(193 days)
QLAB Advanced Quantification Software (K191647)
Yes
The document explicitly states that the device is a "suite of AI algorithms" and utilizes "artificial intelligence including nonadaptive machine learning algorithms trained with clinical and/or artificial data" and "deep learning locked AI algorithm".
No
The device provides automated image analysis and quantification tools to assist healthcare professionals in assessing endotracheal tube placement. It is explicitly stated that it "should not be used in-lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis" and is not intended to replace review by a qualified healthcare professional. Therefore, it is a diagnostic/assistive tool rather than a therapeutic device.
Yes
The device aids in assessing endotracheal tube placement by providing information on the tube's position relative to the carina, which is a diagnostic observation, even though it states it should not be solely relied upon for diagnosis.
Yes
The device is described as a suite of AI algorithms and software for image analysis, and while it operates on various platforms (PACS, On Premise, On Cloud, Digital Projection Radiographic Systems), the core device itself is the software and algorithms, not the underlying hardware.
Based on the provided text, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- Device Function: The Critical Care Suite with Endotracheal Tube Positioning AI Algorithm analyzes medical images (frontal chest X-rays) acquired from a digital x-ray system. It does not analyze biological samples from the patient.
- Intended Use: The intended use is to provide automated image analysis and quantification tools to assist healthcare professionals in assessing endotracheal tube placement. This is a function related to medical imaging interpretation, not laboratory testing of biological samples.
Therefore, the device falls under the category of medical imaging software or AI-powered medical image analysis tools, not In Vitro Diagnostics.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
Critical Care Suite is a suite of AI algorithms for the automated image analysis of frontal chest X-rays acquired on a digital x-ray system.
Critical Care Suite with the Endotracheal Tube Position produces an on-screen image overlay that detects and localizes an endotracheal tube, locates the endotracheal tube tip, locates the carina, and automatically calculates the vertical distance between the endoracheal tube tip and carina. This information is also transmitted to the radiologist for review.
Intended users include licensed qualified healthcare professionals (HCPs) trained to independently place and/or assess endotracheal tube placement and radiologists.
Critical Care Suite with the Endotracheal Tube Positioning AI Algorithm should not be used in-lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. It is not intended to review of the X-ray image by a qualified healthcare professional. Critical Care Suite with the Positioning AI Algorithm is indicated for adult-sized patients.
Product codes (comma separated list FDA assigned to the subject device)
QIH
Device Description
Critical Care Suite with Endotracheal Tube Positioning Al Algorithm is an additional AI Algorithm incorporated into the Critical Care Suite software previously cleared under K183182. It introduces the Endotracheal Tube Positioning Al Algorithm which is a quantification tool that analyzes frontal chest x-ray images and based on the data in the image determines the location of the tip of an intubated patient's endotracheal tube, determines the location of the carina, and then calculates and displays the vertical distance between them. The distance provided is within the x-ray detector imaging plane and does not take into account the geometric magnification resultant from the geometry of the x-ray acquisition based on source to image distance (SID), patient size, or any impacts due to patient rotation or tube rotation. This information can aide clinical care teams and radiologists to determine the proper placement of the endotracheal tube in an intubated patient. All algorithms previously cleared under K183182 are still available with Critical Care Suite, including the Pneumothorax Detection Algorithm for triage and notification.
The benefit of the proposed modification is not specific to the platform on which it is deployed. This benefit applies to all previously cleared computational platforms for Critical Care Suite, including PACS, On Premise, On Cloud and Digital Projection Radiographic Systems. The Optima XR240amx was chosen as the initial platform for deployment because endotracheal tube placement images are almost exclusively acquired on mobile X-ray systems due to the immobilization of the patients being intubated with an endotracheal tube.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
Frontal Chest X-Ray Imaging
Anatomical Site
Chest/Lung
Indicated Patient Age Range
adult-sized patients
Intended User / Care Setting
licensed qualified healthcare professionals (HCPs) trained to independently place and/or assess endotracheal tube placement and radiologists.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
The performance of the Endotracheal Tube Positioning Al Algorithm was tested against a ground truth dataset. The ground truth dataset contained a sufficient number of images to adequately analyze all the primary and secondary endpoints and the results met the defined passing criteria.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
The Endotracheal Tube Positioning Al Algorithm achieved an AUC of 0.9999 (0.9998, 1.0000), a sensitivity of 0.9941 (0.9859, 1.0000) and a specificity of 1.0000 (1.0000, 1.0000) for ETT detection. Additionally, the Endotracheal Tube Positioning Al Algorithm achieved an ETT tip to Carina distance measurement success rate of 0.9851 (0.9722, 0.9981), a carina localization success rate 0.9851 (0.9722, 0.9981), an ETT tip localization success rate of 0.9524 (0.9296, 0.9752) and an ETT localization success rate (DICE) of 0.9881 (0.9765, 0.9997).
