To compare the effectiveness of expert-designed machine learning models and code-free automated machine learning (AutoML) models in classifying optical coherence tomography (OCT) images for detecting age-related macular degeneration (AMD) and distinguishing between its dry and wet forms. Custom models were developed by an artificial intelligence expert using the EfficientNet V2 architecture, while AutoML models were created by an ophthalmologist utilizing LobeAI with transfer learning via ResNet-50 V2. Both models were designed to differentiate normal OCT images from AMD and to also distinguish between dry and wet AMD. The models were trained and tested using an 80:20 split, with each diagnostic group containing 500 OCT images. Performance metrics, including sensitivity, specificity, accuracy, and F1 scores, were calculated and compared. The expert-designed model achieved an overall accuracy of 99.67% for classifying all images, with F1 scores of 0.99 or higher across all binary class comparisons. In contrast, the AutoML model achieved an overall accuracy of 89.00%, with F1 scores ranging from 0.86 to 0.90 in binary comparisons. Notably lower recall was observed for dry AMD vs. normal (0.85) in the AutoML model, indicating challenges in correctly identifying dry AMD. While the AutoML models demonstrated acceptable performance in identifying and classifying AMD cases, the expert-designed models significantly outperformed them. The use of advanced neural network architectures and rigorous optimization in the expert-developed models underscores the continued necessity of expert involvement in the development of high-precision diagnostic tools for medical image classification.

The peripheral immune system is essential for maintaining central nervous system homeostasis. This study investigates the effects of peripheral immune markers on accelerated brain aging and dementia using brain-predicted age difference based on neuroimaging. By leveraging data from the UK Biobank, Cox regression was used to explore the relationship between peripheral immune markers and dementia, and multivariate linear regression to assess associations between peripheral immune biomarkers and brain structure. Additionally, we established a brain age prediction model using Simple Fully Convolutional Network (SFCN) deep learning architecture. Analysis of the resulting brain-Predicted Age Difference (PAD) revealed relationships between accelerated brain aging, peripheral immune markers, and dementia. During the median follow-up period of 14.3 years, 4, 277 dementia cases were observed among 322, 761 participants. Both innate and adaptive immune markers correlated with dementia risk. NLR showed the strongest association with dementia risk (HR = 1.14; 95% CI: 1.11-1.18, P<0.001). Multivariate linear regression revealed significant associations between peripheral immune markers and brain regional structural indices. Utilizing the deep learning-based SFCN model, the estimated brain age of dementia subjects (MAE = 5.63, r2 = - 0.46, R = 0.22) was determined. PAD showed significant correlation with dementia risk and certain peripheral immune markers, particularly in individuals with positive brain age increment. This study employs brain age as a quantitative marker of accelerated brain aging to investigate its potential associations with peripheral immunity and dementia, highlighting the importance of early intervention targeting peripheral immune markers to delay brain aging and prevent dementia.

This study introduces a novel evaluation framework, GPTRadScore, to systematically assess the performance of multimodal large language models (MLLMs) in generating clinically accurate findings from CT imaging. Specifically, GPTRadScore leverages LLMs as an evaluation metric, aiming to provide a more accurate and clinically informed assessment than traditional language-specific methods. Using this framework, we evaluate the capability of several MLLMs, including GPT-4 with Vision (GPT-4V), Gemini Pro Vision, LLaVA-Med, and RadFM, to interpret findings in CT scans. This retrospective study leverages a subset of the public DeepLesion dataset to evaluate the performance of several multimodal LLMs in describing findings in CT slices. GPTRadScore was developed to assess the generated descriptions (location, body part, and type) using GPT-4, alongside traditional metrics. RadFM was fine-tuned using a subset of the DeepLesion dataset with additional labeled examples targeting complex findings. Post fine-tuning, performance was reassessed using GPTRadScore to measure accuracy improvements. Evaluations demonstrated a high correlation of GPTRadScore with clinician assessments, with Pearson's correlation coefficients of 0.87, 0.91, 0.75, 0.90, and 0.89. These results highlight its superiority over traditional metrics, such as BLEU, METEOR, and ROUGE, and indicate that GPTRadScore can serve as a reliable evaluation metric. Using GPTRadScore, it was observed that while GPT-4V and Gemini Pro Vision outperformed other models, significant areas for improvement remain, primarily due to limitations in the datasets used for training. Fine-tuning RadFM resulted in substantial accuracy gains: location accuracy increased from 3.41% to 12.8%, body part accuracy improved from 29.12% to 53%, and type accuracy rose from 9.24% to 30%. These findings reinforce the hypothesis that fine-tuning RadFM can significantly enhance its performance. GPT-4 effectively correlates with expert assessments, validating its use as a reliable metric for evaluating multimodal LLMs in radiological diagnostics. Additionally, the results underscore the efficacy of fine-tuning approaches in improving the descriptive accuracy of LLM-generated medical imaging findings.

Differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) is essential for selecting the most effective treatment strategies. However, traditional imaging modalities and serum biomarkers often lack sufficient specificity. Radiomics, a sophisticated image analysis approach that derives quantitative data from medical imaging, has emerged as a promising non-invasive tool. To systematically review and meta-analyze the radiomics diagnostic accuracy in differentiating ICC from HCC. PubMed, EMBASE, and Web of Science databases were systematically searched through January 24, 2025. Studies evaluating radiomics models for distinguishing ICC from HCC were included. Assessing the quality of included studies was done by using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and METhodological RadiomICs Score tools. Pooled sensitivity, specificity, and area under the curve (AUC) were calculated using a bivariate random-effects model. Subgroup and publication bias analyses were also performed. 12 studies with 2541 patients were included, with 14 validation cohorts entered into meta-analysis. The pooled sensitivity and specificity of radiomics models were 0.82 (95% CI: 0.76-0.86) and 0.90 (95% CI: 0.85-0.93), respectively, with an AUC of 0.88 (95% CI: 0.85-0.91). Subgroup analyses revealed variations based on segmentation method, software used, and sample size, though not all differences were statistically significant. Publication bias was not detected. Radiomics demonstrates high diagnostic accuracy in distinguishing ICC from HCC and offers a non-invasive adjunct to conventional diagnostics. Further prospective, multicenter studies with standardized workflows are needed to enhance clinical applicability and reproducibility.

Cone-beam computed tomography (CBCT) is widely used in dentistry, surgery, radiotherapy and other medical fields. However, repeated CBCT scans expose patients to additional radiation doses, increasing the risk of secondary malignant tumors. Low-dose CBCT image reconstruction technology, which employs advanced algorithms to reduce radiation dose while enhancing image quality, has emerged as a focal point of recent research. This review systematically examined deep learning-based methods for low-dose CBCT reconstruction. It compared different network architectures in terms of noise reduction, artifact removal, detail preservation, and computational efficiency, covering three approaches: image-domain, projection-domain, and dual-domain techniques. The review also explored how emerging technologies like multimodal fusion and self-supervised learning could enhance these methods. By summarizing the strengths and weaknesses of current approaches, this work provides insights to optimize low-dose CBCT algorithms and support their clinical adoption.

Artificial intelligence, including deep learning models, will play a transformative role in automated medical image analysis for the diagnosis of cardiac disorders and their management. Automated accurate delineation of cardiac images is the first necessary initial step for the quantification and automated diagnosis of cardiac disorders. In this paper, we propose a deep learning based enhanced UNet model, U-R-Veda, which integrates convolution transformations, vision transformer, residual links, channel-attention, and spatial attention, together with edge-detection based skip-connections for an accurate fully-automated semantic segmentation of cardiac magnetic resonance (CMR) images. The model extracts local-features and their interrelationships using a stack of combination convolution blocks, with embedded channel and spatial attention in the convolution block, and vision transformers. Deep embedding of channel and spatial attention in the convolution block identifies important features and their spatial localization. The combined edge information with channel and spatial attention as skip connection reduces information-loss during convolution transformations. The overall model significantly improves the semantic segmentation of CMR images necessary for improved medical image analysis. An algorithm for the dual attention module (channel and spatial attention) has been presented. Performance results show that U-R-Veda achieves an average accuracy of 95.2%, based on DSC metrics. The model outperforms the accuracy attained by other models, based on DSC and HD metrics, especially for the delineation of right-ventricle and left-ventricle-myocardium.

Periodontitis, a chronic inflammatory disease causing alveolar bone loss, significantly affects oral health and quality of life. Accurate assessment of bone loss severity and pattern is critical for diagnosis and treatment planning. In this study, we propose a novel AI-based deep learning framework to automatically detect and quantify alveolar bone loss and its patterns using intraoral periapical (IOPA) radiographs. Our method combines YOLOv8 for tooth detection with Keypoint R-CNN models to identify anatomical landmarks, enabling precise calculation of bone loss severity. Additionally, YOLOv8x-seg models segment bone levels and tooth masks to determine bone loss patterns (horizontal vs. angular) via geometric analysis. Evaluated on a large, expertly annotated dataset of 1000 radiographs, our approach achieved high accuracy in detecting bone loss severity (intra-class correlation coefficient up to 0.80) and bone loss pattern classification (accuracy 87%). This automated system offers a rapid, objective, and reproducible tool for periodontal assessment, reducing reliance on subjective manual evaluation. By integrating AI into dental radiographic analysis, our framework has the potential to improve early diagnosis and personalized treatment planning for periodontitis, ultimately enhancing patient care and clinical outcomes.

