التفاصيل البيبلوغرافية
| العنوان: |
Gross failure rates and failure modes for a commercial AI‐based auto‐segmentation algorithm in head and neck cancer patients. |
| المؤلفون: |
Temple, Simon W. P., Rowbottom, Carl G. |
| المصدر: |
Journal of Applied Clinical Medical Physics; Jun2024, Vol. 25 Issue 6, p1-10, 10p |
| مصطلحات موضوعية: |
HEAD & neck cancer, FAILURE mode & effects analysis, ARTIFICIAL intelligence, RADIOTHERAPY treatment planning, CANCER patients |
| مستخلص: |
Purpose: Artificial intelligence (AI) based commercial software can be used to automatically delineate organs at risk (OAR), with potential for efficiency savings in the radiotherapy treatment planning pathway, and reduction of inter‐ and intra‐observer variability. There has been little research investigating gross failure rates and failure modes of such systems. Method: 50 head and neck (H&N) patient data sets with "gold standard" contours were compared to AI‐generated contours to produce expected mean and standard deviation values for the Dice Similarity Coefficient (DSC), for four common H&N OARs (brainstem, mandible, left and right parotid). An AI‐based commercial system was applied to 500 H&N patients. AI‐generated contours were compared to manual contours, outlined by an expert human, and a gross failure was set at three standard deviations below the expected mean DSC. Failures were inspected to assess reason for failure of the AI‐based system with failures relating to suboptimal manual contouring censored. True failures were classified into 4 sub‐types (setup position, anatomy, image artefacts and unknown). Results: There were 24 true failures of the AI‐based commercial software, a gross failure rate of 1.2%. Fifteen failures were due to patient anatomy, four were due to dental image artefacts, three were due to patient position and two were unknown. True failure rates by OAR were 0.4% (brainstem), 2.2% (mandible), 1.4% (left parotid) and 0.8% (right parotid). Conclusion: True failures of the AI‐based system were predominantly associated with a non‐standard element within the CT scan. It is likely that these non‐standard elements were the reason for the gross failure, and suggests that patient datasets used to train the AI model did not contain sufficient heterogeneity of data. Regardless of the reasons for failure, the true failure rate for the AI‐based system in the H&N region for the OARs investigated was low (∼1%). [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of Applied Clinical Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder