This review aims to explore the potential of artificial intelligence (AI) in coronary CT angiography (CCTA), a key tool for diagnosing coronary artery disease (CAD). Because CAD is still a major cause of death worldwide, effective and accurate diagnostic methods are required to identify and manage the condition. CCTA certainly is a noninvasive alternative for diagnosing CAD, but it requires a large amount of data as input. We intend to discuss the idea of incorporating AI into CCTA, which enhances its diagnostic accuracy and operational efficiency. Using such AI technologies as machine learning (ML) and deep learning (DL) tools, CCTA images are automated to perfection and the analysis is significantly refined. It enables the characterization of a plaque, assesses the severity of the stenosis, and makes more accurate risk stratifications than traditional methods, with pinpoint accuracy. Automating routine tasks through AI-driven CCTA will reduce the radiologists\' workload considerably, which is a standard benefit of such technologies. More importantly, it would enable radiologists to allocate more time and expertise to complex cases, thereby improving overall patient care. However, the field of AI in CCTA is not without its challenges, which include data protection, algorithm transparency, as well as criteria for standardization encoding. Despite such obstacles, it appears that the integration of AI technology into CCTA in the future holds great promise for keeping CAD itself in check, thereby aiding the fight against this disease and begetting better clinical outcomes and more optimized modes of healthcare. Future research on AI algorithms for CCTA, making ethical use of AI, and thereby overcoming the technical and clinical barriers to widespread adoption of this new tool, will hopefully pave the way for profound AI-driven transformations in healthcare.

BACKGROUND: Despite a demonstrated high accuracy and reported successful implementations, radiographer preliminary image evaluation (PIE) has been slow and infrequent in its rollout across Australia. A key barrier reported to hamper radiographer PIE service implementation is lack of adequate time to review radiographs and provide an accurate interpretation. This study sought to conduct a correlational analysis between radiographer imaging workload and PIE service accuracy.
METHODS: A total of 45,373 exams and 1152 PIE comments evenly distributed each month from January 1, 2022, to December 31, 2022, were reviewed. PIE comments were assessed for consistency with the radiologist\'s report. The imaging workload (average exams completed per hour) was separated into three, eight-hour \'shifts\' based on time of imaging. Correlational analysis was performed using linear regression models and assessed for normality using the Shapiro-Wilks test.
RESULTS: The study reported no significant linear association between increasing average workload and reduced service accuracy (P = 0.136). It was however noted that when the average workload increased beyond 7 exams/hour, average service accuracy for PIE was always below 85%.
CONCLUSIONS: This study has demonstrated that, although perceived, there is no statistically significant correlation between x-ray imaging workload and radiographer PIE service accuracy. Consideration of this correlation to be a significant barrier to participation in such a service was not reported at this site.

The scaphotrapeziotrapezoid (STT) joint is involved in load transmission between the wrist and thumb. A quantitative description of baseline STT joint morphometrics is needed to capture the variation of normal anatomy as well as to guide staging of osteoarthritis. Statistical shape modeling (SSM) techniques quantify variations in three-dimensional shapes and relative positions. The objectives of this study are to describe the morphology of the STT joint using a multi-domain SSM. We asked: (1) What are the dominant modes of variation that impact bone and articulation morphology at the STT joint, and (2) what are the morphometrics of SSM-generated STT joints? Thirty adult participants were recruited to a computed tomography study of normal wrist imaging and biomechanics. Segmentations of the carpus were converted to three-dimensional triangular surface meshes. A multi-domain, particle-based entropy system SSM was used to quantify variation in carpal bone shape and position as well as articulation morphology. Articular surface areas and interosseous proximity distributions were calculated between mesh vertex pairs on adjacent bones within distance (2.0 mm) and surface-normal angular (35°) thresholds. In the SSM, the first five modes of variation captured 76.2% of shape variation and contributed to factors such as bone scale, articular geometries, and carpal tilt. Median interosseous proximities-a proxy for joint space-were 1.39 mm (scaphotrapezium), 1.42 mm (scaphotrapezoid), and 0.61 mm (trapeziotrapezoid). This study quantifies morphological and articular variations at the STT joint, presenting a range of normative anatomy. The range of estimated interosseous proximities may guide interpretation of imaging-derived STT joint space.

