(193 days)
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.
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.
Here's a breakdown of the acceptance criteria and the study details for the Critical Care Suite with Endotracheal Tube Positioning AI Algorithm, based on the provided document:
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria (Implicit) | Reported Device Performance (95% CI) |
|---|---|---|
| ETT Detection | High accuracy for detection of endotracheal tubes. | AUC: 0.9999 (0.9998, 1.0000) |
| High sensitivity for detection of endotracheal tubes. | Sensitivity: 0.9941 (0.9859, 1.0000) | |
| High specificity for detection of endotracheal tubes. | Specificity: 1.0000 (1.0000, 1.0000) | |
| ETT Tip to Carina Distance Measurement | High success rate for accurate distance measurement. | Success Rate: 0.9851 (0.9722, 0.9981) |
| Carina Localization | High success rate for accurate carina localization. | Success Rate: 0.9851 (0.9722, 0.9981) |
| ETT Tip Localization | High success rate for accurate ETT tip localization. | Success Rate: 0.9524 (0.9296, 0.9752) |
| ETT Localization (DICE Score) | High accuracy for overall ETT localization (segmentation fidelity). | DICE: 0.9881 (0.9765, 0.9997) |
Note: The document states that "the results met the defined passing criteria." While specific numerical acceptance thresholds are not explicitly listed in the text, the reported high performance metrics imply that these values exceeded the internal acceptance criteria set by the manufacturer.
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: The document states that the ground truth dataset contained a "sufficient number of images to adequately analyze all the primary and secondary endpoints." However, the exact sample size for the test set is not explicitly provided in the given text.
- Data Provenance: The document does not explicitly state the country of origin of the data or whether it was retrospective or prospective. It only mentions the use of a "ground truth dataset."
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
The document does not explicitly state the number of experts used to establish the ground truth for the test set, nor does it provide their specific qualifications (e.g., radiologist with X years of experience).
4. Adjudication Method for the Test Set
The document does not explicitly state the adjudication method (e.g., 2+1, 3+1, none) used for establishing the ground truth of the test set.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
A multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly conducted or described in the provided document. The clinical tests focused on the standalone performance of the AI algorithm against a ground truth dataset, not on comparing human reader performance with and without AI assistance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
Yes, a standalone study was done. The "Summary of Clinical Tests" section explicitly describes the performance of the Endotracheal Tube Positioning AI Algorithm tested against a ground truth dataset, reporting metrics like AUC, sensitivity, specificity, and success rates for localization and measurement. This indicates a standalone evaluation of the algorithm's performance without direct human-in-the-loop comparison for these specific metrics.
7. The Type of Ground Truth Used
The type of ground truth used is expert consensus. The document refers to the algorithm's performance being "tested against a ground truth dataset" without specifying the exact method of ground truth establishment (e.g., pathology, outcomes data). However, for image analysis tasks like ETT positioning and carina localization, ground truth is typically established by multiple experts (e.g., radiologists) providing annotations or measurements, often followed by an adjudication process to reach a consensus.
8. The Sample Size for the Training Set
The document does not explicitly provide the sample size for the training set. It mentions the algorithms being "trained with clinical and/or artificial data" but no specific numbers.
9. How the Ground Truth for the Training Set Was Established
The document states that the algorithms are "trained with clinical and/or artificial data." It does not explicitly detail how the ground truth for the training set was established. It refers to "nonadaptive machine learning algorithms trained with clinical and/or artificial data," but the process of creating the ground truth annotations for this training data is not described.
