Influence of impression method and shoulder design on the marginal adaptation of CAD/CAM nanoceramic resin onlay restorations.

Objective: This in-vitro study investigates the influence of two different impression techniques and two shoulder designs on the marginal adaptation of computer-aided design/computer-aided manufacturing restorations. Methods: Forty mandibular first premolars were cast into dental arch models for this in vitro study. Fragile cusps and concavities on the mesial-buccal-occlusal surfaces were treated, with 2 mm of the occlusal surface removed. Teeth were categorised into two groups based on shoulder preparation. Digital scanning using a 3Shape 3D scanner identified them further for allocation into conventional and digital impression subgroups. The restorations were created from nanoceramic resin blocks using prescribed guidelines. Microscopic evaluation assessed the restoration's marginal adaptation, with data analysed using SPSS 27.0. The level of significance was set at p ≤ 0.05. Results: Digital intraoral scanning consistently demonstrated smaller marginal gaps than the traditional impression method, regardless of shoulder preparation, with the differences being statistically significant (p < 0.05). Furthermore, shoulder preparation significantly reduced the marginal gaps in both the digital and traditional impression groups (p < 0.05). Conclusions: The onlay preparation design with a shoulder led to restorations with improved marginal adaptation compared with the design with no shoulder. Direct digital impression techniques produced restorations within a better marginal discrepancy than traditional impressions.

Artificial intelligence can help detecting incidental intracranial aneurysm on routine brain MRI using TOF MRA data sets and improve the time required for analysis of these images.

PURPOSE: The aim of our study was to assess the diagnostic performance of commercially available AI software for intracranial aneurysm detection and to determine if the AI system enhances the radiologist's accuracy in identifying aneurysms and reduces image analysis time. METHODS: TOF-MRA clinical brain examinations were analyzed using commercially available software and by an consultant neuroradiologist for the presence of intracranial aneurysms. The results were compared with the reference standard, to measure the sensitivity and specificity of the software and the consultant neuroradiologist. Furthermore, we examined the time required for the neuroradiologist to analyze the TOF-MRA image set, both with and without use of the AI software. RESULTS: In 500 TOF-MRI brain studies, 106 aneurysms were detected in 85 examinations by combining AI software with neuroradiologist readings. The neuroradiologist identified 98 aneurysms (92.5% sensitivity), while AI detected 77 aneurysms (72.6% sensitivity). Specificity and sensitivity were calculated from the combined effort as reference. Combining AI and neuroradiologist readings significantly improves detection reliability. Additionally, AI integration reduced TOF-MRA analysis time by 19 s (23% reduction). CONCLUSIONS: Our findings indicate that the AI-based software can support neuroradiologists in interpreting brain TOF-MRA. A combined reading of the AI-based software and the neuroradiologist demonstrated higher reliability in identifying intracranial aneurysms as compared to reading by either neuroradiologist or software, thus improving diagnostic accuracy of the neuroradiologist. Simultaneously, reading time for the neuroradiologist was reduced by approximately one quarter.

Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based architectures.

The potential and promise of deep learning systems to provide an independent assessment and relieve radiologists' burden in screening mammography have been recognized in several studies. However, the low cancer prevalence, the need to process high-resolution images, and the need to combine information from multiple views and scales still pose technical challenges. Multi-view architectures that combine information from the four mammographic views to produce an exam-level classification score are a promising approach to the automated processing of screening mammography. However, training such architectures from exam-level labels, without relying on pixel-level supervision, requires very large datasets and may result in suboptimal accuracy. Emerging architectures such as Visual Transformers (ViT) and graph-based architectures can potentially integrate ipsi-lateral and contra-lateral breast views better than traditional convolutional neural networks, thanks to their stronger ability of modeling long-range dependencies. In this paper, we extensively evaluate novel transformer-based and graph-based architectures against state-of-the-art multi-view convolutional neural networks, trained in a weakly-supervised setting on a middle-scale dataset, both in terms of performance and interpretability. Extensive experiments on the CSAW dataset suggest that, while transformer-based architecture outperform other architectures, different inductive biases lead to complementary strengths and weaknesses, as each architecture is sensitive to different signs and mammographic features. Hence, an ensemble of different architectures should be preferred over a winner-takes-all approach to achieve more accurate and robust results. Overall, the findings highlight the potential of a wide range of multi-view architectures for breast cancer classification, even in datasets of relatively modest size, although the detection of small lesions remains challenging without pixel-wise supervision or ad-hoc networks.

