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28 September 2023 AI research in medical Imaging of cancer, brain Injuries, and COVID-19
Maryellen L. Giger
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Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC1265512 (2023) https://doi.org/10.1117/12.2679202
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States
Abstract
Artificial Intelligence in medical imaging involves research in task-based discovery, predictive modeling, and robust clinical translation. Quantitative radiomic analyses, an extension of computer-aided detection (CADe) and computer-aided diagnosis (CADx) methods, are yielding novel image-based tumor characteristics, i.e., signatures that may ultimately contribute to the design of patient-specific cancer diagnostics and treatments. Beyond human-engineered features, deep networks are being investigated in the diagnosis of disease on radiography, ultrasound, and MRI. The method of extracting characteristic radiomic features of a region can be referred to as “virtual biopsies”. Various AI methods are evolving as aids to radiologists as a second reader or a concurrent reader, or as a primary autonomous reader. This presentation will discuss the development, validation, database needs, and ultimate future implementation of AI in the clinical radiology workflow including examples from cancer, brain injuries, and COVID-19, including the creation and benefits of MIDRC (midrc.org).
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Maryellen L. Giger "AI research in medical Imaging of cancer, brain Injuries, and COVID-19", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC1265512 (28 September 2023); https://doi.org/10.1117/12.2679202
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KEYWORDS
Artificial intelligence
Cancer
COVID 19
Medical imaging
Medical research
Traumatic brain injury
Computer aided detection
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