Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

The Safety Challenges of Deep Learning in Real-World Type 1 Diabetes Management

Blood glucose simulation allows the effectiveness of type 1 diabetes (T1D) management strategies to be evaluated without patient harm. Deep learning algorithms provide a promising avenue for extending simulator capabilities; however, these algorithms are limited in that they do not necessarily learn physiologically correct glucose dynamics and can learn incorrect and potentially dangerous relationships from confounders in training data. This is likely to be more important in real-world scenarios, as data is not collected under strict research protocol. This work explores the implications of using deep learning algorithms trained on real-world data to model glucose dynamics. Free-living data was processed from the OpenAPS Data Commons and supplemented with patient-reported tags of challenging diabetes events, constituting one of the most detailed real-world T1D datasets. This dataset was used to train and evaluate state-of-the-art glucose simulators, comparing their prediction error across safety critical scenarios and assessing the physiological appropriateness of the learned dynamics using Shapley Additive Explanations (SHAP). While deep learning prediction accuracy surpassed the widely-used mathematical simulator approach, the model deteriorated in safety critical scenarios and struggled to leverage self-reported meal and exercise information. SHAP value analysis also indicated the model had fundamentally confused the roles of insulin and carbohydrates, which is one of the most basic T1D management principles. This work highlights the importance of considering physiological appropriateness when using deep learning to model real-world systems in T1D and healthcare more broadly, and provides recommendations for building models that are robust to real-world data constraints.Comment: 15 pages, 3 figure

Automating FDA Regulation

In the twentieth century, the Food and Drug Administration (“FDA”) rose to prominence as a respected scientific agency. By the middle of the century, it transformed the U.S. medical marketplace from an unregulated haven for dangerous products and false claims to a respected exemplar of public health. More recently, the FDA’s objectivity has increasingly been questioned. Critics argue the agency has become overly political and too accommodating to industry while lowering its standards for safety and efficacy. The FDA’s accelerated pathways for product testing and approval are partly to blame. They require lower-quality evidence, such as surrogate endpoints, and shift the FDA’s focus from premarket clinical trials toward postmarket surveillance, requiring less evidence up front while promising enhanced scrutiny on the back end. To further streamline product testing and approval, the FDA is adopting outputs from computer models, enhanced by artificial intelligence (“AI”), as surrogates for direct evidence of safety and efficacy. This Article analyzes how the FDA uses computer models and simulations to save resources, reduce costs, infer product safety and efficacy, and make regulatory decisions. To test medical products, the FDA assembles cohorts of virtual humans and conducts digital clinical trials. Using molecular modeling, it simulates how substances interact with cellular targets to predict adverse effects and determine how drugs should be regulated. Though legal scholars have commented on the role of AI as a medical product that is regulated by the FDA, they have largely overlooked the role of AI as a medical product regulator. Modeling and simulation could eventually reduce the exposure of volunteers to risks and help protect the public. However, these technologies lower safety and efficacy standards and may erode public trust in the FDA while undermining its transparency, accountability, objectivity, and legitimacy. Bias in computer models and simulations may prioritize efficiency and speed over other values such as maximizing safety, equity, and public health. By analyzing FDA guidance documents and industry and agency simulation standards, this Article offers recommendations for safer and more equitable automation of FDA regulation

The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

On designing an algorithmically enhanced NHS: towards a conceptual model for the successful implementation of algorithmic clinical decision support software in the National Health Service

Established in 1948, the National Health Service (NHS) has lasted 75 years. It is, however, under considerable strain: facing chronic staff shortages; record numbers of emergency attendances; an ambulance wait-time crisis; and more. Increasingly, policymakers are of the view that the solution to these problems is to rely more heavily on one of the NHS’s greatest resources: its data. It is hoped that by combining the NHS’s data riches with the latest techniques in artificial intelligence (AI), that the means to make the NHS more effective, more efficient, and more consistent, can be identified and acted upon via the implementation of Algorithmic Clinical Decision Support Software (ACDSS). Yet, getting this implementation right will be both technically and ethically difficult. It will require a careful re-design of the NHS’s information infrastructure to ensure the implementation of ACDSS results in intended positive emergence (benefits), and not unintended negative emergence (harms and risks). This then is the purpose of my thesis. I seek to help policymakers with this re-design process by answering the research question ‘What are the information infrastructure requirements for the successful implementation of ACDSS in the NHS?’. I adopt a mixed-methods, theory-informed, and interpretive approach, and weave the results into a narrative policy synthesis. I start with an analysis of why current attempts to implement ACDSS into the NHS’s information infrastructure are failing and what needs to change to increase the chances of success; anticipate what might happen if these changes are not made; identify the exact requirements for bringing forth the changes; explain why the likelihood of these requirements being met by current policy is limited; and conclude by explaining how the likelihood of policy meeting the identified requirements can be increased by designing the ACDSS’s supporting information infrastructure around the core concepts of ‘utility, usability, efficacy, and trustworthiness’

