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.

Multidetector computer tomography and magnetic resonance imaging of double superior mesenteric veins:A case report

BACKGROUND This study aimed to describe the findings of double superior mesenteric veins(SMVs),a rare anatomical variation,on multidetector computer tomography(MDCT)and magnetic resonance imaging(MRI)images.CASE SUMMARY We describe the case of a 34-year-old male,who underwent both MDC and MRI examinations of the upper abdomen because of liver cirrhosis.MDCT and MRI angiography images of the upper abdomen revealed an anatomic variation of the superior mesenteric vein(SMV),the double SMVs.CONCLUSION The double SMVs are a congenital abnormality without potential clinical manifestation.Physicians need to be aware of this anatomical variation during abdominal surgery to avoid iatrogenic injury.

Medical imaging for the diagnosis,recurrence and metastasis evaluation of clear cell sarcoma

Clear cell sarcoma(CCS)of soft tissue is extremely rare,accounting for approximately 1%of all soft tissue tumours.It is very difficult to diagnose CCS based on clinical manifestations.Magnetic resonance imaging(MRI)provides highresolution images of soft tissues and pathological features such as mucus,necrosis,bleeding,and fat through high and low signals on T1 weighted image(T1WI)and T2 weighted image(T2WI).On the other hand,the paramagnetism of melanin in CCS shortens the relaxation time of T1 and T2,and high signal intensity on T1WI and low signal intensity on T2WI can be found.This is different from most other soft tissue sarcomas.At present,the treatment method for CCS is surgical resection.MRI can effectively display the tumour edge,extent of surrounding oedema,and extent of fat involvement,which is highly important for guiding surgical resection and predicting postoperative recurrence.As an invasive sarcoma,CCS has a high risk of metastasis.Regardless of the pathological condition of the resected tumour,MRI or computed tomography(CT)should be performed every 1-2 years to assess recurrence at the primary site and to screen for metastasis in the lungs,liver,and bones.If necessary,PET-CT can be performed to evaluate the overall condition of the patient.

Relevance of Medical Imaging in the Pre-Therapeutic Evaluation of Acute Intestinal Obstruction at CIMED

Objective: We initiated this work with the aim of studying the contribution of imaging in the diagnosis of acute intestinal obstruction at CIMED. Patients and methods: This was a prospective, descriptive and analytical study involving 96 patients collected at the radiology and medical imaging department of CIMED, from January 2022 to January 2023. Result: The age of our patients varied between 11 and 86 years with an average age of 36 years. There was a male predominance of 64.6% compared to 35.4% for women, i.e. a sex ratio of 1.82. The notion of previous surgery was found in 61.5% of our patients. Pain was present in all patients. Radiography of the ASP was performed in 89.6% of patients. It showed hydro-aerial levels in 96.5% of patients. Abdominopelvic CT was performed in 12 patients and made it possible to make the diagnosis of occlusion in all patients. The results of the positive diagnosis were concordant with those intraoperatively in 92% of cases. 8% of our patients, compared to the treatment, spontaneously resumed their transit, 91% benefited from surgical treatment and 1% died before surgery. The outcome was favorable in 80 patients or 83.3%, poor with death in 16 patients or 16.7% of cases. Conclusion: Acute intestinal obstruction remains a serious pathology for which ASP radiography often remains the only radiological examination performed urgently. However, abdominopelvic CT seems widely indicated thanks to its contribution both for the positive diagnosis and for the serious and etiological diagnoses. However, this imaging technique is largely underused in our practice due to its high cost and lack of availability.

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.

The Artificial Intelligence-Enabled Medical Imaging:Today and Its Future

Medical imaging is now being reshaped by artificial intelligence (AI) and progressing rapidly toward future.In this article,we review the recent progress of AI-enabled medical imaging.Firstly,we briefly review the background about AI in its way of evolution.Then,we discuss the recent successes of AI in different medical imaging tasks,especially in image segmentation,registration,detection and recognition.Also,we illustrate several representative applications of AI-enabled medical imaging to show its advantage in real scenario,which includes lung nodule in chest CT,neuroimaging,mammography,and etc.Finally,we report the way of human-machine interaction.We believe that,in the future,AI will not only change the traditional way of medical imaging,but also improve the clinical routines of medical care and enable many aspects of the medical society.

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.

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.

