Incorporating Reflective Learning Practices In Medical Imaging Curriculum.

PURPOSE: To provide an overview of the reflective learning cycle, as well as common reflective learning models, as a means of informing future implementation of reflective learning assignments in medical imaging curriculum. METHODS: Journal articles were searched for in Google Scholar, ScienceDirect, and ResearchGate, as well as the university's library databases using the keywords reflective learning, Kolb's model of learning, reflective learning practices in health care, and reflective learning in radiography. Out of 19 articles found, 12 articles were selected based on inclusion and exclusion criteria. RESULTS: The literature search yielded results in health care education, nursing, medicine, medical imaging and radiography, pharmacy, physical therapy, and occupational therapy. DISCUSSION: Studies have shown that reflection is an integral aspect of learning and has substantial implications for learners' clinical practice. Reflection is a cognitive process that facilitates learning, assists in the understanding and application of knowledge to clinical situations, and develops new clinical knowledge in student radiographers. When reflective activities, such as journaling, portfolios, and problem-based learning, are scaffolded throughout the curriculum, students develop critical reflection skills that positively affect their clinical practice. CONCLUSION: Reflective learning practices can positively affect student learning, clinical decision-making skills, and patient outcomes. When reflective learning activities are incorporated throughout the curriculum, students are more effectively able to bridge the gap between theoretical knowledge and clinical practice. In addition, the reflective learning process allows learners to examine their clinical experiences while providing context for application and future clinical practice and continued learning.

A comparative study of an on premise AutoML solution for medical image classification.

Automated machine learning (AutoML) allows for the simplified application of machine learning to real-world problems, by the implicit handling of necessary steps such as data pre-processing, feature engineering, model selection and hyperparameter optimization. This has encouraged its use in medical applications such as imaging. However, the impact of common parameter choices such as the number of trials allowed, and the resolution of the input images, has not been comprehensively explored in existing literature. We therefore benchmark AutoKeras (AK), an open-source AutoML framework, against several bespoke deep learning architectures, on five public medical datasets representing a wide range of imaging modalities. It was found that AK could outperform the bespoke models in general, although at the cost of increased training time. Moreover, our experiments suggest that a large number of trials and higher resolutions may not be necessary for optimal performance to be achieved.

Consumer electronics based smart technologies for enhanced terahertz healthcare having an integration of split learning with medical imaging.

The proposed work contains three major contribution, such as smart data collection, optimized training algorithm and integrating Bayesian approach with split learning to make privacy of the patent data. By integrating consumer electronics device such as wearable devices, and the Internet of Things (IoT) taking THz image, perform EM algorithm as training, used newly proposed slit learning method the technology promises enhanced imaging depth and improved tissue contrast, thereby enabling early and accurate disease detection the breast cancer disease. In our hybrid algorithm, the breast cancer model achieves an accuracy of 97.5 percent over 100 epochs, surpassing the less accurate old models which required a higher number of epochs, such as 165.

Bacterial imaging in tumour diagnosis.

Some bacteria, such as Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium), have an inherent ability to locate solid tumours, making them a versatile platform that can be combined with other tools to improve the tumour diagnosis and treatment. In anti-cancer therapy, bacteria function by carrying drugs directly or expressing exogenous therapeutic genes. The application of bacterial imaging in tumour diagnosis, a novel and promising research area, can indeed provide dynamic and real-time monitoring in both pre-treatment assessment and post-treatment detection. Different imaging techniques, including optical technology, acoustic imaging, magnetic resonance imaging (MRI) and nuclear medicine imaging, allow us to observe and track tumour-associated bacteria. Optical imaging, including bioluminescence and fluorescence, provides high-sensitivity and high-resolution imaging. Acoustic imaging is a real-time and non-invasive imaging technique with good penetration depth and spatial resolution. MRI provides high spatial resolution and radiation-free imaging. Nuclear medicine imaging, including positron emission tomography (PET) and single photon emission computed tomography (SPECT) can provide information on the distribution and dynamics of bacterial population. Moreover, strategies of synthetic biology modification and nanomaterial engineering modification can improve the viability and localization ability of bacteria while maintaining their autonomy and vitality, thus aiding the visualization of gut bacteria. However, there are some challenges, such as the relatively low bacterial abundance and heterogeneously distribution within the tumour, the high dimensionality of spatial datasets and the limitations of imaging labeling tools. In summary, with the continuous development of imaging technology and nanotechnology, it is expected to further make in-depth study on tumour-associated bacteria and develop new bacterial imaging methods for tumour diagnosis.

