Catheter manipulation analysis for objective performance and technical skills assessment in transcatheter aortic valve implantation

The Emerging and Important Role of Artificial Intelligence in Cardiac Surgery

Artificial Intelligence (AI) has greatly affected our everyday lives and holds great promise to change the landscape of medicine. AI is particularly positioned to improve care for the increasingly complex patients undergoing cardiac surgery utilizing immense amount of data generated in the course of their care. When deployed, AI can be used to analyze this information at the patient's bedside more expediently and accurately, all while providing new insights. This review summarizes the current applications of AI in cardiac surgery, from the vantage point of a patient's journey. Applications of AI include pre-operative risk assessment, intraoperative planning, post-operative patient care and out-patient telemonitoring, encompassing the spectrum of cardiac surgical care. Offloading of administrative processes and enhanced experience with information gathering also represent a unique and underrepresented avenue for future utilization of AI. As clinicians, understanding the nomenclature and applications of AI is important to contextualize problems, to ensure problem-driven solutions and for clinical benefit. Precision medicine, and thus clinically relevant AI, remains dependent on data curation and warehousing to gather insights from large multicenter repositories while treating privacy with the utmost importance. AI tasks should not be siloed but rather holistically integrated into clinical workflow to retain context and relevance. As cardiac surgeons, AI allows us to look forward to a bright future of more efficient utilization of our clinical expertise toward high-level decision making and technical prowess.

Advances in TEE-Centric Intraprocedural Multimodal Image Guidance for Congenital and Structural Heart Disease

Percutaneous interventions are gaining rapid acceptance in cardiology and revolutionizing the treatment of structural heart disease (SHD). As new percutaneous procedures of SHD are being developed, their associated complexity and anatomical variability demand a high-resolution special understanding for intraprocedural image guidance. During the last decade, three-dimensional (3D) transesophageal echocardiography (TEE) has become one of the most accessed imaging methods for structural interventions. Although 3D-TEE can assess cardiac structures and functions in real-time, its limitations (e.g., limited field of view, image quality at a large depth, etc.) must be addressed for its universal adaptation, as well as to improve the quality of its imaging and interventions. This review aims to present the role of TEE in the intraprocedural guidance of percutaneous structural interventions. We also focus on the current and future developments required in a multimodal image integration process when using TEE to enhance the management of congenital and SHD treatments.

An Image Information-Based Objective Assessment Method of Technical Manipulation Skills for Intravascular Interventions

The clinical success of vascular interventional surgery relies heavily on a surgeon's catheter/guidewire manipulation skills and strategies. An objective and accurate assessment method plays a critical role in evaluating the surgeon's technical manipulation skill level. Most of the existing evaluation methods incorporate the use of information technology to find more objective assessment models based on various metrics. However, in these models, sensors are often attached to the surgeon's hands or to interventional devices for data collection, which constrains the surgeon's operational movements or exerts an influence on the motion trajectory of interventional devices. In this paper, an image information-based assessment method is proposed for the evaluation of the surgeon's manipulation skills without the requirement of attaching sensors to the surgeon or catheters/guidewires. Surgeons are allowed to use their natural bedside manipulation skills during the data collection process. Their manipulation features during different catheterization tasks are derived from the motion analysis of the catheter/guidewire in video sequences. Notably, data relating to the number of speed peaks, slope variations, and the number of collisions are included in the assessment. Furthermore, the contact forces, resulting from interactions between the catheter/guidewire and the vascular model, are sensed by a 6-DoF F/T sensor. A support vector machine (SVM) classification framework is developed to discriminate the surgeon's catheterization skill levels. The experimental results demonstrate that the proposed SVM-based assessment method can obtain an accuracy of 97.02% to distinguish between the expert and novice manipulations, which is higher than that of other existing research achievements. The proposed method has great potential to facilitate skill assessment and training of novice surgeons in vascular interventional surgery.

Biomechanical analysis of expert anesthesiologists and novice residents performing a simulated central venous access procedure

BACKGROUND

Central venous access (CVA) is a frequent procedure taught in medical residencies. However, since CVA is a high-risk procedure requiring a detailed teaching and learning process to ensure trainee proficiency, it is necessary to determine objective differences between the expert's and the novice's performance to guide novice practitioners during their training process. This study compares experts' and novices' biomechanical variables during a simulated CVA performance.

