Hasil untuk "Computer applications to medicine. Medical informatics"

Qiwei Li, Jie Zhou

BackgroundNews portrayals of physicians, especially in China, often depict them as vulnerable—overworked, with inadequate compensation, or as victims of violence. These portrayals may send mixed signals to the public, yet their impact remains underexplored. Understanding their impact is essential for informing media strategies and improving physician-patient relationships. ObjectiveThis study investigated how portrayals of physicians as vulnerable influence public evaluations of their competence, warmth, morality, and overall trust and considered the moderating effects of involvement (ie, hospital visit frequency). MethodsFour studies were conducted. Study 1 (N=492) examined the effects of daily exposure to vulnerable portrayals, and study 2 (N=710) experimentally exposed participants to vulnerable portrayals to directly investigate the causal relationship between exposure and evaluations with involvement as a hypothesized moderator. Study 3 (N=565) manipulated situational involvement using an imagination task, whereas study 4 (N=436) embedded involvement-enhancing content into news articles to improve ecological validity. ResultsStudy 1 revealed that among individuals with low or moderate involvement, greater exposure to vulnerable physician portrayals in everyday life predicted more favorable overall evaluations of physicians (low involvement: B=0.11 and P=.04; moderate involvement: B=0.20 and P<.001). No significant effect was found among high-involvement individuals (P>.68 in all cases), suggesting an inverted U-shaped moderating effect of involvement. Study 2 supported this pattern—vulnerable portrayals had no significant impact among individuals with low or high involvement (t702<0.49 in all cases; P>.15 in all cases) but had marginally positive effects on individuals with moderate involvement (t702=1.67; P=.10; d=0.26). Notably, individuals with superhigh involvement (ie, those in hospital settings) evaluated physicians more negatively following vulnerable portrayals (t702=2.49; P=.01; d=0.44). Given that nearly 80% of the general population reports low to moderate hospital visits, which is the positive moderating effect range for involvement, studies 3 and 4 targeted this group and tested whether manipulated situational involvement could enhance the effects of vulnerable portrayals. In studies 3a and 3b, participants in the high–situational involvement condition evaluated physicians more positively in the vulnerable portrayal group than in the control group (3a: t401=2.71, P=.007, d=0.37; 3b: t154=3.48, P<.001, d=0.93), with no effects under low-involvement conditions. Study 4 confirmed that involvement-enhancing vulnerable portrayals elicited more favorable evaluations compared to the control group (t433=3.14; P=.002; d=0.37). Across all 4 studies, overall evaluation significantly predicted trust in the medical profession (B≥0.38 in all cases; P<.001 in all cases), supporting the hypothesized mediation pathway. ConclusionsThe findings reveal a complex relationship between news portrayals of vulnerable physicians and public perceptions moderated by involvement. These results have practical implications for leveraging media to increase public trust and improve physician-patient relationships.

Zhigao Zeng, Jiale Xiao, Shengqiu Yi et al.

Medical image segmentation presents substantial challenges arising from the diverse scales and morphological complexities of target anatomical structures. Although existing Transformer-based models excel at capturing global dependencies, they encounter critical bottlenecks in multi-scale feature representation, spatial relationship modeling, and cross-layer feature fusion. To address these limitations, we propose the M³-TransUNet architecture, which incorporates three key innovations: (1) MSGA (Multi-Scale Gate Attention) and MSSA (Multi-Scale Selective Attention) modules to enhance multi-scale feature representation; (2) ME-MSA (Manhattan Enhanced Multi-Head Self-Attention) to integrate spatial priors into self-attention computations, thereby overcoming spatial modeling deficiencies; and (3) MKGAG (Multi-kernel Gated Attention Gate) to optimize skip connections by precisely filtering noise and preserving boundary details. Extensive experiments on public datasets—including Synapse, CVC-ClinicDB, and ISIC—demonstrate that M³-TransUNet achieves state-of-the-art performance. Specifically, on the Synapse dataset, our model outperforms recent TransUNet variants such as J-CAPA, improving the average DSC to 82.79% (compared to 82.29%) and significantly reducing the average HD95 from 19.74 mm to 10.21 mm.

