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The LumiNE US software is intended for the visualization of medical images to provide insights in anatomy and pathology in preparation of surgical treatment. As such, the software allows for the conversion of 2D patient imaging into 3D models and for the visualization of 2D and 3D patient imaging including Augmented Reality. When accessing the LumiNE US software from a wireless Head-Mounted Display (HMD) or PC monitor, images viewed are for informational purposes only and not intended for diagnostic use.

The LumiNE US software is intended for use by a (neuro)surgical resident or a medical professional that is qualified by a hospital to prepare medical imaging for surgeons. For the conversion of medical imaging into 3D models, Magnetic Resonance Imaging (MRI) and/or Computed Tomography (CT) imaging of adult patients are required. The LumiNE US software is intended to be used for visualization of surgery, and not for diagnostic use. Therefore, segmentation and visualization of tumors or any other pathology can only be used for previously known and pre-diagnosed conditions.

LumiNE US can only be used for contrast-enhanced T1 MR scans (sem-automatic segmentation of known tumor, skin, brain, and ventricles), or for CT scans (threshold-based segmentation).

The LumiNE US MRI T1 tumor segmentation function can only be used in case of a single intracranial contrast enhancing tumor, diagnosed by a neuroradiologist or a neurosurgeon, with a minimal volume of 2.0 cc (0.1 in3) and a minimal diameter in any direction of 15 mm (0.6 inch), and a maximum volume of 100cc (6.1 in3) and a maximal diameter in any direction of 75 mm (3.0 inch).

LumiNE US is a software device for the visualization of medical images to provide insights in anatomy and pathology in preparation of surgical treatment. As such, the software allows for the conversion of 2D patient imaging into 3D models and for the visualization of 2D and 3D patient imaging including Augmented Reality. When accessing the LumiNE US software from a wireless Head-Mounted Display (HMD) or PC monitor, images viewed are for informational purposes only and not intended for diagnostic use. Applicable pathology includes scans with known intracranial lesions that are diagnosed as Glioblastoma, Meninqioma, or Metastasis by a neurosurgeon or neuroradiologist.

The provided text describes the acceptance criteria and the study conducted for the LumiNE US device, specifically focusing on its MRI T1 tumor segmentation function (T1cSF).

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The study evaluates the automatic segmentation of Brain, Skin (as a proxy for the entire head/skull), Tumor, and Ventricles.

Note: The 95% CI for Skin DSC is listed as "0.99-0.1" which is likely a typo and should be "0.99-1.0" given the median of 0.99 and the acceptance criteria.
Note: The 95% CI for Skin 95% HD is listed as "0.99- 5.51" which is unusual given the median of 3.36 and the typical way confidence intervals are presented. It's possible "0.99" is a typo for a lower bound, or it references a different aspect.

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

The document states that a "specific independent U.S. test dataset of MRI-T1 scans was created with each scan belonging to a unique patient." The exact numerical sample size for this U.S. test set is not explicitly stated in the provided text.

Data Provenance:

• Country of Origin: U.S. (collected from institutions covering a wide range of regions across the U.S., from the West Coast to the East Coast and the Southern region).
• Retrospective or Prospective: Not explicitly stated, but the description "was created" and "collected from institutions" strongly suggests a retrospective collection of existing patient scans.

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

• Number of Experts: 3
• Qualifications: U.S. based neurosurgeons with relevant experience including fellowships.

4. Adjudication Method for the Test Set

The U.S. ground truth test set was established by mutual agreement after internal discussion and signed off per scan per truther. This indicates a consensus-based adjudication method.

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

No, a comparative effectiveness study comparing human readers with and without AI assistance (MRMC) was not explicitly mentioned or described in the provided text. The study focuses on evaluating the standalone performance of the AI segmentation algorithm against expert-established ground truth.

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

Yes, a standalone performance study of the T1cSF segmentation function was conducted. The results presented (DSC and 95% HD values) directly evaluate the algorithm's performance against the ground truth.

7. The Type of Ground Truth Used

Expert Consensus (Manual Segmentation): The ground truth for the test set was established by "3 U.S. based neurosurgeons" who "individually truthed" the data, and the definitive ground truth was reached by "mutual agreement after internal discussion and signed off per scan per truther."

8. The Sample Size for the Training Set

The sample size for the training set is not explicitly stated in the provided text. It is mentioned that the algorithms were "originally trained using machine learning (nnUnet)."

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

The method for establishing ground truth for the training set is not explicitly stated. It only mentions that the algorithms were trained using nnUnet and that the U.S. test set "was not used for training of the T1cSF algorithms."

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