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Abstract:
Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.

Keywords:
Artificial Intelligence,Machine Learning,Metaverse,Virtual Reality,Extended Reality,Augmented Reality,Digital Twin,Health Digital Twin,personalized medicine,cardiology

Abstract:
This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practice in interventional cardiology (IC) with special recognition of its most recent advancements. Thus, recent years have been exceptionally abundant in advancements in computational tools, including the development of AI. The application of AI development is currently in its early stages, nevertheless new technologies have proven to be a promising concept, particularly considering IC showing great impact on patient safety, risk stratification and outcomes during the whole therapeutic process. The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients. In a simplified way, two main areas of AI utilization in IC may be distinguished, namely, virtual and physical. Consequently, numerous studies have provided data regarding AI utilization in terms of automated interpretation and analysis from various cardiac modalities, including electrocardiogram, echocardiography, angiography, cardiac magnetic resonance imaging, and computed tomography as well as data collected during robotic-assisted percutaneous coronary intervention procedures. Thus, this paper aims to thoroughly discuss the impact of AI on clinical practice in IC with special recognition of its most recent advancements.

Keywords:
artificial intelligence (AI), interventional cardiology (IC), cardiac modalities, augmented and/or virtual realities, automatic medical systems

Abstract:
The most commonly applied way of teaching students to convey the foundations of human anatomy and physiology involves textbooks and lectures. This way of transmitting knowledge causes difficulties for students, especially in the context of three-dimensional imaging of organ structures, and as a consequence translates into difficulties with imagining them. Even despite the rapid uptake of knowledge dissemination provided by online materials, including courses and webinars, there is a clear need for learning programs featuring first-hand immersive experiences tailored to suit individual study paces. In this paper, we present an approach to enhance a classical study program by combining multi-modality data and representing them in a Mixed Reality (MR)-based environment. The advantages of the proposed approach have been proven by the conducted investigation of the relationship between atrial anatomy, its electrophysiological characteristics, and resulting P wave morphology on the electrocardiogram (ECG). Another part of the paper focuses on the role of the sinoatrial node in ECG formation, while the MR-based visualization of combined micro-computed tomography (micro-CT) data with non-invasive CineECG imaging demonstrates the educational application of these advanced technologies for teaching cardiac anatomy and ECG correlations.

Keywords:
mixed reality, CineECG, micro-CT, P wave, ECG imaging

Abstract:

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Open AccessFeature PaperReview
The Influence of Emerging Technologies on Distance Education
by Magdalena Garlinska
1,†, Magdalena Osial
1 [ORCID] , Klaudia Proniewska
2,3 [ORCID] and Agnieszka Pregowska
1,*,† [ORCID]
1
Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, 02-106 Warsaw, Poland
2
Center for Digital Medicine and Robotics, Jagiellonian University Medical College, Kopernika 7E Str., 31-034 Krakow, Poland
3
Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Medyczna 7 Str., 30-688 Krakow, Poland
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Electronics 2023, 12(7), 1550; https://doi.org/10.3390/electronics12071550
Received: 17 February 2023 / Revised: 22 March 2023 / Accepted: 23 March 2023 / Published: 25 March 2023
(This article belongs to the Special Issue Feature Papers in Computer Science & Engineering)
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Abstract
Recently, during the COVID-19 pandemic, distance education became mainstream. Many students were not prepared for this situation—they lacked equipment or were not even connected to the Internet. Schools and government institutions had to react quickly to allow students to learn remotely. They had to provide students with equipment (e.g., computers, tablets, and goggles) but also provide them with access to the Internet and other necessary tools. On the other hand, teachers were trying to adopt new technologies in the teaching process to enable more interactivity, mitigate feelings of isolation and disconnection, and enhance student engagement. New technologies, including Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Extended Reality (XR, so-called Metaverse), Big Data, Blockchain, and Free Space Optics (FSO) changed learning, teaching, and assessing. Despite that, some tools were implemented fast, and the COVID-19 pandemic was the trigger for this process; most of these technologies will be used further, even in classroom teaching in both schools and universities. This paper presents a concise review of the emerging technologies applied in distance education. The main emphasis was placed on their influence on the efficiency of the learning process and their psychological impact on users. It turned out that both students and teachers were satisfied with remote learning, while in the case of undergraduate children and high-school students, parents very often expressed their dissatisfaction. The limitation of the availability of remote learning is related to access to stable Internet and computer equipment, which turned out to be a rarity. In the current social context, the obtained results provided valuable insights into factors affecting the acceptance and emerging technologies applied in distance education. Finally, this paper suggests a research direction for the development of effective remote learning techniques.

