Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery

Background Artificial intelligence (AI) has the potential to enhance patient safety in surgery, and all its aspects, including education and training, will derive considerable benefit from AI. In the present study, deep-learning models were used to predict the rates of proficiency acquisition in robot-assisted surgery (RAS), thereby providing surgical programs directors information on the levels of the innate ability of trainees to facilitate the implementation of flexible personalized training. Methods 176 medical students, without prior experience with surgical simulators, were trained to reach proficiency in five tasks on a virtual simulator for RAS. Ensemble deep neural networks (DNN) models were developed and compared with other ensemble AI algorithms, i.e., random forests and gradient boosted regression trees (GBRT). Results DNN models achieved a higher accuracy than random forests and GBRT in predicting time to proficiency, 0.84 vs. 0.70 and 0.77, respectively (Peg board 2), 0.83 vs. 0.79 and 0.78 (Ring walk 2), 0.81 vs 0.81 and 0.80 (Match board 1), 0.79 vs. 0.75 and 0.71 (Ring and rail 2), and 0.87 vs. 0.86 and 0.84 (Thread the rings 2). Ensemble DNN models outperformed random forests and GBRT in predicting number of attempts to proficiency, with an accuracy of 0.87 vs. 0.86 and 0.83, respectively (Peg board 2), 0.89 vs. 0.88 and 0.89 (Ring walk 2), 0.91 vs. 0.89 and 0.89 (Match board 1), 0.89 vs. 0.87 and 0.83 (Ring and rail 2), and 0.96 vs. 0.94 and 0.94 (Thread the rings 2). Conclusions Ensemble DNN models can identify at an early stage the acquisition rates of surgical technical proficiency of trainees and identify those struggling to reach the required expected proficiency level

Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review

BACKGROUND: There is a need to standardize training in robotic surgery, including objective assessment for accreditation. This systematic review aimed to identify objective tools for technical skills assessment, providing evaluation statuses to guide research and inform implementation into training curricula. METHODS: A systematic literature search was conducted in accordance with the PRISMA guidelines. Ovid Embase/Medline, PubMed and Web of Science were searched. Inclusion criterion: robotic surgery technical skills tools. Exclusion criteria: non-technical, laparoscopy or open skills only. Manual tools and automated performance metrics (APMs) were analysed using Messick's concept of validity and the Oxford Centre of Evidence-Based Medicine (OCEBM) Levels of Evidence and Recommendation (LoR). A bespoke tool analysed artificial intelligence (AI) studies. The Modified Downs-Black checklist was used to assess risk of bias. RESULTS: Two hundred and forty-seven studies were analysed, identifying: 8 global rating scales, 26 procedure-/task-specific tools, 3 main error-based methods, 10 simulators, 28 studies analysing APMs and 53 AI studies. Global Evaluative Assessment of Robotic Skills and the da Vinci Skills Simulator were the most evaluated tools at LoR 1 (OCEBM). Three procedure-specific tools, 3 error-based methods and 1 non-simulator APMs reached LoR 2. AI models estimated outcomes (skill or clinical), demonstrating superior accuracy rates in the laboratory with 60 per cent of methods reporting accuracies over 90 per cent, compared to real surgery ranging from 67 to 100 per cent. CONCLUSIONS: Manual and automated assessment tools for robotic surgery are not well validated and require further evaluation before use in accreditation processes.PROSPERO: registration ID CRD42022304901

Learning of Surgical Gestures for Robotic Minimally Invasive Surgery Using Dynamic Movement Primitives and Latent Variable Models

Full and partial automation of Robotic Minimally Invasive Surgery holds significant promise to improve patient treatment, reduce recovery time, and reduce the fatigue of the surgeons. However, to accomplish this ambitious goal, a mathematical model of the intervention is needed. In this thesis, we propose to use Dynamic Movement Primitives (DMPs) to encode the gestures a surgeon has to perform to achieve a task. DMPs allow to learn a trajectory, thus imitating the dexterity of the surgeon, and to execute it while allowing to generalize it both spatially (to new starting and goal positions) and temporally (to different speeds of executions). Moreover, they have other desirable properties that make them well suited for surgical applications, such as online adaptability, robustness to perturbations, and the possibility to implement obstacle avoidance. We propose various modifications to improve the state-of-the-art of the framework, as well as novel methods to handle obstacles. Moreover, we validate the usage of DMPs to model gestures by automating a surgical-related task and using DMPs as the low-level trajectory generator. In the second part of the thesis, we introduce the problem of unsupervised segmentation of tasks' execution in gestures. We will introduce latent variable models to tackle the problem, proposing further developments to combine such models with the DMP theory. We will review the Auto-Regressive Hidden Markov Model (AR-HMM) and test it on surgical-related datasets. Then, we will propose a generalization of the AR-HMM to general, non-linear, dynamics, showing that this results in a more accurate segmentation, with a less severe over-segmentation. Finally, we propose a further generalization of the AR-HMM that aims at integrating a DMP-like dynamic into the latent variable model

