Data-driven computational approaches have evolved to enable extraction of information from medical images with a reliability, accuracy and speed which is already transforming their interpretation and exploitation in clinical practice. While similar benefits are longed for in the field of surgery and interventional imaging, this ambition is challenged by a much higher heterogeneity. Clinical workflows within interventional suites and operating theatres are extremely complex and typically rely on poorly integrated intra-operative devices, sensors, and support infrastructures. Recognising that patient-specific management requires patient-specific decision support tools, in this talk, we present our ongoing interdisciplinary work to develop novel machine learning strategies that support, augment and integrate in the surgical workflow while providing the flexibility required by clinical management.
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