[The use of medical imaging as a therapeutic patient education tool in radiotherapy : a feasibility study]. / L'utilisation de l'imagerie médicale en tant qu'outil d'éducation thérapeutique du patient en radiothérapie : étude de faisabilité.

Because of its prevalence and high mortality rate, cancer is a major public health challenge. Radiotherapy is an important treatment option, and makes extensive use of medical imaging. Until now, this type of tool has been reserved to professionals, but it is now opening up to wider use, including by patients themselves for educational purposes. However, this type of usage has been little explored so far. An experimental feasibility study was carried out in the radiotherapy department of the University Hospital of Liège on adult patients with cancer or pulmonary metastases, assigned to two randomized groups. In addition to the usual information given by the radiotherapist, the patients of the experimental group benefited from an intervention consisting in the 3D visualization of their own medical images via the free and open-source computer software «Stone of Orthanc¼. The study results show a low refuse rate (8.2 %) for the 15 patients recruited. Although non-significant, the experimental group showed a median gain in global perception of knowledge, a decrease in anxiety scores and emotional distress. A significant reduction (p = 0.043) was observed for the depression score. The positive results of the feasibility study encourage further work and reinforce the positioning of medical imaging as a tool for therapeutic patient education.

De par sa fréquence et son taux de mortalité élevé, le cancer représente un problème de santé publique majeur. Parmi les traitements possibles, la radiothérapie tient une place importante et fait appel massivement à l'imagerie médicale. Jusqu'ici réservé aux professionnels, ce type d'outil s'ouvre à un usage plus large, y compris par le patient lui-même dans une perspective éducative. Mais cette utilisation est restée peu explorée jusqu'à présent. Une étude expérimentale de faisabilité a ainsi été menée au sein du service de Radiothérapie du CHU de Liège sur des patients adultes avec cancer ou métastases pulmonaires, répartis en deux groupes randomisés. En plus des informations habituellement données par le radiothérapeute, le groupe expérimental a bénéficié d'une intervention consistant en la visualisation en 3D de ses propres images médicales via le logiciel libre et open-source «Stone of Orthanc¼. Les résultats de l'étude indiquent un taux de refus faible (8,2 %) pour les 15 patients recrutés. Bien que non significatif, le groupe expérimental a montré, par rapport au groupe contrôle, un gain médian dans la perception globale de connaissances ainsi qu'une diminution des scores liés à l'anxiété et à la détresse émotionnelle. Une réduction significative (p = 0,043) est observée pour le score de dépression. Les résultats positifs de l'étude de faisabilité encouragent la poursuite des travaux et renforcent le positionnement de l'usage de l'imagerie médicale en tant qu'outil d'éducation thérapeutique du patient.

[Contribution of free software to medical imaging]. / Apport du logiciel libre en imagerie médicale.

Medical research uses increasingly massive, complex and interdependent data, the analysis of which requires the use of specialized algorithms. In order to independently reproduce and validate the results of a scientific study, it is no longer sufficient to share the text of the article as an open-access document, together with the raw research data according to the open-data approach. It is now also needed to share the algorithms used to analyze the data with other research teams. Free and open-source software precisely responds to this need to disseminate technical knowledge at a large scale. In this paper, we present several examples of free software used in medicine, with a particular focus on medical imaging.

La recherche médicale recourt à des données de plus en plus massives, complexes et interdépendantes dont l'analyse nécessite l'usage d'algorithmes spécialisés. Afin de reproduire et valider les résultats d'une étude scientifique de manière indépendante, il n'est, dès lors, plus suffisant de partager le texte de l'article en «open-access¼ complété avec les données brutes en «open-data¼. Il convient désormais d'également partager les algorithmes qui ont servi à l'analyse des données avec d'autres équipes de chercheurs. Le logiciel libre et «open-source¼ répond précisément à ce besoin de diffuser les connaissances techniques à grande échelle. Dans cet article, nous présentons plusieurs exemples de logiciels libres utilisés en médecine, avec une attention particulière portée à l'imagerie médicale.

Research on Medical Image Segmentation Method Based on Improved U-Net3.

