Search Results

AVIEW RT ACS provides deep-learning-based auto-segmented organs and generates contours in RT-DICOM format from CT images which could be used as an initial contour for the clinicians to approve and edit by the radiation oncology department for treatment planning or other professions where a segmented mask of organs is needed.

• a. Deep learning contouring from four body parts (Head & Neck, Breast, Abdomen, and Pelvis)
• b. Generates RT-DICOM structure of contoured organs
• c. Rule-based auto pre-processing
  Receive/Send/Export medical images and DICOM data
  Note that the Breast (Both right and left lung, Heart) were validated with non-contrast CT. Head & Neck (Both right and left Eyes, Brain and Mandible), Abdomen (Both right and Liver), and Pelvis (Both right and left Femur and Bladder) were validated with Contrast CT only.

The AVIEW RT ACS provides deep-learning-based auto-segmented organs and generates contours in RT-DICOM format from CT images. This software could be used by the radiation oncology department planning, or other professions where a segmented mask of organs is needed.

• Deep learning contouring: it can automatically contour the organ-at-risk (OARs) from four body parts (Head ● & Neck, Breast, Abdomen, and Pelvis)
• . Generates RT-DICOM structure of contoured organs
• . Rule-based auto pre-processing
  Receive/Send/Export medical images and DICOM data

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

The general acceptance criterion for the AVIEW RT ACS device appears to be comparable performance to a predicate device (MIM-MRT Dosimetry) in terms of segmentation accuracy, as measured by Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD). While explicit numerical acceptance thresholds are not stated in the provided text (e.g., "DSC must be greater than X"), the study is structured as a comparative effectiveness study. The expectation is that the AVIEW RT ACS performance should be at least equivalent to, if not better than, the predicate device.

The study's tables (Tables 1-30) consistently show the AVIEW RT ACS achieving higher average DSC values (closer to 1, indicating better overlap) and generally lower average 95% HD values (closer to 0, indicating less maximum distance between contours), across various organs, demographic groups, and scanner parameters, compared to the predicate device.

Table of Acceptance Criteria and Reported Device Performance:

Note: The tables throughout the document provide specific performance metrics for individual organs and sub-groups (race, vendors, slice thickness, kernel types). The general conclusion from these tables is that the AVIEW RT ACS consistently performs as well as or better than the predicate device across most metrics and categories.

Study Details for Acceptance Criteria Proof:

1. Sample Size Used for the Test Set: 120 cases.

   • Data Provenance: The dataset included cases from both South Korea and the USA. It was constructed with various ethnicities (White, Black, Asian, Hispanic, Latino, African, American, etc.), and from four major vendors (GE, Siemens, Toshiba, and Philips).
   • Retrospective/Prospective: Not explicitly stated, but the mention of a data set constructed for validation suggests a retrospective collection.

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

   • Number of Experts: 3 radiation oncology physicians.
   • Qualifications: All were trained by "The Korean Society for Radiation Oncology," board-certified by the "Ministry of Health and Welfare," with a range of 9-21 years of experience in radiotherapy. The experts included attending assistant professors (n=2) and professors (n=1) from three institutions.

3. Adjudication Method for the Test Set:

   • The method was a sequential editing process:
     1. One expert manually delineated the organs.
     2. The segmentation results from the first expert were then sequentially edited by the other two experts.
     3. The first expert made corrections.
     4. The result was then received by another expert who finalized the gold standard.
   • This can be considered a form of sequential consensus or collaborative review rather than a strict N+1 or M+N+1 method.

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

   • Yes, a comparative effectiveness study was done. The study directly compares the AVIEW RT ACS against a predicate device (MIM-MRT Dosimetry).
   • Effect Size of Human Readers Improvement with AI vs. Without AI Assistance:
     • The study does not measure the improvement of human readers with AI assistance. Instead, it evaluates the standalone performance of the AI device against the standalone performance of a predicate AI device, both compared to expert-generated ground truth. The "human readers" (the three experts) were used solely to create the ground truth, not to evaluate their performance with or without AI assistance.

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

   • Yes, a standalone performance study was done. The study compares the auto-segmentation results of the AVIEW RT ACS directly to the expert-derived "gold standard" and also compares it to the auto-segmentation of the predicate device. This is purely an algorithm-only evaluation.

6. The Type of Ground Truth Used:

   • Expert Consensus. The ground truth was established by three radiation oncology physicians through a sequential delineation and editing process to create a "robust gold standard."

7. The Sample Size for the Training Set:

   • Not specified within the provided text. The document refers only to the validation/test set.

8. How the Ground Truth for the Training Set Was Established:

   • Not specified within the provided text. Since the training set size and characteristics are not mentioned, neither is the method for establishing its ground truth.

Ask a specific question about this device