Evaluation of Medical Prescribers’ Theoretical Knowledge on Medical Imaging in the Northern Region of Burkina Faso

Introduction: Medical imaging is a medical specialty that involves producing images of the human body and interpreting them for diagnostic, therapeutic purposes, and for monitoring the progress of pathologies. We aimed to assess the theoretical knowledge of doctors and interns in medical imaging in the northern region of Burkina Faso. Methodology: This was a descriptive cross-sectional survey based on a self-administered questionnaire. Prescribers knowledge was estimated based on scores derived from questionnaire responses. Results: We collected 106 questionnaires out of 163, i.e. a participation rate of 65.03%. The average knowledge score was 81.71% for the contribution of medical imaging to patient management. It was 60.02% for the indications/counter-indications of radiological examinations and 72.56% for the risks associated with exposure to radiation during these examinations. The score was 59.83% for the methods used to select the appropriate radiological examination. As regards the completeness of the clinical and biological information on the forms requesting imaging examinations, the score was 96.65%. Specialist doctors had the highest overall level of knowledge (74.68%). Conclusion: Improved technical facilities, good initial and in-service training, and interdisciplinary collaboration will help to ensure that imaging tests are properly prescribed, leading to better patient care.

Deep Learning for Image Segmentation: A Focus on Medical Imaging

Image segmentation is crucial for various research areas. Manycomputer vision applications depend on segmenting images to understandthe scene, such as autonomous driving, surveillance systems, robotics, andmedical imaging. With the recent advances in deep learning (DL) and itsconfounding results in image segmentation, more attention has been drawnto its use in medical image segmentation. This article introduces a surveyof the state-of-the-art deep convolution neural network (CNN) models andmechanisms utilized in image segmentation. First, segmentation models arecategorized based on their model architecture and primary working principle.Then, CNN categories are described, and various models are discussed withineach category. Compared with other existing surveys, several applicationswith multiple architectural adaptations are discussed within each category.A comparative summary is included to give the reader insights into utilizedarchitectures in different applications and datasets. This study focuses onmedical image segmentation applications, where the most widely used architecturesare illustrated, and other promising models are suggested that haveproven their success in different domains. Finally, the present work discussescurrent limitations and solutions along with future trends in the field.

Exploration and Practice of Postgraduate Training Mode Based on Medical Imaging Information Security

In the process of continuous maturity and development of medical imaging diagnosis,it is common to transmit images through public networks.How to ensure the security of transmission,cultivate talents who combine medical imaging and information security,and explore and cultivate new discipline growth points are difficult problems and challenges for schools and educators.In order to cope with industrial changes,a new round of scientific and technological revolution,and the challenges of the further development of artificial intelligence in medicine,this article will analyze the existing problems in the training of postgraduates in medical imaging information security by combining the actual conditions and characteristics of universities,and put forward countermeasures and suggestions to promote the progress of technology in universities.

Application Research of Medical Imaging Practical Teaching Based on Virtual Simulation Teaching Platform

Objective:To explore the application effect of virtual simulation teaching platform in the practical teaching of medical imaging.Methods:A total of 97 students majoring in medical imaging technology of class 2022 were selected and divided into two groups according to the random number method:control group(n=48)and observation group(n=49).The observation group was under the practical teaching mode based on the virtual simulation teaching platform,while the control group was under the traditional multimedia teaching mode.Questionnaire survey and teaching assessment were carried out after the teaching period,and the application effects of the two teaching modes were compared.Results:The reading and theoretical scores of the students in the observation group were significantly higher than those of the students in the control group(P<0.01);there were statistically significant differences in the results of the questionnaire survey(improved learning interest,improved language expression,improved ability to comprehensively analyze problems,and improved teamwork awareness)between the two groups of students(P<0.05);the students in the observation group were markedly more satisfied with the teaching content,teaching methods,and teaching quality than the students in the control group(P<0.05).Conclusion:The medical imaging practical teaching mode based on virtual simulation platform not only helps improve students’theoretical understanding and practical ability in medical imaging technology,but also improves students’learning interest,language expression ability,ability to comprehensively analyze problems,communication skills,teamwork awareness,and satisfaction with the teaching content,teaching methods,and teaching quality.Therefore,it has wide application value in medical specialty education.

