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IRISeg is intended for use as a software application that receives DICOM compliant contrastenhanced CT images, provides manual and machine learning-enabled tools for image analysis and segmentation, and creates an output file that can be used to render a 3D model for preoperative surgical planning and intraoperative display. The use of IRISeq may include the generation of preliminary segmentations using machine learning algorithms. IRISeg is intended for use by qualified professionals. The output file is meant for visual, non-diagnostic use and shall be reviewed by clinicians who are responsible for all final patient management decisions.

The machine learning enabled kidney CT auto-seamentation tool is intended for use for adult patients with contrast-enhanced, axial kidney CT images with slice thickness 3mm or less.

IRISeg Version 3.1 ("IRISeg") is a standalone software application created by Intuitive Surgical for segmentation of contrast-enhanced kidney CT images and generation of output files that can be rendered as virtual 3D models of kidney organs. IRISeg is designed to professionals ("users") with manual and machine learning (ML)-based tools for segmentation of kidney anatomy based on CT scans.

Let's break down the acceptance criteria and study details for the IRISeg device, based on the provided FDA 510(k) summary.

Device Name: IRISeg
Intended Use: Software application for image analysis and segmentation of contrast-enhanced kidney CT images, creating an output file for rendering a 3D model for pre-operative surgical planning and intraoperative display. The machine learning enabled kidney CT auto-segmentation tool is intended for adult patients with contrast-enhanced, axial kidney CT images with slice thickness 3mm or less. The output is for visual, non-diagnostic use and requires review by clinicians.

1. Table of Acceptance Criteria and Reported Device Performance

The document characterizes performance using the Sørensen–Dice coefficient (DSC) for segmentation accuracy of anatomical structures and Mean Distance to Agreement (MDA) for the collecting system. While explicit "acceptance criteria" are not given as numerical cutoffs, the reported performance demonstrates the algorithm's accuracy relative to expert consensus.

(Note: The document states "test results showed that all tests met the acceptance criteria" generally for software V&V, and then presents these quantified performance metrics for the ML algorithm. We infer that these reported intervals fulfill the internal, unstated numerical acceptance thresholds for clinical performance.)

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

• Test Set Sample Size: 81 kidney CT scans.
• Data Provenance: The document states that the clinical data for training was "sourced from clinical data processed during commercial operation of the cleared IRIS 1.0 system (K182643)." For the test set, it explicitly states "No imaging study used to verify performance was used for training; independence of training and testing data were enforced at the level of the scanning institution, namely, studies sourced from a specific institution were used for either training or testing but could not be used in testing ensured diversity in patient population, scan parameters and scanner manufacturers." This implies a retrospective study using existing clinical CT scans. The country of origin is not explicitly stated, but given the FDA submission and the use of "U.S board certified radiologist," it can be inferred that the data is likely from the U.S. or at least relevant to U.S. clinical practice. The data is retrospective.

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

• Number of Experts: Three (3) U.S. Board Certified Radiologists.
• Qualifications: U.S. Board Certified Radiologists. (No further details on years of experience or subspecialty provided beyond "Board Certified").

4. Adjudication Method for the Test Set

The ground truth was established by the "consensus of three U.S Board Certified Radiologists." This implies a form of consensus-based adjudication, but the specific mechanics (e.g., majority vote, sequential review with final agreement, arbitration by a fourth reader) are not detailed beyond "consensus."

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

No, an MRMC comparative effectiveness study involving human readers with vs. without AI assistance was not reported in this summary. The AI performance was evaluated in a standalone manner against expert consensus ground truth. The device output is explicitly non-diagnostic and requires review by clinicians, emphasizing a human-in-the-loop workflow, but the study focused on the algorithm's raw segmentation accuracy.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, a standalone performance study was conducted. The performance metrics (DSC and MDA) characterize the ML algorithm's output directly when compared to expert-derived ground truth. The summary states, "Performance was evaluated by comparing segmentations generated by the kidney CT machine learning algorithm against segmentations generated by a consensus of three U.S Board Certified Radiologists for the same imaging study." This is a direct measurement of the algorithm's standalone accuracy.

7. Type of Ground Truth Used

The ground truth used was expert consensus by three U.S. Board Certified Radiologists, based on their segmentation of the medical images.

8. The Sample Size for the Training Set

The sample size for the training set is not explicitly stated in the provided summary. It states the model was "trained on segmented kidney CT models that were sourced from clinical data processed during commercial operation of the cleared IRIS 1.0 system (K182643)."

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

The ground truth for the training set was established by review from one U.S. board certified radiologist per 3D model. The summary states: "Each 3D model was reviewed by one U.S board certified radiologist."

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