Abstract
Data-driven computational approaches have evolved to enable extraction of information from medical images with 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 interventional imaging, this ambition is challenged by a much higher heterogeneity. Clinical workflows within interventional suites and operating theaters are extremely complex and typically rely on poorly integrated intraoperative devices, sensors, and support infrastructures. Taking stock of some of the most exciting developments in machine learning and artificial intelligence for computer-assisted interventions, we highlight the crucial need to take the context and human factors into account in order to address these challenges. Contextual artificial intelligence for computer-assisted intervention (CAI4CAI) arises as an emerging opportunity feeding into the broader field of surgical data science. Central challenges being addressed in CAI4CAI include how to integrate the ensemble of prior knowledge and instantaneous sensory information from experts, sensors, and actuators; how to create and communicate a faithful and actionable shared representation of the surgery among a mixed human–AI actor team; and how to design interventional systems and associated cognitive shared control schemes for online uncertainty-aware collaborative decision-making ultimately producing more precise and reliable interventions.
Highlights
Contemporary progresses in machine learning and artificial intelligence have permitted the development of tools that can assist clinicians in exploiting and quantifying clinical data including images, textual reports, and genetic information
In this article, keeping a focus on imaging data, we review existing work and share insights on future developments of machine learning strategies that decipher, support, augment, and integrate into various surgical and interventional workflows while providing the flexibility required by clinical management
Imaging sources of particular interest for surgery and intervention include a wide range of well-known interventional modalities, such as surgical microscopy, video endoscopy, X-ray fluoroscopy, and ultrasound, more emerging biophotonics imaging modalities, such as hyperspectral imaging, endomicroscopy, and photoacoustic imaging, and span classical radiology modalities, such as MRI and Computed tomography (CT), that remain the main sources of imaging data for preoperative intervention planning and postoperative assessment
Summary
Contemporary progresses in machine learning and artificial intelligence have permitted the development of tools that can assist clinicians in exploiting and quantifying clinical data including images, textual reports, and genetic information. State-of-the-art algorithms are becoming mature enough to provide automated analysis when applied to well-controlled clinical studies and trials [1], [2], but adapting these tools for patient-specific management remains an active research area, with the bulk of the research community having focused on fully automated machine learning tools These considerations become especially critical in the highly heterogeneous context of surgery and interventional procedures that require patient- and team-specific decision support tools being able to draw information from nonstandardized interventional devices integrated into diverse interventional suites. In these sections, we will highlight how flexible deep learning-based tools are becoming critical for the design of effective and efficient intervention planning solutions.
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