Selene Tomassini, Damiano Duranti, Abdallah Zeggada et al.

The high volume of emergency room patients often necessitates head CT examinations to rule out ischemic, hemorrhagic, or other organic pathologies. A system that enhances the diagnostic efficacy of head CT imaging in emergency settings through structured reporting would significantly improve clinical decision making. Currently, no AI solutions address this need. Thus, our research aims to develop an automatic radiology reporting system by directly analyzing brain anomalies in head CT data. We propose a multi-branch CNN-LSTM fusion network-driven system for enhanced radiology reporting in emergency settings. We preprocessed head CT scans by resizing all slices, selecting those with significant variability, and applying PCA to retain 95% of the original data variance, ultimately saving the most representative five slices for each scan. We linked the reports to their respective slice IDs, divided them into individual captions, and preprocessed each. We performed an 80-20 split of the dataset for ten times, with 15% of the training set used for validation. Our model utilizes a pretrained VGG16, processing groups of five slices simultaneously, and features multiple end-to-end LSTM branches, each specialized in predicting one caption, subsequently combined to form the ordered reports after a BERT-based semantic evaluation. Our system demonstrates effectiveness and stability, with the postprocessing stage refining the syntax of the generated descriptions. However, there remains an opportunity to empower the evaluation framework to more accurately assess the clinical relevance of the automatically-written reports. Part of future work will include transitioning to 3D and developing an improved version based on vision-language models.

Michiko Moriyama, K A T M Ehsanul Huq, Lucy Mondol et al.

BackgroundThe global burden of chronic diseases is increasing and becoming a public health issue throughout the world. The use of telenursing is increasing significantly during and after the COVID-19 pandemic to treat and prevent chronic diseases. Telenursing is growing in many countries to reduce health care costs, increase the number of aging and chronically ill populations, and increase health care coverage to distant, rural, small, or sporadically populated regions. Among its many benefits, telenursing may help to solve increasing shortages of nurses, reduce distances, save travel time, and keep patients out of the hospital. ObjectiveThe objective of this study is to apply the self-management telenursing program and telenursing system developed by the researchers to Bangladesh and to evaluate its feasibility and efficacy (improved diabetes control in participants). MethodsThis is a pilot, quasi-experimental pre- and post-intervention study. Diabetes patients who will attend the Grameen Primary Health Centers (PHCs) in Bangladesh will be enrolled between September 2024 and August 2025. We include patients who have been diagnosed with type 2 diabetes, both sexes, ages 18-75 years old, all types of treatment, willing to participate and give us consent. We exclude patients who have been diagnosed with gestational diabetes, diabetes as a secondary cause, complication of chronic kidney disease (CKD) stage 5, Hemoglobin A1c (HbA1c) is less than 7% for the past 1 year with CKD stage 1 or 2, no complications or complications with good control, having enough knowledge (had education before) and implemented good practice regarding diabetes management assessed by the research nurses, and disabled persons who need other person’s support for daily living. The sample size was calculated and found 70. Written informed consent will be obtained from all the participants. The study protocol got approval from the National Research Ethics Committee of the Bangladesh Medical Research Council (BMRC/NREC/2022-2025/336) on September 08, 2024. The outcome of this study is to evaluate the effects of telenursing intervention by controlling HbA1c. ResultsThe project was funded in 2024. The enrollment of the participants started on October 26, 2024, and the required sample (n=70) enrollment was completed in February 2025. Data analysis will be started after completion of data collection and results will be expected to be submitted for publication in 2026. ConclusionsDiabetic patients will acquire disease-specific management skills. Setting and monitoring goals ensures the continuation of the desired behavior and gives the patients control over their lifestyle. After developing self-management skills, patients assess their lab data and lifestyles including diet, and understand their condition so that they can work with their physiological data by acquiring knowledge of both the disease and self-care. By making self-supported decisions, the patients will be able to manage their diet, exercise, and medication. Trial RegistrationClinicalTrials.gov NCT06632652; https://clinicaltrials.gov/study/NCT06632652 International Registered Report Identifier (IRRID)DERR1-10.2196/71849

Hassan S. Al Khatib, Subash Neupane, Sudip Mittal et al.

