Search Results

The MOZI Treatment Planning System (MOZI TPS) is used to plan radiotherapy treatments with malignant or benign diseases. MOZI TPS is used to plan external beam irradiation with photon beams.

The proposed device, MOZI Treatment Planning System (MOZI TPS), is a standalone software which is used to plan radiotherapy treatments (RT) for patients with malignant or benign diseases. Its core functions include image processing, structure delineation, plan design, optimization and evaluation. Other functions include user login, graphical interface, system and patient management. It can provide a platform for completing the related work of the whole RT plan.

The provided text describes the performance data for the MOZI TPS device, focusing on its automatic contouring (structure delineation) feature. Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided document:

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

The primary acceptance criterion mentioned for structure delineation (automatic contouring) is based on the Mean Dice Similarity Coefficient (DSC). The study aimed to demonstrate non-inferiority compared to a reference device (AccuContour™ - K191928). While explicit thresholds for "acceptable" Mean DSC values are not given as numerical acceptance criteria in the table below, the text states "The result demonstrated that they have equivalent performance," implying that the reported DSC values met the internal non-inferiority standard set by the manufacturer against the performance of the reference device.

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

• Test Set Sample Size: 187 image sets (CT structure models).
• Data Provenance: The testing image source is from the United States. The data is retrospective, as it consists of existing CT datasets.
  • Patient demographics: 57% male, 43% female. Ages: 21-30 (0.3%), 31-50 (31%), 51-70 (51.3%), 71-100 (14.4%). Race: 78% White, 12% Black or African American, 10% Other.
  • Anatomical regions: Head and Neck (20.3%), Esophageal and Lung (Thorax, 20.3%), Gastrointestinal (Abdomen, 20.3%), Prostate (Male Pelvis, 20.3%), Female Pelvis (18.7%).
  • Scanner models: GE (28.3%), Philips (33.7%), Siemens (38%).
  • Slice thicknesses: 1mm (5.3%), 2mm (28.3%), 2.5mm (2.7%), 3mm (23%), 5mm (40.6%).

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

• Number of experts: Six
• Qualifications of experts: Clinically experienced radiation therapy physicists.

4. Adjudication method for the test set

• Adjudication method: Consensus. The ground truth was "generated manually using consensus RTOG guidelines as appropriate by six clinically experienced radiation therapy physicists." This implies that the experts agreed upon the ground truth for each case.

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, a multi-reader, multi-case comparative effectiveness study was not performed to assess human reader improvement with AI assistance. The study focused on the standalone performance of the AI algorithm (automatic contouring) and its comparison to a reference device.

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

• Standalone Performance: Yes, a standalone performance evaluation of the automatic segmentation algorithm was performed. The reported Mean DSC values are for the MOZI TPS device's auto-segmentation function without direct human-in-the-loop interaction during the segmentation process. The comparison to the reference device AccuContour™ (K191928) was also a standalone comparison.

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

• Type of Ground Truth: Expert consensus. The ground truth was "generated manually using consensus RTOG guidelines as appropriate by six clinically experienced radiation therapy physicists."

8. The sample size for the training set

• Training Set Sample Size: 560 image sets (CT structure models).

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

• The document states that the training image set source is from China. It does not explicitly detail the method for establishing ground truth for the training set. However, given that the ground truth for the test set was established by "clinically experienced radiation therapy physicists" using "consensus RTOG guidelines," it is highly probable that a similar methodology involving expert delineation and review was used for the training data to ensure high-quality labels for the deep learning model. The statement that "They are independent of each other" (training and testing sets) implies distinct data collection and ground truth establishment processes, but the specific details for the training set are not provided.

Ask a specific question about this device