The skull is highly durable and plays a significant role in sex determination as one of the most dimorphic bones. The facial canal (FC), a clinically significant canal within the temporal bone, houses the facial nerve. This study aims to estimate sex using morphometric measurements from the FC through machine learning (ML) and artificial neural networks (ANNs). The study utilized Computed Tomography (CT) images of 200 individuals (100 females, 100 males) aged 19-65 years. These images were retrospectively retrieved from the Picture Archiving and Communication Systems (PACS) at Düzce University Faculty of Medicine, Department of Radiology, covering 2021-2024. Bilateral measurements of nine temporal bone parameters were performed in axial, coronal, and sagittal planes. ML algorithms including Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), Decision Tree (DT), Extra Tree Classifier (ETC), Random Forest (RF), Logistic Regression (LR), Gaussian Naive Bayes (GaussianNB), and k-Nearest Neighbors (k-NN) were used, alongside a multilayer perceptron classifier (MLPC) from ANN algorithms. Except for QDA (Acc 0.93), all algorithms achieved an accuracy rate of 0.97. SHapley Additive exPlanations (SHAP) analysis revealed the five most impactful parameters: right SGAs, left SGAs, right TSWs, left TSWs and, the inner mouth width of the left FN, respectively. FN-centered morphometric measurements show high accuracy in sex determination and may aid in understanding FN positioning across sexes and populations. These findings may support rapid and reliable sex estimation in forensic investigations-especially in cases with fragmented craniofacial remains-and provide auxiliary diagnostic data for preoperative planning in otologic and skull base surgeries. They are thus relevant for surgeons, anthropologists, and forensic experts. Not applicable.

It is challenging to preoperatively diagnose follicular thyroid carcinoma (FTC) on ultrasound images. This study aimed to develop an end-to-end diagnostic model that can classify thyroid tumors into benign tumors, FTC and other malignant tumors based on deep learning. This retrospective multi-center study included 10,771 consecutive adult patients who underwent conventional ultrasound and postoperative pathology between January 2018 and September 2021. We proposed a novel data augmentation method and a mixed loss function to solve an imbalanced dataset and applied them to a pre-trained convolutional neural network and transformer model that could effectively extract image features. The proposed model can directly identify FTC from other malignant subtypes and benign tumors based on ultrasound images. The testing dataset included 1078 patients (mean age, 47.3 years ± 11.8 (SD); 811 female patients; FTCs, 39 of 1078 (3.6%); Other malignancies, 385 of 1078 (35.7%)). The proposed classification model outperformed state-of-the-art models on differentiation of FTC from other malignant sub-types and benign ones, achieved an excellent diagnosis performance with balanced-accuracy 0.87, AUC 0.96 (95% CI: 0.96, 0.96), mean sensitivity 0.87 and mean specificity 0.92. Meanwhile, it was superior to radiologists included in this study for thyroid tumor diagnosis (balanced-accuracy: Junior 0.60, p < 0.001; Mid-level 0.59, p < 0.001; Senior 0.66, p < 0.001). The developed classification model addressed the class-imbalanced problem and achieved higher performance in differentiating FTC from other malignant subtypes and benign tumors compared with existing methods. Question Deep learning has the potential to improve preoperatively diagnostic accuracy for follicular thyroid carcinoma (FTC). Findings The proposed model achieved high accuracy, sensitivity and specificity in diagnosing follicular thyroid carcinoma, outperforming other models. Clinical relevance The proposed model is a promising computer-aided diagnostic tool for the clinical diagnosis of FTC, which potentially could help reduce missed diagnosis and misdiagnosis for FTC.

