Multi-branch CNN and grouping cascade attention for medical image classification.

Visual Transformers(ViT) have made remarkable achievements in the field of medical image analysis. However, ViT-based methods have poor classification results on some small-scale medical image classification datasets. Meanwhile, many ViT-based models sacrifice computational cost for superior performance, which is a great challenge in practical clinical applications. In this paper, we propose an efficient medical image classification network based on an alternating mixture of CNN and Transformer tandem, which is called Eff-CTNet. Specifically, the existing ViT-based method still mainly relies on multi-head self-attention (MHSA). Among them, the attention maps of MHSA are highly similar, which leads to computational redundancy. Therefore, we propose a group cascade attention (GCA) module to split the feature maps, which are provided to different attention heads to further improves the diversity of attention and reduce the computational cost. In addition, we propose an efficient CNN (EC) module to enhance the ability of the model and extract the local detail information in medical images. Finally, we connect them and design an efficient hybrid medical image classification network, namely Eff-CTNet. Extensive experimental results show that our Eff-CTNet achieves advanced classification performance with less computational cost on three public medical image classification datasets.

Imaging Modalities in the Preoperative Assessment of Femoroacetabular Impingement and Hip Dysplasia: Determining Best Practices.

¼ In the last decade, significant progress has been made in understanding hip pain, especially related to femoroacetabular impingement (FAI) and hip dysplasia (HD), which collectively affect over 20% of the population.¼ Preoperative imaging, including plain radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US), plays a pivotal role in diagnosing FAI and HD. Imaging precision, standardized techniques, and accurate interpretation are crucial for effective treatment planning.¼ The continual advancements in imaging techniques, especially seen in MRI (arthrograms, application of leg traction, and delayed gadolinium-enhanced MRI of cartilage), represent important strides in the precise assessment of pathology associated with FAI and HD.¼ By incorporating these advancements into routine imaging protocols, healthcare providers can ensure a comprehensive understanding of hip joint dynamics, enabling more accurate diagnosis and effective management strategies for patients with FAI and HD, ultimately leading to improved clinical outcomes.

[A Survey of the Current Status and the Needs of Medical Imaging Technicians in China]. / ä¸­å›½åŒ»å­¦å½±åƒæŠ€æœ¯ä»Žä¸šäººå‘˜çŽ°çŠ¶å’Œéœ€æ±‚è°ƒæŸ¥ç ”ç©¶.

Objective: To investigate the status quo and the needs of medical imaging technicians (MITs) in the radiology department of secondary and tertiary hospitals in China, so as to provide references and support for the development of the medical imaging technology industry and the relevant policymaking by health administrative departments. Methods: The questionnaire was developed by the Chinese Society of Imaging Technology. The radiology department of each hospital involved in the survey recommended one MIT to fill out the online questionnaire. The contents included: (a) the basic information of the hospital; (b) a general overview of the MITs in the hospital; (c) daily work; (d) career development and promotion; (e) research status and needs, etc. Differences in the number of MIT staff were compared using the Mann-Whitney U test and the chi-square test was used to compare the differences in the selected numbers of MITs in need between regions or between different levels of hospitals. Results: In this investigation, valid questionnaires were finally obtained from a total of 5403 hospitals in 31 provinces in China. The total number of MITs of the hospitals covered in the sample was 67481. The number of MITs in each hospital was 9 (5, 16). The male-to-female ratio was 1.41:1. MITs who were 20 to 40 years old accounted for 78%. The proportions of MITs who had completed doctorate, master's, undergraduate, junior college, and technical secondary school or lower level education were 0.6%, 3.3%, 60.7%, 30.8%, and 4.55%, respectively. The proportions of chief MITs, deputy chief MITs, supervisor MITs, primary MITs, assistant technician and those below were 1.0%, 4.21%, 22.1%, 51.8%, and 20.9%, respectively. The overall professional satisfaction of MITs was good. "Lack of opportunities for learning and communication" was quoted as the main problem MITs encountered in regard to improving their job-related competency. 59.2% of the respondents had not published any academic papers in the past five years, and only 7.0% of the respondents had published in journals included in the Science Citation Index (SCI) in the past five years. Conclusion: MITs in China are on average relatively young and the number of MITs has greatly increased. At this stage, more attention should be given to the cultivation of talents and continuing education of MITs and the construction of the discipline should be further strengthened, so as to provide strong support for the development of the medical imaging technology industry in China.

