Who is the manufacturer of Critical Care Suite?

Ge Medical Systems, LLC filed the 510(k) submission for Critical Care Suite (K183182).

What is the FDA product code for Critical Care Suite?

QFM. It is classified under 21 CFR 892.2080 as a Class 2 device in the Radiology panel.

What is the regulatory pathway for Critical Care Suite?

510(k) clearance (submission type: Traditional).

Does Critical Care Suite use AI or machine learning?

Yes. Critical Care Suite (K183182) is flagged as an AI/ML-enabled medical device in the FDA's AI/ML device list.

Who can help prepare an FDA 510(k) submission like Critical Care Suite?

Innolitics — a medical-device software consultancy — provides regulatory and engineering support for FDA 510(k) submissions. See https://innolitics.com/services/510ks/ or contact https://innolitics.com/contact.

Can an AI/ML device like Critical Care Suite get a guaranteed FDA 510(k) clearance?

Yes. Innolitics offers a guaranteed FDA 510(k) clearance engagement for AI/ML Software as a Medical Device — combining engineering and regulatory execution under one agreement with a guaranteed clearance outcome. Eligibility: the device must be an AI/ML Software as a Medical Device (SaMD), and its performance must meet the criteria Innolitics determines during an initial regulatory strategy engagement to be necessary for FDA clearance. See https://innolitics.com/services/end-to-end-samd/ or contact https://innolitics.com/contact.

Critical Care Suite

Q: What are the predicate devices for Critical Care Suite?

HealthPNX by Zebra Medical Vision (K190362).

K183182 · Ge Medical Systems, LLC · QFM · Aug 12, 2019 · Radiology

Device Facts

Record ID	K183182
Device Name	Critical Care Suite
Applicant	Ge Medical Systems, LLC
Product Code	QFM · Radiology
Decision Date	Aug 12, 2019
Decision	SESE
Submission Type	Traditional
Regulation	21 CFR 892.2080
Device Class	Class 2
Attributes	AI/ML, Software as a Medical Device

Intended Use

Critical Care Suite is a computer aided triage and notification device that analyzes frontal chest x-ray images for the presence of prespecified critical findings (pneumothorax). Critical Care Suite identifies images with critical findings to enable case prioritization or triage in the PACS/workstation. Critical Care Suite is intended for notification only and does not provide diagnostic information beyond the notification. Critical Care Suite 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 physician. Critical Care Suite is indicated for adult-size patients.

Device Story

Software module using AI-based image analysis to identify pneumothorax in frontal chest X-rays; flags images in PACS/workstation for prioritized radiologist review. Input: frontal chest X-ray images. Processing: automated quality check for image compatibility/lung field coverage; AI algorithm detects pneumothorax. Output: passive notification in PACS/workstation; optional secondary capture DICOM image. When deployed on Digital Projection Radiographic Systems (e.g., Optima XR240amx), provides on-device technologist notification if lung field positioning is atypical. Used in clinical settings (radiology/PACS workflows). Benefits: enables case prioritization, potentially reducing report turnaround time for critical findings compared to standard first-in, first-out workflows.

Clinical Evidence

Retrospective evaluation on 804 frontal chest X-rays (376 with pneumothorax, 428 without). Ground truth established by 3 independent US-board certified radiologists. Results: AUC 0.9607 (95% CI [0.9491, 0.9724]), sensitivity 84.3% (95% CI [80.6%, 88.0%]), specificity 93.5% (95% CI [91.1%, 95.8%]). Large pneumothorax sensitivity: 96.3%; small pneumothorax sensitivity: 75%. Performance consistent across AP/PA views and system manufacturers.

Technological Characteristics

Software module; AI-based image analysis; deep learning algorithm; vendor/system agnostic; integrates with PACS and Digital Projection Radiographic Systems (e.g., Optima XR240amx); supports on-premise or cloud deployment; DICOM-compatible.

Indications for Use

Indicated for adult-size patients requiring triage of frontal chest X-ray images for the presence of pneumothorax.

Regulatory Classification

Identification

Radiological computer aided triage and notification software is an image processing prescription device intended to aid in prioritization and triage of radiological medical images. The device notifies a designated list of clinicians of the availability of time sensitive radiological medical images for review based on computer aided image analysis of those images performed by the device. The device does not mark, highlight, or direct users' attention to a specific location in the original image. The device does not remove cases from a reading queue. The device operates in parallel with the standard of care, which remains the default option for all cases.

