Search Results

The AI Platform is intended for noninvasive processing of ultrasound images to detect, measure, and calculate relevant medical parameters of structures and function of adult patients with suspected disease. The device is intended to be used on images from adult patients.

Exo Al Platform is a software as a medical device (SaMD) that helps qualified users with image-based assessment of ultrasound examinations in adult patients. It is designed to simplify workflow by helping trained healthcare providers evaluate, quantify, and generate reports for ultrasound images. The device is intended to generate images and a report that can be reviewed in a typical standard of care setting.

Al Platform takes as an input imported Digital Imaging and Communications in Medicine (DICOM) images from ultrasound scanners of a specific range and allows users to detect, measure, and calculate relevant medical parameters of structures and function of patients with suspected disease. It provides users with a specific toolset for viewing ultrasound images of the lung and heart, placing landmarks, and creating reports.

Key features of the software are

• LVEF AI: an Al-assisted tool for quantification of ejection on cardiac ultrasound images.
• . Lung Al: an Al-assisted tool to suggest presence of lung structures and artifacts on ultrasound images.

Exo Al Platform does not perform any function that could not be accomplished by a trained user manually. It's important to note that patient management decisions should not be made solely on the results of the Al Platform analysis.

Acceptance Criteria & Device Performance Study for Exo AI Platform (AIP001)

The Exo AI Platform (AIP001) is a software as a medical device (SaMD) intended for noninvasive processing of ultrasound images to detect, measure, and calculate relevant medical parameters of structures and function in adult patients with suspected disease. This document outlines the acceptance criteria and the studies performed to demonstrate the device meets these criteria for both its cardiac (LVEF AI) and lung (Lung AI) functionalities.

1. Table of Acceptance Criteria and Reported Device Performance

For Cardiac Ultrasound (LVEF AI - Ejection Fraction Measurement):

For Lung Ultrasound (Lung AI - A-lines and B-lines detection/quantification):

(Note: Specific quantitative targets for "High" ICC and "Low" RMSD/Kappa are not explicitly stated in the provided text, but the reported values demonstrate strong performance in common clinical contexts for these metrics.)

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

• LVEF AI (Cardiac Function): 151 subjects
• Lung AI (Lung Function): 125 subjects

Data Provenance: The data was acquired during routine clinical practice from multiple clinical sites in metropolitan cities, ensuring diverse racial patient populations. The data encompassed diverse demographic variables, including gender, age (20-96 years), and BMI (15.3-52.8). The images were acquired from both cart-based and portable ultrasound devices. The test data was explicitly stated to be entirely separated from the training/validation datasets and not used for any part of the training. This suggests a retrospective collection of data designed for independent validation. The countries of origin are not specified, but "metropolitan cities with diverse racial patient populations" implies a multi-site, potentially multi-national, collection or at least a highly diverse domestic setting.

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

• LVEF AI (Ejection Fraction): The ground truth was obtained as the average ejection fraction measurement of three experts.
• Lung AI (A-line Presence): The ground truth was determined by consensus of two or more experts.
• Lung AI (B-line Counts): The ground truth was determined as the average of B-line counts from three experts.

Qualifications of Experts: The document does not explicitly state the specific qualifications of these experts (e.g., number of years of experience, specific board certifications like radiologist or cardiologist). However, the context of "routine clinical practice" and "experts" implies highly qualified medical professionals experienced in interpreting cardiac and lung ultrasound images.

4. Adjudication Method for the Test Set

• LVEF AI (Ejection Fraction): The adjudication method for the reference data (ground truth) was established by taking the average ejection fraction measurement of three experts. This implies a method akin to "average of multiple readers."
• Lung AI (A-line Presence): The adjudication method for the ground truth was determined by consensus of two or more experts. This suggests a qualitative agreement, where at least two experts had to concur.
• Lung AI (B-line Counts): The adjudication method for the ground truth was established by taking the average of B-line counts from three experts. (Similar to LVEF AI).

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

No, a Multi Reader Multi Case (MRMC) comparative effectiveness study was not done or reported in the provided text. The performance assessment focused on the standalone performance of the AI tool against expert-established ground truth, not on how human readers' performance improved with AI assistance.

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

Yes, a standalone performance study was done. The reported ICC, RMSD, and Kappa values directly assess the AI Platform's accuracy and reliability in generating measurements and detections independently, against the expert-derived ground truth. The statement that "Exo AI Platform does not perform any function that could not be accomplished by a trained user manually" also reinforces its role as an automated tool, evaluated on its own.

7. The Type of Ground Truth Used

The type of ground truth used was expert consensus / expert measurement.

• For Cardiac Ejection Fraction: Average measurements from three experts.
• For Lung A-line Presence: Consensus of two or more experts.
• For Lung B-line Counts: Average counts from three experts.