Zogala D. Artificial intelligence in medical imaging.

The current era witnesses a highly dynamic development of Artificial Intelligence (AI) applications, impacting various human activities. Medical imaging techniques are no exception. AI can find application in image acquisition, image processing and augmentation, as well as in the actual interpretation of images. Moreover, within the domain of radiomics, AI can be instrumental in advanced analysis surpassing the capacities of the human eye and experience. While several certified commercial solutions are available, the validation and accumulation of sufficient evidence regarding their positive impact on healthcare is currently constrained. The role of AI presently leans towards being assistive, yet further evolution is anticipated. Risks and disadvantages encompass dependency on computational power, the quality of input data, and their annotation for learning purposes. The transparency of algorithmic functioning is lacking, and issues pertaining to portability may arise. The integration and utilization of AI introduce entirely new ethical and legislative aspects. Predicting the future development of AI in imaging methods is challenging, with a further increase in implementation appearing more probable.

Tuberculosis extrapulmonar en paciente embarazada. Reporte de caso / Extrapulmonary Tuberculosis in Pregnant Patient. Case Report

Introducción: La tuberculosis (TB) extrapulmonar es la afectación de cualquier órgano, sin compromiso pulmonar demostrado, como consecuencia de la diseminación hematógena/linfática del bacilo de Koch. Presentación de caso: Paciente en puerperio inmediato cursando cuadro clínico de gonalgia que se estudió con resonancia magnética que mostró lesión endomedular en región distal del fémur izquierdo. Se estudió con tomografía de tórax, abdomen y pelvis que evidenciaron otras lesiones a nivel esplénico, sin compromiso hepático ni pulmonar. Se realizó punción diagnóstica femoral con evidencia de granulomas con necrosis central. Se interpretó tuberculosis extrapulmonar y se inició tratamiento antifímico con mejora sintomática. Discusión: La TB extrapulmonar puede impactar a nivel de pleura, ganglios linfáticos, vías urinarias, sistema osteoarticular, sistema nervioso central y abdomen. En el embarazo, la prevalencia de TB extrapulmonar es baja. Conclusión: La TB femoral y esplénica concomitante en pacientes embarazadas es un hallazgo infrecuente por lo que su análisis resulta de gran importancia. Arribar al diagnóstico requiere un elevado índice de sospecha. El retraso diagnóstico conlleva a un aumento de la morbimortalidad

Introduction: Extrapulmonary tuberculosis (TB) is the involvement of any organ, without demonstrated pulmonary involvement, as a consequence of the hematogenous/lymphatic dissemination of the Koch bacillus. Case presentation: Patient in the immediate postpartum period with clinical symptoms of gonalgia that was studied with magnetic resonance imaging showing intramedullary lesion in the distal region of the left femur. A CT scan of the chest, abdomen and pelvis showed other lesions at the splenic level, without liver or lung involvement. A femoral diagnostic puncture was performed with evidence of granulomas with central necrosis. Extrapulmonary tuberculosis was interpreted and antifimic treatment was started with symptomatic improvement. Discussion: Extrapulmonary TB can impact the pleura, lymph nodes, urinary tract, osteoarticular system, central nervous system and abdomen. During pregnancy, the prevalence of extrapulmonary TB is low. Conclusion: Concomitant femoral and splenic TB in pregnant patients is a rare finding, which is why its analysis is of great importance. Arriving at a diagnosis requires a high index of suspicion. Delayed diagnosis leads to an increase in morbidity and mortalit

Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis.

