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Seg Pro V3 is a software device intended to assist trained radiation oncology professionals, including, but not limited to, radiation oncologists, medical physicists, and dosimetrists, during their clinical workflows of radiation therapy treatment planning by providing initial contours of organs at risk on DICOM images. Seg Pro V3 is intended to be used on adult patients only.

The contours are generated by deep-learning algorithms and then transferred to radiation therapy treatment planning systems. Seg Pro V3 must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results generated. Seg Pro V3 is not intended to be used for decision making or to detect lesions.

Seg Pro V3 is an adjunct tool and is not intended to replace a clinician's judgment and manual contouring of the normal organs on DICOM images. Clinicians must not use the software generated output alone without review as the primary interpretation.

The proposed device, Seg Pro V3, is a standalone software that is designed to be used by trained radiation oncology professionals to automatically delineate (segment/contour) organs-at-risk (OARs) on DICOM images. This auto-contouring of OARs is intended to facilitate radiation therapy workflows.

The device receives images in DICOM format as input and automatically generates the contours of OARs, which are stored in DICOM format and in RTSTRUCT modality. The device must be used in conjunction with a DICOM-compliant treatment planning system (TPS) to review and edit results. Once data is routed to Seg Pro V3, the data will be processed and no user interaction is required, nor provided.

The deployment environment is recommended to be in a local network with an existing hospital-grade IT system in place. Seg Pro V3 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.
• Presentation of labels and their color.
• Processed image management and output (RTSTRUCT) file management.

Here's an analysis of the acceptance criteria and study proving the device meets those criteria, based on the provided FDA 510(k) clearance letter for Seg Pro V3 (RT-300):

Acceptance Criteria and Reported Device Performance

Study Details Proving Device Meets Acceptance Criteria

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

• Sample Size: 175 cases.
• Data Provenance: Consecutively collected from the Cancer Imaging Archive (TCIA) datasets. The data was acquired independently from product development training and internal testing. Race and ethnic distribution within the study data patient population was unavailable.
• Geographic Origin (inferred): TCIA is primarily a US-based resource, so data is likely from the United States or a diverse international collection.

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

• Number of Experts: Three.
• Qualifications of Experts: Board-certified radiation oncologists.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• Adjudication Method: "Each OAR contour used as ground truth (GT) was independently generated by three board-certified radiation oncologists." This implies a consensus or agreement among all three experts was used to define the ground truth, effectively a 3-way consensus. The document does not explicitly state an adjudication method like 2+1, but the independent generation by three experts suggests a high-quality, agreed-upon ground truth.

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:

• MRMC Study: No. The study primarily evaluated the standalone performance of the AI algorithm. The clinical validation mentions that Seg Pro V3 "operates as intended within a clinical workflow and supports its intended use as an adjunct tool," but it does not present data from an MRMC study 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:

• Standalone Performance: Yes. "a standalone performance evaluation was conducted to assess the Organ-at-Risk (OAR) contouring capabilities of Seg Pro V3. The observed results indicated that Seg Pro V3 by itself, in the absence of any interaction with a clinician, can contour developed OARs with satisfactory results." The reported DSC and HD metrics are from this standalone evaluation.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• Ground Truth Type: Expert consensus. The ground truth (GT) for each OAR contour was "independently generated by three board-certified radiation oncologists."

8. The sample size for the training set:

• The document explicitly states that the 175 cases used for the standalone performance evaluation were "acquired independently from product development training and internal testing." However, the document does not specify the sample size of the training set used to develop the deep learning models.

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

• The document does not specify how the ground truth for the training set was established. It only describes the ground truth establishment for the test set.