METHODS

Seven experts and seven novices were part of this study. The participants' motion data during a CVA simulation procedure was collected using the Vicon Motion System. The procedure was divided into four stages for analysis, and each hand's speed, acceleration, and jerk were obtained. Also, the procedural time was analyzed. Descriptive analysis and multilevel linear models with random intercept and interaction were used to analyze group, hand, and stage differences.

RESULTS

There were statistically significant differences between experts and novices regarding time, speed, acceleration, and jerk during a simulated CVA performance. These differences vary significantly by the procedure stage for right-hand acceleration and left-hand jerk.

CONCLUSIONS

Experts take less time to perform the CVA procedure, which is reflected in higher speed, acceleration, and jerk values. This difference varies according to the procedure's stage, depending on the hand and variable studied, demonstrating that these variables could play an essential role in differentiating between experts and novices, and could be used when designing training strategies.

3D Printing, Computational Modeling, and Artificial Intelligence for Structural Heart Disease

Structural heart disease (SHD) is a new field within cardiovascular medicine. Traditional imaging modalities fall short in supporting the needs of SHD interventions, as they have been constructed around the concept of disease diagnosis. SHD interventions disrupt traditional concepts of imaging in requiring imaging to plan, simulate, and predict intraprocedural outcomes. In transcatheter SHD interventions, the absence of a gold-standard open cavity surgical field deprives physicians of the opportunity for tactile feedback and visual confirmation of cardiac anatomy. Hence, dependency on imaging in periprocedural guidance has led to evolution of a new generation of procedural skillsets, concept of a visual field, and technologies in the periprocedural planning period to accelerate preclinical device development, physician, and patient education. Adaptation of 3-dimensional (3D) printing in clinical care and procedural planning has demonstrated a reduction in early-operator learning curve for transcatheter interventions. Integration of computation modeling to 3D printing has accelerated research and development understanding of fluid mechanics within device testing. Application of 3D printing, computational modeling, and ultimately incorporation of artificial intelligence is changing the landscape of physician training and delivery of patient-centric care. Transcatheter structural heart interventions are requiring in-depth periprocedural understanding of cardiac pathophysiology and device interactions not afforded by traditional imaging metrics.

Machine learning-based operation skills assessment with vascular difficulty index for vascular intervention surgery

An accurate assessment of surgical operation skills is essential for improving the vascular intervention surgical outcome and the performance of endovascular surgery robots. In existing studies, subjective and objective assessments of surgical operation skills use a variety of indicators, such as the operation speed and operation smoothness. However, the vascular conditions of particular patients have not been considered in the assessment, leading to deviations in the evaluation. Therefore, in this paper, an operation skills assessment method including the vascular difficulty level index for catheter insertion at the aortic arch in endovascular surgery is proposed. First, the model describing the difficulty of the vascular anatomical structure is established with characteristics of different aortic arch branches based on machine learning. Afterwards, the vascular difficulty level is set as an objective index combined with operating characteristics extracted from the operations performed by surgeons to evaluate the surgical operation skills at the aortic arch using machine learning. The accuracy of the assessment improves from 86.67 to 96.67% after inclusion of the vascular difficulty as an evaluation indicator to more objectively and accurately evaluate skills. The method described in this paper can be adopted to train novice surgeons in endovascular surgery, and for studies of vascular interventional surgery robots. Graphical abstract Operation skill assessment with vascular difficulty for vascular interventional surgery.

Electrothermally Driven Hydrogel-on-Flex-Circuit Actuator for Smart Steerable Catheters

This paper reports an active catheter-tip device functionalized by integrating a temperature-responsive smart polymer onto a microfabricated flexible heater strip, targeting at enabling the controlled steering of catheters through complex vascular networks. A bimorph-like strip structure is enabled by photo-polymerizing a layer of poly(N-isopropylacrylamide) hydrogel (PNIPAM), on top of a 20 × 3.5 mm² flexible polyimide film that embeds a micropatterned heater fabricated using a low-cost flex-circuit manufacturing process. The heater activation stimulates the PNIPAM layer to shrink and bend the tip structure. The bending angle is shown to be adjustable with the amount of power fed to the device, proving the device’s feasibility to provide the integrated catheter with a controlled steering ability for a wide range of navigation angles. The powered device exhibits uniform heat distribution across the entire PNIPAM layer, with a temperature variation of <2 °C. The operation of fabricated prototypes assembled on commercial catheter tubes demonstrates their bending angles of up to 200°, significantly larger than those reported with other smart-material-based steerable catheters. The temporal responses and bending forces of their actuations are also characterized to reveal consistent and reproducible behaviors. This proof-of-concept study verifies the promising features of the prototyped approach to the targeted application area.