Keywords:
distance education, artificial intelligence (AI), virtual reality, augmented reality, mixed reality, free space optics (FSO), blockchain, big data

Abstract:
The priority of modern dentistry is to keep patients’ teeth for as long as possible. Tooth extraction is a procedure performed as a last resort when conservative methods and endodontic surgery procedures have not brought the expected results. As a consequence, the number of patients in dental offices, who require first and repeated endodontic treatment, is increasing. Thus, the development of new technologies in the medical industry, including microscopy, computer tomography (CT), as well as diode and neodymium-YAG-erbium lasers, enables dentists to increase the percentage of successful treatments. Moreover, mixed reality (MR) is a very new technology, in which the 3D view can help plan or simulate various types of tasks before they will be carried out in real life. In dentistry, 3D holography can be applied to display CT data to plan endodontic treatment. The most important element in effective root canal treatment is the precise imaging of the root canal. The CT scans allow dentists to view the anatomy of the patient’s tooth with much higher precision and understanding than using 2D radiography (RTG-radiographic photo) pictures. Recently, the development of new 3D technologies allows dentists to obtain even more data from existing CT scans. In this paper, the CT scan data were applied to generate patient teeth in 3D and simulate the view of the root canal’s anatomy in MR devices, i.e., Microsoft HoloLens 2. Using DICOM RAW data from the CT exam, we generated a 3D model of the jaw with a tooth. In the next step, the crown of the tooth was removed in a similar way to how a dentist would do this using a dental handpiece. Furthermore, all root canals were cleaned of everything inside. This way we achieved empty tunnels, namely root canals. Finally, we added appropriate lighting, similar to the type of lighting that dentists use. The proposed approach enables to display of the root canals in the same way as during the endodontic procedure using a microscope. It allows for the visualization of the root canal and changing its direction, in which dimensional accuracy is crucial. It turns out that mixed reality can be considered a complementary method to the traditional approach, which reduces the amount of time for the root canal treatment procedure by up to 72.25%, depending on the complexity of the case, and increases its effectiveness. Thus, the mixed reality-based system can be considered an effective tool for planning dental treatment.

Keywords:
3D holography,root canal treatment,tooth,augmented reality,mixed reality

Abstract:
The dynamic COVID-19 pandemic has destabilized education and forced academic centers to explore non-traditional teaching modalities. A key challenge this creates is in reconciling the fact that hands-on time in lab settings has been shown to increase student understanding and peak their interests. Traditional visualization methods are already limited and topics such as 3D molecular structures remain difficult to understand. This is where advances in Information and Communication Technologies (ICT), including remote meetings, Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Extended Reality (XR, so-called Metaverse) offer vast potential to revolutionize the education landscape. Specifically, how MR merges real and virtual life in a uniquely promising way and offers opportunities for entirely new educational applications. In this paper, we briefly overview and report our initial experience using MR to teach medical and pharmacy students. We also explore the future usefulness of MR in pharmacy education. MR mimics real-world experiences both in distance education and traditional laboratory classes. We also propose ICT-based systems designed to run on the Microsoft HoloLens2 MR goggles and can be successfully applied in medical and pharmacy coursework. The models were developed and implemented in Autodesk Maya and exported to Unity. Our findings demonstrate that MR-based solutions can be an excellent alternative to traditional classes, notably in medicine, anatomy, organic chemistry, and biochemistry (especially 3D molecular structures), in both remote and traditional in-person teaching modalities. MR therefore has the potential to become an integral part of medical education in both remote learning and in-person study

