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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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