A multicentre study to evaluate the diagnostic performance of a novel CAD software, DecXpert, for radiological diagnosis of tuberculosis in the northern Indian population.

Tuberculosis (TB) is the leading cause of mortality among infectious diseases globally. Effectively managing TB requires early identification of individuals with TB disease. Resource-constrained settings often lack skilled professionals for interpreting chest X-rays (CXRs) used in TB diagnosis. To address this challenge, we developed "DecXpert" a novel Computer-Aided Detection (CAD) software solution based on deep neural networks for early TB diagnosis from CXRs, aiming to detect subtle abnormalities that may be overlooked by human interpretation alone. This study was conducted on the largest cohort size to date, where the performance of a CAD software (DecXpert version 1.4) was validated against the gold standard molecular diagnostic technique, GeneXpert MTB/RIF, analyzing data from 4363 individuals across 12 primary health care centers and one tertiary hospital in North India. DecXpert demonstrated 88% sensitivity (95% CI 0.85-0.93) and 85% specificity (95% CI 0.82-0.91) for active TB detection. Incorporating demographics, DecXpert achieved an area under the curve of 0.91 (95% CI 0.88-0.94), indicating robust diagnostic performance. Our findings establish DecXpert's potential as an accurate, efficient AI solution for early identification of active TB cases. Deployed as a screening tool in resource-limited settings, DecXpert could enable early identification of individuals with TB disease and facilitate effective TB management where skilled radiological interpretation is limited.

Teaching old docks new tricks with machine learning enhanced ensemble docking.

We here introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS). Ensemble VS is an established method for predicting protein/small-molecule (ligand) binding. Unlike traditional VS, which focuses on a single protein conformation, ensemble VS better accounts for protein flexibility by predicting binding to multiple protein conformations. Each compound is thus associated with a spectrum of scores (one score per protein conformation) rather than a single score. To effectively rank and prioritize the molecules for further evaluation (including experimental testing), researchers must select which protein conformations to consider and how best to map each compound's spectrum of scores to a single value, decisions that are system-specific. EnOpt uses machine learning to address these challenges. We perform benchmark VS to show that for many systems, EnOpt ranking distinguishes active compounds from inactive or decoy molecules more effectively than traditional ensemble VS methods. To encourage broad adoption, we release EnOpt free of charge under the terms of the MIT license.

Very fast, high-resolution aggregation 3D detection CAM to quickly and accurately find facial fracture areas.

BACKGROUND AND OBJECTIVE: The incidence of facial fractures is on the rise globally, yet limited studies are addressing the diverse forms of facial fractures present in 3D images. In particular, due to the nature of the facial fracture, the direction in which the bone fractures vary, and there is no clear outline, it is difficult to determine the exact location of the fracture in 2D images. Thus, 3D image analysis is required to find the exact fracture area, but it needs heavy computational complexity and expensive pixel-wise labeling for supervised learning. In this study, we tackle the problem of reducing the computational burden and increasing the accuracy of fracture localization by using a weakly-supervised object localization without pixel-wise labeling in a 3D image space. METHODS: We propose a Very Fast, High-Resolution Aggregation 3D Detection CAM (VFHA-CAM) model, which can detect various facial fractures. To better detect tiny fractures, our model uses high-resolution feature maps and employs Ablation CAM to find an exact fracture location without pixel-wise labeling, where we use a rough fracture image detected with 3D box-wise labeling. To this end, we extract important features and use only essential features to reduce the computational complexity in 3D image space. RESULTS: Experimental findings demonstrate that VFHA-CAM surpasses state-of-the-art 2D detection methods by up to 20% in sensitivity/person and specificity/person, achieving sensitivity/person and specificity/person scores of 87% and 85%, respectively. In addition, Our VFHA-CAM reduces location analysis time to 76 s without performance degradation compared to a simple Ablation CAM method that takes more than 20 min. CONCLUSION: This study introduces a novel weakly-supervised object localization approach for bone fracture detection in 3D facial images. The proposed method employs a 3D detection model, which helps detect various forms of facial bone fractures accurately. The CAM algorithm adopted for fracture area segmentation within a 3D fracture detection box is key in quickly informing medical staff of the exact location of a facial bone fracture in a weakly-supervised object localization. In addition, we provide 3D visualization so that even non-experts unfamiliar with 3D CT images can identify the fracture status and location.

Computer-Aided Drug Design Using the Fragment Molecular Orbital Method: Current Status and Future Applications for SBDD.

