Search Results

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.

Ask a specific question about this device