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

Global research trends of the application of artificial intelligence in bladder cancer since the 21st century: a bibliometric analysis

IntroductionSince the significant breakthroughs in artificial intelligence (AI) algorithms, the application of AI in bladder cancer has rapidly expanded. AI can be used in all aspects of the bladder cancer field, including diagnosis, treatment and prognosis prediction. Nowadays, these technologies have an excellent medical auxiliary effect and are in explosive development, which has aroused the intense interest of researchers. This study will provide an in-depth analysis using bibliometric analysis to explore the trends in this field.MethodDocuments regarding the application of AI in bladder cancer from 2000 to 2022 were searched and extracted from the Web of Science Core Collection. These publications were analyzed by bibliometric analysis software (CiteSpace, Vosviewer) to visualize the relationship between countries/regions, institutions, journals, authors, references, keywords.ResultsWe analyzed a total of 2368 publications. Since 2016, the number of publications in the field of AI in bladder cancer has increased rapidly and reached a breathtaking annual growth rate of 43.98% in 2019. The U.S. has the largest research scale, the highest study level and the most significant financial support. The University of North Carolina is the institution with the highest level of research. EUROPEAN UROLOGY is the most influential journal with an impact factor of 24.267 and a total citation of 11,848. Wiklund P. has the highest number of publications, and Menon M. has the highest number of total citations. We also find hot research topics within the area through references and keywords analysis, which include two main parts: AI models for the diagnosis and prediction of bladder cancer and novel robotic-assisted surgery for bladder cancer radicalization and urinary diversion.ConclusionAI application in bladder cancer is widely studied worldwide and has shown an explosive growth trend since the 21st century. AI-based diagnostic and predictive models will be the next protagonists in this field. Meanwhile, the robot-assisted surgery is still a hot topic and it is worth exploring the application of AI in it. The advancement and application of algorithms will be a massive driving force in this field

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

For the Love of Robots: Posthumanism in Latin American Science Fiction Between 1960-1999

Posthumanism—understood as a symbiotic relationship between humans and technology—is quickly and surely becoming an inextricable part of daily life. In an era where technology can be worn as an extension of—and an enhancement to—our bodies, traditional science fiction tropes such as robots and cyborgs resurface and reformulate questions on critical aspects of human experience: who are we and what do our (imagined) technologies say about our world? Such questions are far more complex than they appear. Their answers should not come from one source alone, as humanness is experienced differently across time and cultural systems. In this sense, it is imperative to focus critical attention on works beyond the English-language canon in order to discover alternative readings of the posthuman, understand how varying historical, social, and economic contexts give new meanings to robots, cyborgs and hyper-technological imaginaries, and provide balancing perspectives to the ideas presented in canon posthuman science fiction from the developed world. To this end, this study centers on posthuman science fiction from Latin America. The primary works included here are limited only to Mexico, Chile, and Argentina—three of the countries with the greatest science fiction output in the region. This study explores the intersections of gender, sexualities, and posthumanism, as well as the underlying sociopolitical implications of such narratives. They exhibit an undeniable influence of canon Anglophone science fiction in terms of tropes (robots as mates for humans, cybernetic doppelgangers, technological utopias and dystopias) as well as problematic representations of gender, sex, and race. Yet, at the same time, posthuman elements in these Latin American narratives exhibit distinct local traits. Moreover, robot and cyborg figures enhance and renew discourses of political corruption, dictatorial trauma, surveillance, social and ecological decline. This study aims to outline the ways in which Latin American posthuman science fiction stands apart from the canon and proves itself as a legitimate genre. Simultaneously, this project seeks to supplement the nascent critical corpus on Latin American science fiction. It is my hope that this study’s insights will contribute to the field’s growth and success with scholars and readers alike

