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Critical Care Suite with Pneumothorax Detection AI Algorithm is a computer-aided triage, notification, and diagnostic device that analyzes frontal chest X-ray images for the presence of a pneumothorax. Critical Care Suite identifies and highlights images with a pneumothorax to enable case prioritization or triage and assist as a concurrent reading aide during interpretation of radiographs.

Intended users include qualified independently licensed healthcare professionals (HCPs) trained to independently assess the presence of pneumothoraxes in radiographic images and radiologists.

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 adults and Transitional Adolescents (18 to

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 for the presence of critical findings. Critical Care Suite with Pneumothorax Detection Al Algorithm is indicated for adults and transitional adolescents (18 to

Here's a summary of the acceptance criteria and study details for the GE Medical Systems, LLC Critical Care Suite with Pneumothorax Detection AI Algorithm, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document primarily focuses on reporting the device's performance against its own established criteria rather than explicitly listing pre-defined "acceptance criteria" tables. However, we can infer the acceptance criteria from the reported performance goals.

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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

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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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.

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.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample size and Data Provenance for the Test Set:

• Sample Size: 804 frontal chest X-ray images (N=376 for pneumothorax present; N=428 for pneumothorax absent).
• Data Provenance: Collected in North America, representative of the intended population. The text does not explicitly state if it was retrospective or prospective, but the nature of a "collected dataset" for evaluation typically implies retrospective analysis of existing images.

3. Number of Experts and Qualifications for Ground Truth of the Test Set:

• Number of Experts: 3 independent US-board certified radiologists.
• Qualifications: "US-board certified radiologists." No specific years of experience or subspecialty are mentioned beyond board certification.

4. Adjudication Method for the Test Set:

• The text states the ground truth was "established by 3 independent US-board certified radiologists." It does not explicitly detail a specific adjudication method like 2+1 or 3+1. This implies a consensus-based approach where the radiologists independently reviewed images to establish the ground truth, likely resolving discrepancies through discussion to reach a final determination.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• No, a multi-reader multi-case (MRMC) comparative effectiveness study directly comparing human readers with AI assistance vs. without AI assistance was not reported in this summary. The study focused on the standalone diagnostic performance of the AI algorithm.

6. Standalone (Algorithm Only) Performance Study:

• Yes, a standalone performance study of the algorithm without human-in-the-loop was done. The reported metrics (ROC AUC, Sensitivity, Specificity) are direct measurements of the algorithm's performance against the established ground truth.

7. Type of Ground Truth Used:

• Expert Consensus: The ground truth was established by "3 independent US-board certified radiologists." This indicates an expert consensus approach.

8. Sample Size for the Training Set:

• The document does not explicitly state the sample size for the training set. It mentions the algorithm was "trained on annotated medical images" but provides no further details on the quantity of images used for training.

9. How the Ground Truth for the Training Set Was Established:

• The document states the device utilizes a "deep learning algorithm trained on annotated medical images." While it doesn't explicitly describe the method for establishing ground truth for the training set, it is implied that these "annotated medical images" had pre-existing labels or were labeled by experts for the purpose of training the AI.

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