The healthcare industry is moving towards a patient-centric paradigm that requires advanced methods for managing and representing patient data. This paper presents a Patient Journey Ontology (PJO), a framework that aims to capture the entirety of a patient's healthcare encounters. Utilizing ontologies, the PJO integrates different patient data sources like medical histories, diagnoses, treatment pathways, and outcomes; it enables semantic interoperability and enhances clinical reasoning. By capturing temporal, sequential, and causal relationships between medical encounters, the PJO supports predictive analytics, enabling earlier interventions and optimized treatment plans. The ontology's structure, including its main classes, subclasses, properties, and relationships, as detailed in the paper, demonstrates its ability to provide a holistic view of patient care. Quantitative and qualitative evaluations by Subject Matter Experts (SMEs) demonstrate strong capabilities in patient history retrieval, symptom tracking, and provider interaction representation, while identifying opportunities for enhanced diagnosis-symptom linking. These evaluations reveal the PJO's reliability and practical applicability, demonstrating its potential to enhance patient outcomes and healthcare efficiency. This work contributes to the ongoing efforts of knowledge representation in healthcare, offering a reliable tool for personalized medicine, patient journey analysis and advancing the capabilities of Generative AI in healthcare applications.

Michel Génard, Françoise Lescourret

In the spring of 1987, point-count surveys of breeding birds (passerines and picidae) were conducted, resulting in a dataset of 197 counts. The purpose was to analyze the effects of forest fragmentation on bird community composition in a mountain pine forest located in the Néouvielle National Nature Reserve in the central French Pyrenees between 1800 and 2400 metres. The study aimed to differentiate between the impacts of landscape factors (patch area, isolation) and habitat characteristics (altitude, vegetation structure). Additional information was gathered regarding the presence of Common Crossbill (Loxia curvirostra), Great Spotted Woodpecker (Dendrocopos major), Red Squirrel (Sciurus vulgaris), and Capercaillie (Tetrao urogallus) in the forest. The sampling design ensured that the selected patches represented a wide range of sizes and distances to the nearest large pine patch or low-altitude forest stand. Bird sampling utilized the point-count technique [3], focusing on singing passerines and Picidae within a 50-metre radius. The altitude, the percentage of open areas, of stones, boulders and of herbaceous and ligneous plant cover at various heights, the canopy height and number of dead trees, along with landscape variables describing patch size and isolation from large pine stands or low-altitude forests, were assessed for each point count. This dataset offers insight into the breeding bird community and squirrel occurrence in a typical high-altitude mountain pine forest in the Pyrenees in 1987, serving as a baseline for future comparisons to study changes in bird and squirrel populations, the impact of climate change, habitat fragmentation, and conservation priorities. These data aim to inspire further research and enhance our understanding of bird and squirrel ecology in mountain regions.

Hanya Ahmed, Qianni Zhang, Robert Donnan et al.