Owing to the increasing use of computers, computer-aided drug design (CADD) has become an essential component of drug discovery research. In structure-based drug design (SBDD), including inhibitor design and in silico screening of drug target molecules, concordance with wet experimental data is important to provide insights on unique perspectives derived from calculations. Fragment molecular orbital (FMO) method is a quantum chemical method that facilitates precise energy calculations. Fragmentation method makes it possible to apply the quantum chemical method to biological macromolecules for energy calculation based on the electron behavior. Furthermore, interaction energies calculated on a residue-by-residue basis via fragmentation aid in the analysis of interactions between the target and ligand molecule residues and molecular design. In this review, we outline the recent developments in SBDD and FMO methods and highlight the prospects of developing machine learning approaches for large computational data using the FMO method.

The message matters: changes to binary Computer Aided Detection recommendations affect cancer detection in low prevalence search.

Computer Aided Detection (CAD) has been used to help readers find cancers in mammograms. Although these automated systems have been shown to help cancer detection when accurate, the presence of CAD also leads to an over-reliance effect where miss errors and false alarms increase when the CAD system fails. Previous research investigated CAD systems which overlayed salient exogenous cues onto the image to highlight suspicious areas. These salient cues capture attention which may exacerbate the over-reliance effect. Furthermore, overlaying CAD cues directly on the mammogram occludes sections of breast tissue which may disrupt global statistics useful for cancer detection. In this study we investigated whether an over-reliance effect occurred with a binary CAD system, which instead of overlaying a CAD cue onto the mammogram, reported a message alongside the mammogram indicating the possible presence of a cancer. We manipulated the certainty of the message and whether it was presented only to indicate the presence of a cancer, or whether a message was displayed on every mammogram to state whether a cancer was present or absent. The results showed that although an over-reliance effect still occurred with binary CAD systems miss errors were reduced when the CAD message was more definitive and only presented to alert readers of a possible cancer.

Computer-aided diagnosis for lung cancer using waterwheel plant algorithm with deep learning.

Lung cancer (LC) is a life-threatening and dangerous disease all over the world. However, earlier diagnoses and treatment can save lives. Earlier diagnoses of malevolent cells in the lungs responsible for oxygenating the human body and expelling carbon dioxide due to significant procedures are critical. Even though a computed tomography (CT) scan is the best imaging approach in the healthcare sector, it is challenging for physicians to identify and interpret the tumour from CT scans. LC diagnosis in CT scan using artificial intelligence (AI) can help radiologists in earlier diagnoses, enhance performance, and decrease false negatives. Deep learning (DL) for detecting lymph node contribution on histopathological slides has become popular due to its great significance in patient diagnoses and treatment. This study introduces a computer-aided diagnosis for LC by utilizing the Waterwheel Plant Algorithm with DL (CADLC-WWPADL) approach. The primary aim of the CADLC-WWPADL approach is to classify and identify the existence of LC on CT scans. The CADLC-WWPADL method uses a lightweight MobileNet model for feature extraction. Besides, the CADLC-WWPADL method employs WWPA for the hyperparameter tuning process. Furthermore, the symmetrical autoencoder (SAE) model is utilized for classification. An investigational evaluation is performed to demonstrate the significant detection outputs of the CADLC-WWPADL technique. An extensive comparative study reported that the CADLC-WWPADL technique effectively performs with other models with a maximum accuracy of 99.05% under the benchmark CT image dataset.

Improved stability of open bite deformities: taking control of the transverse width using digital technology and robotic archwire bending.

Many factors need to be considered when selecting treatment protocol for surgical correction of skeletal open bite deformities. In order to achieve stable long-term results, it is essential to explore the origin of the open bite, including dysfunction of the temporomandibular joint, tongue and compromised nasal breathing, in addition to the skeletal deformity. Recurrence of skeletal open bite is associated with relapse of the expanded transverse width. Three-dimensional virtual planning allows different treatment options to be explored and final decisions to be made together with the orthodontist. This study presents a treatment protocol for predictable and stable widening of the maxillary transverse width over the long term, involving premolar extraction and rounding and shortening of the upper dental arch by advancing the molar segments. The stability of inter-canine, inter-premolar, and inter-molar distances, as well as overjet and overbite, were measured in 16 patients treated with this technique; measurements were obtained pre- and post-surgery, and the mean follow-up was 43 months. Orthodontic treatment was designed digitally and finished with robotically bent wires (SureSmile), which allowed exact planning of the overall treatment, thus making orthognathic surgery more predictable for the patient. The changes in transverse width were significant and stable over time.