Special Controls

Radiological computer aided triage and notification software must comply with the following special controls: 1. Design verification and validation must include: i. A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations. ii. A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage (e.g., improved time to review of prioritized images for pre-specified clinicians). iii. Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment. iv. Standalone performance testing protocols and results of the device. v. Appropriate software documentation (e.g., device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results). 2. Labeling must include the following: i. A detailed description of the patient population for which the device is indicated for use. ii. A detailed description of the intended user and user training that addresses appropriate use protocols for the device. iii. Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (e.g., poor image quality for certain subpopulations), as applicable. iv. A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images. v. Device operating instructions. vi. A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness (e.g., improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (e.g., confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.

*Classification.* Class II (special controls). The special controls for this device are:(1) Design verification and validation must include: (i) A detailed description of the notification and triage algorithms and all underlying image analysis algorithms including, but not limited to, a detailed description of the algorithm inputs and outputs, each major component or block, how the algorithm affects or relates to clinical practice or patient care, and any algorithm limitations. (ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide effective triage ( *e.g.,* improved time to review of prioritized images for pre-specified clinicians).(iii) Results from performance testing that demonstrate that the device will provide effective triage. The performance assessment must be based on an appropriate measure to estimate the clinical effectiveness. The test dataset must contain sufficient numbers of cases from important cohorts ( *e.g.,* subsets defined by clinically relevant confounders, effect modifiers, associated diseases, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals for these individual subsets can be characterized with the device for the intended use population and imaging equipment.(iv) Stand-alone performance testing protocols and results of the device. (v) Appropriate software documentation ( *e.g.,* device hazard analysis; software requirements specification document; software design specification document; traceability analysis; description of verification and validation activities including system level test protocol, pass/fail criteria, and results).(2) Labeling must include the following: (i) A detailed description of the patient population for which the device is indicated for use; (ii) A detailed description of the intended user and user training that addresses appropriate use protocols for the device; (iii) Discussion of warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level ( *e.g.,* poor image quality for certain subpopulations), as applicable;(iv) A detailed description of compatible imaging hardware, imaging protocols, and requirements for input images; (v) Device operating instructions; and (vi) A detailed summary of the performance testing, including: test methods, dataset characteristics, triage effectiveness ( *e.g.,* improved time to review of prioritized images for pre-specified clinicians), diagnostic accuracy of algorithms informing triage decision, and results with associated statistical uncertainty (*e.g.,* confidence intervals), including a summary of subanalyses on case distributions stratified by relevant confounders, such as lesion and organ characteristics, disease stages, and imaging equipment.

Predicate Devices

HealthPNX by Zebra Medical Vision (K190362)

Related Devices

K211733 — Lunit INSIGHT CXR Triage · Lunit, Inc. · Nov 10, 2021
K223491 — Critical Care Suite with Pneumothorax Detection AI Algorithm, Critical Care Suite 2.1, Critical Care Suite · Ge Medical Systems, LLC · May 25, 2023
K191556 — Red Dot · Behold.Ai Technologies Limited · Feb 28, 2020
K250831 — Annalise Enterprise · Annalise-Ai · Apr 23, 2025
K193300 — AIMI-Triage CXR PTX · Radlogics, Inc. · Apr 8, 2020

Submission Summary (Full Text)