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
AUC of 0.9999 (0.9998, 1.0000)
sensitivity of 0.9941 (0.9859, 1.0000)
specificity of 1.0000 (1.0000, 1.0000)
ETT tip to Carina distance measurement success rate of 0.9851 (0.9722, 0.9981)
carina localization success rate 0.9851 (0.9722, 0.9981)
ETT tip localization success rate of 0.9524 (0.9296, 0.9752)
ETT localization success rate (DICE) of 0.9881 (0.9765, 0.9997)
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
QLAB Advanced Quantification Software (K191647)
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Critical Care Suite (K183182)
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
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October 29, 2021
GE Medical Systems, LLC Chris Paulik Regulatory Affairs Program Manager 3000 N. Grandview Blvd WAUKESHA, WI 53188
Re: K211161
Trade/Device Name: Critical Care Suite with Endotracheal Tube Positioning AI Algorithm Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management and Processing System Regulatory Class: Class II Product Code: QIH Dated: September 27, 2021 Received: September 28, 2021
Dear Chris Paulik:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's
1
requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801 and Part 809); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (OS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Indications for Use
510(k) Number (if known)
K21161
Device Name
Critical Care Suite with Endotracheal Tube Positioning AI Algorithm
Indications for Use (Describe)
Critical Care Suite is a suite of AI algorithms for the automated image analysis of frontal chest X-rays acquired on a digital x-ray system.
Critical Care Suite with the Endotracheal Tube Position produces an on-screen image overlay that detects and localizes an endotracheal tube, locates the endotracheal tube tip, locates the carina, and automatically calculates the vertical distance between the endoracheal tube tip and carina. This information is also transmitted to the radiologist for review.
Intended users include licensed qualified healthcare professionals (HCPs) trained to independently place and/or assess endotracheal tube placement and radiologists.
Critical Care Suite with the Endotracheal Tube Positioning AI Algorithm should not be used in-lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. It is not intended to review of the X-ray image by a qualified healthcare professional. Critical Care Suite with the Positioning AI Algorithm is indicated for adult-sized patients.
Type of Use (Select one or both, as applicable) Prescription Use (Part 21 CFR 801 Subpart D)
Over-The-Counter Use (21 CFR 801 Subpart C)
CONTINUE ON A SEPARATE PAGE IF NEEDED.
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510(k) Summary
In accordance with 21 CFR 807.92 the following summary of information is provided:
| Date: | September 27, 2021 |
|---|---|
| Submitter: | GE Healthcare, (GE Medical Systems, LLC) |
| 3000 N. Grandview Blvd | |
| Waukesha, WI 53188 USA | |
| Primary | |
| Contact | |
| Person: | Chris Paulik |
| Regulatory Affairs Program Manager | |
| GE Healthcare | |
| 262-894-5415 | |
| Christopher.A.Paulik@ge.com | |
| Secondary | |
| Contact | |
| Person: | Diane Uriell |
| Regulatory Affairs Director | |
| GE Healthcare | |
| 262-290-8218 | |
| Diane.Uriell@ge.com | |
| Device Trade | |
| Name: | Critical Care Suite with Endotracheal Tube Positioning Al Algorithm |
| Common / | |
| Usual Name: | Automated Radiological Image Processing Software |
| Classification | |
| Names and | |
| Product Code: | Regulation Name: Medical Image Management and Processing System |
| Regulation: 21 CFR 892.2050 | |
| Classification: Class II | |
| Product Codes: QIH | |
| Predicate | |
| Device: | QLAB Advanced Quantification Software (K191647) |
| Regulation Name: Picture archiving and communications system | |
| Regulation: 21 CFR 892.2050 | |
| Classification: Class II | |
| Product Codes: QIH | |
| Reference | |
| Device: | Critical Care Suite (K183182) |
| Regulation Name: Radiological computer aided triage and notification software | |
| Regulation: 21 CFR 892.2080 | |
| Classification: Class II | |
| Product Codes: QFM | |
| Device | |
| Description: | Critical Care Suite with Endotracheal Tube Positioning Al Algorithm is an additional AI |
| Algorithm incorporated into the Critical Care Suite software previously cleared under | |
| K183182. It introduces the Endotracheal Tube Positioning Al Algorithm which is a | |
| quantification tool that analyzes frontal chest x-ray images and based on the data in the | |
| image determines the location of the tip of an intubated patient's endotracheal tube, | |
| determines the location of the carina, and then calculates and displays the vertical | |