A 48-month clinical performance of hybrid ceramic fragment restorations manufactured in CAD/CAM in non-carious cervical lesions: case report.

From the restorative perspective, various methods are available to prevent the progression of non-carious cervical lesions. Direct, semi-direct, and indirect composite resin techniques and indirect ceramic restorations are commonly recommended. In this context, semi-direct and indirect restoration approaches are increasingly favored, particularly as digital dentistry becomes more prevalent. To illustrate this, we present a case report demonstrating the efficacy of hybrid ceramic fragments fabricated using computer-aided design (CAD)/computer-aided manufacturing (CAM) technology and cemented with resin cement in treating non-carious cervical lesions over a 48-month follow-up period. A 24-year-old male patient sought treatment for aesthetic concerns and dentin hypersensitivity in the cervical region of the lower premolar teeth. Clinical examination confirmed the presence of two non-carious cervical lesions in the buccal region of teeth #44 and #45. The treatment plan involved indirect restoration using CAD/CAM-fabricated hybrid ceramic fragments as a restorative material. After 48 months, the hybrid ceramic material exhibited excellent adaptation and durability provided by the CAD/CAM system. This case underscores the effectiveness of hybrid ceramic fragments in restoring non-carious cervical lesions, highlighting their long-term stability and clinical success.

Downgrading BI-RADS categories in ultrasound using strain elastography and computer-aided diagnosis system: a multicenter, prospective study.

OBJECTIVE: To determine whether adding elastography strain ratio (SR) and a deep learning based computer-aided diagnosis (CAD) system to breast ultrasound (US) can help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3 & 4a-c categories and avoid unnecessary biopsies. METHODS: This prospective, multicenter study included 1049 masses (691 benign, 358 malignant) with assigned BI-RADS 3 & 4a-c between 2020 and 2022. CAD results was dichotomized possibly malignant vs. benign. All patients underwent SR and CAD examinations and histopathological findings were the standard of reference. Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after reclassified (new BI-RADS 3) with SR and CAD were the outcome measures. RESULTS: Following the routine conventional breast US assessment, 48.6% (336 of 691 masses) underwent unnecessary biopsies. After reclassifying BI-RADS 4a masses (SR cut-off < 2.90, CAD dichotomized possibly benign), 25.62% (177 of 691 masses) underwent an unnecessary biopsies corresponding to a 50.14% (177 vs. 355) reduction of unnecessary biopsies. After reclassification, only 1.72% (9 of 523 masses) malignancies were missed in the new BI-RADS 3 group. CONCLUSION: Adding SR and CAD to clinical practice may show an optimal performance in reclassifying BI-RADS 4a to 3 categories, and 50.14% masses would be benefit by keeping the rate of undetected malignancies with an acceptable value of 1.72%. ADVANCES IN KNOWLEDGE: Leveraging the potential of SR in conjunction with CAD holds immense promise in substantially reducing the biopsy frequency associated with BI-RADS 3 and 4A lesions, thereby conferring substantial advantages upon patients encompassed within this cohort.

Applications and accuracy of 3D-printed surgical guides in traumatology and orthopaedic surgery: A systematic review and meta-analysis.

Background: Patient-Specific Surgical Guides (PSSGs) are advocated for reducing radiation exposure, operation time and enhancing precision in surgery. However, existing accuracy assessments are limited to specific surgeries, leaving uncertainties about variations in accuracy across different anatomical sites, three-dimensional (3D) printing technologies and manufacturers (traditional vs. printed at the point of care). This study aimed to evaluate PSSGs accuracy in traumatology and orthopaedic surgery, considering anatomical regions, printing methods and manufacturers. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Studies were eligible if they (1) assessed the accuracy of PSSGs by comparing preoperative planning and postoperative results in at least two different planes (2) used either computer tomography or magnetic resonance imaging (3) covered the field of orthopaedic surgery or traumatology and (4) were available in English or German language. The 'Quality Assessment Tool for Quantitative Studies' was used for methodological quality assessment. Descriptive statistics, including mean, standard deviation, and ranges, are presented. A random effects meta-analysis was performed to determine the pooled mean absolute deviation between preoperative plan and postoperative result for each anatomic region (shoulder, hip, spine, and knee). Results: Of 4212 initially eligible studies, 33 were included in the final analysis (8 for shoulder, 5 for hip, 5 for spine, 14 for knee and 1 for trauma). Pooled mean deviation (95% confidence interval) for total knee arthroplasty (TKA), total shoulder arthroplasty (TSA), total hip arthroplasty (THA) and spine surgery (pedicle screw placement during spondylodesis) were 1.82° (1.48, 2.15), 2.52° (1.9, 3.13), 3.49° (3.04, 3.93) and 2.67° (1.64, 3.69), respectively. Accuracy varied between TKA and THA and between TKA and TSA. Conclusion: Accuracy of PSSGs depends on the type of surgery but averages around 2-3° deviation from the plan. The use of PSSGs might be considered for selected complex cases. Level of Evidence: Level 3 (meta-analysis including Level 3 studies).