Using Machine Learning to Assess Physician Competence: A Systematic Review

PURPOSE

To identify the different machine learning (ML) techniques that have been applied to automate physician competence assessment and evaluate how these techniques can be used to assess different competence domains in several medical specialties.

METHOD

In May 2017, MEDLINE, EMBASE, PsycINFO, Web of Science, ACM Digital Library, IEEE Xplore Digital Library, PROSPERO, and Cochrane Database of Systematic Reviews were searched for articles published from inception to April 30, 2017. Studies were included if they applied at least one ML technique to assess medical students', residents', fellows', or attending physicians' competence. Information on sample size, participants, study setting and design, medical specialty, ML techniques, competence domains, outcomes, and methodological quality was extracted. MERSQI was used to evaluate quality, and a qualitative narrative synthesis of the medical specialties, ML techniques, and competence domains was conducted.

RESULTS

Of 4,953 initial articles, 69 met inclusion criteria. General surgery (24; 34.8%) and radiology (15; 21.7%) were the most studied specialties; natural language processing (24; 34.8%), support vector machine (15; 21.7%), and hidden Markov models (14; 20.3%) were the ML techniques most often applied; and patient care (63; 91.3%) and medical knowledge (45; 65.2%) were the most assessed competence domains.

CONCLUSIONS

A growing number of studies have attempted to apply ML techniques to physician competence assessment. Although many studies have investigated the feasibility of certain techniques, more validation research is needed. The use of ML techniques may have the potential to integrate and analyze pragmatic information that could be used in real-time assessments and interventions.

Wearable technology-based metrics for predicting operator performance during cardiac catheterisation

Introduction

Unobtrusive metrics that can auto-assess performance during clinical procedures are of value. Three approaches to deriving wearable technology-based metrics are explored: (1) eye tracking, (2) psychophysiological measurements [e.g. electrodermal activity (EDA)] and (3) arm and hand movement via accelerometry. We also measure attentional capacity by tasking the operator with an additional task to track an unrelated object during the procedure.

Methods

Two aspects of performance are measured: (1) using eye gaze and psychophysiology metrics and (2) measuring attentional capacity via an additional unrelated task (to monitor a visual stimulus/playing cards). The aim was to identify metrics that can be used to automatically discriminate between levels of performance or at least between novices and experts. The study was conducted using two groups: (1) novice operators and (2) expert operators. Both groups made two attempts at a coronary angiography procedure using a full-physics virtual reality simulator. Participants wore eye tracking glasses and an E4 wearable wristband. Areas of interest were defined to track visual attention on display screens, including: (1) X-ray, (2) vital signs, (3) instruments and (4) the stimulus screen (for measuring attentional capacity).

Results

Experts provided greater dwell time (63% vs 42%, p = 0.03) and fixations (50% vs 34%, p = 0.04) on display screens. They also provided greater dwell time (11% vs 5%, p = 0.006) and fixations (9% vs 4%, p = 0.007) when selecting instruments. The experts’ performance for tracking the unrelated object during the visual stimulus task negatively correlated with total errors (r = − 0.95, p = 0.0009). Experts also had a higher standard deviation of EDA (2.52 µS vs 0.89 µS, p = 0.04).

Conclusions

Eye tracking metrics may help discriminate between a novice and expert operator, by showing that experts maintain greater visual attention on the display screens. In addition, the visual stimulus study shows that an unrelated task can measure attentional capacity.

Trial registration This work is registered through clinicaltrials.gov, a service of the U.S. National Health Institute, and is identified by the trial reference: NCT02928796.

Assessment of distal access catheter performance during neuroendovascular procedures: measuring force in three-dimensional patient specific phantoms

BACKGROUND

The amount of force applied on a device is an important measure to evaluate the endovascular and surgical device manipulations. The measure has not been evaluated for neuroenodvascular procedures.

PURPOSE

We aimed to study the use of force measure as a novel approach to test distal access catheter (DAC) performance during catheterization of cervical and intracranial vessels using patient specific 3-dimentional (3D) phantoms.