Both cancer and fibrosis are diseases involving dysregulation of cell signaling pathways resulting in an altered cellular microenvironment which ultimately leads to progression of the condition. The two disease entities share common molecular pathophysiology and recent research has illuminated the how each promotes the other. Multiple imaging techniques have been developed to aid in the early and accurate diagnosis of each disease, and given the commonalities between the pathophysiology of the conditions, advances in imaging one disease have opened new avenues to study the other. Here, we detail the most up-to-date advances in imaging techniques for each disease and how they have crossed over to improve detection and monitoring of the other. We explore techniques in positron emission tomography (PET), magnetic resonance imaging (MRI), second generation harmonic Imaging (SGHI), ultrasound (US), radiomics, and artificial intelligence (AI). A new diagnostic imaging tool in PET/computed tomography (CT) is the use of radiolabeled fibroblast activation protein inhibitor (FAPI). SGHI uses high-frequency sound waves to penetrate deeper into the tissue, providing a more detailed view of the tumor microenvironment. Artificial intelligence with the aid of advanced deep learning (DL) algorithms has been highly effective in training computer systems to diagnose and classify neoplastic lesions in multiple organs. Ultimately, advancing imaging techniques in cancer and fibrosis can lead to significantly more timely and accurate diagnoses of both diseases resulting in better patient outcomes.

Magnetic Nanoparticles for Biomedical and Imaging Applications.

Magnetic nanoparticles (MNPs) are a class of nanomaterials composed of metals such as cobalt, nickel, and iron with paramagnetic, ferromagnetic, or superparamagnetic properties [...].

Factors influencing imaging clinical decision-making in low back pain management. A scoping review.

BACKGROUND: The use of diagnostic imaging in low back pain (LBP) management is often inappropriate, despite recommendations from clinical practice guidelines. There is a limited understanding of factors that influence the imaging clinical decision-making (CDM) process. AIM: Explore the literature on factors influencing imaging CDM for people with LBP and consider how these findings could be used to reduce inappropriate use of imaging in LBP management. DESIGN: Scoping review. METHOD: This review followed the Preferred Reporting Items for Systematic Review extension for scoping reviews. A digital search was conducted in Medline, the Cumulative Index of Nursing and Allied Health Literature, Scopus, and the Cochrane Central Register of Controlled Trials for eligible studies published between January 2010-2023. Data reporting influences on imaging CDM were extracted. Data were then analysed through an inductive process to group the influencing factors into categories. RESULTS: After screening, 35 studies (5 qualitative and 30 quantitative) were included in the review, which reported factors influencing imaging CDM. Three categories were developed: clinical features (such as red flags, pain, and neurological deficit), non-modifiable factors (such as age, sex, and ethnicity) and modifiable factors (such as beliefs about consequences and clinical practice). Most studies reported non-modifiable factors. CONCLUSIONS: The results of this scoping review challenge the perception that imaging CDM is purely based on clinical history and objective findings. There is a complex interplay between clinical features, patient and clinician characteristics, beliefs, and environment. These findings should be considered when designing strategies to address inappropriate imaging behaviour.

What makes a good scientific presentation on artificial intelligence in medical imaging?

