(139 days)
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, 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. It is intended to be used by trained professionals at various settings of patient point of care such as clinical admission, periodic evaluations, and prior to hospitalization discharge,
The Lumify system is compatible with iOS or Android operating systems. The B-lines feature is compatible only with Android operating systems and 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 software provides various imaging features, including an Android-specific feature with a guided scan protocol for comprehensive exams and real-time automated B-line assessment during lung exams.
Acceptance Criteria and Device Performance Study for Philips Lumify Diagnostic Ultrasound System (K223771)
1. Acceptance Criteria and Reported Device Performance
The acceptance criteria for the Philips Lumify Diagnostic Ultrasound System with the Expanded B-lines Software Feature were pre-defined success criteria for the AI algorithm's agreement with clinician readings (ground truth).
| Metric | Acceptance Criteria (Lower Confidence Limit) | Reported Device Performance (All Transducers) |
|---|---|---|
| Merged B-line Detection | 0.746 | |
| Sensitivity | 0.83 (0.77, 0.88) | |
| Specificity | 0.92 (0.88, 0.96) | |
| B-line Counting | ||
| ICC | Not explicitly stated as 0.746, but implies good agreement based on successful clinical user needs | 0.91 (0.89, 0.93) |
Note: The lower confidence limit of 0.746 for both sensitivity and specificity was explicitly determined through a pilot study to assess clinician agreement, forming the basis for the acceptance criteria for merged B-line detection. While an explicit numerical acceptance criterion for ICC in B-line counting isn't provided, the conclusion states the device met clinical user needs as intended, implying satisfactory performance against an internal threshold.
2. Sample Size and Data Provenance for Test Set
- Sample Size: 416 lung ultrasound (LUS) video loops.
- Data Provenance:
- Collected from 157 subjects presenting with shortness of breath in a hospital setting.
- Each subject may have contributed up to 4 video loops.
- The study data were collected to ensure full coverage of the lung (posterior and anterior, left and right).
- Representative of videos collected from three different transducers (C5-2, L12-4, S4-1).
- The study used previously collected clinical ultrasound images.
- Retrospective: The phrasing "previously collected clinical ultrasound images" suggests the data was retrospective, gathered before the specific performance study was conducted.
3. Number of Experts and Qualifications for Ground Truth
The document does not explicitly state the number of experts used to establish the ground truth for the test set. However, it indicates that the ground truth was established by "clinicians" and refers to "clinician readings."
The qualifications of these clinicians are not specifically detailed (e.g., "radiologist with 10 years of experience").
4. Adjudication Method for the Test Set
The adjudication method is described as "majority agreement" among clinicians. The document states: "ground truth: majority agreement target was determined through pilot study conducted to assess clinician's agreement". This suggests a method where ground truth was established by the consensus of multiple clinicians, likely a 2+1 or similar majority-based approach, though the exact number of clinicians involved in each decision is not specified.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The document does not describe a multi-reader, multi-case (MRMC) comparative effectiveness study designed to measure the effect size of human readers improving with AI vs. without AI assistance. The study focuses solely on the standalone performance of the AI algorithm against clinician-established ground truth.
6. Standalone (Algorithm Only) Performance
Yes, a standalone performance study was conducted. The "Artificial Intelligence Summary" explicitly states: "A study using previously collected clinical ultrasound images with prospective reads by clinicians was conducted to evaluate the performance (including merged B-lines and B-line counting)." The reported sensitivity, specificity, and ICC values represent the algorithm's performance independent of real-time human intervention or assistance.
7. Type of Ground Truth Used
The type of ground truth used was expert consensus, specifically "clinician readings" based on "majority agreement."
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.
9. How Ground Truth for the Training Set Was Established
The document does not provide details on how the ground truth for the training set was established. It only mentions that the training data was distinct from the test data. Given that the algorithm uses machine learning, it is highly probable that the training data also had a ground truth established by experts, but the specifics are not included in this document.
§ 892.1550 Ultrasonic pulsed doppler imaging system.
(a)
Identification. An ultrasonic pulsed doppler imaging system is a device that combines the features of continuous wave doppler-effect technology with pulsed-echo effect technology and is intended to determine stationary body tissue characteristics, such as depth or location of tissue interfaces or dynamic tissue characteristics such as velocity of blood or tissue motion. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.