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EFAI CHESTSUITE XR MALPOSITIONED ETT ASSESSMENT SYSTEM (EFAI ETTXR) is a radiological computer-aided triage and notification software indicated for use in the analysis of chest X-ray (CXR) images in adults. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communicating suspected positive cases of vertically malpositioned endotracheal tube (ETT) in relation to the carina. Findings are flagged when the ETT distal tip is assessed as being more than 7 cm above the carina, less than 3 cm above the carina, or when it is below the carina (i.e in the right or left mainstem bronchus). The device assesses solely the vertical position of the ETT distal tip relative to the carina, does not factor patient positioning, and cannot detect esophageal intubation. The device is tested in the single lumen endotracheal tube, while it may trigger a false prioritization alert in the case of properly positioned double lumen ETT.

EFAI ETTXR analyzes cases using algorithms to identify suspected malpositioned ETT findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI ETTXR is not intended to direct attention to specific portions of an image or to 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 malpositioned ETT or otherwise preclude clinical assessment of chest radiographs.

EFAI CHESTSUITE XR MALPOSITIONED ETT ASSESSMENT SYSTEM (EFAI ETTXR) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze chest radiographs and alerts the PACS/RIS workstation once images with features suggestive of malpositioned ETT are identified.

Through the use of EFAI ETTXR, a radiologist is able to review studies with features suggestive of malpositioned ETT earlier than in standard of care workflow.

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 radiographs. The device aims to aid in prioritization and triage of radiological medical images only.

Here's a breakdown of the acceptance criteria and the study details for the EFAI ETTXR device, based on the provided document:

Acceptance Criteria and Reported Device Performance

Study Details

1. Sample Size Used for the Test Set and Data Provenance:
* Sample Size: 940 studies (each patient included only one study).
* Data Provenance: Retrospective, consecutively collected from multiple clinical sites across the United States. None of the studies were used in model development or analytical validation.

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:
* Number of Experts: Three.
* Qualifications: U.S. board-certified radiologists.

3. Adjudication Method for the Test Set:
* Method: Majority agreement among the three U.S. board-certified radiologists.
* Resulting Ground Truth: 259 positive cases for malpositioned ETT, 681 negative cases (316 correctly positioned ETTs, 365 with no ETT).

4. 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 explicitly described in this document. The study described is a standalone performance validation of the AI model.

5. If a Standalone (Algorithm Only) Performance Study Was Done:
* Yes, a standalone performance validation study was done. The document states: "The observed results of the standalone performance validation study demonstrated that EFAI ETTXR by itself, in the absence of any interaction with a clinician, can provide case-level notifications with features suggestive of malpositioned ETT with satisfactory results."

6. The Type of Ground Truth Used:
* Expert Consensus: The ground truth was established by the majority agreement of three U.S. board-certified radiologists.

7. The Sample Size for the Training Set:
* The document does not specify the exact sample size for the training set. It mentions that "None of the studies [in the test set] was used as part of the EFAI ETTXR model development or analytical validation testing," implying a separate training set was used, but its size is not provided.

8. 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 implies the use of "deep learning techniques" and a "database of images" for the algorithm. It's common in AI development studies for the training set ground truth to also be established by expert review, but this is not detailed for EFAI ETTXR's training data.

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EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM (EFAI MLSCT) is a software workflow tool designed to aid in prioritizing the clinical assessment of non-contrast head CT cases with features suggestive of midline shift (MLS) in individuals aged 18 years and above. EFAI MLSCT analyzes cases using deep learning algorithms to identify suspected MLS findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage.

EFAI MLSCT is not intended to direct attention to specific portions of an image or to anomalies other than MLS. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out MLS or otherwise preclude clinical assessment of CT studies.

EFAI NEUROSUITE CT MIDLINE SHIFT ASSESSMENT SYSTEM (EFAI MLSCT) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze non-contrast head CTs and alerts the PACS/RIS workstation once images with features suggestive of MLS are identified.

Through the use of EFAI MLSCT, a radiologist is able to review studies with features suggestive of MLS earlier than in standard of care workflow.

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 non-contrast head CT. The device aims to aid in prioritization and triage of radiological medical images only.