Keywords:
information and communication technologies, immersive technologies, information and communication technologies in education, immersive technologies in education, Mixed Reality, 3D human–computer interaction, advanced medical education, pharmacy, Metaverse

Abstract:
Background: The Augmented Reality (AR) blends digital information with the real world. Thanks to cameras, sensors, and displays it can supplement the physical world with holographic images. Nowadays, the applications of AR range from navigated surgery to vehicle navigation. Development: The purpose of this feasibility study was to develop an AR holographic system implementing Vertucci’s classification of dental root morphology to facilitate the study of tooth anatomy. It was tailored to run on the AR HoloLens 2 (Microsoft) glasses. The 3D tooth models were created in Autodesk Maya and exported to Unity software. The holograms of dental roots can be projected in a natural setting of the dental office. The application allowed to display 3D objects in such a way that they could be rotated, zoomed in/out, and penetrated. The advantage of the proposed approach was that students could learn a 3D internal anatomy of the teeth without environmental visual restrictions. Conclusions: It is feasible to visualize internal dental root anatomy with AR holographic system. AR holograms seem to be attractive adjunct for learning of root anatomy.

Keywords:
mixed reality, augmented reality, holography, tooth, dental root, root canal, visualization

Abstract:
Background: Immersive technologies like Mixed Reality (MR), Virtual Reality (VR) and Augmented Reality (AR) are becoming increasingly popular and gain user trust across various fields, particularly in medicine. In this paper we will use the general term Mixed Reality (MR) to refer to the various virtual reality methods, namely VR and AR. These new immersive technologies require varying degrees of instruction, both in their practice use, as well as in how to adjust to interacting with 3D virtual spaces. This study assesses the pedagogical value of these immersive technologies in medical education. Method: We surveyed a group of 211 students and 47 academic faculty at a medical college regarding potential applications of MR in the medical curriculum by using a questionnaire comprised of eight questions. Results were analyzed accounting for user age and professional position, i.e., student vs faculty. Results: 70% of students and 60% of the academic faculty think that MR-supplemented education is advantageous over a classical instruction. Most highly valued were the 3D visualization capabilities of MR, especially in anatomy classes. There was no significant statistical difference between students and faculty responders. Moreover, screensharing between faculty and students contributed to better, longer lasting absorption of knowledge. Surprisingly, the main issue was related to availability, i.e., only 5% of students had access to MR, while 17% of faculty use MR regularly, and 36% occasionally. Conclusions: MR technology can be a valuable resource that supports traditional medical education, especially via 3D anatomy classes, however MR availability needs to be increased. Moreover, MR expands the capabilities and effectiveness of remote learning, which was normalized during the COVID-19 pandemic, to ensure effective student and patient education. MR-based lessons, or even select modules, provide a unique opportunity to ex-change experiences inside and outside the medical community.

Keywords:
mixed reality, e-learning, remote learning, real-time rendering, 3D visualization, medical education

Abstract:
Background: General data protection regulation (GDPR) provides rules according to which data should be managed and processed in a secure and appropriate way for patient requirements and security. Currently, everyone in Europe is covered by GDPR. Thus, the medical practice also requires access to patient data in a safe and secure way. Methods: Holographic technology allows users to see everything visible ona computer screen in a new and less restricted way, i. e. without the limitations of traditional computers and screens. Results: In this study, a three-dimensional holographic doctors' assistant is designed and implemented in a way that meets the GDPR requirements. The HoloView application, which is tailored to run on Microsoft HoloLens, is proposed toallow display and access to personal data and so-called sensitive information of all individual patients without the risk that it will be presented to unauthorized persons. Conclusions: To enhance the user experience and remain consistent with GSPR, a holographic desk is proposed that allows displaying patient data and sensitive information only in front of the doctor's eyes using mixed reality glasses. Last but not least, it boasts of a reduction in infection risk for the staff during the COVID-19 pandemic, affording medical care to be carried out by as few doctors as possible.