Advancements in diagnostic technology are required to improve patient outcomes and facilitate early diagnosis, as breast cancer is a substantial global health concern. This research discusses the creation of a unique Deep Learning (DL) Ensemble Deep Learning based on a Clinical Decision Support System (EDL-CDSS) that enables the precise and expeditious diagnosis of breast cancer. Numerous DL models are combined in the proposed EDL-CDSS to create an ensemble method that optimizes the advantages and reduces the disadvantages of individual techniques. The team improves its capacity to extricate intricate patterns and features from medical imaging data by incorporating the Kelm Extreme Learning Machine (KELM), Deep Belief Network (DBN), and other DL architectures. Comprehensive testing has been conducted across various datasets to assess the efficacy of this system in comparison to individual DL models and traditional diagnostic methods. Among other objectives, the evaluation prioritizes precision, sensitivity, specificity, F1-score, accuracy, and overall accuracy to mitigate false positives and negatives. The experiment's conclusion exhibits a remarkable accuracy of 96.14% in comparison to prior advanced methodologies.

Artificial intelligence (AI) is already having a significant impact on healthcare. For example, AI-guided imaging can improve the diagnosis/treatment of vascular diseases, which affect over 200 million people globally. Recently, Chiu and colleagues (2024) developed an AI algorithm that supports nurses with no ultrasound training in diagnosing abdominal aortic aneurysms (AAA) with similar accuracy as ultrasound-trained physicians. This technology can therefore improve AAA screening; however, achieving clinical impact with new AI technologies requires careful consideration of commercialization strategies, including funding, compliance with safety and regulatory frameworks, health technology assessment, regulatory approval, reimbursement, and clinical guideline integration.

Advancements in cardiac computed tomography angiography (CCTA) have enabled the extraction of physiological data from an anatomy-based imaging modality. This review outlines the key methodologies for deriving fractional flow reserve (FFR) from CCTA, with a focus on two primary methods: 1) computational fluid dynamics-based FFR (CT-FFR) and 2) plaque-derived ischemia assessment using artificial intelligence and quantitative plaque metrics. These techniques have expanded the role of CCTA beyond anatomical assessment, allowing for concurrent evaluation of coronary physiology without the need for invasive testing. This review provides an overview of the principles, workflows, and limitations of each technique and aims to inform on the current state and future direction of non-invasive coronary physiology assessment.

Accurate classification of computed tomography (CT) images is essential for diagnosis and treatment planning, but existing methods often struggle with the subtle and spatially diverse nature of pathological features. Current approaches typically process images uniformly, limiting their ability to detect localized abnormalities that require focused analysis. We introduce UGPL, an uncertainty-guided progressive learning framework that performs a global-to-local analysis by first identifying regions of diagnostic ambiguity and then conducting detailed examination of these critical areas. Our approach employs evidential deep learning to quantify predictive uncertainty, guiding the extraction of informative patches through a non-maximum suppression mechanism that maintains spatial diversity. This progressive refinement strategy, combined with an adaptive fusion mechanism, enables UGPL to integrate both contextual information and fine-grained details. Experiments across three CT datasets demonstrate that UGPL consistently outperforms state-of-the-art methods, achieving improvements of 3.29%, 2.46%, and 8.08% in accuracy for kidney abnormality, lung cancer, and COVID-19 detection, respectively. Our analysis shows that the uncertainty-guided component provides substantial benefits, with performance dramatically increasing when the full progressive learning pipeline is implemented. Our code is available at: https://github.com/shravan-18/UGPL

Mild Cognitive Impairment (MCI) serves as a prodromal stage of Alzheimer's Disease (AD), where early identification and intervention can effectively slow the progression to dementia. However, diagnosing AD remains a significant challenge in neurology due to the confounders caused mainly by the selection bias of multimodal data and the complex relationships between variables. To address these issues, we propose a novel visual-language causal intervention framework named Alzheimer's Disease Prediction with Cross-modal Causal Intervention (ADPC) for diagnostic assistance. Our ADPC employs large language model (LLM) to summarize clinical data under strict templates, maintaining structured text outputs even with incomplete or unevenly distributed datasets. The ADPC model utilizes Magnetic Resonance Imaging (MRI), functional MRI (fMRI) images and textual data generated by LLM to classify participants into Cognitively Normal (CN), MCI, and AD categories. Because of the presence of confounders, such as neuroimaging artifacts and age-related biomarkers, non-causal models are likely to capture spurious input-output correlations, generating less reliable results. Our framework implicitly eliminates confounders through causal intervention. Experimental results demonstrate the outstanding performance of our method in distinguishing CN/MCI/AD cases, achieving state-of-the-art (SOTA) metrics across most evaluation metrics. The study showcases the potential of integrating causal reasoning with multi-modal learning for neurological disease diagnosis.