White Paper 11: Artificial intelligence, robotics & data science

198 p. : 17 cmSIC white paper on Artificial Intelligence, Robotics and Data Science sketches a preliminary roadmap for addressing current R&D challenges associated with automated and autonomous machines. More than 50 research challenges investigated all over Spain by more than 150 experts within CSIC are presented in eight chapters. Chapter One introduces key concepts and tackles the issue of the integration of knowledge (representation), reasoning and learning in the design of artificial entities. Chapter Two analyses challenges associated with the development of theories –and supporting technologies– for modelling the behaviour of autonomous agents. Specifically, it pays attention to the interplay between elements at micro level (individual autonomous agent interactions) with the macro world (the properties we seek in large and complex societies). While Chapter Three discusses the variety of data science applications currently used in all fields of science, paying particular attention to Machine Learning (ML) techniques, Chapter Four presents current development in various areas of robotics. Chapter Five explores the challenges associated with computational cognitive models. Chapter Six pays attention to the ethical, legal, economic and social challenges coming alongside the development of smart systems. Chapter Seven engages with the problem of the environmental sustainability of deploying intelligent systems at large scale. Finally, Chapter Eight deals with the complexity of ensuring the security, safety, resilience and privacy-protection of smart systems against cyber threats.18 EXECUTIVE SUMMARY ARTIFICIAL INTELLIGENCE, ROBOTICS AND DATA SCIENCE Topic Coordinators Sara Degli Esposti ( IPP-CCHS, CSIC ) and Carles Sierra ( IIIA, CSIC ) 18 CHALLENGE 1 INTEGRATING KNOWLEDGE, REASONING AND LEARNING Challenge Coordinators Felip Manyà ( IIIA, CSIC ) and Adrià Colomé ( IRI, CSIC – UPC ) 38 CHALLENGE 2 MULTIAGENT SYSTEMS Challenge Coordinators N. Osman ( IIIA, CSIC ) and D. López ( IFS, CSIC ) 54 CHALLENGE 3 MACHINE LEARNING AND DATA SCIENCE Challenge Coordinators J. J. Ramasco Sukia ( IFISC ) and L. Lloret Iglesias ( IFCA, CSIC ) 80 CHALLENGE 4 INTELLIGENT ROBOTICS Topic Coordinators G. Alenyà ( IRI, CSIC – UPC ) and J. Villagra ( CAR, CSIC ) 100 CHALLENGE 5 COMPUTATIONAL COGNITIVE MODELS Challenge Coordinators M. D. del Castillo ( CAR, CSIC) and M. Schorlemmer ( IIIA, CSIC ) 120 CHALLENGE 6 ETHICAL, LEGAL, ECONOMIC, AND SOCIAL IMPLICATIONS Challenge Coordinators P. Noriega ( IIIA, CSIC ) and T. Ausín ( IFS, CSIC ) 142 CHALLENGE 7 LOW-POWER SUSTAINABLE HARDWARE FOR AI Challenge Coordinators T. Serrano ( IMSE-CNM, CSIC – US ) and A. Oyanguren ( IFIC, CSIC - UV ) 160 CHALLENGE 8 SMART CYBERSECURITY Challenge Coordinators D. Arroyo Guardeño ( ITEFI, CSIC ) and P. Brox Jiménez ( IMSE-CNM, CSIC – US )Peer reviewe

Applications of Medical Physics

Applications of Medical Physics” is a Special Issue of Applied Sciences that has collected original research manuscripts describing cutting-edge physics developments in medicine and their translational applications. Reviews providing updates on the latest progresses in this field are also included. The collection includes a total of 20 contributions by authors from 9 different countries, which cover several areas of medical physics, spanning from radiation therapy, nuclear medicine, radiology, dosimetry, radiation protection, and radiobiology