Artificial Intelligence in Oncological Hybrid Imaging

Abstract

Background Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes. Methods and Results The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations. Conclusion AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation. Key Points: Citation Format

Smart scanning: automatic detection of superficially located lymph nodes using ultrasound – initial results

“…Research on the integration of AI into ultrasound as well as other imaging methods is increasingly becoming a focus of various disciplines [24,25]. In the field of ultrasound, most studies analyze whether the integration of AI can improve the differential diagnosis of lesions, for example of the thyroid gland [14,15,16,17] or the breast [12,13].…”

The radiologist as a physician – artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians – a narrative review

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