Experiment Combined with Picture Archiving and Communication System in the Practical Teaching of Medical Imaging

Objective:To explore the application effect of virtual simulation experiment combined with picture archiving and communication system(PACS)in medical imaging practical teaching.Methods:97 students from the medical imaging class of 2022 were divided into two groups;the control group(n=48)was taught by the traditional teaching method,whereas the research group was taught by virtual simulation experiment combined with PACS(n=49).The teaching achievements and teaching effects of the two groups were compared to define the advantages of the two teaching modes.Results:Initially,there were no significant differences in the basic theory,image analysis,report writing,and differential diagnosis scores between the two groups of students(P>0.05);however,after 16 weeks of teaching,the scores of the research group were better than those of the control group(P<0.05);the pass rate of students in the study group(93.88%)was higher than that in the control group(81.25%);the scores of students in the research group in terms of clinical inquiry skills,X-ray/computed tomography/magnetic resonance imaging(X-ray/CT/MRI)operation skills,and doctor-patient communication skills were significantly higher than those in the control group(P<0.05).Conclusion:In medical imaging practical teaching,the application of virtual simulation experiment combined with PACS can effectively address several problems in the traditional teaching mode,including the single teaching method,the single teaching content,and the lack of innovation,and,at the same time,improve students’basic theoretical knowledge,X-ray/CT/MRI operation skills,consultation skills,and doctor-patient communication skills,thereby effectively improving the teaching quality and learning effect.

Importance of Features Selection,Attributes Selection,Challenges and Future Directions for Medical Imaging Data:A Review

In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of data.These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality.On the other hand,this growth also made it difficult to comprehend and utilize data for various purposes.The results of imaging data can become biased because of extraneous features present in larger datasets.Feature selection gives a chance to decrease the number of components in such large datasets.Through selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization precision.The correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast control.This paper presents a review of feature selection techniques and attributes selection measures for medical imaging.This review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining algorithms.The review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced data.Moreover,this review provides the importance of feature selection for correct classification of medical infections.In the end,critical analysis and future directions are provided.

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers

Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply artificial intelligence(AI)techniques to multiple clinical scenarios of ovarian cancer,especially in the field of medical imaging.AI-assisted imaging studies have involved computer tomography(CT),ultrasonography(US),and magnetic resonance imaging(MRI).In this review,we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer,and bring up the advances in terms of four clinical aspects,including medical diagnosis,pathological classification,targeted biopsy guidance,and prognosis prediction.Meanwhile,current status and existing issues of the researches on AI application in ovarian cancer are discussed.

Challenges and optimization strategies in medical imaging service delivery during COVID-19

In coronavirus disease 2019(COVID-19),medical imaging plays an essential role in the diagnosis,management and disease progression surveillance.Chest radiography and computed tomography are commonly used imaging techniques globally during this pandemic.As the pandemic continues to unfold,many healthcare systems worldwide struggle to balance the heavy strain due to overwhelming demand for healthcare resources.Changes are required across the entire healthcare system and medical imaging departments are no exception.The COVID-19 pandemic had a devastating impact on medical imaging practices.It is now time to pay further attention to the profound challenges of COVID-19 on medical imaging services and develop effective strategies to get ahead of the crisis.Additionally,preparation for operations and survival in the post-pandemic future are necessary considerations.This review aims to comprehensively examine the challenges and optimization of delivering medical imaging services in relation to the current COVID-19 global pandemic,including the role of medical imaging during these challenging times and potential future directions post-COVID-19.

Deep Learning in Medical Imaging and Drug Design

Over the last decade,deep learning(DL)methods have been extremely successful and widely used in almost every domain.Researchers are now focusing on the convergence of medical imaging and drug design using deep learning to revolutionize medical diagnostic and improvement in the monitoring from response to therapy.DL a new machine learning paradigm that focuses on learning with deep hierarchical models of data.Medical imaging has transformed healthcare science,it was thought of as a diagnostic tool for disease,but now it is also used in drug design.Advances in medical imaging technology have enabled scientists to detect events at the cellular level.The role of medical imaging in drug design includes identification of likely responders,detection,diagnosis,evaluation,therapy monitoring,and follow-up.A qualitative medical image is transformed into a quantitative biomarker or surrogate endpoint useful in drug design decision-making.For this,a parameter needs to be identified that characterizes the disease baseline and its subsequent response to treatment.The result is a quantifiable improvement in healthcare quality in most therapeutic areas,resulting in improvements in quality and life duration.This paper provides an overview of recent studies on applying the deep learning method in medical imaging and drug design.We briefly discuss the fields related to the history of deep learning,medical imaging,and drug design.

