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qCT LN Quant is a software device used in the tracking, assessment, and quantitative characterization of detected pulmonary nodules. This automatically analyzes user-selected regions within lung CT to provide volumetric, diameter and computer analysis based on morphological characteristics in a single study, or over the time course of several thoracic studies. The system performs the measurements, allowing the preview of lung nodules in 2D and 3D reconstructed views and the respective measurements to be displayed. It is indicated for the evaluation of user detected solid pulmonary nodules.

Qure.ai's computed tomography (CT) scan software, the qCT LN Quant, is a deep-learning-based device that can process non-contract CT (NCCT) scans and assists in quantitative characterization of solid lung nodules of size ≥ 6mm on Chest CTs.

qCT LN Quant consists of a cloud module that can interacts with DICOM modality or the user's picture archiving and communication system (PACS) to receive de-identified scans and returns the results to the same destination. In addition, solid nodules are segmented by the user semi-automatically using double seed points on the nodule, followed by interactive fine-tuning of the boundaries. The segmented region is quantitatively characterized by qCT LN Quant and presented to users as an additional overlay by highlighting and labelling respectively. User-assisted segmentation generated by qCT LN Quant can be presented in two ways to the users:

a. PACS-based mode: As a new series (secondary capture) which are returned to the originating PACS system with segmentation burnt on the series. This can be done only at PACS which supports GSPS Output.

b. Web-based mode: On Qure's web application where the segmentation is overlaid on top of the original scan.

qCT LN Quant deep learning algorithm has been trained to quantify the target structures on CT scans and is coupled with pre-and post-processing functionality that allows the device to work directly with the radiology workflow. The user is presented with 2D view and 3D reconstructed view of solid nodule images labelling the quantitative characteristics based on the user-segmented structures. The output consists of information on average diameter and volumes of user identified solid nodules. The additional features include long axis diameter (mm), short axis diameter (mm), Effective diameter (mm), and Mean/Minimum/Max HU (HU) and volume change overtime with matched nodules. In addition, qCT LN Quant consists of a Brock Score - Risk Calculator that uses diameter of the nodule and clinician's input. The Lung-RADS™ calculator feature is based on ACR guideline, which can assist the physician in decision making. qCT LN Quant also provides recommendations based on Fleischner's Society guideline. Thus, qCT LN Quant offers functionality to calculate Brock and LungRADS score as part of integrated or cleared devices with capability to display such output.

qCT LN Quant is limited to analysis of imaging data and results generated are meant for information purposes only. The device is not intended for clinical diagnosis of any disease. It does not replace the role of physician or of other testing in the standard of care for lung abnormalities.

Here's a detailed breakdown of the acceptance criteria and study information for the qCT LN Quant device, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in a structured table format with specific thresholds before the study was conducted. Instead, it presents the results of the performance testing. However, we can infer the implied acceptance criteria from the reported performance, suggesting that these values were considered "good performance" and met "predefined success criteria."

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

• Sample Size: 216 solid nodules identified from a total of 118 chest CT scans.
• Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective.

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

• Number of Experts: Three expert radiologists.
• Qualifications: The document states "three expert radiologists," but does not explicitly detail their years of experience or specific subspecialty certifications.

4. Adjudication Method for the Test Set:

• Adjudication Method: "The truthers independently read the scans and mark out the boundaries of the nodule in all slices." This implies a consensus-based approach after independent marking, but the specific adjudication rules (e.g., how disagreements were resolved, 2+1, 3+1, or simple majority) are not explicitly stated. It is a form of expert consensus, but the mechanism for reaching the final ground truth from independent readings isn't fully detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

• No MRMC study was done with AI assistance vs. without AI assistance. The study described is a standalone performance study of the device.

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

• Yes, a standalone study was done. The document explicitly states: "Performance of the qCT LN Quant device in quantitative characterization of solid nodules was assessed using the standalone study."