Computer assisted diagnostic technology has been widely used in clinical practice, specifically focusing on medical image segmentation. Its purpose is to segment targets with certain special meanings in medical images and extract relevant features, providing reliable basis for subsequent clinical diagnosis and research. However, because of different shapes and complex structures of segmentation targets in different medical images, some imaging techniques have similar characteristics, such as intensity, color, or texture, for imaging different organs and tissues. The localization and segmentation of targets in medical images remains an urgent technical challenge to be solved. As such, an improved full scale skip connection network structure for the CT liver image segmentation task is proposed. This structure includes a biomimetic attention module between the shallow encoder and the deep decoder, and the feature fusion proportion coefficient between the two is learned to enhance the attention of the overall network to the segmented target area. In addition, based on the traditional point sampling mechanism, an improved point sampling strategy is proposed for characterizing medical images to further enhance the edge segmentation effect of CT liver targets. The experimental results on the commonly used combined (CT-MR) health absolute organ segmentation (CHAOS) dataset show that the average dice similarity coefficient (DSC) can reach 0.9467, the average intersection over union (IOU) can reach 0.9623, and the average F1 score can reach 0.9351. This indicates that the model can effectively learn image detail features and global structural features, leading to improved segmentation of liver images.

Reliability of a Musculoskeletal Imaging Competency Examination for Physical Therapists.

INTRODUCTION: The Burley Readiness Examination (BRE) for Musculoskeletal (MSK) Imaging Competency assesses physical therapists' baseline MSK imaging competency. Establishing its reliability is essential to its value in determining MSK imaging competency. The purpose of this study was to test the reliability of the BRE for MSK Imaging Competency among physical therapists (PTs) with varying levels of training and education. REVIEW OF LITERATURE: Previous literature supports PTs' utility concerning diagnostic imaging; however, no studies directly measure their competency. With PTs expanding their practice scope and professional PT education programs, increasing their MSK imaging instruction, assessing competency becomes strategic in determining the future of MSK education and training. SUBJECTS: One hundred twenty-three United States licensed PTs completed the BRE. METHODS: Physical therapists completed the BRE through an online survey platform. Point biserial correlation (rpb) was calculated for each examination question. Final analyses were based on 140 examination questions. Examination scores were compared using independent sample t-test and one-way analysis of variance. Chi-square tests and odds ratios (ORs) assessed the relationship of a passing examination score (≥75%) and the type of training. Reliability of the BRE was assessed using Cronbach's alpha (α). RESULTS: Mean overall examination score was 75.89 ± 8.56%. Seventy PTs (56.9%) obtained a passing score. Physical therapists with additional MSK imaging training, board certification, and residency or fellowship training scored significantly higher (P < .001) compared with those with only entry-level PT program education. Physical therapists with additional MSK imaging training scored significantly higher (x̄ = 81.07% ± 8.93%) and were almost 5 times (OR = 4.74, 95% CI [1.95-11.50]) as likely to achieve a passing score than those without. The BRE demonstrated strong internal consistency (Cronbach's α = 0.874). DISCUSSION AND CONCLUSIONS: The BRE was reliable, consistently identifying higher examination scores among those with increased MSK imaging training. Training in MSK imaging influenced competency more than other factors. The BRE may be of analytical value to PT professional and postprofessional programs.

Imaging Assessment of Radiation Therapy-Related Normal Tissue Injury in Children: A PENTEC Visionary Statement.

The Pediatric Normal Tissue Effects in the Clinic (PENTEC) consortium has made significant contributions to understanding and mitigating the adverse effects of childhood cancer therapy. This review addresses the role of diagnostic imaging in detecting, screening, and comprehending radiation therapy-related late effects in children, drawing insights from individual organ-specific PENTEC reports. We further explore how the development of imaging biomarkers for key organ systems, alongside technical advancements and translational imaging approaches, may enhance the systematic application of imaging evaluations in childhood cancer survivors. Moreover, the review critically examines knowledge gaps and identifies technical and practical limitations of existing imaging modalities in the pediatric population. Addressing these challenges may expand access to, minimize the risk of, and optimize the real-world application of, new imaging techniques. The PENTEC team envisions this document as a roadmap for the future development of imaging strategies in childhood cancer survivors, with the overarching goal of improving long-term health outcomes and quality of life for this vulnerable population.

Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imaging.

In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification.

Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is not only costly but also demands substantial time from clinical specialists. Addressing this issue, we introduce the S4MI (Self-Supervision and Semi-Supervision for Medical Imaging) pipeline, a novel approach that leverages advancements in self-supervised and semi-supervised learning. These techniques engage in auxiliary tasks that do not require labeling, thus simplifying the scaling of machine supervision compared to fully-supervised methods. Our study benchmarks these techniques on three distinct medical imaging datasets to evaluate their effectiveness in classification and segmentation tasks. Notably, we observed that self-supervised learning significantly surpassed the performance of supervised methods in the classification of all evaluated datasets. Remarkably, the semi-supervised approach demonstrated superior outcomes in segmentation, outperforming fully-supervised methods while using 50% fewer labels across all datasets. In line with our commitment to contributing to the scientific community, we have made the S4MI code openly accessible, allowing for broader application and further development of these methods. The code can be accessed at https://github.com/pranavsinghps1/S4MI .