METHODS

Using patient specific 3D phantoms of the cervical and intracranial circulation, we recorded measure of force required to deliver three types of DACs beyond the ophthalmic segment of the internal carotid artery. Six different combinations of DAC-microcatheter-guidewire were tested. We intentionally included what we considered suboptimal combinations of DACs, microcatheters, and guidewires during our experiments to test the feasibility of measuring force under different conditions. A six axis force sensor was secured to the DAC with an adjustable torque used to track axially directed push and pull forces required to navigate the DAC to the target site.

RESULTS

In a total of 55 experiments, we found a significant difference in the amount of force used between different DACs (mean force for DAC A, 1.887±0.531N; for DAC B, 2.153±1.280 N; and for DAC C, 1.194±0.521 N, P=0.007). There was also a significant difference in force measures among the six different catheter systems (P=0.035).

CONCLUSIONS

Significant difference in the amount of force used between different DACs and catheter systems were recorded. Use of force measure in neuroendovascular procedures on 3D printed phantoms is feasible.

Biomechanical profiles of tracheal intubation: a mannequin-based study to make an objective assessment of clinical skills by expert anesthesiologists and novice residents

BACKGROUND

Tracheal intubation (TI) is a key medical skill used by anesthesiologists and critical care physicians in airway management in operating rooms and critical care units. An objective assessment of dexterity in TI procedures would greatly enhance the quality of medical training. This study aims to investigate whether any biomechanical parameters obtained by 3D-motion analysis of body movements during TI procedures can objectively distinguish expert anesthesiologists from novice residents.

METHODS

Thirteen expert anesthesiologists and thirteen residents attempted TI procedures on an airway mannequin using a Macintosh laryngoscope. Motion capturing technology was utilized to digitally record movements during TI procedures. The skill with which experts and novices measured biomechanical parameters of body motions were comparatively examined.

RESULTS

The two groups showed similar outcomes (success rates and mean time needed to complete the TI procedures) as well as similar mean absolute velocity values in all 21 body parts examined. However, the experts exhibited significantly lower mean absolute acceleration values at the head and the left hand than the residents. In addition, the mean-absolute-jerk measurement revealed that the experts commanded potentially smoother motions at the head and the left hand. The Receiver Operating Characteristic (ROC) curves analysis demonstrated that mean-absolute-acceleration and -jerk measurements provide excellent measures for discriminating between experts and novices.

CONCLUSIONS

Biomechanical parameter measurements could be used as a means to objectively assess dexterity in TI procedures. Compared with novice residents, expert anesthesiologists possess a better ability to control their body movements during TI procedures, displaying smoother motions at the selected body parts.

Robotics in percutaneous cardiovascular interventions

INTRODUCTION

The fundamental technique of performing percutaneous cardiovascular (CV) interventions has remained unchanged and requires operators to wear heavy lead aprons to minimize exposure to ionizing radiation. Robotic technology is now being utilized in interventional cardiology partially as a direct result of the increasing appreciation of the long-term occupational hazards of the field. This review was undertaken to report the clinical outcomes of percutaneous robotic coronary and peripheral vascular interventions. Areas covered: A systematic literature review of percutaneous robotic CV interventions was undertaken. The safety and feasibility of percutaneous robotically-assisted CV interventions has been validated in simple to complex coronary disease, and iliofemoral disease. Studies have shown that robotically-assisted PCI significantly reduces operator exposure to harmful ionizing radiation without compromising procedural success or clinical efficacy. In addition to the operator benefits, robotically-assisted intervention has the potential for patient advantages by allowing more accurate lesion length measurement, precise stent placement and lower patient radiation exposure. However, further investigation is required to fully elucidate these potential benefits. Expert commentary: Incremental improvement in robotic technology and telecommunications would enable treatment of an even broader patient population, and potentially provide remote robotic PCI.

The Development of Robotic Technology in Cardiac and Vascular Interventions

Robotic technology has been used in cardiovascular medicine for over a decade, and over that period its use has been expanded to interventional cardiology and percutaneous coronary and peripheral vascular interventions. The safety and feasibility of robotically assisted interventions has been demonstrated in multiple studies ranging from simple to complex coronary lesions, and in the treatment of iliofemoral and infrapopliteal disease. These studies have shown a reduction in operator exposure to harmful ionizing radiation, and the use of robotics has the intuitive benefit of alleviating the occupational hazard of operator orthopedic injuries. In addition to the interventional operator benefits, robotically assisted intervention has the potential to also be beneficial for patients by allowing more accurate lesion length measurement, stent placement, and patient radiation exposure; however, more investigation is required to elucidate these benefits fully.