| distance between them. The distance provided is within the x-ray detector imaging | |
| plane and does not take into account the geometric magnification resultant from the | |
| geometry of the x-ray acquisition based on source to image distance (SID), patient size, | |
| or any impacts due to patient rotation or tube rotation. This information can aide | |
| clinical care teams and radiologists to determine the proper placement of the | |
| endotracheal tube in an intubated patient. All algorithms previously cleared under | |
| K183182 are still available with Critical Care Suite, including the Pneumothorax Detection | |
| Algorithm for triage and notification. | |
| The benefit of the proposed modification is not specific to the platform on which it is | |
| deployed. This benefit applies to all previously cleared computational platforms for | |
| Critical Care Suite, including PACS, On Premise, On Cloud and Digital Projection | |
| Radiographic Systems. The Optima XR240amx was chosen as the initial platform for | |
| deployment because endotracheal tube placement images are almost exclusively | |
| acquired on mobile X-ray systems due to the immobilization of the patients being | |
| intubated with an endotracheal tube. | |
| Intended Use: | Critical Care Suite with Endotracheal Tube Positioning Al Algorithm is intended to |
| provide automated radiological image processing and analysis tools implementing | |
| artificial intelligence including nonadaptive machine learning algorithms trained with | |
| clinical and/or artificial data. | |
| Indications for | |
| Use: | Critical Care Suite is a suite of Al algorithms for the automated image analysis of frontal |
| chest X-rays acquired on a digital x-ray system. | |
| Critical Care Suite with the Endotracheal Tube Positioning AI algorithm produces an on- | |
| screen image overlay that detects and localizes an endotracheal tube, locates the | |
| endotracheal tube tip, locates the carina, and automatically calculates the vertical | |
| distance between the endotracheal tube tip and carina. This information is also | |
| transmitted to the radiologist for review. | |
| Intended users include licensed qualified healthcare professionals (HCPs) trained to independently place and/or assess endotracheal tube placement and radiologists. | |
| Critical Care Suite with Endotracheal Tube Positioning Al Algorithm should not be used in-lieu of full patient evaluation or solely relied upon to make or confirm a diagnosis. It is not intended to replace the review of the X-ray image by a qualified healthcare professional. Critical Care Suite with the Endotracheal Tube Positioning Al Algorithm is indicated for adult-size patients. | |
| Technology: | Critical Care Suite with Endotracheal Tube Positioning Al Algorithm employs the same fundamental scientific technology as its predicate device. It is a deep learning locked AI algorithm that can be deployed on several computing platforms such as PACS, On Premise, On Cloud or Imaging Systems. The patient and user populations are identical to what is provided with Critical Care Suite, adult-sized patients. The Endotracheal Tube Positioning Al Algorithm is an automated radiological image processing and analysis tool, which is equivalent to the image analysis and quantification algorithms provided in the QLAB Advanced Quantification Software. |
| The differences between Critical Care Suite with Endotracheal Tube Positioning AI Algorithm and QLAB Advanced Quantification Software are the acquisition systems that provide the images as well as the specific anatomies that are being analyzed. Critical Care Suite with Endotracheal Tube Positioning Al Algorithm analyzes chest radiographic images where QLAB Advanced Quantification Software analyzes ultrasound images of the heart. This difference does not impact the safety or efficacy of Critical Care Suite with Endotracheal Tube Positioning Al Algorithm since both devices analyze images using deep learning Al technology to identify/visualize anatomical structure and then provide quantification measurements based on that data to aide qualified healthcare professionals trained on endotracheal tube placement and radiologists. |
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| Product Device | Critical Care Suite with Endotracheal Tube | QLAB Advanced Quantification Software |
|---|---|---|
| Comparison | Positioning Al Algorithm | (K191647) |
| Device | Picture archiving and communications system | Picture archiving and communications system |
| Classification | Class II, QIH | Class II, QIH |
| Targeted clinical | ||
| condition, | ||
| anatomy, and | ||
| imaging modality | Endotracheal Tube Positioning Visualization and | |
| Quantification | ||
| Chest/Lung | ||
| Frontal Chest X-Ray Imaging | Right Ventricle Visualization and Quantification | |