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review.

Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.

Non-gastrointestinal stromal tumor, mesenchymal neoplasms of the gastrointestinal tract: a review of tumor genetics, pathology, and cross-sectional imaging findings.

There is a diverse group of non-gastrointestinal stromal tumor (GIST), mesenchymal neoplasms of the gastrointestinal (GI) tract that demonstrate characteristic pathology and histogenesis as well as variable imaging findings and biological behavior. Recent advancements in tumor genetics have unveiled specific abnormalities associated with certain tumors, influencing their molecular pathogenesis, biology, response to treatment, and prognosis. Notably, giant fibrovascular polyps of the esophagus, identified through MDM2 gene amplifications, are now classified as liposarcomas. Some tumors exhibit distinctive patterns of disease distribution. Glomus tumors and plexiform fibromyxomas exhibit a pronounced affinity for the gastric antrum. In contrast, smooth muscle tumors within the GI tract are predominantly found in the esophagus and colorectum, surpassing the incidence of GISTs in these locations. Surgical resection suffices for symptomatic benign tumors; multimodality treatment may be necessary for frank sarcomas. This article aims to elucidate the cross-sectional imaging findings associated with a wide spectrum of these tumors, providing insights that align with their histopathological features.

Neurogenic thoracic outlet syndrome and controversies in diagnosis and management.

Compression of the neurovascular structures at the level of the scalene triangle and pectoralis minor space is rare, but increasing awareness and understanding is allowing for the treatment of more individuals than in the past. We outlined the recognition, preoperative evaluation, and treatment of patients with neurogenic thoracic outlet syndrome. Recent work has illustrated the role of imaging and centrality of the physical examination on the diagnosis. However, a fuller understanding of the spatial biomechanics of the shoulder, scalene triangle, and pectoralis minor musculotendinous complex has shown that, although physical therapy is a mainstay of treatment, a poor response to physical therapy with a sound diagnosis should not preclude decompression. Modes of failure of surgical decompression stress the importance of full resection of the anterior scalene muscle and all posterior rib impinging elements to minimize the risk of recurrence of symptoms. Neurogenic thoracic outlet syndrome is a rare but critical cause of disability of the upper extremity. Modern understanding of the pathophysiology and evaluation have led to a sounder diagnosis. Although physical therapy is a mainstay, surgical decompression remains the gold standard to preserve and recover function of the upper extremity. Understanding these principles will be central to further developments in the treatment of this patient population.

Current concepts in clinical features and diagnosis of thoracic outlet syndrome.

The diagnosis and clinical features of thoracic outlet syndrome have long confounded clinicians, owing to heterogeneity in symptom presentation and many overlapping competing diagnoses that are "more common." Despite the advent and prevalence of high-resolution imaging, along with the increasing awareness of the syndrome itself, misdiagnoses and untimely diagnoses can result in significant patient morbidity. The authors aimed to summarize the current concepts in the clinical features and diagnosis of thoracic outlet syndrome.

A novel deep learning technique for medical image analysis using improved optimizer.

Application of Convolutional neural network in spectrum of Medical image analysis are providing benchmark outputs which converges the interest of many researchers to explore it in depth. Latest preprocessing technique Real ESRGAN (Enhanced super resolution generative adversarial network) and GFPGAN (Generative facial prior GAN) are proving their efficacy in providing high resolution dataset. Objective: Optimizer plays a vital role in upgrading the functioning of CNN model. Different optimizers like Gradient descent, Stochastic Gradient descent, Adagrad, Adadelta and Adam etc. are used for classification and segmentation of Medical image but they suffer from slow processing due to their large memory requirement. Stochastic Gradient descent suffers from high variance and is computationally expensive. Dead neuron problem also proves to detrimental to the performance of most of the optimizers. A new optimization technique Gradient Centralization is providing the unparalleled result in terms of generalization and execution time. Method: Our paper explores the next factor which is the employment of new optimization technique, Gradient centralization (GC) to our integrated framework (Model with advanced preprocessing technique). Result and conclusion: Integrated Framework of Real ESRGAN and GFPGAN with Gradient centralization provides an optimal solution for deep learning models in terms of Execution time and Loss factor improvement.

Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative.

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at  https://radiomic.github.io/CLEAR-E3/ .

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.