Visual expertise refers to advanced visual skills demonstrated when performing domain-specific visual tasks. Prior research has emphasized the fact that medical experts rely on such perceptual pattern-recognition skills when interpreting medical images, particularly in the field of electrocardiogram (ECG) interpretation. Analyzing and modeling cardiology practitioners' visual behavior across different levels of expertise in the health care sector is crucial. Namely, understanding such acquirable visual skills may help train less experienced clinicians to interpret ECGs accurately.This study aims to quantify and analyze through the use of eye-tracking technology differences in the visual behavior and methodological practices for different expertise levels of cardiology practitioners such as medical students, cardiology nurses, technicians, fellows, and consultants when interpreting several types of ECGs.A total of 63 participants with different levels of clinical expertise took part in an eye-tracking study that consisted of interpreting 10 ECGs with different cardiac abnormalities. A counterbalanced within-subjects design was used with one independent variable consisting of the expertise level of the cardiology practitioners and two dependent variables of eye-tracking metrics (fixations count and fixation revisitations). The eye movements data revealed by specific visual behaviors were analyzed according to the accuracy of interpretation and the frequency with which interpreters visited different parts/leads on a standard 12-lead ECG. In addition, the median and SD in the IQR for the fixations count and the mean and SD for the ECG lead revisitations were calculated.Accuracy of interpretation ranged between 98% among consultants, 87% among fellows, 70% among technicians, 63% among nurses, and finally 52% among medical students. The results of the eye fixations count, and eye fixation revisitations indicate that the less experienced cardiology practitioners need to interpret several ECG leads more carefully before making any decision. However, more experienced cardiology practitioners rely on their skills to recognize the visual signal patterns of different cardiac abnormalities, providing an accurate ECG interpretation.The results show that visual expertise for ECG interpretation is linked to the practitioner's role within the health care system and the number of years of practical experience interpreting ECGs. Cardiology practitioners focus on different ECG leads and different waveform abnormalities according to their role in the health care sector and their expertise levels.
The increasing use of artificial intelligence (AI) in healthcare has brought about numerous ethical considerations that push for reflection. Humanizing AI in medical training is crucial to ensure that the design and deployment of its algorithms align with ethical principles and promote equitable healthcare outcomes for both medical practitioners trainees and patients. This perspective article provides an ethical framework for responsibly designing AI systems in medical training, drawing on our own past research in the fields of electrocardiogram interpretation training and e-health wearable devices. The article proposes five pillars of responsible design: transparency, fairness and justice, safety and wellbeing, accountability, and collaboration. The transparency pillar highlights the crucial role of maintaining the explainabilty of AI algorithms, while the fairness and justice pillar emphasizes on addressing biases in healthcare data and designing models that prioritize equitable medical training outcomes. The safety and wellbeing pillar however, emphasizes on the need to prioritize patient safety and wellbeing in AI model design whether it is for training or simulation purposes, and the accountability pillar calls for establishing clear lines of responsibility and liability for AI-derived decisions. Finally, the collaboration pillar emphasizes interdisciplinary collaboration among stakeholders, including physicians, data scientists, patients, and educators. The proposed framework thus provides a practical guide for designing and deploying AI in medicine generally, and in medical training specifically in a responsible and ethical manner.
Accurate interpretation of a 12-lead electrocardiogram (ECG) demands high levels of skill and expertise. Early training in medical school plays an important role in building the ECG interpretation skill. Thus, understanding how medical students perform the task of interpretation is important for improving this skill.We aimed to use eye tracking as a tool to research how eye fixation can be used to gain a deeper understanding of how medical students interpret ECGs.In total, 16 medical students were recruited to interpret 10 different ECGs each. Their eye movements were recorded using an eye tracker. Fixation heatmaps of where the students looked were generated from the collected data set. Statistical analysis was conducted on the fixation count and duration using the Mann-Whitney U test and the Kruskal-Wallis test.The average percentage of correct interpretations was 55.63%, with an SD of 4.63%. After analyzing the average fixation duration, we found that medical students study the three lower leads (rhythm strips) the most using a top-down approach: lead II (mean=2727 ms, SD=456), followed by leads V1 (mean=1476 ms, SD=320) and V5 (mean=1301 ms, SD=236). We also found that medical students develop a personal system of interpretation that adapts to the nature and complexity of the diagnosis. In addition, we found that medical students consider some leads as their guiding point toward finding a hint leading to the correct interpretation.The use of eye tracking successfully provides a quantitative explanation of how medical students learn to interpret a 12-lead ECG.