Standard diagnostic report of medical imaging

1 BackgroundIt is well known that the radiology diagnostic report as the essential component of the patient′s permanent health record,which radiography is an indispensable diagnostic tool.Our duties are observe the imaging carefully and write a

Contribution of Medical Imaging in the Diagnosis of Urinary Disorders in Children at the Charles de Gaulle University Hospital in Ouagadougou

Purpose: The purpose of our study, which focused on the contribution of medical imaging in the diagnosis of urinary tract diseases in children at the Charles de Gaulle University Hospital of Ouagadougou, was to study the role of medical imaging in the diagnosis of urinary tract diseases in patients aged 15 years or less at the CHUP-CDG. Patients and Methods: This was a descriptive cross-sectional study with the retrospective collection covering the period from January 1, 2009 to December 31, 2018, i.e., 10 years. We collected a total of 833 medical imaging examinations, performed in 735 patients. The mean age of the patients was 40 months, infants accounted for 37.69% of the cases. Male patients were more numerous with a sex ratio of 1.53. Results: Ultrasonography was performed in 652 patients or 78.27%, ASP RX in 128 patients or 10.88%. URC and UIV were used in 6.53% and 0.68% of patients, respectively. CT and MRI were not performed in our study. The most frequent clinical urinary signs were dysuria (58.13%) and hematuria (43.94%). Ultrasonography was the most requested examination (78.27%), followed by conventional radiography (15.37%). Urinary lithiasis was by far the most common urinary condition (46.86%), followed by urinary tract infections (32.19%) and malformative uropathies (14.93%), of which the posterior urethral valve was the most frequent. Imaging was also used to find other conditions associated with urinary tract diseases. Conclusion: Medical imaging plays a major role in the diagnosis and management of urinary tract diseases in children. It has limitations, that is why a formal meeting between clinicians and radiologists is necessary for a better choice of imaging techniques and efficient management of these conditions.

Application of AI Technology in Tumor medical Imaging

Artificial intelligence AI has many algorithms, , there are many applicationsin central nervous system tumors, lung cancer, breast cancer,prostate cancer, orthopaedic tumors, etc., with the norms and support ofnational policies,AI technology in tumor medical imaging will be ushedbroadly.

Reliability of Medical Imaging in the Pre-Therapeutic Assessment of Acute Intestinal Obstruction at the Centre Hospitalier Universitaire Du Point “G”.

Introduction: Acute intestinal obstruction is a serious pathology, a surgical emergency for which medical imaging plays an important role in the management. We initiated this work in order to study the contribution of imaging in the diagnosis of acute intestinal obstruction at the Point-G University Hospital. Patients and Methods: This was a prospective, descriptive and analytical study of 96 patients collected at the radiology and medical imaging department of CHU Point-G from January 2018 to January 2019. Results: The age of our patients varied from 11 to 86 years, with an average of 36 years old. There was a male predominance of 64.6% against 35.4% for women, i.e., a sex ratio of 1.82. Previous surgery was found in 61.5% of our patients. The pain was present in all patients. An unprepared abdominal X-ray was performed in 89.6% of patients. Hydroaerobic levels were found in 96.5% of patients. Abdominopelvic CT scans were performed on 12 patients, all of whom were diagnosed with occlusion. These positive diagnostic findings were consistent with intraoperative findings in 92% of cases. The causes were dominated by bridges in 46 patients and tumors in 9 patients. Signs of severity on CT were dominated by signs of distress of the upstream bile ducts in 8.3%. Exactly 8% of our patients spontaneously resumed transit, 91% received surgical treatment and 1% died before surgery. The outcome was favorable in 80 patients (83.3%) and poor with death in 16 patients (16.7%). Conclusion: Acute intestinal obstruction remains a serious pathology for which the X-ray of the PSA is often the only radiological examination performed in an emergency. However, abdominopelvic CT seems to us to be widely indicated thanks to its contribution both to the positive diagnosis and to the diagnosis of severity and etiology. However, this imaging technique is widely underused in our practice because of its high cost and lack of availability.

New Year's greeting and overview of Artificial Intelligence in Medical Imaging in 2021

As editors of Artificial Intelligence in Medical Imaging(AIMI),it is our great pleasure to take this opportunity to wish all of our authors,subscribers,readers,Editorial Board members,independent expert referees,and staff of the Editorial Office a Very Happy New Year.On behalf of the Editorial Team,we would like to express our gratitude to all of the authors who have contributed their valuable manuscripts,our independent referees,and our subscribers and readers for their continuous support,dedication,and encouragement.Together with an excellent of team effort by our Editorial Board members and staff of the Editorial Office,AIMI advanced in 2020 and we look forward to greater achievements in 2021.