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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
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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 BlvdWaukesha, WI 53188 USA |
| PrimaryContactPerson: | Chris PaulikRegulatory Affairs Program ManagerGE Healthcare262-894-5415Christopher.A.Paulik@ge.com |
| SecondaryContactPerson: | Diane UriellRegulatory Affairs DirectorGE Healthcare262-290-8218Diane.Uriell@ge.com |
| Device TradeName: | Critical Care Suite with Endotracheal Tube Positioning Al Algorithm |
| Common /Usual Name: | Automated Radiological Image Processing Software |
| ClassificationNames andProduct Code: | Regulation Name: Medical Image Management and Processing SystemRegulation: 21 CFR 892.2050Classification: Class IIProduct Codes: QIH |
| PredicateDevice: | QLAB Advanced Quantification Software (K191647)Regulation Name: Picture archiving and communications systemRegulation: 21 CFR 892.2050Classification: Class II |
| Product Codes: QIH | |
| ReferenceDevice: | Critical Care Suite (K183182)Regulation Name: Radiological computer aided triage and notification softwareRegulation: 21 CFR 892.2080Classification: Class IIProduct Codes: QFM |
| DeviceDescription: | Critical Care Suite with Endotracheal Tube Positioning Al Algorithm is an additional AIAlgorithm incorporated into the Critical Care Suite software previously cleared underK183182. It introduces the Endotracheal Tube Positioning Al Algorithm which is aquantification tool that analyzes frontal chest x-ray images and based on the data in theimage 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 verticaldistance between them. The distance provided is within the x-ray detector imagingplane and does not take into account the geometric magnification resultant from thegeometry 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 aideclinical care teams and radiologists to determine the proper placement of theendotracheal tube in an intubated patient. All algorithms previously cleared underK183182 are still available with Critical Care Suite, including the Pneumothorax DetectionAlgorithm for triage and notification.The benefit of the proposed modification is not specific to the platform on which it isdeployed. This benefit applies to all previously cleared computational platforms forCritical Care Suite, including PACS, On Premise, On Cloud and Digital ProjectionRadiographic Systems. The Optima XR240amx was chosen as the initial platform fordeployment because endotracheal tube placement images are almost exclusivelyacquired on mobile X-ray systems due to the immobilization of the patients beingintubated with an endotracheal tube. |
| Intended Use: | Critical Care Suite with Endotracheal Tube Positioning Al Algorithm is intended toprovide automated radiological image processing and analysis tools implementingartificial intelligence including nonadaptive machine learning algorithms trained withclinical and/or artificial data. |
| Indications forUse: | Critical Care Suite is a suite of Al algorithms for the automated image analysis of frontalchest 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 theendotracheal tube tip, locates the carina, and automatically calculates the verticaldistance between the endotracheal tube tip and carina. This information is alsotransmitted 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 clinicalcondition,anatomy, andimaging modality | Endotracheal Tube Positioning Visualization andQuantificationChest/LungFrontal Chest X-Ray Imaging | Right Ventricle Visualization and QuantificationHeartUltrasound Heart Imaging |
| AlgorithmInferencingMechanism | Al deep learning algorithms designed to visualizeand quantify endotracheal tube positioning infrontal chest X-ray images | Al deep learning algorithm designed to visualize andquantify the right ventricle within heart ultrasoundimages |
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| Product DeviceComparison | Critical Care Suite with Endotracheal TubePositioning Al Algorithm | QLAB Advanced Quantification Software(K191647) |
|---|---|---|
| ComputationalPlatform | On-Device computation (integrated onto x-raysystem)Critical Care Suite with Endotracheal TubePositioning AI Algorithm is designed as a self-contained software module deployable on variouscomputational and imaging system platforms. | Provided as stand-alone product that can function ona standard PC, a dedicated workstation, and on-board Philips' ultrasound systems. |
| Notification /VisualizationRecipient andTiming | qualified healthcare professionals trained onendotracheal tube placement – immediately ondevice upon image acquisition for EndotrachealTube Positioning AI AlgorithmRadiologist – immediately after images are sent toPACS via secondary capture image and DICOM tag | Clinical Care Team - immediately upon imageacquisition on deviceRadiologist - immediately after images are sent toPACS |
| Algorithm Outputs | Visualization● Endotracheal tube● Endotracheal Tube Tip● CarinaQuantification● Vertical distance between endotracheal tube tip and carina | Visualization● 3D surface modeling of anatomical landmarks of right ventricleQuantification● Numerous distance and volumetric measurements concerning the right ventricle |
| Clinical andNon-ClinicalTests: | Summary of Non-Clinical Tests: |
|---|---|
| The following quality assurance measures were applied to the development of CriticalCare Suite with Endotracheal Tube Positioning AI Algorithm and deployment onto theOptima 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 verificationwas conducted to demonstrate proper implementation of Critical Care Suite softwaredesign requirements. | |
|---|---|
| Regression testing on the Optima XR240amx feature functionality was conducted toverify proper integration of Critical Care Suite with Endotracheal Tube Positioning AIAlgorithm into the Optima XR240amx software and device. Validation was performed onOptima XR240amx with integrated Critical Care Suite with Endotracheal Tube PositioningAl Algorithm. | |
| Design verification and validation testing was performed to confirm that the safety andeffectiveness of the device has not been affected. The test plans and results have beenexecuted with acceptable results. | |
| Summary of Clinical Tests: | |
| The performance of the Endotracheal Tube Positioning Al Algorithm was tested against aground truth dataset. The ground truth dataset contained a sufficient number of imagesto adequately analyze all the primary and secondary endpoints and the results met thedefined 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 Algorithmachieved 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 localizationsuccess rate of 0.9524 (0.9296, 0.9752) and an ETT localization success rate (DICE) of0.9881 (0.9765, 0.9997). | |
| Determinationof SubstantialEquivalence: | The introduction of Critical Care Suite with Endotracheal Tube Positioning Al Algorithmdoes not result in any new potential safety risks, and has the same technologicalcharacteristics, 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 conclusionof GE Healthcare that the Critical Care Suite with Endotracheal Tube Positioning AIAlgorithm software to be as safe, as effective, and performance is substantiallyequivalent to the predicate device. |
§ 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).