Imaging from optical coherence tomography (OCT) is widely used for detecting retinal diseases, localization of intra-retinal boundaries, etc. It is, however, degraded by speckle noise. Deep learning models can aid with denoising, allowing clinicians to clearly diagnose retinal diseases. Deep learning models can be considered as an end-to-end framework. We selected denoising studies that used deep learning models with retinal OCT imagery. Each study was quality-assessed through image quality metrics (including the peak signal-to-noise ratio—<i>PSNR</i>, contrast-to-noise ratio—<i>CNR</i>, and structural similarity index metric—<i>SSIM</i>). Meta-analysis could not be performed due to heterogeneity in the methods of the studies and measurements of their performance. Multiple databases (including Medline via PubMed, Google Scholar, Scopus, Embase) and a repository (ArXiv) were screened for publications published after 2010, without any limitation on language. From the 95 potential studies identified, a total of 41 were evaluated thoroughly. Fifty-four of these studies were excluded after full text assessment depending on whether deep learning (DL) was utilized or the dataset and results were not effectively explained. Numerous types of OCT images are mentioned in this review consisting of public retinal image datasets utilized purposefully for denoising OCT images (<i>n</i> = 37) and the Optic Nerve Head (ONH) (<i>n</i> = 4). A wide range of image quality metrics was used; <i>PSNR</i> and <i>SNR</i> that ranged between 8 and 156 dB. The minority of studies (<i>n</i> = 8) showed a low risk of bias in all domains. Studies utilizing ONH images produced either a <i>PSNR</i> or <i>SNR</i> value varying from 8.1 to 25.7 dB, and that of public retinal datasets was 26.4 to 158.6 dB. Further analysis on denoising models was not possible due to discrepancies in reporting that did not allow useful pooling. An increasing number of studies have investigated denoising retinal OCT images using deep learning, with a range of architectures being implemented. The reported increase in image quality metrics seems promising, while study and reporting quality are currently low.

I. M. Chernenkiy, Y. A. Drach, S. R. Mustakimova et al.

Colorectal cancer is the third-most common cancer in the Western Hemisphere. The segmentation of colorectal and colorectal cancer by computed tomography is an urgent problem in medicine. Indeed, a system capable of solving this problem will enable the detection of colorectal cancer at early stages of the disease, facilitate the search for pathology by the radiologist, and significantly accelerate the process of diagnosing the disease. However, scientific publications on medical image processing mostly use closed, non-public data. This paper presents an extension of the Medical Decathlon dataset with colorectal markups in order to improve the quality of segmentation algorithms. An experienced radiologist validated the data, categorized it into subsets by quality, and published it in the public domain. Based on the obtained results, we trained neural network models of the UNet architecture with 5-part cross-validation and achieved a Dice metric quality of $0.6988 \pm 0.3$. The published markups will improve the quality of colorectal cancer detection and simplify the radiologist's job for study description.

Yushen Xu, Xiaosong Li, Yuchan Jie et al.

In clinical practice, tri-modal medical image fusion, compared to the existing dual-modal technique, can provide a more comprehensive view of the lesions, aiding physicians in evaluating the disease's shape, location, and biological activity. However, due to the limitations of imaging equipment and considerations for patient safety, the quality of medical images is usually limited, leading to sub-optimal fusion performance, and affecting the depth of image analysis by the physician. Thus, there is an urgent need for a technology that can both enhance image resolution and integrate multi-modal information. Although current image processing methods can effectively address image fusion and super-resolution individually, solving both problems synchronously remains extremely challenging. In this paper, we propose TFS-Diff, a simultaneously realize tri-modal medical image fusion and super-resolution model. Specially, TFS-Diff is based on the diffusion model generation of a random iterative denoising process. We also develop a simple objective function and the proposed fusion super-resolution loss, effectively evaluates the uncertainty in the fusion and ensures the stability of the optimization process. And the channel attention module is proposed to effectively integrate key information from different modalities for clinical diagnosis, avoiding information loss caused by multiple image processing. Extensive experiments on public Harvard datasets show that TFS-Diff significantly surpass the existing state-of-the-art methods in both quantitative and visual evaluations. Code is available at https://github.com/XylonXu01/TFS-Diff.