Ideological and Political Exploration of Academic Continuing Education Professional Diploma in Medical Imaging

In alignment with the 20th National Congress of the Communist Party of China’s commitment to establish a healthier nation,the focus on healthcare strategically prioritizes human well-being.This necessitates redefining medical service delivery.Consequently,the training of medical students and continuing education is Challenged.In recent years,the notion of incorporating“ideological and political education within the curriculum”is becoming more explicit,especially within university talent training.This paper,exploring the unique attributes of professional medical imaging student training and continuing education,proposes the innovative model of“3+4+2”ideological and political education at the First Affiliated Hospital of Dalian Medical University.It introduces a new system of“school-family-society”tripartite co-education,a new mechanism of“party committee-functional departments-teaching and research office-teachers”four-level linkage,and a novel approach of“party branch-teaching and research office”dual-wheel drive,integrating value shaping,knowledge imparting,and ability cultivation.The goal is to nurture Party talents and educate patriots,thereby enhancing the effectiveness of“comprehensive education”and bolstering the construction of new medical sciences.

Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects adults.Treatment depends on the type of leukemia and the extent to which cancer has established throughout the body.Identifying leukemia in the initial stage is vital to providing timely patient care.Medical image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive processes.It can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human error.Many researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its subgroups.This study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical Images.The projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical images.The MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature extraction.Lastly,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia cancer.The hyperparameter tuning process using MPA helps enhance leukemia cancer classification performance.Simulation results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

Robust zero-watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

In the intricate network environment,the secure transmission of medical images faces challenges such as information leakage and malicious tampering,significantly impacting the accuracy of disease diagnoses by medical professionals.To address this problem,the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi-stage discrete wavelet transform(DWT),Daisy descriptor,and discrete cosine transform(DCT).The algorithm initially encrypts the original medical image through DWT-DCT and Logistic mapping.Subsequently,a 3-stage DWT transformation is applied to the encrypted medical image,with the centre point of the LL3 sub-band within its low-frequency component serving as the sampling point.The Daisy descriptor matrix for this point is then computed.Finally,a DCT transformation is performed on the Daisy descriptor matrix,and the low-frequency portion is processed using the perceptual hashing algorithm to generate a 32-bit binary feature vector for the medical image.This scheme utilises cryptographic knowledge and zero-watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image,which meets the basic re-quirements of medical image watermarking.The embedding and extraction of water-marks are accomplished in a mere 0.160 and 0.411s,respectively,with minimal computational overhead.Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks,with a notable performance in resisting rotation attacks.

Fractional-order heterogeneous memristive Rulkov neuronal network and its medical image watermarking application

This article proposes a novel fractional heterogeneous neural network by coupling a Rulkov neuron with a Hopfield neural network(FRHNN),utilizing memristors for emulating neural synapses.The study firstly demonstrates the coexistence of multiple firing patterns through phase diagrams,Lyapunov exponents(LEs),and bifurcation diagrams.Secondly,the parameter related firing behaviors are described through two-parameter bifurcation diagrams.Subsequently,local attraction basins reveal multi-stability phenomena related to initial values.Moreover,the proposed model is implemented on a microcomputer-based ARM platform,and the experimental results correspond to the numerical simulations.Finally,the article explores the application of digital watermarking for medical images,illustrating its features of excellent imperceptibility,extensive key space,and robustness against attacks including noise and cropping.

DCFNet:An Effective Dual-Branch Cross-Attention Fusion Network for Medical Image Segmentation

Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis.Notably,most existing methods that combine the strengths of convolutional neural networks(CNNs)and Transformers have made significant progress.However,there are some limitations in the current integration of CNN and Transformer technology in two key aspects.Firstly,most methods either overlook or fail to fully incorporate the complementary nature between local and global features.Secondly,the significance of integrating the multiscale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer.To address this issue,we present a groundbreaking dual-branch cross-attention fusion network(DCFNet),which efficiently combines the power of Swin Transformer and CNN to generate complementary global and local features.We then designed the Feature Cross-Fusion(FCF)module to efficiently fuse local and global features.In the FCF,the utilization of the Channel-wise Cross-fusion Transformer(CCT)serves the purpose of aggregatingmulti-scale features,and the Feature FusionModule(FFM)is employed to effectively aggregate dual-branch prominent feature regions from the spatial perspective.Furthermore,within the decoding phase of the dual-branch network,our proposed Channel Attention Block(CAB)aims to emphasize the significance of the channel features between the up-sampled features and the features generated by the FCFmodule to enhance the details of the decoding.Experimental results demonstrate that DCFNet exhibits enhanced accuracy in segmentation performance.Compared to other state-of-the-art(SOTA)methods,our segmentation framework exhibits a superior level of competitiveness.DCFNet’s accurate segmentation of medical images can greatly assist medical professionals in making crucial diagnoses of lesion areas in advance.

A novel medical image data protection scheme for smart healthcare system

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the Internet.Recently,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning systems.Smart healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical data.In smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance Imaging.The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI.Moreover,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission delays.To address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory.The results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,respectively.This validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.