In recent decades, the use of 4D Flow MRI images has enabled the quantification of velocity fields within a volume of interest and along the cardiac cycle. However, the lack of resolution and the presence of noise in these biomarkers are significant issues. As indicated by recent studies, it appears that biomarkers such as wall shear stress are particularly impacted by the poor resolution of vessel segmentation. The Phase Contrast Magnetic Resonance Angiography (PC-MRA) is the state-of-the-art method to facilitate segmentation. The objective of this work is to introduce a new handcraft feature that provides a novel visualisation of 4D Flow MRI images, which is useful in the segmentation task. This feature, termed Weighted Mean Frequencies (WMF), is capable of revealing the region in three dimensions where a voxel has been passed by pulsatile flow. Indeed, this feature is representative of the hull of all pulsatile velocity voxels. The value of the feature under discussion is illustrated by two experiments. The experiments involved segmenting 4D Flow MRI images using optimal thresholding and deep learning methods. The results obtained demonstrate a substantial enhancement in terms of IoU and Dice, with a respective increase of 0.12 and 0.13 in comparison with the PC-MRA feature, as evidenced by the deep learning task. This feature has the potential to yield valuable insights that could inform future segmentation processes in other vascular regions, such as the heart or the brain.

Sarcopenia, defined as the progressive loss of skeletal muscle mass and function, has been associated with poor prognosis in patients with pancreatic cancer, particularly those with borderline resectable pancreatic cancer (BRPC). Although body composition can be extracted from routine CT imaging, sarcopenia assessment remains underused in clinical practice. Recent advances in artificial intelligence (AI) offer the potential to automate and standardize this process, but their clinical translation remains limited. This narrative review aims to critically evaluate (1) the clinical impact of CT-defined sarcopenia in BRPC, and (2) the performance and maturity of AI-based methods for automated muscle and fat segmentation on CT images. A dual-axis literature search was conducted to identify clinical studies assessing the prognostic role of sarcopenia in BRPC, and technical studies developing AI-based segmentation models for body composition analysis. Structured data extraction was applied to 13 clinical and 71 technical studies. A PRISMA-inspired flow diagram was included to ensure methodological transparency. Sarcopenia was consistently associated with worse survival and treatment tolerance in BRPC, yet clinical definitions and cut-offs varied widely. AI models-mostly 2D U-Nets trained on L3-level CT slices-achieved high segmentation accuracy (mean DSC >0.93), but external validation and standardization were often lacking. CT-based AI assessment of sarcopenia holds promise for improving patient stratification in BRPC. However, its clinical adoption will require standardization, integration into decision-support frameworks, and prospective validation across diverse populations.

<b>Aims/Background</b> Pulmonary embolism (PE) is a life-threatening condition with significant diagnostic challenges due to high rates of missed or delayed detection. Computed tomography pulmonary angiography (CTPA) is the current standard for diagnosing PE, however, demand for imaging places strain on healthcare systems and increases error rates. This systematic review aims to assess the diagnostic accuracy and clinical applicability of artificial intelligence (AI)-based models for PE detection on CTPA, exploring their potential to enhance diagnostic reliability and efficiency across clinical settings. <b>Methods</b> A systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Excerpta Medica Database (EMBASE), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cochrane, PubMed, and Google Scholar were searched for original articles from inception to September 2024. Articles were included if they reported successful AI integration, whether partial or full, alongside CTPA scans for PE detection in patients. <b>Results</b> The literature search identified 919 articles, with 745 remaining after duplicate removal. Following rigorous screening and appraisal aligned with inclusion and exclusion criteria, 12 studies were included in the final analysis. A total of three primary AI modalities emerged: convolutional neural networks (CNNs), segmentation models, and natural language processing (NLP), collectively used in the analysis of 341,112 radiographic images. CNNs were the most frequently applied modality in this review. Models such as AdaBoost and EmbNet have demonstrated high sensitivity, with EmbNet achieving 88-90.9% per scan and reducing false positives to 0.45 per scan. <b>Conclusion</b> AI shows significant promise as a diagnostic tool for identifying PE on CTPA scans, particularly when combined with other forms of clinical data. However, challenges remain, including ensuring generalisability, addressing potential bias, and conducting rigorous external validation. Variability in study methodologies and the lack of standardised reporting of key metrics complicate comparisons. Future research must focus on refining models, improving peripheral emboli detection, and validating performance across diverse settings to realise AI's potential fully.