Vijay Kumar, Kolin Paul

Continuous advancements in medical technology have led to the creation of affordable mobile imaging devices suitable for telemedicine and remote monitoring. However, the rapid examination of large populations poses challenges, including the risk of fraudulent practices by healthcare professionals and social workers exchanging unverified images via mobile applications. To mitigate these risks, this study proposes using watermarking techniques to embed a device fingerprint (DFP) into captured images, ensuring data provenance. The DFP, representing the unique attributes of the capturing device and raw image, is embedded into raw images before storage, thus enabling verification of image authenticity and source. Moreover, a robust remote validation method is introduced to authenticate images, enhancing the integrity of medical image data in interconnected healthcare systems. Through a case study on mobile fundus imaging, the effectiveness of the proposed framework is evaluated in terms of computational efficiency, image quality, security, and trustworthiness. This approach is suitable for a range of applications, including telemedicine, the Internet of Medical Things (IoMT), eHealth, and Medical Cyber-Physical Systems (MCPS) applications, providing a reliable means to maintain data provenance in diagnostic settings utilizing medical images or videos.

Zehua Dong, Junxiao Wang, Yanxia Li et al.

Lotta Virtanen, Anu-Marja Kaihlanen, Petra Saukkonen et al.

Abstract Background Physicians’ work is often stressful. The digitalization of healthcare aims to streamline work, but not all physicians have experienced its realization. We examined associations of perceived changes in work due to digitalization and the amount of digital work with job strain among physicians. The moderating role of the length of work experience was investigated for these associations. Methods We used representative survey data on Finnish physicians’ (N = 4271) experiences of digitalization from 2021. The independent variables included perceptions on statements about work transformations aligned with digitalization goals, and the extent that information systems and teleconsultations were utilized. Stress related to information systems (SRIS), time pressure, and psychological stress were the dependent variables. We analyzed the associations using multivariable linear and logistic regressions. Results Respondents had a mean SRIS score of 3.5 and a mean time pressure score of 3.7 on a scale of 1–5. Psychological stress was experienced by 60%. Perceptions associated with higher SRIS comprised disagreements with statements asserting that digitalization accelerates clinical encounters (b = .23 [95% CI: .16–.30]), facilitates access to patient information (b = .15 [.07–.23]), and supports decision-making (b = .11 [.05–.18]). Disagreement with accelerated clinical encounters (b = .12 [.04–.20]), and agreements with patients’ more active role in care (b = .11 [.04–.19]) and interprofessional collaboration (b = .10 [.02–.18]) were opinions associated with greater time pressure. Disagreeing with supported decision-making (OR = 1.26 [1.06–1.48]) and agreeing with patients’ active role (OR = 1.19 [1.02–1.40]) were associated with greater psychological stress. However, perceiving improvements in the pace of clinical encounters and access to patient information appeared to alleviate job strain. Additionally, extensive digital work was consistently linked to higher strain. Those respondents who held teleconsultations frequently and had less than 6 years of work experience reported the greatest levels of time pressure. Conclusions Physicians seem to be strained by frequent teleconsultations and work that does not meet the goals of digitalization. Improving physicians’ satisfaction with digitalization through training specific to the stage of career and system development can be crucial for their well-being. Schedules for digital tasks should be planned and allocated to prevent strain related to achieving the digitalization goals.

Maxime Ryckewaert, Daphné Héran, Carole Feilhes et al.

In the dataset presented in this article, two hundred and seventy four trays containing one hundred berries were measured by a hyperspectral camera in the visible/near-infrared spectral domain. This dataset was formed to study the use of hyperspectral imaging for maturity monitoring of grape berries [2]. This dataset contains reflectance spectra from hyperspectral camera of grape berries of three different varieties and chemical composition (sugar content).

Jie Xu, Lu Lu, Sen Yang et al.

