Robustness Evaluation of Computer-aided Clinical Trials for Medical Devices

Medical cyber-physical systems, such as the implantable cardioverter defibrillator (ICD), require evaluation of safety and efficacy in the context of a patient population in a clinical trial. Advances in computer modeling and simulation allow for generation of a simulated cohort or virtual cohort which mimics a patient population and can be used as a source of prior information. A major obstacle to acceptance of simulation results as a source of prior information is the lack of a framework for explicitly modeling sources of uncertainty in simulation results and quantifying the effect on trial outcomes. In this work, we formulate the Computer-Aided Clinical Trial (CACT) within a Bayesian statistical framework allowing explicit modeling of assumptions and utilization of simulation results at all stages of a clinical trial. To quantify the robustness of the CACT outcome with respect to a simulation assumption, we define δ-robustness as the minimum perturbation of the base prior distribution resulting in a change of the CACT outcome and provide a method to estimate the δ-robustness. We demonstrate the utility of the framework and how the results of δ-robustness evaluation can be utilized at various stages of a clinical trial through an application to the Rhythm ID Goes Head-to-head Trial (RIGHT), which was a comparative evaluation of the safety and efficacy of specific software algorithms across different implantable cardiac devices. Finally, we introduce a hardware interface that allows for direct interaction with the physical device in order to validate and confirm the results of a CACT for implantable cardiac devices

Computer- and robot-assisted Medical Intervention

Medical robotics includes assistive devices used by the physician in order to make his/her diagnostic or therapeutic practices easier and more efficient. This chapter focuses on such systems. It introduces the general field of Computer-Assisted Medical Interventions, its aims, its different components and describes the place of robots in that context. The evolutions in terms of general design and control paradigms in the development of medical robots are presented and issues specific to that application domain are discussed. A view of existing systems, on-going developments and future trends is given. A case-study is detailed. Other types of robotic help in the medical environment (such as for assisting a handicapped person, for rehabilitation of a patient or for replacement of some damaged/suppressed limbs or organs) are out of the scope of this chapter.Comment: Handbook of Automation, Shimon Nof (Ed.) (2009) 000-00

The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

Artificial intelligence (AI) and Machine learning (ML) systems in medicine are poised to significantly improve health care, for example, by offering earlier diagnoses of diseases or recommending optimally individualized treatment plans. However, the emergence of AI/ML in medicine also creates challenges, which regulators must pay attention to. Which medical AI/ML-based products should be reviewed by regulators? What evidence should be required to permit marketing for AI/ML-based software as a medical device (SaMD)? How can we ensure the safety and effectiveness of AI/ML-based SaMD that may change over time as they are applied to new data? The U.S. Food and Drug Administration (FDA), for example, has recently proposed a discussion paper to address some of these issues. But it misses an important point: we argue that regulators like the FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition

Computer-Aided Clinical Trials For Medical Devices

Life-critical medical devices require robust safety and efficacy to treat patient populations with potentially large patient heterogeneity. Today, the de facto standard for evaluating medical devices is the randomized controlled trial. However, even after years of device development many clinical trials fail. For example, in the Rhythm ID Goes Head to Head Trial (RIGHT) the risk for inappropriate therapy by implantable cardioverter defibrillators (ICDs) actually increased relative to control treatments. With recent advances in physiological modeling and devices incorporating more complex software components, population-level device outcomes can be obtained with scalable simulations. Consequently, there is a need for data-driven approaches to provide early insight prior to the trial, lowering the cost of trials using patient and device models, and quantifying the robustness of the outcome. This work presents a clinical trial modeling and statistical framework which utilizes simulation to improve the evaluation of medical device software, such as the algorithms in ICDs. First, a method for generating virtual cohorts using a physiological simulator is introduced. Next, we present our framework which combines virtual cohorts with real data to evaluate the efficacy and allows quantifying the uncertainty due to the use of simulation. Results predicting the outcome of RIGHT and improving statistical power while reducing the sample size are shown. Finally, we improve device performance with an approach using Bayesian optimization. Device performance can degrade when deployed to a general population despite success in clinical trials. Our approach improves the performance of the device with outcomes aligned with the MADIT-RIT clinical trial. This work provides a rigorous approach towards improving the development and evaluation of medical treatments

Comparison of symmetrical hemodialysis catheters using computational fluid dynamics

Purpose: Symmetric-tip dialysis catheters have become alternative devices because of low access recirculation and ease of tip positioning. Flow characteristics of three symmetric catheters were compared based on computational fluid dynamics (CFD) as they relate to catheter function. Materials And Methods: In Palindrome, GlidePath, and VectorFloW catheters, a computational fluid dynamics based approach was used to assess W regions of flow separation, which are prone to thrombus development; (ii) shear-induced platelet activation potency; (iii) recirculation; and (iv) venous outflow deflection. A steady-state, laminar flow model simulated: catheter tip position within the superior vena cava. Catheter performance was investigated at high hemodialysis flow rate (400 mL/min). Blood was assumed as a Newtonian fluid. Results: Wide regions of flow separation downstream of the Palindrome side slot and close to the distal tip were observed in forward and reversed line configurations. Geometric asymmetry of the distal guide wire aperture of the GlidePath catheter produced the highest levels of inverted velocity flow when run in reversed configuration. The lowest mean shear-induced platelet activation was exhibited by GlidePath and VectorFloW catheters; the Palindrome catheter exhibited 152% higher overall platelet activation potency. All catheters were associated with a recirculation close to zero; the helically contoured lumens of the VectorFlow catheter produced the greatest amount of deflection of venous flow away from the arterial lumen. Conclusions: The VectorFlow catheter produced less shear-induced platelet activation than the Palindrome catheter and less flow separation than the Palindrome and GlidePath catheters irrespective of line configuration These findings have,potential implications for differences in thrombogenic risk during clinical performance of these catheters