Distant Operational Care Centre: Design Project Report

The goal of this project is to outline the design of the Distant Operational Care Centre (DOCC), a modular medical facility to maintain human health and performance in space, that is adaptable to a range of remote human habitats. The purpose of this project is to outline a design, not to go into a complete technical specification of a medical facility for space. This project involves a process to produce a concise set of requirements, addressing the fundamental problems and issues regarding all aspects of a space medical facility for the future. The ideas presented here are at a high level, based on existing, researched, and hypothetical technologies. Given the long development times for space exploration, the outlined concepts from this project embodies a collection of identified problems, and corresponding proposed solutions and ideas, ready to contribute to future space exploration efforts. In order to provide a solid extrapolation and speculation in the context of the future of space medicine, the extent of this project's vision is roughly within the next two decades. The Distant Operational Care Centre (DOCC) is a modular medical facility for space. That is, its function is to maintain human health and performance in space environments, through prevention, diagnosis, and treatment. Furthermore, the DOCC must be adaptable to meet the environmental requirements of different remote human habitats, and support a high quality of human performance. To meet a diverse range of remote human habitats, the DOCC concentrates on a core medical capability that can then be adapted. Adaptation would make use of the DOCC's functional modularity, providing the ability to replace, add, and modify core functions of the DOCC by updating hardware, operations, and procedures. Some of the challenges to be addressed by this project include what constitutes the core medical capability in terms of hardware, operations, and procedures, and how DOCC can be adapted to different remote habitats

Haptic Enhancement of Sensorimotor Learning for Clinical Training Applications

Modern surgical training requires radical change with the advent of increasingly complex procedures, restricted working hours, and reduced ‘hands-on’ training in the operating theatre. Moreover, an increased focus on patient safety means there is a greater need to objectively measure proficiency in trainee surgeons. Indeed, the existing evidence suggests that surgical sensorimotor skill training is not adequate for modern surgery. This calls for new training methodologies which can increase the acquisition rate of sensorimotor skill. Haptic interventions offer one exciting possible avenue for enhancing surgical skills in a safe environment. Nevertheless, the best approach for implementing novel training methodologies involving haptic intervention within existing clinical training curricula has yet to be determined. This thesis set out to address this issue. In Chapter 2, the development of two novel tools which enable the implementation of bespoke visuohaptic environments within robust experimental protocols is described. Chapters 3 and 4 report the effects of intensive, long-term training on the acquisition of a compliance discrimination skill. The results indicate that active behaviour is intrinsically linked to compliance perception, and that long-term training can help to improve the ability of detecting compliance differences. Chapter 5 explores the effects of error augmentation and parameter space exploration on the learning of a complex novel task. The results indicate that error augmentation can help improve learning rate, and that physical workspace exploration may be a driver for motor learning. This research is a first step towards the design of objective haptic intervention strategies to help support the rapid acquisition of sensorimotor skill. The work has applications in clinical settings such as surgical training, dentistry and physical rehabilitation, as well as other areas such as sport

Towards Patient Specific Mitral Valve Modelling via Dynamic 3D Transesophageal Echocardiography

Mitral valve disease is a common pathologic problem occurring increasingly in an aging population, and many patients suffering from mitral valve disease require surgical intervention. Planning an interventional approach from diagnostic imaging alone remains a significant clinical challenge. Transesophageal echocardiography (TEE) is the primary imaging modality used diagnostically, it has limitations in image quality and field-of-view. Recently, developments have been made towards modelling patient-specific deformable mitral valves from TEE imaging, however, a major barrier to producing accurate valve models is the need to derive the leaflet geometry through segmentation of diagnostic TEE imaging. This work explores the development of volume compounding and automated image analysis to more accurately and quickly capture the relevant valve geometry needed to produce patient-specific mitral valve models. Volume compounding enables multiple ultrasound acquisitions from different orientations and locations to be aligned and blended to form a single volume with improved resolution and field-of-view. A series of overlapping transgastric views are acquired that are then registered together with the standard en-face image and are combined using a blending function. The resulting compounded ultrasound volumes allow the visualization of a wider range of anatomical features within the left heart, enhancing the capabilities of a standard TEE probe. In this thesis, I first describe a semi-automatic segmentation algorithm based on active contours designed to produce segmentations from end-diastole suitable for deriving 3D printable molds. Subsequently I describe the development of DeepMitral, a fully automatic segmentation pipeline which leverages deep learning to produce very accurate segmentations with a runtime of less than ten seconds. DeepMitral is the first reported method using convolutional neural networks (CNNs) on 3D TEE for mitral valve segmentations. The results demonstrate very accurate leaflet segmentations, and a reduction in the time and complexity to produce a patient-specific mitral valve replica. Finally, a real-time annulus tracking system using CNNs to predict the annulus coordinates in the spatial frequency domain was developed. This method facilitates the use of mitral annulus tracking in real-time guidance systems, and further simplifies mitral valve modelling through the automatic detection of the annulus, which is a key structure for valve quantification, and reproducing accurate leaflet dynamics