METHODS: First, a set of evaluation criteria is designed based on a comprehensive literature review. Second, existing candidate criteria are optimized for using a Delphi method by five experts in medicine and engineering. Third, three clinical experts design a set of medical datasets to interact with LLMs. Finally, benchmarking experiments are conducted on the datasets. The responses generated by chatbots based on LLMs are recorded for blind evaluations by five licensed medical experts. RESULTS: The obtained evaluation criteria cover medical professional capabilities, social comprehensive capabilities, contextual capabilities, and computational robustness, with sixteen detailed indicators. The medical datasets include twenty-seven medical dialogues and seven case reports in Chinese. Three chatbots are evaluated, ChatGPT by OpenAI, ERNIE Bot by Baidu Inc., and Doctor PuJiang (Dr. PJ) by Shanghai Artificial Intelligence Laboratory. Experimental results show that Dr. PJ outperforms ChatGPT and ERNIE Bot in both multiple-turn medical dialogue and case report scenarios.

Qidong Liu, Xian Wu, Xiangyu Zhao et al.

The recent surge in Large Language Models (LLMs) has garnered significant attention across numerous fields. Fine-tuning is often required to fit general LLMs for a specific domain, like the web-based healthcare system. However, two problems arise during fine-tuning LLMs for medical applications. One is the task variety problem, which involves distinct tasks in real-world medical scenarios. The variety often leads to sub-optimal fine-tuning for data imbalance and seesaw problems. Besides, the large amount of parameters in LLMs leads to huge time and computation consumption by fine-tuning. To address these two problems, we propose a novel parameter efficient fine-tuning framework for multi-task medical applications, dubbed as MOELoRA. The designed framework aims to absorb both the benefits of mixture-of-expert (MOE) for multi-task learning and low-rank adaptation (LoRA) for parameter efficient fine-tuning. For unifying MOE and LoRA, we devise multiple experts as the trainable parameters, where each expert consists of a pair of low-rank matrices to retain the small size of trainable parameters. Then, a task-motivated gate function for all MOELoRA layers is proposed, which can control the contributions of each expert and produce distinct parameters for various tasks. We conduct experiments on a multi-task medical dataset, indicating MOELoRA outperforms the existing parameter efficient fine-tuning methods. The code is available online.

Yizhe Zhang, Shuo Wang, Yejia Zhang et al.

Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e.g., 99.5\% of the time). CP provides comprehensive predictions on possible labels of a given test sample, and the size of the set indicates how certain the predictions are (e.g., a set larger than one is `uncertain'). Such distinct properties of CP enable effective collaborations between human experts and medical AI models, allowing efficient intervention and quality check in clinical decision-making. In this paper, we propose a new method called Reliable-Region-Based Conformal Prediction (RR-CP), which aims to impose a stronger statistical guarantee so that the user-specified error rate (e.g., 0.5\%) can be achieved in the test time, and under this constraint, the size of the prediction set is optimized (to be small). We consider a small prediction set size an important measure only when the user-specified error rate is achieved. Experiments on five public datasets show that our RR-CP performs well: with a reasonably small-sized prediction set, it achieves the user-specified error rate (e.g., 0.5\%) significantly more frequently than exiting CP methods.

Tomoyuki Yamakami

Quantum computing has been studied over the past four decades based on two computational models of quantum circuits and quantum Turing machines. To capture quantum polynomial-time computability, a new recursion-theoretic approach was taken lately by Yamakami [J. Symb. Logic 80, pp.~1546--1587, 2020] by way of recursion schematic definition, which constitutes six initial quantum functions and three construction schemes of composition, branching, and multi-qubit quantum recursion. By taking a similar approach, we look into quantum polylogarithmic-time computability and further explore the expressing power of elementary schemes designed for such quantum computation. In particular, we introduce an elementary form of the quantum recursion, called the fast quantum recursion, and formulate $EQS$ (elementary quantum schemes) of ``elementary'' quantum functions. This class $EQS$ captures exactly quantum polylogarithmic-time computability, which forms the complexity class BQPOLYLOGTIME. We also demonstrate the separation of BQPOLYLOGTIME from NLOGTIME and PPOLYLOGTIME. As a natural extension of $EQS$, we further consider an algorithmic procedural scheme that implements the well-known divide-and-conquer strategy. This divide-and-conquer scheme helps compute the parity function but the scheme cannot be realized within our system $EQS$.