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Nail fold capillaroscopy is an important means of monitoring human health. Panoramic nail fold images improve the efficiency and accuracy of examinations. However, the acquisition of panoramic nail fold images is seldom studied and the problem manifests of few matching feature points when image stitching is used for such images. Therefore, this paper presents a method for panoramic nail fold image stitching based on vascular contour enhancement, which first solves the problem of few matching feature points by pre-processing the image with contrast-constrained adaptive histogram equalization (CLAHE), bilateral filtering (BF), and sharpening algorithms. The panoramic images of the nail fold blood vessels are then successfully stitched using the fast robust feature (SURF), fast library of approximate nearest neighbors (FLANN) and random sample agreement (RANSAC) algorithms. The experimental results show that the panoramic image stitched by this paper\'s algorithm has a field of view width of 7.43 mm, which improves the efficiency and accuracy of diagnosis.

BACKGROUND: The spatiotemporal progression and patterns of tissue deformation in ventilator-induced lung injury (VILI) remain understudied. Our aim was to identify lung clusters based on their regional mechanical behavior over space and time in lungs subjected to VILI using machine-learning techniques.
RESULTS: Ten anesthetized pigs (27 ± 2 kg) were studied. Eight subjects were analyzed. End-inspiratory and end-expiratory lung computed tomography scans were performed at the beginning and after 12 h of one-hit VILI model. Regional image-based biomechanical analysis was used to determine end-expiratory aeration, tidal recruitment, and volumetric strain for both early and late stages. Clustering analysis was performed using principal component analysis and K-Means algorithms. We identified three different clusters of lung tissue: Stable, Recruitable Unstable, and Non-Recruitable Unstable. End-expiratory aeration, tidal recruitment, and volumetric strain were significantly different between clusters at early stage. At late stage, we found a step loss of end-expiratory aeration among clusters, lowest in Stable, followed by Unstable Recruitable, and highest in the Unstable Non-Recruitable cluster. Volumetric strain remaining unchanged in the Stable cluster, with slight increases in the Recruitable cluster, and strong reduction in the Unstable Non-Recruitable cluster.
CONCLUSIONS: VILI is a regional and dynamic phenomenon. Using unbiased machine-learning techniques we can identify the coexistence of three functional lung tissue compartments with different spatiotemporal regional biomechanical behavior.

This comprehensive review explores the transformative role of quantitative angiography in the landscape of cardiovascular medicine. Tracing the historical evolution of cardiovascular diagnostics, we emphasize the significance of angiography in diagnosis and treatment. The primary focus on quantitative angiography reveals its capacity to move beyond qualitative assessments, providing clinicians with precise measurements and objective parameters. This paradigm shift enhances diagnostic accuracy, promising far-reaching implications for the future of cardiovascular medicine. The ability to tailor interventions based on meticulous measurements optimizes therapeutic strategies and positions the field on the brink of a new era where personalized approaches become the norm. However, challenges such as image quality, radiation exposure, and interpretation variability persist, necessitating a collective call to action for continued research and development. As we confront these issues, collaborative efforts across disciplines are essential to refine existing technologies and usher in innovative solutions. This review concludes with a resounding call for ongoing research initiatives, large-scale clinical studies, and collective commitment to propel quantitative angiography into a universally accepted standard, ensuring its full realization in enhancing patient care and outcomes in cardiovascular medicine.

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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

Ameloblastic fibrodentinoma (AFD) is a rare benign odontogenic tumor that resembles an ameloblastic fibroma with dysplastic dentin. This report presents a rare case of mandibular AFD with imaging features in a young patient. Panoramic radiography and computed tomography revealed a well-defined lesion with internal septa and calcified foci, causing inferior displacement of the adjacent molars as well as buccolingual cortical thinning and expansion of the posterior mandible. The lesion was surgically removed via mass excision, and the involved tooth was extracted under general anesthesia. During the 5-year follow-up period, no evidence of recurrence was observed. Radiologic features of AFD typically reveal a moderately to well-defined mixed lesion with varying degrees of radiopacity, reflecting the extent of dentin formation. Radiologists should consider AFD in the differential diagnosis when encountering a multilocular lesion with little dense radiopacity, particularly if it is associated with delayed eruption, impaction, or absence of involved teeth, on radiographic images of young patients.