Evaluation of 3D Printed Immobilisation Shells for Head and Neck IMRT

This paper presents the preclinical evaluation of a novel immobilization system for patients undergoing external beam radiation treatment of head and neck tumors. An immobilization mask is manufactured directly from a 3-D model, built using the CT data routinely acquired for treatment planning so there is no need to take plaster of Paris moulds. Research suggests that many patients find the mould room visit distressing and so rapid prototyping could potentially improve the overall patient experience. Evaluation of a computer model of the immobilization system using an anthropomorphic phantom shows that >99% of vertices are within a tolerance of ±0.2 mm. Hausdorff distance was used to analyze CT slices obtained by rescanning the phantom with a printed mask in position. These results show that for >80% of the slices the median “worse-case” tolerance is approximately 4 mm. These measurements suggest that printed masks can achieve similar levels of immobilization to those of systems currently in clinical use

Expanding the medical physicist curricular and professional programme to include Artificial Intelligence

Purpose: To provide a guideline curriculum related to Artificial Intelligence (AI), for the education and training of European Medical Physicists (MPs). Materials and methods: The proposed curriculum consists of two levels: Basic (introducing MPs to the pillars of knowledge, development and applications of AI, in the context of medical imaging and radiation therapy) and Advanced. Both are common to the subspecialties (diagnostic and interventional radiology, nuclear medicine, and radiation oncology). The learning outcomes of the training are presented as knowledge, skills and competences (KSC approach). Results: For the Basic section, KSCs were stratified in four subsections: (1) Medical imaging analysis and AI Basics; (2) Implementation of AI applications in clinical practice; (3) Big data and enterprise imaging, and (4) Quality, Regulatory and Ethical Issues of AI processes. For the Advanced section instead, a common block was proposed to be further elaborated by each subspecialty core curriculum. The learning outcomes were also translated into a syllabus of a more traditional format, including practical applications. Conclusions: This AI curriculum is the first attempt to create a guideline expanding the current educational framework for Medical Physicists in Europe. It should be considered as a document to top the sub-specialties' curriculums and adapted by national training and regulatory bodies. The proposed educational program can be implemented via the European School of Medical Physics Expert (ESMPE) course modules and - to some extent - also by the national competent EFOMP organizations, to reach widely the medical physicist community in Europe.Peer reviewe

Computer-Aided Detection, Pulmonary Embolism, Computerized Tomography Pulmonary Angiography: Current Status

Angiography (mostly computed tomography, but in some cases, conventional) is still the gold diagnostic standard in the clinical diagnosis of pulmonary embolism (PE). Computer-aided detection (CAD) is software that alerts radiologists the presence of PE during computerized tomography pulmonary angiography (CTPA) examinations. Interpreting CTPA scans with the aid of commercially available CTPA-CAD has improved the detectability of PE patients. This chapter aims to complete the scope of this book by explaining the clinical evidences of PE, the CTPA technology, the role of CTPA-CAD software in improving the diagnostic abilities of CTPA and the role of conventional pulmonary angiography in daily clinical practice. The reader will be introduced to the performance of diagnosing PE with or without the aid of CTPA-CAD algorithms. Differences among CTPA-CAD’s output will be compared and tabled according to “per patient,” “per clot,” “first reader,” and “second reader” basis. This includes, but not limited to, the CTPA-CAD’s sensitivity and specificity in comparison to human observer performance (i.e., radiologist). These topics cover the current status practice at the pulmonary angiography clinic

Artificial Intelligence and Endo-Histo-OMICs: New Dimensions of Precision Endoscopy and Histology in Inflammatory Bowel Disease

Integrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to revolutionise clinical practice and research. Artificial intelligence harnesses advanced algorithms to deliver accurate assessments of IBD endoscopy and histology, offering precise evaluations of disease activity, standardised scoring, and outcome prediction. Furthermore, artificial intelligence offers the potential for a holistic endo-histo-omics approach by interlacing and harmonising endoscopy, histology, and omics data towards precision medicine. The emerging applications of artificial intelligence could pave the way for personalised medicine in IBD, offering patient stratification for the most beneficial therapy with minimal risk. Although artificial intelligence holds promise, challenges remain, including data quality, standardisation, reproducibility, scarcity of randomised controlled trials, clinical implementation, ethical concerns, legal liability, and regulatory issues. The development of standardised guidelines and interdisciplinary collaboration, including policy makers and regulatory agencies, is crucial for addressing these challenges and advancing artificial intelligence in IBD clinical practice and trials