Keywords:
augmented reality, mixed reality, pandemic

Abstract:
Regarding sleep research, polysomnography (PSG) also called a sleep study, is a gold standard. It incorporates brain waves, the oxygen level in the blood, heart rate and breathing, and leg movement recordings. PSG is a complicated and expensive laboratory-based procedure, usually done in hospitals or special sleep center. In this study, an alternative technique for Sleep-Related Breathing Disorders (SRBD) based on selected cardiac and acoustic parameters and the Random Forest (RF) has been studied. A system dedicated to the detection of simultaneously acquired ECG and acoustic signals, which are collected during sleep at home environment is proposed. Results obtained indicate that classification and regression tree models such as RF are appropriate for the evaluation of sleep disorders like SRBD. The best identification of sleep irregularities at level 89.00 percent for the raw database was obtained. Thus, statistical predictive models allow identification of breathing events with high levels of sensitivity and specificity, providing an inexpensive and accurate diagnosis.

Keywords:
patient monitoring, random forest, disorders, biomarkers

Abstract:
Immersive technologies, like Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) have undergone technical evolutions over the last few decades. Their rapid development and dynamic changes enable their effective applications in medicine, in fields like imaging, preprocedural planning, treatment, operations planning, medical students training, and active support during therapeutic and rehabilitation procedures. Within this paper, a comprehensive analysis of VR/AR/MR application in the medical industry and education is presented. We overview and discuss our previous experience with AR/MR and 3D visual environment and MR-based imaging systems in cardiology and interventional cardiology. Our research shows that using immersive technologies users can not only visualize the heart and its structure but also obtain quantitative feedback on their location. The MR-based imaging system proposed offers better visualization to interventionists and potentially helps users understand complex operational cases. The results obtained suggest that technology using VR/AR/MR can be successfully used in the teaching process of future doctors, both in aspects related to anatomy and clinical classes. Moreover, the system proposed provides a unique opportunity to break the boundaries, interact in the learning process, and exchange experiences inside the medical community.

Keywords:
augmented reality, improving the education process, interaction, intraprocedural visualization, mixed reality, preprocedural planning, teaching

Abstract:
Background and objective: People suffer from sleep disorders caused by work-related stress, irregular lifestyle or mental health problems. Therefore, development of effective tools to diagnose sleep disorders is important. Recently, to analyze biomedical signals Information Theory is exploited. We propose efficient classification method of sleep anomalies by applying entropy estimating algorithms to encoded ECGs signals coming from patients suffering from Sleep-Related Breathing Disorders (SRBD). Methods: First, ECGs were discretized using the encoding method which captures the biosignals variability. It takes into account oscillations of ECG measurements around signals averages. Next, to estimate entropy of encoded signals Lempel–Ziv complexity algorithm (LZ) which measures patterns generation rate was applied. Then, optimal encoding parameters, which allow distinguishing normal versus abnormal events during sleep with high sensitivity and specificity were determined numerically. Simultaneously, subjects' states were identified using acoustic signal of breathing recorded in the same period during sleep. Results: Random sequences show normalized LZ close to 1 while for more regular sequences it is closer to 0. Our calculations show that SRBDs have normalized LZ around 0.32 (on average), while control group has complexity around 0.85. The results obtained to public database are similar, i.e. LZ for SRBDs around 0.48 and for control group 0.7. These show that signals within the control group are more random whereas for the SRBD group ECGs are more deterministic. This finding remained valid for both signals acquired during the whole duration of experiment, and when shorter time intervals were considered. Proposed classifier provided sleep disorders diagnostics with a sensitivity of 93.75 and specificity of 73.00%. To validate our method we have considered also different variants as a training and as testing sets. In all cases, the optimal encoding parameter, sensitivity and specificity values were similar to our results above. Conclusions: Our pilot study suggests that LZ based algorithm could be used as a clinical tool to classify sleep disorders since the LZ complexities for SRBD positives versus healthy individuals show a significant difference. Moreover, normalized LZ complexity changes are related to the snoring level. This study also indicates that LZ technique is able to detect sleep abnormalities in early disorders stage.

