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EFAI PNXXR is a software workflow tool designed to aid the clinical assessment of adult (22 years of age or older) Posteroanterior (PA) view Chest X-Ray cases with features suggestive of pneumothorax in the medical care environment. EFAI PNXXR analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI PNXXR is not intended to direct attention to specific portions or anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out pneumothorax or otherwise preclude clinical assessment of X-Ray cases.

EFAI ChestSuite XR Pneumothorax Assessment System, herein referred to as EFAI PNXXR, is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze PA chest x-rays and sends notification messages to the picture archiving and communication system (PACS)/workstation to allow suspicious findings of pneumothorax to be identified.

The device is intended to provide a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from the prioritization. It does not mark, highlight, or direct users' attention to a specific location on the original chest X-ray. The device aims to aid in prioritization and triage of radiological medical images only.

The deployment environment is recommended to be in a local network with an existing hospitalgrade IT system in place. EFAI PNXXR should be installed on a specialized server supporting deep learning processing. The configurations are only being operated by the manufacturer:

• · Local network setting of input and output destinations;

Here's a breakdown of the acceptance criteria and study details for EFAI ChestSuite XR Pneumothorax Assessment System, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance

1. Acceptance Criteria Table and Reported Device Performance

2. Sample Size and Data Provenance for Test Set

• Sample Size: 800 anonymized Chest X-ray images.
• Data Provenance: Retrospective, multi-center study. Data was collected from 3 institutions in the US and 1 institution outside the US (OUS). The dataset was explicitly stated as not being used for model development or analytical validation.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Three.
• Qualifications of Experts: US board-certified radiologists.

4. Adjudication Method for Test Set

• Adjudication Method: Majority agreement among the three board-certified radiologists. (This generally implies a 2-out-of-3 consensus.)

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

• Was an MRMC study done? No, an MRMC comparative effectiveness study was not reported. The study focused on the standalone performance of the algorithm against a defined ground truth and compared its performance metrics (sensitivity, specificity, AUC, processing time) to a predicate device.

6. Standalone Performance (Algorithm Only)

• Was a standalone performance study done? Yes, a retrospective, blinded, multicenter study was performed to establish the standalone performance of EFAI PNXXR. The study compared the device's pneumothorax classification performance and processing time against the predicate device (red dot™).

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. Specifically, the reference standard (ground truth) was generated by the majority agreement between the three board-certified radiologists.

8. Sample Size for Training Set

• The sample size for the training set is not specified in the provided document. The document mentions that the test set was "Neither of the datasets were used as part of the EFAI PNXXR model development or analytical validation testing," implying a separate (and unquantified) training dataset.

9. How Ground Truth for Training Set Was Established

• The method for establishing ground truth for the training set is not specified in the provided document. It only states that the test set (from section 2 above) was not part of the model development.

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EFAI Intelligent Cardiothoracic Ratio Assessment System (or iCTR) is a software for use by hospital and clinics to automatically assess the cardiothoracic ratio (CTR) of a chest X-ray image from the X-ray imager subject. The iCTR is designed to measure the maximal transverse diameter of heart and maximal inner transverse diameter of thoracic cavity and calculate the CTR of a chest X-ray image in posterior (PA) chest view using an artificial intelligence algorithm.

Intended users of the software are aimed to the physicians or other licensed practitioners in the healthcare institutions, such as clinics, hospitals, healthcare facilities, residential care facilities and long-term care services. The system is suitable for adults between 20 - 80 years of age.

Its results are not intended to be used on a stand-alone basis for clinical-decision making or otherwise preclude clinical assessment of cardiothoracic ratio (CTR) cases.

The iCTR is a non-invasive software medical device designed to be installed on the computer with specific system requirements. It is a radiological computer-assisted software system that automatically analyzes DICOM chest X-ray images in PA view and outputs the CTR through an artificial intelligence algorithm. The structure report includes a preview of the compressed chest X-ray image with the automatically-derived CTR result and annotation line, indicating the maximal transverse diameter of heart and maximal inner transverse diameter of thoracic cavity, and the trajectory of CTR records. The trajectory of CTR does not implement a predictive or prognostic feature.

Here's an analysis of the acceptance criteria and study proving the device meets them, based on the provided text:

Device Name: EFAI Intelligent Cardiothoracic Ratio (iCTR) Assessment System

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document does not explicitly present a formal "acceptance criteria" table with thresholds. Instead, it describes performance metrics that were achieved and deemed "met" or "greater than" a certain level. For clarity, I've inferenced the acceptance criteria based on these reported performance numbers.

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