PURPOSE: Adequate communication of scientific findings is crucial to enhance knowledge transfer. This study aimed to determine the key features of a good scientific oral presentation on artificial intelligence (AI) in medical imaging. METHODS: A total of 26 oral presentations dealing with original research on AI studies in medical imaging at the 2023 RSNA annual meeting were included and systematically assessed by three observers. The presentation quality of the research question, inclusion criteria, reference standard, method, results, clinical impact, presentation clarity, presenter engagement, and the presentation's quality of knowledge transfer were assessed using five-point Likert scales. The number of slides, the average number of words per slide, the number of interactive slides, the number of figures, and the number of tables were also determined for each presentation. Mixed-effects ordinal regression was used to assess the association between the above-mentioned variables and the quality of knowledge transfer of the presentation. RESULTS: A significant positive association was found between the quality of the presentation of the research question and the presentation's quality of knowledge transfer (odds ratio [OR]: 2.5, P = 0.005). The average number of words per slide was significantly negatively associated with the presentation's quality of knowledge transfer (OR: 0.9, P < 0.001). No other significant associations were found. CONCLUSION: Researchers who orally present their scientific findings in the field of AI and medical imaging should pay attention to clearly communicating their research question and minimizing the number of words per slide to maximize the value of their presentation.

Audit and feedback to change diagnostic image ordering practices: A systematic review and meta-analysis.

BACKGROUND: Up to 30% of diagnostic imaging (DI) tests may be unnecessary, leading to increased healthcare costs and the possibility of patient harm. The primary objective of this systematic review was to assess the effect of audit and feedback (AF) interventions directed at healthcare providers on reducing image ordering. The secondary objective was to examine the effect of AF on the appropriateness of DI ordering. METHODS: Studies were identified using MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials and ClinicalTrials.gov registry on December 22nd, 2022. Studies were included if they were randomized control trials (RCTs), targeted healthcare professionals, and studied AF as the sole intervention or as the core component of a multi-faceted intervention. Risk of bias for each study was evaluated using the Cochrane risk of bias tool. Meta-analyses were completed using RevMan software and results were displayed in forest plots. RESULTS: Eleven RCTs enrolling 4311 clinicians or practices were included. AF interventions resulted in 1.5 fewer image test orders per 1000 patients seen than control interventions (95% confidence interval (CI) for the difference -2.6 to -0.4, p-value = 0.009). The effect of AF on appropriateness was not statistically significant, with a 3.2% (95% CI -1.5 to 7.7%, p-value = 0.18) greater likelihood of test orders being considered appropriate with AF vs control interventions. The strength of evidence was rated as moderate for the primary objective but was very low for the appropriateness outcome because of risk of bias, inconsistency in findings, indirectness, and imprecision. CONCLUSION: AF interventions are associated with a modest reduction in total DI ordering with moderate certainty, suggesting some benefit of AF. Individual studies document effects of AF on image order appropriateness ranging from a non-significant trend toward worsening to a highly significant improvement, but the weighted average effect size from the meta-analysis is not statistically significant with very low certainty.

The Role of Imaging for Gastrointestinal Bleeding: Consensus Recommendations From the American College of Gastroenterology and Society of Abdominal Radiology.

Gastrointestinal (GI) bleeding is the most common GI diagnosis leading to hospitalization within the United States. Prompt diagnosis and treatment of GI bleeding is critical to improving patient outcomes and reducing high healthcare utilization and costs. Radiologic techniques including computed tomography angiography, catheter angiography, computed tomography enterography, magnetic resonance enterography, nuclear medicine red blood cell scan, and technetium-99m pertechnetate scintigraphy (Meckel scan) are frequently used to evaluate patients with GI bleeding and are complementary to GI endoscopy. However, multiple management guidelines exist which differ in the recommended utilization of these radiologic examinations. This variability can lead to confusion as to how these tests should be used in the evaluation of GI bleeding. In this document, a panel of experts from the American College of Gastroenterology and Society of Abdominal Radiology provide a review of the radiologic examinations used to evaluate for GI bleeding including nomenclature, technique, performance, advantages, and limitations. A comparison of advantages and limitations relative to endoscopic examinations is also included. Finally, consensus statements and recommendations on technical parameters and utilization of radiologic techniques for GI bleeding are provided.

On the evaluation of deep learning interpretability methods for medical images under the scope of faithfulness.

