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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 < 22 years old but treated like adults).

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 <22 years old but treated like adults) and is intended to be used by licensed qualified healthcare professionals (HCPs) trained to independently assess the presence of pneumothoraxes in radiographic images and radiologists. Critical Care Suite is a software module that can be deployed on several computing platforms such as PACS, On Premise, On Cloud or X-ray Imaging Systems.

Today's clinical workflow, hospitals are overburdened by large volume of orders and long turnaround times for radiologist reports. Critical Care Suite with the Pneumothorax Detection Al Algorithm enables effective prioritization and assists in the detection / diagnosis of pneumothoraxes for radiologists and HCPs that have been trained to independently assess the presence of pneumothoraxes in radiographic images. It performs this task by flagging images with a suspicious finding and providing a localization overlay of the suspected pneumothorax as well as a graphical representation of the algorithm's confidence in the resultant finding. These outputs can be displayed wherever the reviewing physician normally conducts their reads per their standard of care, including PACS, On Premise, On Cloud and Digital Projection Radiographic Systems.

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.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: 804 images
• Data Provenance: The test set included images from two North American sites.
• Retrospective/Prospective: The document does not explicitly state if the test set was retrospective or prospective. However, given it's a "final validation ground truth dataset" that was not used in training, it's highly likely to be retrospective.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

• Number of Experts: Three blinded radiologists.
• Qualifications of Experts: Radiologists (no specific experience level mentioned, but "blinded radiologists" implies qualified professionals).

4. Adjudication Method for the Test Set

• Adjudication Method: The ground truth was established by "three blinded radiologists." This implies a consensus method, likely majority rule or a process where discrepancies were resolved to arrive at a single ground truth label. The specific phrase "consensus" or "adjudication" is not used, but the description points to this approach.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance

• MRMC Study Done: Yes, a multi-reader multi-case study was conducted.
• Effect Size of Human Reader Improvement with AI vs. Without AI Assistance:
  • Mean AUC: Improved by 14.5% (from 76.8% non-aided to 91.3% aided; p=0.002).
  • Sensitivity: Increased by 16.3% (from 67.4% non-aided to 83.7% aided; p<0.001).
  • Specificity: Increased by 12.4% (from 76.6% non-aided to 89.0% aided; p<001).
  • Large Pneumothorax (Mean AUC): Improved by 10.5% (p=0.009).
  • Large Pneumothorax (Sensitivity): Improved by 13.4% (p<0.001).
  • Small Pneumothorax (Mean AUC): Improved by 17.6% (p<0.001).
  • Small Pneumothorax (Sensitivity): Improved by 18.7% (p<0.001).

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Standalone Study Done: Yes, the "standalone performance of the Pneumothorax Detection AI Algorithm was tested against this dataset."

7. The Type of Ground Truth Used

• Type of Ground Truth: Expert consensus by three blinded radiologists.

8. The Sample Size for the Training Set

• Sample Size for Training Set: The algorithm was developed using "over 12,000 images." This number includes images used for training, verification, and validation, but the specific breakdown for the training set alone is not provided. It's implied that the majority would be for training.

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

• Ground Truth for Training Set: The document states that the "Pneumothorax Detection AI Algorithm was developed using over 12,000 images from six sources, including the National Institute of Health and sites within the United States, Canada, and India." It then clarifies this data was "segregated into training, verification, and validation datasets." While it doesn't explicitly detail the methodology for establishing ground truth for the training set, it's standard practice that such large datasets for deep learning and medical imaging are meticulously annotated by medical experts (e.g., radiologists) or derived from existing clinical reports and pathology, which would then be reviewed or confirmed by experts. Given the rigor for the validation set, it's reasonable to infer a similar expert-driven process for the training data, although the specifics are not provided in this excerpt.

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