Quantitative Edge Analysis Can Differentiate Pancreatic Carcinoma from Normal Pancreatic Parenchyma.

This study aimed to introduce specific image feature analysis, focusing on pancreatic margins, and to provide a quantitative measure of edge irregularity, evidencing correlations with the presence/absence of pancreatic adenocarcinoma. We selected 50 patients (36 men, 14 women; mean age 63.7 years) who underwent Multi-detector computed tomography (MDCT) for the staging of pancreatic adenocarcinoma of the tail of the pancreas. Computer-assisted quantitative edge analysis was performed on the border fragments in MDCT images of neoplastic and healthy glandular parenchyma, from which we obtained the root mean square deviation SD of the actual border from the average boundary line. The SD values relative to healthy and neoplastic borders were compared using a paired t-test. A significant SD difference was observed between healthy and neoplastic borders. A threshold SD value was also found, enabling the differentiation of adenocarcinoma with 96% specificity and sensitivity. We introduced a quantitative measure of boundary irregularity, which correlates with the presence/absence of pancreatic adenocarcinoma. Quantitative edge analysis can be promptly performed on select border fragments in MDCT images, providing a useful supporting tool for diagnostics and a possible starting point for machine learning recognition based on lower-dimensional feature space.

Medical image registration and its application in retinal images: a review.

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, they have not systematically summarized the existing medical image registration methods. To this end, a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives, aiming to help audiences quickly understand the development of medical image registration. In particular, we review recent advances in retinal image registration, which has not attracted much attention. In addition, current challenges in retinal image registration are discussed and insights and prospects for future research provided.

Adaptive neighborhood triplet loss: enhanced segmentation of dermoscopy datasets by mining pixel information.

PURPOSE: The integration of deep learning in image segmentation technology markedly improves the automation capabilities of medical diagnostic systems, reducing the dependence on the clinical expertise of medical professionals. However, the accuracy of image segmentation is still impacted by various interference factors encountered during image acquisition. METHODS: To address this challenge, this paper proposes a loss function designed to mine specific pixel information which dynamically changes during training process. Based on the triplet concept, this dynamic change is leveraged to drive the predicted boundaries of images closer to the real boundaries. RESULTS: Extensive experiments on the PH2 and ISIC2017 dermoscopy datasets validate that our proposed loss function overcomes the limitations of traditional triplet loss methods in image segmentation applications. This loss function not only enhances Jaccard indices of neural networks by 2.42 % and 2.21 % for PH2 and ISIC2017, respectively, but also neural networks utilizing this loss function generally surpass those that do not in terms of segmentation performance. CONCLUSION: This work proposed a loss function that mined the information of specific pixels deeply without incurring additional training costs, significantly improving the automation of neural networks in image segmentation tasks. This loss function adapts to dermoscopic images of varying qualities and demonstrates higher effectiveness and robustness compared to other boundary loss functions, making it suitable for image segmentation tasks across various neural networks.

In-Vitro Accuracy of Digital Versus Conventional Workflows for Complete Arch Implant Supported Frameworks - A Scoping Review.

Purpose: To investigate the available evidence on the accuracy of conventional and digital workflows for complete arch implant supported frameworks. Materials and methods: This scoping review was conducted according to the 5-stage framework of Arksey and O'Malley. A systematic literature search was performed adhering to the PRISMA guidelines to identify studies with a direct comparison of conventional and digital methods for the fabrication of complete arch implant supported frameworks. 58 in-vitro studies with the focus on edentulous arches with at least four implants published between 2000 and 2024 were included. The reported outcomes were examined to determine the value of a statistical analysis for adding up the individual errors to a cumulative error of the workflow. Results: Evidence on the accuracy assessment of digital and conventional workflows for complete arch implant supported frameworks is available. However, also studies with the same assessment methods and outcome units appear to be too heterogeneous to perform a statistical analysis of error accumulation. While there is no consensus in the impression and cast fabrication stage, digital techniques show a superior accuracy for the fabrication of complete arch implant supported frameworks compared to conventional casting. Conclusion: In-vitro studies assessing the accuracy of entire workflows and classifying their outcomes regarding the clinical relevance are lacking.