{0}------------------------------------------------ August 12, 2019 Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left, there is a symbol representing the Department of Health & Human Services - USA. To the right, the FDA logo is displayed in blue, with the words "U.S. FOOD & DRUG" stacked above the word "ADMINISTRATION". The logo is simple and professional, conveying the organization's role in regulating food and drugs. GE Medical Systems, LLC. % Camille Vidal Director of Regulatory Affairs Strategy 3000 N. Grandview Blvd. WAUKESHA WI 53188 Re: K183182 Trade/Device Name: Critical Care Suite Regulation Number: 21 CFR 892.2080 Regulation Name: Radiological computer aided triage and notification software Regulatory Class: Class II Product Code: QFM Dated: July 12, 2019 Received: July 12, 2019 Dear Camille Vidal: 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/cfpmp/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 requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); 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 {1}------------------------------------------------ https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) 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 medical devices and radiation-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, For 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 {2}------------------------------------------------ ### Indications for Use 510(k) Number (if known) K183182 Device Name Critical Care Suite #### Indications for Use (Describe) Critical Care Suite is a computer aided triage and notification device that analyzes frontal chest x-ray images for the presence of prespecified critical findings (pneumothorax). Critical Care Suite identifies images with critical findings to enable case prioritization or triage in the PACS/workstation. Critical Care Suite is intended for notification only and does not information beyond the notification. Critical Care Suite 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 physician. Critical Care Suite is indicated for adult-size patients. X Prescription Use (Part 21 CFR 801 Subpart D) Over-The-Counter Use (21 CFR 801 Subpart C) ### CONTINUE ON A SEPARATE PAGE IF NEEDED. This section applies only to requirements of the Paperwork Reduction Act of 1995. ### *DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.* The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to: > Department of Health and Human Services Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff PRAStaff@fda.hhs.gov "An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number." {3}------------------------------------------------ Image /page/3/Picture/1 description: The image shows the logo for General Electric (GE). The logo consists of the letters "GE" in a stylized script, enclosed within a circular frame. The frame is adorned with swirling, wave-like elements, giving it a dynamic and fluid appearance. The color scheme is primarily blue, with the letters and frame rendered in a vibrant shade of blue. ### 510(k) Summary K183182 In accordance with 21 CFR 807.92 the following summary of information is provided: | Date: | August 7th, 2019 | |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Submitter: | GE Medical Systems, LLC 3000 N. Grandview Blvd Waukesha, WI 53188, USA | | Primary Contact Person: | Camille Vidal Director of Regulatory Affairs Strategy GE Healthcare 240-280-5356 Camille.Vidal@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 | | Common/Usual Name: | Radiological Computer Assisted Triage and Notification Software | | Classification Names: | Class II, Radiological Computer Assisted Triage and Notification Software, 21 CFR 892.2080 | | Product Code: | QFM | | Predicate Device(s): | HealthPNX by Zebra Medical Vision, K190362 Class II, 21 CFR 892.2080, Product code: QFM | | Indications for use | Critical Care Suite is a computer aided triage and notification device that analyzes frontal chest x-ray images for the presence of prespecified critical findings (pneumothorax). Critical Care Suite identifies images with critical findings to enable case prioritization or triage in the PACS/workstation. Critical Care Suite is intended for notification only and does not provide diagnostic information beyond the notification. Critical Care Suite 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 physician. Critical Care Suite is indicated for adult-size patients. | | Device Description: | Critical Care Suite is a software module that employs Al-based image analysis algorithms to identify pre-specified critical findings (pneumothorax) in frontal chest X-ray images and flag the images in the PACS/workstation to enable prioritized review by the radiologist. Critical Care Suite employs a sequence of vendor and system agnostic AI algorithms to ensure that the input images are suitable for the pneumothorax detection algorithm and to detect the presence of pneumothorax in frontal chest X-rays: - The Quality Care Suite algorithms conduct an automated check to confirm that the input image is compatible with the pneumothorax detection algorithm and that the lung field coverage is adequate; - the PTX Classifier determines whether a pneumothorax is present in the image. If a pneumothorax is detected, Critical Care Suite enables case prioritization or triage through direct communication of the Critical Care Suite notification during image transfer to the PACS. It can also produce a Secondary Capture DICOM Image that presents the Al results to the radiologist. When deployed on a Digital Projection Radiographic Systems such as Optima XR240amx, Critical Care Suite is automatically run after image acquisition. Quality Care Suite algorithms produce an on-device