Telesurgical robotics, as a technical solution for robot-assisted minimally invasive surgery (RAMIS), has become the first domain within medicosurgical robotics that achieved a true global clinical adoption. Its relative success (still at a low single-digit percentile total market penetration) roots in the particular human-in-the-loop control, in which the trained surgeon is always kept responsible for the clinical outcome achieved by the robot-actuated invasive tools. Nowadays, this paradigm is challenged by the need for improved surgical performance, traceability, and safety reaching beyond the human capabilities. Partially due to the technical complexity and the financial burden, the adoption of telesurgical robotics has not reached its full potential, by far. Apart from the absolutely market-dominating da Vinci surgical system, there are already 60+ emerging RAMIS robot types, out of which 15 have already achieved some form of regulatory clearance. This article aims to connect the technological advancement with the principles of commercialization, particularly looking at engineering components that are under development and have the potential to bring significant advantages to the clinical practice. Current RAMIS robots often do not exceed the functionalities deriving from their mechatronics, due to the lack of data-driven assistance and smart human–machine collaboration. Computer assistance is gradually gaining more significance within emerging RAMIS systems. Enhanced manipulation capabilities, refined sensors, advanced vision, task-level automation, smart safety features, and data integration mark together the inception of a new era in telesurgical robotics, infiltrated by machine learning (ML) and artificial intelligence (AI) solutions. Observing other domains, it is definite that a key requirement of a robust AI is the good quality data, derived from proper data acquisition and sharing to allow building solutions in real time based on ML. Emerging RAMIS technologies are reviewed both in a historical and a future perspective.

Robot-Assisted Minimally Invasive Surgery—Surgical Robotics in the Data Age

Robust deep learning-based semantic organ segmentation in hyperspectral images

Semi-supervised learning with progressive unlabeled data excavation for label-efficient surgical workflow recognition.

Hundreds of millions of operations are performed worldwide each year, and the rising uptake in minimally invasive surgery has enabled fiber optic cameras and robots to become both important tools to conduct surgery and sensors from which to capture information about surgery. Computer vision (CV), the application of algorithms to analyze and interpret visual data, has become a critical technology through which to study the intraoperative phase of care with the goals of augmenting surgeons' decision-making processes, supporting safer surgery, and expanding access to surgical care. While much work has been performed on potential use cases, there are currently no CV tools widely used for diagnostic or therapeutic applications in surgery. Using laparoscopic cholecystectomy as an example, we reviewed current CV techniques that have been applied to minimally invasive surgery and their clinical applications. Finally, we discuss the challenges and obstacles that remain to be overcome for broader implementation and adoption of CV in surgery. 

/pdf/computer-vision-in-surgery-from-potential-to-clinical-value-1xl1kndi.pdf

Computer vision in surgery: from potential to clinical value

The use of digital technology is increasing rapidly across surgical specialities, yet there is no consensus for the term 'digital surgery'. This is critical as digital health technologies present technical, governance, and legal challenges which are unique to the surgeon and surgical patient. We aim to define the term digital surgery and the ethical issues surrounding its clinical application, and to identify barriers and research goals for future practice. 38 international experts, across the fields of surgery, AI, industry, law, ethics and policy, participated in a four-round Delphi exercise. Issues were generated by an expert panel and public panel through a scoping questionnaire around key themes identified from the literature and voted upon in two subsequent questionnaire rounds. Consensus was defined if >70% of the panel deemed the statement important and <30% unimportant. A final online meeting was held to discuss consensus statements. The definition of digital surgery as the use of technology for the enhancement of preoperative planning, surgical performance, therapeutic support, or training, to improve outcomes and reduce harm achieved 100% consensus agreement. We highlight key ethical issues concerning data, privacy, confidentiality and public trust, consent, law, litigation and liability, and commercial partnerships within digital surgery and identify barriers and research goals for future practice. Developers and users of digital surgery must not only have an awareness of the ethical issues surrounding digital applications in healthcare, but also the ethical considerations unique to digital surgery. Future research into these issues must involve all digital surgery stakeholders including patients. 

/pdf/a-delphi-consensus-statement-for-digital-surgery-1fgm5x31.pdf

Hani J. Marcus

Papers

Surgical Data Science - from Concepts toward Clinical Translation