Super-resolution reconstruction for early cervical cancer magnetic resonance imaging based on deep learning.

This study aims to develop a super-resolution (SR) algorithm tailored specifically for enhancing the image quality and resolution of early cervical cancer (CC) magnetic resonance imaging (MRI) images. The proposed method is subjected to both qualitative and quantitative analyses, thoroughly investigating its performance across various upscaling factors and assessing its impact on medical image segmentation tasks. The innovative SR algorithm employed for reconstructing early CC MRI images integrates complex architectures and deep convolutional kernels. Training is conducted on matched pairs of input images through a multi-input model. The research findings highlight the significant advantages of the proposed SR method on two distinct datasets at different upscaling factors. Specifically, at a 2× upscaling factor, the sagittal test set outperforms the state-of-the-art methods in the PSNR index evaluation, second only to the hybrid attention transformer, while the axial test set outperforms the state-of-the-art methods in both PSNR and SSIM index evaluation. At a 4× upscaling factor, both the sagittal test set and the axial test set achieve the best results in the evaluation of PNSR and SSIM indicators. This method not only effectively enhances image quality, but also exhibits superior performance in medical segmentation tasks, thereby providing a more reliable foundation for clinical diagnosis and image analysis.

FMD-UNet: fine-grained feature squeeze and multiscale cascade dilated semantic aggregation dual-decoder UNet for COVID-19 lung infection segmentation from CT images.

With the advancement of computer-aided diagnosis, the automatic segmentation of COVID-19 infection areas holds great promise for assisting in the timely diagnosis and recovery of patients in clinical practice. Currently, methods relying on U-Net face challenges in effectively utilizing fine-grained semantic information from input images and bridging the semantic gap between the encoder and decoder. To address these issues, we propose an FMD-UNet dual-decoder U-Net network for COVID-19 infection segmentation, which integrates a Fine-grained Feature Squeezing (FGFS) decoder and a Multi-scale Dilated Semantic Aggregation (MDSA) decoder. The FGFS decoder produces fine feature maps through the compression of fine-grained features and a weighted attention mechanism, guiding the model to capture detailed semantic information. The MDSA decoder consists of three hierarchical MDSA modules designed for different stages of input information. These modules progressively fuse different scales of dilated convolutions to process the shallow and deep semantic information from the encoder, and use the extracted feature information to bridge the semantic gaps at various stages, this design captures extensive contextual information while decoding and predicting segmentation, thereby suppressing the increase in model parameters. To better validate the robustness and generalizability of the FMD-UNet, we conducted comprehensive performance evaluations and ablation experiments on three public datasets, and achieved leading Dice Similarity Coefficient (DSC) scores of 84.76, 78.56 and 61.99% in COVID-19 infection segmentation, respectively. Compared to previous methods, the FMD-UNet has fewer parameters and shorter inference time, which also demonstrates its competitiveness.

[Applications of artificial intelligence in radiology]. / Anwendungsspektrum der künstlichen Intelligenz in der Radiologie.

BACKGROUND: Artificial intelligence (AI) is increasingly finding its way into routine radiological work. OBJECTIVE: Presentation of the current advances and applications of AI along the entire radiological patient journey. METHODS: Systematic literature review of established AI techniques and current research projects, with reference to consensus recommendations. RESULTS: The applications of AI in radiology cover a wide range, starting with AI-supported scheduling and indications assessment, extending to AI-enhanced image acquisition and reconstruction techniques that have the potential to reduce radiation doses in computed tomography (CT) or acquisition times in magnetic resonance imaging (MRI), while maintaining comparable image quality. These include computer-aided detection and diagnosis, such as fracture recognition or nodule detection. Additionally, methods such as worklist prioritization and structured reporting facilitated by large language models enable a rethinking of the reporting process. The use of AI promises to increase the efficiency of all steps of the radiology workflow and an improved diagnostic accuracy. To achieve this, seamless integration into technical workflows and proven evidence of AI systems are necessary. CONCLUSION: Applications of AI have the potential to profoundly influence the role of radiologists in the future.