Machine understanding surgical actions from intervention procedure textbooks

The automatic extraction of procedural surgical knowledge from surgery manuals, academic papers or other high-quality textual resources, is of the utmost importance to develop knowledge-based clinical decision support systems, to automatically execute some procedure’s step or to summarize the procedural information, spread throughout the texts, in a structured form usable as a study resource by medical students. In this work, we propose a first benchmark on extracting detailed surgical actions from available intervention procedure textbooks and papers. We frame the problem as a Semantic Role Labeling task. Exploiting a manually annotated dataset, we apply different Transformer-based information extraction methods. Starting from RoBERTa and BioMedRoBERTa pre-trained language models, we first investigate a zero-shot scenario and compare the obtained results with a full fine-tuning setting. We then introduce a new ad-hoc surgical language model, named SurgicBERTa, pre-trained on a large collection of surgical materials, and we compare it with the previous ones. In the assessment, we explore different dataset splits (one in-domain and two out-of-domain) and we investigate also the effectiveness of the approach in a few-shot learning scenario. Performance is evaluated on three correlated sub-tasks: predicate disambiguation, semantic argument disambiguation and predicate-argument disambiguation. Results show that the fine-tuning of a pre-trained domain-specific language model achieves the highest performance on all splits and on all sub-tasks. All models are publicly released

The Robotic Surgery Procedural Framebank

Robot-Assisted minimally invasive surgery is the gold standard for the surgical treatment of many pathological conditions, and several manuals and academic papers describe how to perform these interventions. These high-quality, often peer-reviewed texts are the main study resource for medical personnel and consequently contain essential procedural domain-specific knowledge. The procedural knowledge therein described could be extracted, e.g., on the basis of semantic parsing models, and used to develop clinical decision support systems or even automation methods for some procedure’s steps. However, natural language understanding algorithms such as, for instance, semantic role labelers have lower efficacy and coverage issues when applied to domain others than those they are typically trained on (i.e., newswire text). To overcome this problem, starting from PropBank frames, we propose a new linguistic resource specific to the robotic-surgery domain, named Robotic Surgery Procedural Framebank (RSPF). We extract from robotic-surgical texts verbs and nouns that describe surgical actions and extend PropBank frames by adding any of new lemmas, frames or role sets required to cover missing lemmas, specific frames describing the surgical significance, or new semantic roles used in procedural surgical language. Our resource is publicly available and can be used to annotate corpora in the surgical domain to train and evaluate Semantic Role Labeling (SRL) systems in a challenging fine-grained domain setting

Automatic extraction of robotic surgery actions from text and kinematic data

The latest generation of robotic systems is becoming increasingly autonomous due to technological advancements and artificial intelligence. The medical field, particularly surgery, is also interested in these technologies because automation would benefit surgeons and patients. While the research community is active in this direction, commercial surgical robots do not currently operate autonomously due to the risks involved in dealing with human patients: it is still considered safer to rely on human surgeons' intelligence for decision-making issues. This means that robots must possess human-like intelligence, including various reasoning capabilities and extensive knowledge, to become more autonomous and credible. As demonstrated by current research in the field, indeed, one of the most critical aspects in developing autonomous systems is the acquisition and management of knowledge. In particular, a surgical robot must base its actions on solid procedural surgical knowledge to operate autonomously, safely, and expertly. This thesis investigates different possibilities for automatically extracting and managing knowledge from text and kinematic data. In the first part, we investigated the possibility of extracting procedural surgical knowledge from real intervention descriptions available in textbooks and academic papers on the robotic-surgical domains, by exploiting Transformer-based pre-trained language models. In particular, we released SurgicBERTa, a RoBERTa-based pre-trained language model for surgical literature understanding. It has been used to detect procedural sentences in books and extract procedural elements from them. Then, with some use cases, we explored the possibilities of translating written instructions into logical rules usable for robotic planning. Since not all the knowledge required for automatizing a procedure is written in texts, we introduce the concept of surgical commonsense, showing how it relates to different autonomy levels. In the second part of the thesis, we analyzed surgical procedures from a lower granularity level, showing how each surgical gesture is associated with a given combination of kinematic data

Gesture Recognition and Control for Semi-Autonomous Robotic Assistant Surgeons

The next stage for robotics development is to introduce autonomy and cooperation with human agents in tasks that require high levels of precision and/or that exert considerable physical strain. To guarantee the highest possible safety standards, the best approach is to devise a deterministic automaton that performs identically for each operation. Clearly, such approach inevitably fails to adapt itself to changing environments or different human companions. In a surgical scenario, the highest variability happens for the timing of different actions performed within the same phases. This thesis explores the solutions adopted in pursuing automation in robotic minimally-invasive surgeries (R-MIS) and presents a novel cognitive control architecture that uses a multi-modal neural network trained on a cooperative task performed by human surgeons and produces an action segmentation that provides the required timing for actions while maintaining full phase execution control via a deterministic Supervisory Controller and full execution safety by a velocity-constrained Model-Predictive Controller

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

Surgical Data Science - from Concepts toward Clinical Translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process