notification if the lung field has atypical positioning to give the | | technologist the opportunity to make correction before sending the image to the PACS. The Critical Care Suite output is then sent directly to | | | PACS upon exam closure where images with a suspicious finding are flagged for prioritized review by the Radiologist. | | | In parallel, an on-device, technologist notification is generated 15 minutes after exam closure, indicating which cases were prioritized by Critical Care Suite in PACS. The technologist notification is contextual and does not provide any diagnostic information. The on-device, technologist notification is not intended to inform any clinical decision, prioritization, or action. | | | The Digital Projection Radiographic System intended use remains unchanged in that the system is used for general purpose diagnostic radiographic imaging. | | {4}------------------------------------------------ Image /page/4/Picture/0 description: The image shows the General Electric (GE) logo. The logo consists of the letters 'GE' intertwined in a stylized monogram. The monogram is enclosed within a circular border, and the entire logo is rendered in a light blue color. The logo is simple and recognizable. # GE Healthcare 510(k) Premarket Notification Submission {5}------------------------------------------------ Image /page/5/Picture/0 description: The image shows the logo for General Electric (GE). The logo is a blue circle with the letters "GE" in a stylized font in the center. There are swirling lines around the letters, giving the logo a dynamic and modern look. The blue color is consistent throughout the logo. | Predicate Device Comparison | Critical Care Suite | HealthPNX (K190362) | |---------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Device classification | Radiological Computer Assisted Triage and Notification software, Class II, QFM | Radiological Computer Assisted Triage and Notification software, Class II, QFM | | Targeted clinical condition, anatomy and modality | Pneumothorax Chest/Lung Frontal Chest X-ray | Pneumothorax Chest/Lung Chest X-ray | | Input Validation | Quality Care Suite algorithms conduct an automated check to confirm image is compatible with processing algorithm (age, frontal chest, lung field) Atypical lung field positioning generates notifications on the X-ray system and Secondary Capture DICOM Image when generated. | Validation feature of HealthPNX verifies that input age, modality and view to ensure compatibility with processing algorithm. In case of failure during data validation, system outputs an error code. | | Algorithm for Pneumothorax detection | AI algorithm designed to detect pneumothorax in frontal chest X-ray images Critical Care Suite uses a vendor agnostic algorithm compatible with DICOM frontal chest X-ray images acquired on fixed or mobile systems. | AI algorithm designed to detect pneumothorax in chest X-ray images. HealthPNX employs a vendor agnostic algorithm compatible with DICOM chest X-ray images. | | Computational Platform | Critical Care Suite is designed as a software module that can be deployed on several computing and X-ray imaging platforms such as Digital Projection Radiographic Systems, PACS. On Premise or On Cloud. | Cloud-based computation upon transfer to PACS of image | {6}------------------------------------------------ Image /page/6/Picture/0 description: The image shows the General Electric (GE) logo. The logo consists of the letters 'GE' intertwined in a stylized, cursive font. The letters are enclosed within a circular border, and the space between the letters and the border is filled with a light blue color. The overall design is simple, clean, and recognizable as the brand identity of General Electric. # GE Healthcare 510(k) Premarket Notification Submission | Predicate Device Comparison | Critical Care Suite | HealthPNX (K190362) | |-----------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Device output in case of positive detection | Critical Care Suite enables case prioritization or triage through direct communication of the Critical Care Suite notification during image transfer to the PACS. | Integration module notifies the PACS/workstation for prioritization through the worklist interface. | | | No markup on original image | No markup on original image | | | Upon image acquisition on a Digital Projection Radiographic System, an on-device, technologist notification is generated 15 minutes after exam closure, indicating which cases were prioritized by Critical Care Suite in PACS. The technologist notification is contextual and does not provide any diagnostic information. The on-device, technologist notification is not intended to inform any clinical decision, prioritization, or action. | | | Notification: Recipient, timing and means of notification | Passive notification to radiologist. Images with suspicion of pneumothorax are flagged in PACS/workstation. | Passive notification to radiologist. Images with suspicion of pneumothorax are flagged in PACS/workstation. | | Performance level - timing of notification | Exams arrive on PACS with the passive notification already incorporated, therefore there is no delay for image transfer or computation. The worklist prioritization happens immediately once the exam is received on the PACS. | Passive notification is visible upon transfer to the PACS with a delay of about 22 seconds for image transfer to the cloud, computation and results transfer. | | Performance level - accuracy of classification | ROC AUC > 0.95 AUC: 0.9607 (95% CI [0.9491, 0.9724]) Specificity 93.5% (95% CI [91.1%, 95.8%]) Sensitivity 84.3% (95% CI [80.6%, 88.0%]) AUC on large pneumothorax 0.9888 (95% CI [0.9810, 0.9965]) Sensitivity