7. The Type of Ground Truth Used:

• Expert Consensus. The ground truth "was established by three expert radiologists" who "independently read the scans and mark out the boundaries of the nodule in all slices."

8. The Sample Size for the Training Set:

• The document does not provide the sample size for the training set. It mentions that the qCT LN Quant deep learning algorithm "has been trained to quantify the target structures on CT scans," but the size of this training dataset is not disclosed in the provided text.

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

• The document does not explicitly state how the ground truth for the training set was established. It only mentions that the algorithm was trained.

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The qXR-LN (qXR Lung nodule) is computer-aided detection software to identify and mark regions in relation to suspected pulmonary nodules from 6 to 30 mm in size. The device is intended to be used in the incidental adult population. It is designed to aid the physician to review the frontal (AP/PA) chest radiographs of adults acquired on digital radiographic systems as a second reader and be used with any DICOM viewer or PACS . qXR-LN provides adjunctive information only and is not a substitute for the original chest radiographic image.

qXR-LN is a Computer-Aided Detection (CADe) device that is designed to perform CAD processing in frontal (PA or AP view) Chest X-ray images for indication of locations for high nodule probability, which has an effective detection size from 6 mm to 30 mm. The device is intended to be a secondary aid to the qualified intended user to identify incidental pulmonary lung nodules chest x-ray images.

The device utilizes a deep learning algorithm. The qXR-LN was trained on a large and diverse dataset of 2.5million scans from 5 countries across the world. The training dataset was from more than 25 manufacturers.

Chest X-rays are sent to qXR-LN by the means of transmission functions within the user's image storage system (e.g., Picture Archiving and Communication System (PACS)) or other radiological imaging equipment (e.g., X-ray systems) and processed by the qXR-LN to detect and localise lung nodules. Following receipt of chest radiographs, the software device automatically analyses each image to detect and localise lung nodules.

qXR-LN receives chest x-ray images in digital imaging and communications in medicine (DICOM) as input. The qXR-LN device produces DICOM format outputs that enable users to view the presence and location of lung nodules.

This device is intended to aid the intended user in review of chest x-rays and detect and localise lung nodules as a secondary reader. The results are not intended to be used on a standalone basis for clinical decision-making nor is it intended to rule out the target conditions or otherwise preclude clinical assessment of X-ray cases.

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

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for qXR-LN are not explicitly stated as distinct numerical targets in the same way performance criteria often are. Instead, the document compares its performance to a predicate device (Samsung Auto Lung Nodule Detection) and demonstrates non-inferiority or improvement. The core principle for acceptance is "substantial equivalence" to the predicate, with performance metrics being a key factor in proving this equivalence.

Based on the provided information, the implicit acceptance criteria are framed around demonstrating performance at least equivalent to the predicate, particularly in terms of improving human reader performance and achieving a competitive standalone sensitivity for nodule detection.

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qXR-CTR is a deep-learning based software for use by hospitals and clinics for automated assessment of the CTR on chest X-ray (CXRs) scans.

qXR-CTR is designed to measure the ratio of the maximal transverse diameter of the heart (CD) and the maximal inner transverse diameter (TD) of the thoracic cavity and calculate the CTR value on posterior-anterior view chest view using an artificial intelligence algorithm.

The intended users of this device are physicians or licensed practitioners in healthcare institutions, such as clinics, hospitals, residential care facilities, long-term care services, and healthcare facilities.

The system is suitable for adults > 22 years of age.

The device is used to aid the intended users and results are not intended to be used on a stand-alone basis for clinical decision making or otherwise preclude clinical assessment of CTR cases.

The qXR-CTR 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 cardiac diameter, thoracic diameter, and CTR through an artificial intelligence algorithm. The structured 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.

Acceptance Criteria and Study Details for qXR-CTR

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for Test Set

• Sample Size: 435 scans.
• Data Provenance: Retrospectively collected chest X-rays from various parts of the US.