BACKGROUND Visual expertise refers to advanced visual skills demonstrated when performing domain-specific visual tasks. Prior research has emphasized the fact that medical experts rely on such perceptual pattern-recognition skills when interpreting medical images, particularly in the field of electrocardiogram (ECG) interpretation. Analyzing and modeling cardiology practitioners’ visual behavior across different levels of expertise in the health care sector is crucial. Namely, understanding such acquirable visual skills may help train less experienced clinicians to interpret ECGs accurately. OBJECTIVE This study aims to quantify and analyze through the use of eye-tracking technology differences in the visual behavior and methodological practices for different expertise levels of cardiology practitioners such as medical students, cardiology nurses, technicians, fellows, and consultants when interpreting several types of ECGs. METHODS A total of 63 participants with different levels of clinical expertise took part in an eye-tracking study that consisted of interpreting 10 ECGs with different cardiac abnormalities. A counterbalanced within-subjects design was used with one independent variable consisting of the expertise level of the cardiology practitioners and two dependent variables of eye-tracking metrics (fixations count and fixation revisitations). The eye movements data revealed by specific visual behaviors were analyzed according to the accuracy of interpretation and the frequency with which interpreters visited different parts/leads on a standard 12-lead ECG. In addition, the median and SD in the IQR for the fixations count and the mean and SD for the ECG lead revisitations were calculated. RESULTS Accuracy of interpretation ranged between 98% among consultants, 87% among fellows, 70% among technicians, 63% among nurses, and finally 52% among medical students. The results of the eye fixations count, and eye fixation revisitations indicate that the less experienced cardiology practitioners need to interpret several ECG leads more carefully before making any decision. However, more experienced cardiology practitioners rely on their skills to recognize the visual signal patterns of different cardiac abnormalities, providing an accurate ECG interpretation. CONCLUSIONS The results show that visual expertise for ECG interpretation is linked to the practitioner’s role within the health care system and the number of years of practical experience interpreting ECGs. Cardiology practitioners focus on different ECG leads and different waveform abnormalities according to their role in the health care sector and their expertise levels.
Over the past decade, the market size of connected wellness devices has grown in an exponential fashion. This trend is due to the advent and affordability of connected wearables and activity trackers. While users see in these health and wellness devices opportunities to nurture a culture of health and wellbeing, several hurdles still exist towards developing this culture. Wearable technologies companies have built closed-gated ecosystems around their products, preventing them to resonate with the clinical guidelines and best practices. Thus, users of these wearables find themselves torn apart between two drastically different environments; The medical healthcare environment and the wellness environment. In this paper, we explore the efforts already done to bridge that gap by analyzing the available health solutions. From this analysis, we draft guidelines for an integrated health and wellness ecosystem. These proposed guidelines focus on the Human-IoT Interaction aspect as an important cornerstone in the success and sustainability of the health and wellness program. These guidelines will also account for scalability and compatibility depending respectively on the advancement of medical practices and technologies available.
