Search Results

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 Lung Application 3 is intended to assist healthcare professionals by providing automated image processing to analyze ultrasound images for lung-related conditions. Specifically, it evaluates the adequacy of ultrasound frames for clinical interpretation and assesses the appearance of pleural lines as normal or irregular.

The Lung Application 3 is a software-only functionality integrated into the Philips Lumify Diagnostic Ultrasound System, designed to support lung ultrasound examinations. It introduces two key features: pleural line assessment and lung image view quality assessment. The Pleural Line feature identifies and assesses the appearance of pleural lines as normal or irregular (defined as thickened, interrupted, fragmented, jagged, uneven, or otherwise non-smooth appearance on ultrasound). The lung view quality tool assesses the adequacy of ultrasound frames based on overall image appearance and the presence of any pleural lines. The application operates on a compatible Android-based commercial off-the-shelf device (e.g., tablet or smartphone) connected to Lumify transducers (C5-2, S4-1, and L12-4 models). It utilizes machine learning algorithms trained on a large dataset of expert-annotated lung ultrasound images to ensure accurate analysis. The workflow includes zone selection, image acquisition, navigation, review, and editing of results, with real-time feedback provided via visual indicators for image quality and pleural line analysis. The Lung Application 3 is intended for use by trained professionals in clinical settings to assist in evaluations of adult patients (18 years and older) with various pulmonary conditions. It does not introduce any new contraindications and is designed to comply with existing safety and operational standards.

Key Features:

• Software-based functionality for lung ultrasound enhancement.
• Pleural line classification as normal or irregular appearance.
• Lung view quality assessment for diagnostic adequacy.
• Real-time feedback via visual indicators.
• Machine learning-based algorithms for accurate image analysis.
• Compatibility with existing Lumify transducers and Android devices.

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 mode (2D), Pulsed Wave Doppler, Color Doppler, Combined (B+ Color), and M modes. The Lumify system is compatible with iOS and Android operating systems.

The Lumify Diagnostic Ultrasound System (iOS) utilizes:

1. A commercial off-the-shelf (COTS) iOS mobile item (smart phone or tablet)
2. The Philips Ultrasound Lumify software running as a medical device application on the COTS device
3. The Philips C5-2 Curved array USB transducer
4. The Philips L12-4 Linear array USB transducer
5. The Philips S4-1 Sector array USB transducer
6. Lumify Micro B Transducer Cable
7. Lumify Micro C Transducer Cable
8. Lumify USB-C to USB-C Transducer Cable
9. Lumify Power Module

Here's a breakdown of the acceptance criteria and the study proving the device's adherence, based on the provided FDA 510(k) clearance letter for the Philips Lumify Diagnostic Ultrasound System with Lung Application 3:

Acceptance Criteria and Reported Device Performance

Study Details

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

• Test Set Sample Size: The document does not explicitly state the exact numerical sample size for the test set. It mentions that the machine learning algorithms were trained on a "large dataset of expert-annotated lung ultrasound images" and that the retrospective data analysis evaluated the performance on a set of images to assess agreement with ground truth. More specific numbers for the test set are not provided.
• Data Provenance: The data was described as "retrospective data analysis study evaluated the performance of two artificial intelligence algorithms integrated into the Philips Lumify Diagnostic Ultrasound System for automated classification of lung view quality and pleural line appearance during clinical LUS examinations." The country of origin for the data is not specified.

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

• Number of Experts: The document does not explicitly state the number of experts used to establish ground truth. It refers to "expert-annotated lung ultrasound images" and "qualified clinical experts" when establishing acceptance criteria based on inter-rater agreement.
• Qualifications of Experts: The experts are referred to as "qualified clinical experts." Specific qualifications (e.g., "radiologist with 10 years of experience") are not provided.

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

• The document does not specify the adjudication method used for establishing ground truth for the test set. It mentions "expert-annotated," implying multiple experts, but the process for resolving disagreements (if any) is not detailed.

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:

• A MRMC comparative effectiveness study involving human readers with vs. without AI assistance was not explicitly described in this document as part of the performance evaluation for this 510(k) clearance. The study focused on the standalone performance of the AI algorithms against expert-established ground truth.

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

• Yes, a standalone study was done. The performance evaluation described in Section 8, "Non-Clinical Performance Data," is a standalone assessment of the AI algorithms. It evaluated "algorithm agreement with ground truth labels." The results presented for PABAK, concordance, and Kappa are all measures of the algorithm's performance independent of real-time human interaction.

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

• The ground truth was established by expert annotation/consensus. The document states, "machine learning algorithms trained on a large dataset of expert-annotated lung ultrasound images" and "evaluated algorithm agreement with ground truth labels." The acceptance criteria were also "established based on published inter-rater agreement ranges for lung view quality and pleural line irregularity among qualified clinical experts."

7. The sample size for the training set:

• The document states, "It utilizes machine learning algorithms trained on a large dataset of expert-annotated lung ultrasound images." A specific numerical sample size for the training set is not provided.

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

• The ground truth for the training set was established through expert annotation. The document explicitly mentions "machine learning algorithms trained on a large dataset of expert-annotated lung ultrasound images."

Ask a specific question about this device