3. Number and Qualifications of Experts for Ground Truth (Test Set)

• Number of Experts: 3
• Qualifications: ABR (American Board of Radiology) thoracic radiologists with a minimum of 10 years of experience.

4. Adjudication Method for Test Set

The adjudication method is not explicitly stated, but with 3 experts establishing ground truth, a consensus method (e.g., majority vote or discussion to agreement) is implied. It's not explicitly stated as 2+1, 3+1, or none.

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

No, an MRMC comparative effectiveness study was not done. The document describes a standalone performance evaluation of the qXR-CTR against ground truth established by experts.

6. Standalone Performance

Yes, a standalone (algorithm only without human-in-the-loop performance) study was done. The reported performance metrics (RMSE, MAE) are intrinsic to the device's measurements compared to expert-established ground truth.

7. Type of Ground Truth Used

Expert consensus. The ground truth was established by three ABR thoracic radiologists.

8. Sample Size for Training Set

The sample size for the training set is not provided in the given text.

9. How Ground Truth for Training Set was Established

The method for establishing ground truth for the training set is not described in the provided text.

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qXR-PTX-PE is a radiological computer-assisted triage and notification software that analyzes adult chest X-ray images for the presence of pre-specified suspected critical findings (pleural effusion and/or pneumothorax). qXR-PTX-PE uses an artificial intelligence algorithm to analyze images for features suggestive of critical findings and provides case-level output available in the PACS/workstation for worklist prioritization or triage.

As a passive notification for prioritization-only software tool within standard of care workflow, qXR-PTX-PE does not send a proactive alert directly to the appropriately trained medical specialists. qXR-PTX-PE is not intended to direct attention to specific portions of an image or to anomalies other than pleural effusion and/or pneumothorax. Its results are not intended to be used on a stand-alone basis for clinical decision-making.

qXR-PTX-PE is a radiological computer aided triage and notification software that analyses adult frontal (AP or PA views) CXR images for the presence of pre-specified suspected target conditions (pleural effusion and/or pneumothorax). The algorithm was trained on training data from across the world. The training dataset consisted of 74% of the data from India, 20.04% from the EU, 3.9% from the US, 1.4% from Brazil and 0.63% from Vietnam. The input for qXR-PTX-PE is a frontal chest X-ray (AP and PA view) in digital imaging and communications in medicine (DICOM) format

Chest X-rays are sent to qXR-PTX-PE by the means of transmission within the user's image storage system (e.g., Picture Archiving and Communication System (PACS)) or other radiological imaging equipment (e.g., X-ray systems) and processed by the qXR-PTX-PE for analysis. Following receipt of chest radiographs, the software device automatically analyses each image to detect features suggestive of pneumothorax and/or pleural effusion.

This would allow the appropriately trained medical specialists to group suspicious exams together that may potentially benefit for their prioritization. Chest radiographs without the suspicious findings are placed in the worklist for routine review, which is the standard of care at present. A secondary capture is available for the information on presence of the suspicious findings.

qXR-PTX-PE does not provide any proactive alerts. qXR-PTX-PE is not intended to direct attention to specific portions of the image. The results are not intended to be used on a standalone basis for clinical decision-making nor is it intended to rule out the target conditions or otherwise preclude clinical assessment of X-Ray cases.

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

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the statement: "The device shows > 95% AUC". The reported performance significantly exceeds this threshold.

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

• Pneumothorax Test Set: 613 scans
  • 201 scans with pneumothorax
  • 412 scans without pneumothorax
• Pleural Effusion Test Set: 1070 scans
  • 344 scans with pleural effusion
  • 726 scans without pleural effusion
• Data Provenance: Retrospective. All test data was obtained from various hospitals across the US. Specific regions mentioned include Midwest, Northeast, and West. The test set was intentionally obtained from sites different from the training data sites to ensure independence.

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

• Number of Experts: 3
• Qualifications: ABR (American Board of Radiology) thoracic radiologists with a minimum of 10 years of experience.

