quantitative data from cardiovascular images acquired from ultrasound scanners. It is exclusively for use by qualified clinicians.

Here's an analysis of the acceptance criteria and study detailed in the provided text:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria & Reported Device Performance

The acceptance criteria for iCardio.ai EchoMeasure's performance were based on the Bi-variate Linear Regression Coefficient Slope (BLRSC). The device was designed to estimate the "worst-case" error, defined as the difference between the software output and the mean of three clinician-derived annotations. The acceptance criterion was that the estimated worst-case BLRSC (based on the 95% CI) for each endpoint must be above a certain predetermined threshold. The study's conclusion explicitly states that "In no instance did the worst-case BLRSC for a given measurement (calculated based on the 95% confidence interval) fall below the predetermined, minimum allowable BLRSC threshold."

2. Sample Size for Test Set and Data Provenance

• Sample Size: 200 comprehensive echocardiography studies from 200 distinct patients. A single DICOM was selected for each relevant view (PLAX, A2C, or A4C).
• Data Provenance: Retrospective, sampled from two independent clinical sites from two different US states. This was done to assure a wide sample of imaging data and patient demographics. No data from these sites was used for the training or tuning of the algorithm.

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

• Number of Experts: Three (3)
• Qualifications: Experienced US-based cardiac sonographers.

4. Adjudication Method for Test Set
The ground truth was established using the mean of three (3) clinician-derived annotations per case. This implies a consensus-based approach or averaging of independent expert measurements.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The provided text does not mention a multi-reader multi-case (MRMC) comparative effectiveness study to assess the effect of AI assistance on human reader performance. The study described is a standalone performance study.

6. Standalone Performance Study
Yes, a standalone performance study was conducted. The objective was to demonstrate successful device performance using prospectively-defined success criteria for each endpoint, specifically evaluating the "worst-case" error for linear and volumetric measurements against clinician-derived ground truth.

7. Type of Ground Truth Used
The ground truth used was expert consensus based on manual measurements and segmentations performed by experienced clinicians (the mean of three experienced US-based cardiac sonographers).

8. Sample Size for Training Set
The text does not specify the sample size for the training set. It only mentions that the sonographers used for the standalone study were independent of those used to annotate the training data, and that data from the two clinical sites used for the test set was not used for training or tuning.

9. How Ground Truth for Training Set was Established
The text does not explicitly detail how the ground truth for the training set was established, other than noting that different sonographers were involved compared to the test set ground truth establishment.

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EchoSolv AS is a machine learning (ML) and artificial intelligence (AI) based decision support software indicated for use as an adjunct to echocardiography for assessment of severe aortic stenosis (AS).

When utilized by an interpreting physician, this device provides information to facilitate rendering an accurate diagnosis of AS. Patient management decisions should not be made solely on the results of the EchoSolv AS analysis.

EchoSolv AS includes both the algorithm based AS phenotype analysis, and the application of recognized AS clinical practice quidelines.

Limitations: EchoSolv AS is not intended for patients under the age of 18 years or those who have previously undergone aortic valve replacement surgery

EchoSolv AS is a standalone, cloud-based decision support software which is intended to be used certified cardiologist to aid in the diagnosis of Severe Aortic Stenosis. EchoSolv AS analyzes basic patient demographic data and measurements obtained from a transthoracic echo examination to provide a categorical assessment as to whether the data are suggestive of a high, medium or low probability of Severe AS. EchoSolv AS is intended for patients who 18 years or older who have an echocardiogram performed as part of routine clinical care (i.e., for the evaluation of structural heart disease).

Patient demographic and echo measurement data is automatically processed through the artificial intelligence algorithm which provides an output regarding the probability of a Severe AS phenotype to aid in the clinical diagnosis of Severe AS during the review of the patient echo study and generation of the final study report, according to current clinical practice guidelines. The software provides an output on the following assessments:

1. Severe AS Phenotype Probability

Whether the patient has a high, medium, or low probability of exhibiting a Severe AS phenotype, based on analysis by the EchoSolv AS proprietary Al algorithm, that the determined predicted AVA is ≤1.0cm². The Al probability score requires a minimum set of data inputs to provide a valid output but is based on all available echocardiographic measurement data and does not rely on the traditional LVOT measurements used to in the continuity equation.

2. Severe AS Guideline Assessment

Whether the patient meets the definition for Severe AS based on direct evaluation of provided echocardiogram data measurements (AV Peak Velocity, AV Mean Gradient and AV Area) with current clinical practice guidelines (2020 ACC/AHA Guideline for the Management of Patients with Valvular Heart Disease).

EchoSolv AS is intended to be used by board-certified cardiologists who review echocardiograms during the evaluation and diagnosis of structural heart disease, namely aortic stenosis. EchoSolv AS is intended to be used in conjunction with current clinical practices and workflows to improve the identification of Severe AS cases.

Here's an analysis of the acceptance criteria and study detailed in the provided document for the EchoSolv AS device:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" in a tabulated format. However, based on the performance data presented, the implicit acceptance criteria can be inferred from the reported performance and comparison to a predicate device. The performance metrics reported are AUROC, Sensitivity, Specificity, Diagnostic Likelihood Ratios (DLR), and improvement in reader AUROC and concordance in the MRMC study.

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