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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic
Di Sun1, Lubomir Hadjiiski1, Ajjai Alva2
1Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
Tomography (Ann Arbor, Mich.)
|March 22, 2022
Summary
Computerized artificial intelligence (AI) decision support systems significantly improve physicians' diagnostic accuracy for bladder cancer treatment response. This AI tool enhances consistency among healthcare providers.
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Accurate assessment of bladder cancer treatment response is crucial for patient management.
- Physician performance in interpreting treatment response can be variable.
- Computerized decision support systems (CDSS) offer potential to aid diagnostic accuracy.
Purpose of the Study:
- To evaluate the impact of an AI-based CDSS on physicians' diagnostic accuracy for bladder cancer treatment response.
- To analyze factors influencing diagnostic performance with and without AI assistance.
Main Methods:
- Observer study involving 17 physicians assessing bladder cancer treatment response.
- Utilized pre- and post-chemotherapy CTU scans from 123 patients.
- Compared diagnostic performance with and without the CDSS-T.
Main Results:
- Physician diagnostic accuracy significantly improved when using the CDSS-T (p = 0.002).
- Factors like case difficulty and physician experience affected performance without AI.
- CDSS-T use led to more consistent performance across observers.
Conclusions:
- AI-based decision support systems can enhance diagnostic accuracy in bladder cancer treatment assessment.
- CDSS-T has the potential to standardize physician performance.
- This technology may improve patient care by ensuring more reliable treatment response evaluations.
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