4. Adjudication Method for the Test Set

The provided text states that "The ground truth was established by 3 ABR thoracic radiologists with a minimum of 10 years of experience." It does not specify a particular adjudication method (e.g., 2+1, 3+1, or simple consensus). Without further detail, it's assumed to be a consensus among these three experts, but the exact process of resolving discrepancies is not described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not reported. The study focuses purely on the standalone performance of the AI algorithm (qXR-PTX-PE) and compares it to the standalone performance of a predicate device.

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

Yes, a standalone performance study was done. The reported AUC, sensitivity, and specificity metrics are for the qXR-PTX-PE algorithm acting independently.

7. The Type of Ground Truth Used

The ground truth used was expert consensus (3 ABR thoracic radiologists with a minimum of 10 years of experience).

8. The Sample Size for the Training Set

The exact total sample size for the training set is not specified. However, the text provides the geographical distribution of the training data:

• 74% from India
• 20.04% from the EU
• 3.9% from the US
• 1.4% from Brazil
• 0.63% from Vietnam

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

The document does not explicitly state how the ground truth for the training set was established. It only mentions that the algorithm was "trained on training data from across the world." It can be inferred that expert labeling or clinical diagnoses were likely used, given the nature of a medical imaging AI, but specific details about the process (e.g., number of readers, their qualifications, adjudication) are not provided for the training data.

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The qXR-BT device is intended to generate a secondary digital chest X-ray image that facilitates confirmation of the position of a breathing tube and an anatomical landmark on adult chest X-rays. This device is intended for use by licensed physicians who are trained in the evaluation of breathing tube placement on chest X-rays. The qXR-BT image provides adjunctive information and is not a substitute for the original PA/AP image.

qXR-BT is a standalone image analysis software used during the review of digital chest radiographic images, intended to facilitate determining the position of the breathing tube relative to the carina. Standard of care medical imaging workflows are well established, and include pre-existing software components such as a PACS, DICOM viewer and imaging worklist; qXR-BT is designed to integrate with these components.

X-rays are sent to qXR-BT by means of transmission functions within the user's PACS system. Upon completion of processing, the qXR-BT device returns results to the user's PACS or other userspecified radiology software system or database.

The input to the qXR-BT device is a chest X-ray (AP and PA, referred to as frontal) in digital imaging and communications in medicine (DICOM) format.

The qXR-BT device produces PDF and DICOM format outputs that enable users to view the position of a breathing tube and an anatomical landmark (carina).

The PDF format output contains preview images that show segmented structures outlined with a textual report describing the structures detected. The text report is restricted to the presence or absence of the breathing tubes and the carina as detected by the software device.

The DICOM format output consists of a single complete additional DICOM series for each input scan. This DICOM output contains labeled overlays indicating the location and extent of the segmentable structures, suitable for viewing in the PACS or radiology viewer.

The qXR-BT analysis module consists of a set of pre-trained convolutional neural networks (CNNs), that form the core processing component shown in Figure 1. This core processing component is coupled with a pre-processing module to prepare input DICOMs for processing by the CNNs and a post-processing module to convert the output into visual and tabular format for users.

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

1. A table of acceptance criteria and the reported device performance:

2. Sample size used for the test set and the data provenance:

• Sample size: 162 Chest X-ray images.
• Data Provenance: Not explicitly stated regarding country of origin or whether it was retrospective or prospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of experts: Three radiologists.
• Qualifications of experts: From the United States; no specific experience level (e.g., years of experience) is mentioned beyond "radiologists."

4. Adjudication method for the test set:

• Not explicitly stated. The text mentions "manual annotation of three radiologists," which implies an expert consensus method, but the specific adjudication rules (e.g., majority vote, independent review followed by consensus) are not detailed.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No, a multi-reader multi-case (MRMC) comparative effectiveness study with human readers was not explicitly mentioned or detailed in this summary. The performance testing described is a standalone evaluation of the algorithm's accuracy against ground truth.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• Yes, a standalone performance study was done. The text explicitly states, "Qure.ai performed standalone performance testing to test the accuracy of qXR-BT's analysis."