| Heart | ||
| Ultrasound Heart Imaging | ||
| Algorithm | ||
| Inferencing | ||
| Mechanism | Al deep learning algorithms designed to visualize | |
| and quantify endotracheal tube positioning in | ||
| frontal chest X-ray images | Al deep learning algorithm designed to visualize and | |
| quantify the right ventricle within heart ultrasound | ||
| images |
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| Product Device
Comparison | Critical Care Suite with Endotracheal Tube
Positioning Al Algorithm | QLAB Advanced Quantification Software
(K191647) |
|------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Computational
Platform | On-Device computation (integrated onto x-ray
system)
Critical Care Suite with Endotracheal Tube
Positioning AI Algorithm is designed as a self-
contained software module deployable on various
computational and imaging system platforms. | Provided as stand-alone product that can function on
a standard PC, a dedicated workstation, and on-
board Philips' ultrasound systems. |
| Notification /
Visualization
Recipient and
Timing | qualified healthcare professionals trained on
endotracheal tube placement – immediately on
device upon image acquisition for Endotracheal
Tube Positioning AI Algorithm
Radiologist – immediately after images are sent to
PACS via secondary capture image and DICOM tag | Clinical Care Team - immediately upon image
acquisition on device
Radiologist - immediately after images are sent to
PACS |
| Algorithm Outputs | Visualization
● Endotracheal tube
● Endotracheal Tube Tip
● Carina
Quantification
● Vertical distance between endotracheal tube tip and carina | Visualization
● 3D surface modeling of anatomical landmarks of right ventricle
Quantification
● Numerous distance and volumetric measurements concerning the right ventricle |
| Clinical and
Non-Clinical
| Tests: | Summary of Non-Clinical Tests: |
|---|---|
| The following quality assurance measures were applied to the development of Critical | |
| Care Suite with Endotracheal Tube Positioning AI Algorithm and deployment onto the | |
| Optima XR240amx system: | |
| 1. Risk Analysis | |
| 2. Requirements Reviews | |
| 3. Design Reviews | |
| 4. Testing on unit level (Module verification) | |
| 5. Integration testing (System verification) | |
| 6. Performance testing (Verification) | |
| 7. Safety testing (Verification) | |
| 8. Simulated use testing (Validation) |
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| | Critical Care Suite with Endotracheal Tube Positioning Al Algorithm specific verification
was conducted to demonstrate proper implementation of Critical Care Suite software
design requirements. |
|-------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| | Regression testing on the Optima XR240amx feature functionality was conducted to
verify proper integration of Critical Care Suite with Endotracheal Tube Positioning AI
Algorithm into the Optima XR240amx software and device. Validation was performed on
Optima XR240amx with integrated Critical Care Suite with Endotracheal Tube Positioning
Al Algorithm. |
| | Design verification and validation testing was performed to confirm that the safety and
effectiveness of the device has not been affected. The test plans and results have been
executed with acceptable results. |
| | Summary of Clinical Tests: |
| | The performance of the Endotracheal Tube Positioning Al Algorithm was tested against a
ground truth dataset. The ground truth dataset contained a sufficient number of images
to adequately analyze all the primary and secondary endpoints and the results met the
defined passing criteria. |
| | The Endotracheal Tube Positioning Al Algorithm achieved an AUC of 0.9999 (0.9998,
1.0000), a sensitivity of 0.9941 (0.9859, 1.0000) and a specificity of 1.0000 (1.0000,
1.0000) for ETT detection. Additionally, the Endotracheal Tube Positioning Al Algorithm
achieved an ETT tip to Carina distance measurement success rate of 0.9851 (0.9722,
0.9981), a carina localization success rate 0.9851 (0.9722, 0.9981), an ETT tip localization
success rate of 0.9524 (0.9296, 0.9752) and an ETT localization success rate (DICE) of
0.9881 (0.9765, 0.9997). |
| Determination
of Substantial
Equivalence: | The introduction of Critical Care Suite with Endotracheal Tube Positioning Al Algorithm
does not result in any new potential safety risks, and has the same technological
characteristics, and performs as well as the predicate devices currently on the market. |
| | After analyzing design verification and validation testing on the bench it is the conclusion
of GE Healthcare that the Critical Care Suite with Endotracheal Tube Positioning AI
Algorithm software to be as safe, as effective, and performance is substantially
equivalent to the predicate device. |