Efficacy of a novel auto-fluorescence imaging system with computer-assisted color analysis for assessment of colorectal lesions

AIM:To evaluate the efficacy of computer-assisted color analysis of colorectal lesions using a novel auto-fluorescence imaging(AFI)system to distinguish neoplastic lesions from non-neoplastic lesions and to predict the depth of invasion.METHODS:From January 2013 to April 2013,consecutive patients with known polyps greater than 5 mm in size who were scheduled to undergo endoscopic treatment at The Jikei University Hospital were prospectively recruited for this study.All lesions were evaluated using a novel AFI system,and color-tone sampling was performed in a region of interest determined from narrow band imaging or from chromoendoscopy findings without magnification.The green/red(G/R)ratio for each lesion on the AFI images was calculated automatically using a computer-assisted color analysis system that permits real-time color analysis during endoscopic procedures.RESULTS:A total of 88 patients with 163 lesions were enrolled in this study.There were significant differences in the G/R ratios of hyperplastic polyps(non-neoplastic lesions),adenoma/intramucosal cancer/submucosal(SM)superficial cancer,and SM deep cancer(P<0.0001).The mean±SD G/R ratios were 0.984±0.118in hyperplastic polyps and 0.827±0.081 in neoplastic lesions.The G/R ratios of hyperplastic polyps were significantly higher than those of neoplastic lesions(P<0.001).When a G/R ratio cut-off value of>0.89 was applied to determine non-neoplastic lesions,the sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV),and accuracy were 83.9%,82.6%,53.1%,95.6%and 82.8%,respectively.For neoplastic lesions,the mean G/R ratio was 0.834±0.080 in adenoma/intramucosal cancer/SM superficial cancer and 0.746±0.045 in SM deep cancer.The G/R ratio of adenoma/intramucosal cancer/SM superficial cancer was significantly higher than that of SM deep cancer(P<0.01).When a G/R ratio cut-off value of<0.77 was applied to distinguish SM deep cancers,the sensitivity,specificity,PPV,NPV,and accuracy were80.0%,84.4%,29.6%,98.1%and 84.1%,respectively.CONCLUSION:The novel AFI system with color analysis was effective in distinguishing non-neoplastic lesions from neoplastic lesions and might allow determination of the depth of invasion.

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.

Recent advances in computerized imaging and its vital roles in liverdisease diagnosis, preoperative planning, and interventional liversurgery: A review

The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes.In clinical settings,screening and determining the extent of a pathology are prominent factors in preparing remedial agents and administering approp-riate therapeutic procedures.Moreover,in a patient undergoing liver resection,a realistic preoperative simulation of the subject-specific anatomy and physiology also plays a vital part in conducting initial assessments,making surgical decisions during the procedure,and anticipating postoperative results.Conventionally,various medical imaging modalities,e.g.,computed tomography,magnetic resonance imaging,and positron emission tomography,have been employed to assist in these tasks.In fact,several standardized procedures,such as lesion detection and liver segmentation,are also incorporated into prominent commercial software packages.Thus far,most integrated software as a medical device typically involves tedious interactions from the physician,such as manual delineation and empirical adjustments,as per a given patient.With the rapid progress in digital health approaches,especially medical image analysis,a wide range of computer algorithms have been proposed to facilitate those procedures.They include pattern recognition of a liver,its periphery,and lesion,as well as pre-and postoperative simulations.Prior to clinical adoption,however,software must conform to regulatory requirements set by the governing agency,for instance,valid clinical association and analytical and clinical validation.Therefore,this paper provides a detailed account and discussion of the state-of-the-art methods for liver image analyses,visualization,and simulation in the literature.Emphasis is placed upon their concepts,algorithmic classifications,merits,limitations,clinical considerations,and future research trends.

Computer Simulation/Practical Models for Human Thyroid Thermographic Imaging

We have demonstrated a successful computer model utilizing ANSIS software that is verified with a practical model using Infrared (IR) sensors. The simulation model incorporates the three heat transfer coefficients: conduction, convection, and radiation. While the conduction component was a major contributor to the simulation model, the other two coefficients have added to the accuracy and precision of the model. Convection heat allows for the influence of blood flow within the study, while the radiation aspect, sensed through IR sensors, links the practical model of the study. This study also compares simulation data with the applied model generated from IR probe sensors. These sensors formed an IR scanner that moved via servo mechanical system, tracking the temperature distribution within and around the thyroid gland. These data were analyzed and processed to produce a thermal image of the thyroid gland. The acquired data were then compared with an Iodine uptake scan for the same patients.

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.

Breast Tumor Computer-Aided Detection System Based on Magnetic Resonance Imaging Using Convolutional Neural Network

Background:The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue.Early diagnosis of tumors has become the most effective way to prevent breast cancer.Method:For distinguishing between tumor and non-tumor in MRI,a new type of computer-aided detection CAD system for breast tumors is designed in this paper.The CAD system was constructed using three networks,namely,the VGG16,Inception V3,and ResNet50.Then,the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system.Result:CAD system built based on VGG16,Inception V3,and ResNet50 has higher performance than mainstream CAD systems.Among them,the system built based on VGG16 and ResNet50 has outstanding performance.We further explore the impact of the secondary migration on the experimental results in the VGG16 system,and these results show that the migration can improve system performance of the proposed framework.Conclusion:The accuracy of CNN represented by VGG16 is as high as 91.25%,which is more accurate than traditional machine learningmodels.The F1 score of the three basic networks that join the secondary migration is close to 1.0,and the performance of the VGG16-based breast tumor CAD system is higher than Inception V3,and ResNet50.

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.

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.

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.