7. The type of ground truth used:

• Expert consensus. The ground truth was "based on manual annotation of three radiologists from United States."

8. The sample size for the training set:

• The sample size for the training set is not provided in the given document. The document only mentions the test set size.

9. How the ground truth for the training set was established:

• How the ground truth for the training set was established is not provided in the given document. The document only describes how the ground truth for the test set was established.

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The qER-Quant device is intended for automatic labeling, visualization of segmentable brain structures from a set of Non-Contrast head CT (NCCT) images. The software is intended to automate the current manual process of identifying, labeling and quantifying the volume of segmentable brain structures identified on NCCT images. qER-Quant provides volumes from NCCT images acquired at a single time point and provides a table with comparative analysis for two or more images that were acquired on the same image acquisition protocol for the same individual at multiple time points.

The qER-Quant software is indicated for use in the following structures: Intracranial Hyperdensities, Lateral Ventricles and Midline Shift.

qER-Quant is a standalone software device that processes non-contrast head CT scans to outline and quantify the structures described in the intended use. The qER-Quant software interacts with the user's picture archiving and communication system (PACS) to receive scans and returns the results to the same destination.

The analysis module of the qER-Quant software contains of a set of pre-trained convolutional neural networks (CNNs), that form the core processing component shown in Figure 1. This core processing component is coupled with a pre-processing module to prepare input digital imaging and communications in medicine (DICOMs) for processing by the CNNs and a post-processing module to convert the output into visual and tabular output for users.

Here's a breakdown of the acceptance criteria and study details for the qER-Quant device, based on the provided text:

qER-Quant Device Performance Study Details

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria were defined based on the accuracy of the qER-Quant system when compared against manually labeled ground truth. The reported device performance met these pre-set criteria.

2. Sample Size and Data Provenance

• Test Set Sample Sizes:
  • Intracranial Hyperdensity: 183 scans
  • Midline Shift: 188 scans
  • Left Lateral Ventricle: 210 scans
  • Right Lateral Ventricle: 210 scans
  • Reproducibility testing was done on 20% of these CT scans.
• Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It uses "a set of head CT scans."

3. Number of Experts and Qualifications for Ground Truth Establishment

• Number of Experts: The document states "experts" (plural) were used but does not specify the exact number.
• Qualifications of Experts: Not specified beyond being "experts" in the context of manually labeling CT scans.

4. Adjudication Method for the Test Set

The document does not explicitly state an adjudication method (e.g., 2+1, 3+1). It only mentions that the ground truth was established by "manually labeled by experts."

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

• No, an MRMC comparative effectiveness study was not reported. The performance testing was a "standalone" evaluation of the device's accuracy against expert-generated ground truth.

6. Standalone Performance (Algorithm Only)

• Yes, a standalone performance study was conducted. The document states: "Qure.ai performed standalone consisted of a set of head CT scans with the outlines of the target structures manually labeled by experts." The results detailed in Table 2 are of this standalone performance.

7. Type of Ground Truth Used

• The ground truth used was expert consensus / manual labeling. The document clearly states: "manually labeled by experts."

8. Sample Size for the Training Set

• The document does not provide the sample size for the training set. It only describes the architecture of the analysis module as "a set of pre-trained convolutional neural networks (CNNs)."

9. How Ground Truth for the Training Set Was Established

• The document does not explicitly state how the ground truth for the training set was established. It describes the CNNs as "pre-trained," which implies a training phase using labeled data, but the method of ground truth establishment for that specific data is not detailed.

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qER is a radiological computer aided triage and notification software in the analysis of non-contrast head CT images.