Here's an analysis of the acceptance criteria and study details for the EFAI Neurosuite CT Midline Shift Assessment System (MLS-CT-100), 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

• Test Set Sample Size: 300 cases (102 positive for MLS, 198 negative for MLS). Each case included only one CT study.
• Data Provenance: Retrospective, consecutively collected from multiple clinical sites across the United States (U.S.). The U.S. cases were solely collected for this study.

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

• Number of Experts: Three (3)
• Qualifications: U.S. board-certified radiologists.

4. Adjudication Method (Test Set)

• Adjudication Method: Majority agreement between the three experts established the reference standard (ground truth).

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

• Was it done? No. The document describes a "standalone performance validation study" and mentions "Reader comparison analysis" for overall safety & effectiveness, but does not detail an MRMC comparative effectiveness study where human readers' performance with and without AI assistance was evaluated for an effect size. The study described focuses on the standalone performance of the AI.

6. Standalone Performance Study

• Was it done? Yes. The document explicitly states: "The observed results of the standalone performance validation study demonstrated that EFAI MLSCT by itself, in the absence of any interaction with a clinician, can provide case-level notifications with features suggestive of MLS with satisfactory results."

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus (majority agreement of three U.S. board-certified radiologists).

8. Sample Size for the Training Set

• The document states that the "model development and validation utilized cases from Taiwan," but it does not specify the sample size for the training set. It only mentions that the U.S. validation cases were not used for model development or analytical validation testing.

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

• The document indicates that the model was developed and validated using cases from Taiwan, but it does not describe how the ground truth for these training cases was established.

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EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT SYSTEM (EFAI AASCTA) is a radiological computer aided triage and notification software indicated for use in the analysis of chest-abdomen CTA in adults aged 22 and older. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communicating suspected positive cases of aortic dissection (AD) or aortic intramural hematoma (IMH) pathology.

EFAI AASCTA uses an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI AASCTA 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 decisionmaking nor is it intended to rule out AAS or otherwise preclude clinical assessment of computed tomography cases.

EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT SYSTEM (EFAI AASCTA) is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze chest or chest-abdomen CTA and alerts the PACS/RIS workstation once images with features suggestive of AD or IMH are identified.

Through the use of EFAI AASCTA, a radiologist is able to review studies with features suggestive of AD or IMH earlier than in standard of care workflow.

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 or chest-abdomen CTA. The device aims to aid in prioritization and triage of radiological medical images only.

Here's a breakdown of the acceptance criteria and study details for the EFAI CARDIOSUITE CTA ACUTE AORTIC SYNDROME ASSESSMENT SYSTEM, based on the provided document:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: 380 CTA studies (156 positive cases, 224 negative cases).
• Data Provenance: Retrospective, multisite clinical validation study. The data was collected in the United States. None of the studies in the test set were used for model development or analytical validation. The study population included 51.58% females and 48.42% males, with a mean age of 62.90 years. CT scanner manufacturers included Philips, Toshiba, Siemens, GE, and others.

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

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

4. Adjudication Method for the Test Set

• Adjudication Method: Majority agreement between the three experts. (Described as "the reference standard (ground truth) was generated by the majority agreement between the three experts.")

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

• No, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not reported. The study focused on the standalone performance of the AI algorithm.

6. Standalone Performance Study

• Yes, a standalone performance study was conducted. The results reported (sensitivity and specificity) are for the EFAI AASCTA by itself, "in the absence of any interaction with a clinician."

7. Type of Ground Truth Used

• Ground Truth Type: Expert consensus. Specifically, the "majority agreement between the three experts" (U.S. board-certified radiologists) determined the presence of AD or IMH for each case.

8. Sample Size for the Training Set

• The document does not explicitly state the sample size for the training set. It only mentions that none of the 380 studies in the validation test set were used for model development (training) or analytical validation.

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 discusses the ground truth establishment for the test set.

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