on large pneumothorax 96.3% (95% CI [93.3%, 99.2%]) AUC on small pneumothorax 0.9389 (95% CI [0.9209, 0.9570]) Sensitivity on small pneumothorax 75% (95% CI [69.2%, 80.8%]) | ROC AUC > 0.95 AUC: 0.983 (95% CI [0.9740, 0.9902]), Specificity: 93% Sensitivity: 93% Stratified results on small vs. large pneumothorax not assessed. | | Clinical and Non- Clinical Tests | Summary of Non-Clinical Tests: | |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | | Critical Care Suite contains a set of AI algorithms that resides within a software module that has been designed to be integrated within several computing and imaging platforms, such as the Optima XR240amx. | | The following quality assurance measures were applied to the development of Critical Care Suite and deployment onto the Optima XR240amx system: | | | ■ Risk Analysis | | | ■ Requirements Reviews | | | ■ Design Reviews | | | ■ Testing on unit level (Module verification) | | | ■ Integration testing (System verification) | | | ■ Performance testing (Verification) | | | ■ Safety testing (Verification) | | | ■ Simulated use testing (Validation) | | | Critical Care Suite specific verification was conducted to demonstrate proper implementation of Critical Care Suite software design requirements. | | | Regression testing of the Optima XR 240amx feature functionality was conducted to verify proper integration of the Critical Care Suite into the Optima XR240amx software and device. Validation was performed on Optima XR240amx with integrated Critical Care Suite. | | | The test plans have been executed with acceptable results. | | | Timing Performance | | | Internal bench testing was conducted with and without Critical Care Suite integrated within the Optima XR240amx. The average time to acquire, annotate, process and transfer an image from the x-ray system to PACS was measured and found to take 42 seconds on average. Whether Critical Care Suite was on or off did not make a statistical difference in the timing. This shows that Critical Care Suite has no timing impact on image acquisition, processing, annotation and transfer to PACS as compared to standard of care when measured in the same conditions. | | | Since the image arrives on PACS with the passive notification already incorporated, the worklist prioritization happens immediately once the image is received on the PACS. | | | | The timing of the processing and prioritization is well within the clinical operational expectations of standard chest radiographic exam and its reading by radiologists. | | | According to Gaskin, Cree M., et al. "Impact of a Reading Priority Scoring System on the Prioritization of Examination Interpretations." American Journal of Roentgenology 206.5 (2016): 1031-1039 and Rachh, Pratik, et al. "Reducing STAT Portable Chest Radiograph Turnaround Times: A Pilot Study." Current problems in diagnostic radiology (2017), the estimated average Report Turnaround Time for non-prioritized or ineffectively prioritized exams is between 7.23 hours and 8.67 hours. | | | Incorporating the Critical Care Suite passive notification to help radiologists prioritize their exam reads would drastically reduce this turnaround time for the cases that have been flagged by Critical Care Suite as compared to standard of care (First-In, First-Out). | | | Summary of Clinical Evaluation: | | | Critical Care Suite was evaluated on a dataset of 804 frontal chest X-rays collected in North America and representative of the intended population. The algorithm prediction is compared to the ground truth established by 3 independent US-board certified radiologists. The algorithm ROC AUC meets the performance requirement of FDA product code QFM (AUC>95%): AUC=96% (95% CI [94.9% - 97.2%]) (PTX present: N=376; PTX absent: N=428). Stratified analyses showed consistent performance across image view (AP/PA), system manufacturer (GE/non- GE) and data sources. | | | Critical Care Suite performs at high specificity 93.5% (95% CI [91.1% - 95.8%]) and high sensitivity 84.3% (95% CI [80.6% – 88.0%]). Stratified analysis by pneumothorax size shows that nearly all large pneumothoraces are detected (96.3% with 95% CI [93.3% - 99.2%]) while 3 out 4 small pneumothoraces are detected (75% with 95% CI [69.2% - 80.8%]) with limited false notifications thanks to the high specificity. | | Substantial Equivalence Discussion: | Critical Care Suite and HealthPNX are software devices intended to aid in triage and prioritization of radiological images. Both devices use artificial intelligence algorithms to identify suspicious findings suggestive of pneumothorax in chest X-ray images. Both devices are intended to | | notify the radiologist by producing a passive notification in the form of a case level flag in the PACS/workstation. | | | Critical Care Suite, when deployed on a Digital Projection Radiographic System, generates an on-device, technologist notification 15 minutes after exam closure, indicating which cases were prioritized by Critical Care Suite in PACS. The technologist notification is contextual and does not provide any diagnostic information. The on-device, technologist notification is not intended to inform any clinical decision, prioritization, or action. | | | The predicate and proposed devices use similar artificial intelligence techniques to process radiological images. Specifically, the proposed and predicate software utilize a deep learning algorithm trained on annotated medical images. Both trained algorithms achieve the high accuracy performance requirement for product code QFM (ROC AUC >0.95) in the detection of pneumothorax in a representative image dataset withheld for testing. Critical Care Suite and HealthPNX operates at high specificity and high sensitivity. |…