The device is intended to assist hospital networks and trained medical specialists in workflow triage by flagging the following suspected positive findings of pathologies in head CT images: intracranial hemorrhage, mass effect, midline shift and cranial fracture.

qER uses an artificial intelligence algorithm to analyze images on a standalone cloud-based application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected findings. Notifications include non-diagnostic preview images that are meant for informational purposes only. The device does not alter the original medical image and is not intended to be used as a diagnostic device.

The results of the device are intended to be used in conjunction information and based on professional judgment, to assist with triage/prioritization of medical images. Notified clinicians are responsible for viewing full images per the standard of care.

Qure.ai Head CT scan interpretation software, qER, is a deep-learning-based software device that analyses head CT scans for signs of intracranial hemorrhage, midline shift, mass effect or cranial fractures in order to prioritize them for clinical review. The standalone software device consists of an on-premise module and a cloud module. qER accepts non-contrast adult head CT scan DICOM files as input and provides a priority flag indicating critical scans. Additionally, the software has the preview of critical scans to the medical specialist.

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

Acceptance Criteria and Device Performance

The core purpose of the qER device is workflow triage by identifying suspected positive findings of pathologies in head CT images. The performance data presented focuses on the device's ability to accurately detect these pathologies in a standalone setting.

1. Table of Acceptance Criteria and Reported Device Performance

While the document doesn't explicitly state "acceptance criteria" as numerical thresholds beyond "exceeded the predefined success criteria, as well as the required performance criteria for triage and notification software as per the special controls for QAS," the reported sensitivities and specificities for each pathology effectively serve as the demonstrated "acceptance" level the device achieved.

Additionally, a key performance metric for a triage device is the time to notification:

Study Details

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

• Sample Size: 1320 head CT scans.
• Data Provenance: Retrospective, multicenter study. Data originated from multiple locations within the United States.

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

• Number of Experts: 3 board-certified radiologists.
• Qualifications: The document explicitly states "board-certified radiologists." No further details on years of experience are provided.

4. Adjudication Method for the Test Set

• The text states that the ground truth was established by "3 board-certified radiologists reading the scans." It does not explicitly mention an adjudication method (e.g., 2+1, 3+1 consensus). It is implied that their readings defined the ground truth, but the process of resolving discrepancies among the three readers is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and what was the effect size of how much human readers improve with AI vs without AI assistance

• The provided text does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance was directly compared. The study primarily focuses on the standalone performance of the qER algorithm and its ability to reduce the "time to notification" compared to standard of care "time to open." While the "time-to-notification" analysis suggests a significant workflow improvement when using qER for triage (2.11 mins vs. 65.54 mins), this is not a direct measure of human reader diagnostic accuracy improvement with AI assistance.

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

• Yes, a standalone performance study was done. The "Performance Data" section explicitly states, "A retrospective, multicenter, blinded clinical study was conducted to test the accuracy of qER at triaging head CT scans... Sensitivity and specificity exceeded the predefined success criteria... demonstrating the ability of the qER device to effectively triage studies containing one of these conditions." The results in Table 2 are for the qER algorithm's accuracy independently.

7. The Type of Ground Truth Used

• Expert Consensus: The ground truth for the pathologies (Intracranial hemorrhage, cranial fractures, mass effect, midline shift, and absence of these abnormalities) was established by "3 board-certified radiologists reading the scans." This indicates an expert consensus approach to defining the ground truth.

8. The Sample Size for the Training Set

• The document does not specify the sample size used for the training set. It mentions that the qER software uses "a pre-trained artificial intelligence algorithm" and "a pre-trained classification convolutional neural network (CNN) that has been trained to detect a specific abnormality from head CT scan images." However, the size of the dataset used for this training is not disclosed in the provided text.

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

• The document does not explicitly describe how the ground truth for the training set was established. It only states that the CNN was "pre-trained" on medical images to detect specific abnormalities. It is common practice for such training to also rely on expert annotations, but this is not detailed for the training set in this document.

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