Chronological age, although commonly used in clinical practice, fails to capture individual variations in rates of ageing and physiological decline. Recent advances in artificial intelligence (AI) have transformed the estimation of biological age using various imaging techniques. This Review consolidates AI developments in age prediction across brain, chest, abdominal, bone, and facial imaging using diverse methods, including MRI, CT, x-ray, and photographs. The difference between predicted and chronological age-often referred to as age deviation-is a promising biomarker for assessing health status and predicting disease risk. In this Review, we highlight consistent associations between age deviation and various health outcomes, including mortality risk, cognitive decline, and cardiovascular prognosis. We also discuss the technical challenges in developing unbiased models and ethical considerations for clinical application. This Review highlights the potential of AI-based age estimation in personalised medicine as it offers a non-invasive, interpretable biomarker that could transform health risk assessment and guide preventive interventions.

Lung neuroendocrine tumours (NETs, also known as carcinoids) are rapidly rising in incidence worldwide but have unknown aetiology and limited therapeutic options beyond surgery. We conducted multi-omic analyses on over 300 lung NETs including whole-genome sequencing (WGS), transcriptome profiling, methylation arrays, spatial RNA sequencing, and spatial proteomics. The integration of multi-omic data provides definitive proof of the existence of four strikingly different molecular groups that vary in patient characteristics, genomic and transcriptomic profiles, microenvironment, and morphology, as much as distinct diseases. Among these, we identify a new molecular group, enriched for highly aggressive supra-carcinoids, that displays an immune-rich microenvironment linked to tumour--macrophage crosstalk, and we uncover an undifferentiated cell population within supra-carcinoids, explaining their molecular and behavioural link to high-grade lung neuroendocrine carcinomas. Deep learning models accurately identified the Ca A1, Ca A2, and Ca B groups based on morphology alone, outperforming current histological criteria. The characteristic tumour microenvironment of supra-carcinoids and the validation of a panel of immunohistochemistry markers for the other three molecular groups demonstrates that these groups can be accurately identified based solely on morphological features, facilitating their implementation in the clinical setting. Our proposed morpho-molecular classification highlights group-specific therapeutic opportunities, including DLL3, FGFR, TERT, and BRAF inhibitors. Overall, our findings unify previously proposed molecular classifications and refine the lung cancer map by revealing novel tumour types and potential treatments, with significant implications for prognosis and treatment decision-making.

BackgroundSubjective cognitive decline (SCD) is recognized as an early phase in the progression of Alzheimer's disease (AD).ObjectiveTo explore the abnormal patterns of morphological and functional connectivity coupling (MC-FC coupling) and their potential diagnostic significance in SCD.MethodsThe data of 52 individuals with SCD and 51 age-gender-education matched healthy controls (HC) who underwent resting-state functional magnetic resonance imaging and high-resolution 3D T₁-weighted imaging were retrieved to build the MC and FC of gray matter. Support vector machine (SVM) methods were used for differentiating between SCD and HC.ResultsSCD individuals exhibited MC-FC decoupling in the frontoparietal network compared with HC (p = 0.002, 5000 permutations). Using these adjusted MC-FC coupling metrics, SVM analysis achieved 74.76% accuracy, 64.71% sensitivity, and 92.31% specificity (p < 0.001, 5000 permutations). Additionally, the stronger MC-FC coupling of the left inferior temporal gyrus (r = 0.294, p = 0.034) and right posterior cingulate gyrus (r = 0.372, p = 0.007) in SCD individuals was positively correlated with subjective memory complaint performance.ConclusionsThe findings of this study provide insight into the idiosyncratic feature of brain organization underlying SCD from the prospective of MC-FC coupling and highlight the potential of MC-FC coupling for the identification of the preclinical stage of AD.