8. The Sample Size for the Training Set

The sample size for the training set is not specified in the provided text. The document only explicitly mentions that the test data was entirely separated from the training/validation datasets.

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. It only mentions that the AI algorithms (Deep Convolutional Neural Networks) were "trained with clinical data."

Ask a specific question about this device

The Philips Lumify Diagnostic Ultrasound System is intended for diagnostic ultrasound imaging in B (2D), Color Doppler, Combined (B+Color), Pulsed Wave Doppler (PWD), and M-modes.

It is indicated for diagnostic ultrasound imaging and fluid flow analysis in the following applications: Fetal/Obstetric, Abdominal, Pediatric, Cephalic, Urology, Gynecological, Cardiac Fetal Echo, Small Organ, Musculoskeletal, Peripheral Vessel, Carotid, Cardiac, Lung,

The Lumify system is a transportable ultrasound system intended for use in environments where healthcare is provided by healthcare professionals.

The Philips Lumify Diagnostic Ultrasound System (Lumify) is a mobile, durable, and reusable, software-controlled medical device, which is intended to acquire high-resolution ultrasound data and to display the data in B (2D), Pulsed Wave Doppler, Color Doppler, Combined (B+ Color), and M modes.

The Lumify Diagnostic Ultrasound System (Android) utilizes:

• A commercial off-the-shelf (COTS) Android mobile device (smart phone or tablet)
• The Philips Ultrasound Lumify software running as an application on the COTS device
• The Philips C5-2 Curved array USB transducer
• The Philips L12-4 Linear array USB transducer
• The Philips S4-1 Sector array USB transducer
• Lumify Micro B Transducer Cable
• Lumify Micro C Transducer Cable

The Lumify system is compatible with iOS or Android operating systems. The Lumify system software provides various imaging features, including an Android-specific feature with artificial intelligence (AI) based, Auto EF Quantification (ejection fraction) technology during cardiac imaging.

Here's a breakdown of the acceptance criteria and the study details for the Philips Lumify Diagnostic Ultrasound System with Auto EF Quantification, based on the provided FDA 510(k) summary:

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: 80 patients' Apical 4 Chamber (A4CH) view clips.
• Data Provenance: The data were acquired with the Lumify Diagnostic Ultrasound System, specifically for this clinical performance study. Patients were selected based on eligibility, and data were acquired consecutively for patients with normal and impaired LV function. This suggests a prospective acquisition for the purpose of the study. The document does not specify the country of origin for the data.

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

The ground truth was established by "manual tracing performed by sonographers."

• Number of Experts: The document refers to "sonographers" in the plural, but does not specify the exact number of sonographers involved in the manual tracing.
• Qualifications of Experts: The document implies that these were qualified "sonographers" experienced in echocardiographic LV function evaluation, but does not provide specific qualifications (e.g., years of experience, board certification). It can be inferred that they are healthcare professionals who routinely perform this task.

4. Adjudication Method for the Test Set

The ground truth for EF, EDV, and ESV was established by the "average results by manual tracing." This implies that IF multiple sonographers performed the manual tracings, their results were averaged. However, it does not explicitly state an adjudication method like 2+1 or 3+1 (where discrepancies are resolved by a third expert or consensus). It refers to "the average results by manual tracing," suggesting a quantitative aggregation rather than a specific adjudicative consensus process if multiple readers were used. If only one sonographer performed the tracing for each case, no adjudication would be necessary.

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

There is no mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study being done to evaluate how much human readers improve with AI vs. without AI assistance. The study focuses on the agreement between the AI's automated measurements and a "manual tracing" ground truth, not on reader performance with and without AI assistance.

6. Standalone (Algorithm Only) Performance

Yes, a standalone study was done. The clinical performance study directly compared the "automated EF evaluation by LVivo EF" (the algorithm's performance) against "Ejection Fraction (EF) evaluation by manual tracing performed by sonographers" (the ground truth). The results (correlation coefficients) reflect the algorithm's performance without a human-in-the-loop scenario. The LVivo EF automatically processed 95% of the clips, indicating its standalone capability.

7. Type of Ground Truth Used

The ground truth used was expert consensus/manual tracing. Specifically, it was defined as "Ejection Fraction (EF) evaluation by manual tracing performed by sonographers" and "the average results by manual tracing" for EF, EDV, and ESV. This is considered an expert-derived ground truth based on conventional, established methods for echocardiographic LV function evaluation.

8. Sample Size for the Training Set

The document explicitly states: "The data used for clinical performance study were completely distinct from that used during training of the algorithm, and there was no overlap between the two data sets." However, it does not provide the sample size for the training set used to develop the LVivo EF algorithm.

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

The document states that the clinical performance study data were "completely distinct from that used during training of the algorithm," but it does not describe how the ground truth for the training set was established. It implies that such a training process occurred ("training of the algorithm"), but details on its ground truth are not provided in this summary.

Ask a specific question about this device