The global expenditure of healthcare systems on the management of chronic diseases is exhausting costs and efforts. This is due to the delicate nature of patients needing care constantly, as well as the chronic nature of the diseases. These two causes force patients to pay visits to their healthcare providers more frequently. Repetitive visits are countless, short in time, unmanageable and ineffective. The state of the art for managing chronic diseases focus on a shared effort between the patient and the physician in an ongoing process of collecting, monitoring, analyzing, and finally adapting the management program by the physician and the patient. Technology along with Artificial Intelligence (AI) made these processes easier. However, studies have rarely attempted to introduce models that integrated AI in health coaching plans for patients with chronic diseases. As health coaching is an integral part of some countries public health strategy. It is now used as a complementary medical intervention to shape healthy behavior change. In this paper we propose a model that puts health coaching along with AI in its core to 1. empower patients to effectively manage their chronic conditions 2. as well as to persuade them to adhere to their care management program for extended periods of time.
As the processing power and the science of robotics advances, robots are becoming more and more available to the masses and convenient to use. One instance of the applications of robots are the moving robots. These robots essentially move and explore the world they are located in, mainly: indoor robots. In our laboratory we are currently working on an indoor robot which serves the purpose of teleperesence. One of the challenges in building such robot is the navigation, mainly planning routes, and avoiding the obstacles that are in front of it. In order to navigate, the robot needs to have a map of the environment where it is located. Plus, it needs to locate itself in that map. This paper discusses an improved obstacle avoidance algorithm to our laboratory's experimental telepresence robot. The improved algorithm is based on Simultaneous Localization and Mapping (SLAM) This new obstacle avoidance algorithm uses a Kinect 2 to collect depth frames and RGB images then synthesize them into Point Cloud files (PCL). These Point clouds are processed and gathered into a 3D map and used as input to the Simultaneous Localization and Mapping (SLAM) algorithm to help the robot navigate as smooth as possible in its indoor environment. The robot navigates by creating a map of its surroundings and constantly localizing itself in that map.
This paper presents a systematic review of relevant primary studies on the use of augmented reality (AR) to improve various skills of children and adolescents diagnosed with autism spectrum disorder (ASD) from years 2005 to 2018 inclusive in eight bibliographic databases. This systematic review attempts to address eleven specific research questions related to the learing skills, participants, AR technology, research design, data collection methods, settings, evaluation parameters, intervention outcomes, generalization, and maintenance. The social communication skill was the highly targeted skill, and individuals with ASD were part of all the studies. Computer, smartphone, and smartglass are more frequently used technologies. The commonly used research design was pre-test and post-test. Almost all the studies used observation as a data collection method, and classroom environment or controlled research environment were used as a setting of evaluation. Most of the evaluation parameters were human-assisted. The results of the studies show that AR benefited children with ASD in learning skills. The generalization test was conducted in one study only, but the results were not reported. The results of maintenance tests conducted in five studies during a short-term period following the withdrawal of intervention were positive. Although the effect of using AR towards the learning of individuals was positive, given the wide variety of skills targeted in the studies, and the heterogeneity of the participants, a summative conclusion regarding the effectiveness of AR for teaching or learning of skills related to ASD based on the existing literature is not possible. The review also proposes the research taxonomy for ASD. Future research addressing the effectiveness of AR among more participants, different technologies supporting AR for the intervention, generalization, and maintenance of learning skills, and the evaluation in the inslusive classroom environment and other settings is warranted.
The electrocardiogram is ranked among the top used medical diagnostic tests worldwide. Although its widespread use in the healthcare sector, there is a restricted body of research that depicts how medical practitioners interpret it. This is primarily due to the limited quantitative research methodologies that were able to capture this subtle skill during the previous two decades. In addition, there is a restricted set of international guidelines that unify the electrocardiogram interpretation process across different health institutions. This set of factors contributes towards the insufficiency of training provided to future medical practitioners. This also leads to the unpreparedness by students who are about to embark into the medical journey. In this paper, we present the overview and the general results for an eye tracking based experimental study that assessed the expertise of medical practitioners with varying expertise levels, namely medical students, nurses, technicians, fellows, and finally cardiology consultants. Those were assessed across ten categories of heart abnormalities depicted through ten 12-lead electrocardiograms. The study is done with an inherent aim in mind, which is to find innovative training solutions for cardiology professionals for a better electrocardiogram interpretation.
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