BACKGROUND AND OBJECTIVE: Evaluating the interpretability of Deep Learning models is crucial for building trust and gaining insights into their decision-making processes. In this work, we employ class activation map based attribution methods in a setting where only High-Resolution Class Activation Mapping (HiResCAM) is known to produce faithful explanations. The objective is to evaluate the quality of the attribution maps using quantitative metrics and investigate whether faithfulness aligns with the metrics results. METHODS: We fine-tune pre-trained deep learning architectures over four medical image datasets in order to calculate attribution maps. The maps are evaluated on a threefold metrics basis utilizing well-established evaluation scores. RESULTS: Our experimental findings suggest that the Area Over Perturbation Curve (AOPC) and Max-Sensitivity scores favor the HiResCAM maps. On the other hand, the Heatmap Assisted Accuracy Score (HAAS) does not provide insights to our comparison as it evaluates almost all maps as inaccurate. To this purpose we further compare our calculated values against values obtained over a diverse group of models which are trained on non-medical benchmark datasets, to eventually achieve more responsive results. CONCLUSION: This study develops a series of experiments to discuss the connection between faithfulness and quantitative metrics over medical attribution maps. HiResCAM preserves the gradient effect on a pixel level ultimately producing high-resolution, informative and resilient mappings. In turn, this is depicted in the results of AOPC and Max-Sensitivity metrics, successfully identifying the faithful algorithm. In regards to HAAS, our experiments yield that it is sensitive over complex medical patterns, commonly characterized by strong color dependency and multiple attention areas.

Types of Evidence Needed to Assess the Clinical Value of Diagnostic Imaging.

AbstractThe evidence underlying the use of advanced diagnostic imaging is based mainly on diagnostic accuracy studies and not on well-designed trials demonstrating improved patient outcomes. This has led to an expansion of low-value and potentially harmful patient care and raises ethical issues around the widespread implementation of tests with incompletely known benefits and harms. Randomized clinical trials are needed to support the safety and effectiveness of imaging tests and should be required for clearance of most new technologies. Large, diverse cohort studies are needed to quantify disease risk associated with many imaging findings, especially incidental findings, to enable evidence-based management. The responsibility to minimize the use of tests with unknown or low value requires engagement of clinicians, medical societies, and the public.

Imaging in pleural Mesothelioma: A review of the 16th International Conference of the International Mesothelioma Interest Group.

Imaging continues to gain a greater role in the assessment and clinical management of patients with mesothelioma. This communication summarizes the oral presentations from the imaging session at the 2023 International Conference of the International Mesothelioma Interest Group (iMig), which was held in Lille, France from June 26 to 28, 2023. Topics at this session included an overview of best practices for clinical imaging of mesothelioma as reported by an iMig consensus panel, emerging imaging techniques for surgical planning, radiologic assessment of malignant pleural effusion, a radiomics-based transfer learning model to predict patient response to treatment, automated assessment of early contrast enhancement, and tumor thickness for response assessment in peritoneal mesothelioma.

An extensive analysis of artificial intelligence and segmentation methods transforming cancer recognition in medical imaging.

Recent advancements in computational intelligence, deep learning, and computer-aided detection have had a significant impact on the field of medical imaging. The task of image segmentation, which involves accurately interpreting and identifying the content of an image, has garnered much attention. The main objective of this task is to separate objects from the background, thereby simplifying and enhancing the significance of the image. However, existing methods for image segmentation have their limitations when applied to certain types of images. This survey paper aims to highlight the importance of image segmentation techniques by providing a thorough examination of their advantages and disadvantages. The accurate detection of cancer regions in medical images is crucial for ensuring effective treatment. In this study, we have also extensive analysis of Computer-Aided Diagnosis (CAD) systems for cancer identification, with a focus on recent research advancements. The paper critically assesses various techniques for cancer detection and compares their effectiveness. Convolutional neural networks (CNNs) have attracted particular interest due to their ability to segment and classify medical images in large datasets, thanks to their capacity for self- learning and decision-making.