Keywords:
information theory, Lempel-Ziv complexity, entropy, ECG, sleep-related breathing disorders, randomness

Abstract:
Because an average human spends one third of his life asleep, it is apparent that the quality of sleep has an important impact on the overall quality of life. To properly understand the influence of sleep, it is important to know how to detect its disorders such as snoring, wheezing, or sleep apnea. The aim of this study is to investigate the predictive capability of a dual-modality analysis scheme for methods of sleep-related breathing disorders (SRBDs) using biosignals captured during sleep. Two logistic regressions constructed using backward stepwise regression to minimize the Akaike information criterion were extensively considered. To evaluate classification correctness, receiver operating characteristic (ROC) curves were used. The proposed classification methodology was validated with constructed Random Forests methodology. Breathing sounds and electrocardiograms of 15 study subjects with different degrees of SRBD were captured and analyzed. Our results show that the proposed classification model based on selected parameters for both logistic regressions determine the different types of acoustic events during sleep. The ROC curve indicates that selected parameters can distinguish normal versus abnormal events during sleep with high sensitivity and specificity. The percentage of prediction for defined SRBDs is very high. The initial assumption was that the quality of result is growing with the number of parameters included in the model. The best recognition reached is more than 89% of good predictions. Thus, sleep monitoring of breath leads to the diagnosis of vital function disorders. The proposed methodology helps find a way of snoring rehabilitation, makes decisions concerning future treatment, and has an influence on the sleep quality.

Keywords:
patient monitoring, sleep-related breathing disorders, vital functions

Abstract:
Early pancreatic cancer diagnosis and therapy drastically increase the chances of survival. Tumor visualization using CT scan images is an important part of these processes. In this paper, we apply Mixed Reality (MR) and Artificial Intelligence, in particular, Machine Learning (ML) to prepare image-guided 3D models of pancreatic cancer in a population of oncology patients. Object detection was based on the convolution neural network, i.e. the You Only Look Once (YOLO) version 7 algorithm, while the semantic segmentation has been done with the 3D-UNET algorithm. Next, the 3D holographic visualization of this model as an interactive, MR object was performed using the Microsoft HoloLens2. The results indicated that the proposed MR and ML-based approach can precisely segment the pancreas along with suspected lesions, thus providing a reliable tool for diagnostics and surgical planning, especially when considering organ reconstruction during pancreaticoduodenectomy.

Keywords:
Extended Reality, Mixed Reality, Augmented Reality, Head-Mounted Displays, Artificial Intelligence, Image-guided surgery

Abstract:
Apart fromoperating tables andmodern surgical instruments, themodern operating rooms are equipped with displays and video surveillance systems. The three-dimensional operating room allows users to watch medics perform surgery from different, individually chosen, points of view. For the first time, it is possible to reproduce/repeat the course of the operations and change the perspective or position, fromwhich it is observed. Here, we proposed a solution based on Microsoft HoloLens and Azure Kinect DK devices as remote support to patient management. The operating room is transferred to the digital form in real-time using Augmented Reality based technologies. Users can move around the digital place like a ghost in real space. The approach proposed allows users to see observe surgery from any point of view they want without disturbing the surgeon's workflow. They can change their positions, angle, and place of observation. All environmental restrictions disappear. The presented solution gives trainees a convenient opportunity to learn. It may make a significant contribution to improving the surgeontraining, patients' outcomes, and may allow virtual medical consultations during the surgery between specialists without them leaving their workplace.

Keywords:
3D operating room, augmented reality, HoloLens

Keywords:
quantify breathing pattern, patient monitoring

Keywords:
adaptive design, biostatistic, information Theory