Evan McKinney, Mingkang Xia, Chao Zhou et al.

Noisy, Intermediate Scale Quantum (NISQ) computers have reached the point where they can show the potential for quantum advantage over classical computing. Unfortunately, NISQ machines introduce sufficient noise that even for moderate size quantum circuits the results can be unreliable. We propose a co-designed superconducting quantum computer using a Superconducting Nonlinear Asymmetric Inductive eLement (SNAIL) modulator. The SNAIL modulator is designed by considering both the ideal fundamental qubit gate operation while maximizing the qubit coupling capabilities. First, the SNAIL natively implements $\sqrt[n]{\texttt{iSWAP}}$ gates realized through proportionally scaled pulse lengths. This naturally includes $\sqrt{\texttt{iSWAP}}$, which provides an advantage over $\texttt{CNOT}$ as a basis gate. Second, the SNAIL enables high-degree couplings that allow rich and highly parallel qubit connection topologies without suffering from frequency crowding. Building on our previously demonstrated SNAIL-based quantum state router we propose a quantum 4-ary tree and a hypercube inspired corral built from interconnected quantum modules. We compare their advantage in data movement based on necessary \texttt{SWAP} gates to the traditional lattice and heavy-hex lattice used in latest commercial quantum computers. We demonstrate the co-design advantage of our SNAIL-based machine with $\sqrt{\texttt{iSWAP}}$ basis gates and rich topologies against $\texttt{CNOT}$/heavy-hex and $\texttt{FSIM}$/lattice for 16-20 qubit and extrapolated designs circa 80 qubit architectures. We compare total circuit time and total gate count to understand fidelity for systems dominated by decoherence and control imperfections, respectively. Finally, we provide a gate duration sensitivity study on further decreasing the SNAIL pulse length to realize $\sqrt[n]{\texttt{iSWAP}}$ qubit systems to reduce decoherence times.

Ling Huang, Su Ruan, Thierry Denoeux

The investigation of uncertainty is of major importance in risk-critical applications, such as medical image segmentation. Belief function theory, a formal framework for uncertainty analysis and multiple evidence fusion, has made significant contributions to medical image segmentation, especially since the development of deep learning. In this paper, we provide an introduction to the topic of medical image segmentation methods using belief function theory. We classify the methods according to the fusion step and explain how information with uncertainty or imprecision is modeled and fused with belief function theory. In addition, we discuss the challenges and limitations of present belief function-based medical image segmentation and propose orientations for future research. Future research could investigate both belief function theory and deep learning to achieve more promising and reliable segmentation results.

Jonathan Álvarez Ariza, Heyson Baez

In the last decade, Single-Board Computers (SBCs) have been employed more frequently in engineering and computer science both to technical and educational levels. Several factors such as the versatility, the low-cost, and the possibility to enhance the learning process through technology have contributed to the educators and students usually employ these devices. However, the implications, possibilities, and constraints of these devices in engineering and Computer Science (CS) education have not been explored in detail. In this systematic literature review, we explore how the SBCs are employed in engineering and computer science and what educational results are derived from their usage in the period 2010-2020 at tertiary education. For that, 154 studies were selected out of n=605 collected from the academic databases Ei Compendex, ERIC, and Inspec. The analysis was carried-out in two phases, identifying, e.g., areas of application, learning outcomes, and students and researchers' perceptions. The results mainly indicate the following aspects: (1) The areas of laboratories and e-learning, computing education, robotics, Internet of Things (IoT), and persons with disabilities gather the studies in the review. (2) Researchers highlight the importance of the SBCs to transform the curricula in engineering and CS for the students to learn complex topics through experimentation in hands-on activities. (3) The typical cognitive learning outcomes reported by the authors are the improvement of the students' grades and the technical skills regarding the topics in the courses. Concerning the affective learning outcomes, the increase of interest, motivation, and engagement are commonly reported by the authors.