Early detection of lung cancer is critical to improving survival outcomes. We present a deep learning framework for automated lung cancer screening from chest computed tomography (CT) images with integrated explainability. Using the IQ-OTH/NCCD dataset (1,197 scans across Normal, Benign, and Malignant classes), we evaluate a custom convolutional neural network (CNN) and three fine-tuned transfer learning backbones: DenseNet121, ResNet152, and VGG19. Models are trained with cost-sensitive learning to mitigate class imbalance and evaluated via accuracy, precision, recall, F1-score, and ROC-AUC. While ResNet152 achieved the highest accuracy (97.3%), DenseNet121 provided the best overall balance in precision, recall, and F1 (up to 92%, 90%, 91%, respectively). We further apply Shapley Additive Explanations (SHAP) to visualize evidence contributing to predictions, improving clinical transparency. Results indicate that CNN-based approaches augmented with explainability can provide fast, accurate, and interpretable support for lung cancer screening, particularly in resource-limited settings.

A detailed understanding of material structure across multiple scales is essential for predictive modeling of textile-reinforced composites. Nesting -- characterized by the interlocking of adjacent fabric layers through local interpenetration and misalignment of yarns -- plays a critical role in defining mechanical properties such as stiffness, permeability, and damage tolerance. This study presents a framework to quantify nesting behavior in dry textile reinforcements under compaction using low-resolution computed tomography (CT). In-situ compaction experiments were conducted on various stacking configurations, with CT scans acquired at 20.22 $\mu$m per voxel resolution. A tailored 3D{-}UNet enabled semantic segmentation of matrix, weft, and fill phases across compaction stages corresponding to fiber volume contents of 50--60 %. The model achieved a minimum mean Intersection-over-Union of 0.822 and an $F1$ score of 0.902. Spatial structure was subsequently analyzed using the two-point correlation function $S_2$, allowing for probabilistic extraction of average layer thickness and nesting degree. The results show strong agreement with micrograph-based validation. This methodology provides a robust approach for extracting key geometrical features from industrially relevant CT data and establishes a foundation for reverse modeling and descriptor-based structural analysis of composite preforms.

Atrial fibrillation, a common cardiac arrhythmia with rapid and irregular atrial electrical activity, requires accurate left atrial segmentation for effective treatment planning. Recently, deep learning methods have gained encouraging success in left atrial segmentation. However, current methodologies critically depend on the assumption of consistently complete centered left atrium as input, which neglects the structural incompleteness and boundary discontinuities arising from random-crop operations during inference. In this paper, we propose BSA-Net, which exploits an adaptive adjustment strategy in both feature position and loss optimization to establish long-range feature relationships and strengthen robust intermediate feature representations in boundary regions. Specifically, we propose a Spatial-adaptive Convolution (SConv) that employs a shuffle operation combined with lightweight convolution to directly establish cross-positional relationships within regions of potential relevance. Moreover, we develop the dual Boundary Prioritized loss, which enhances boundary precision by differentially weighting foreground and background boundaries, thus optimizing complex boundary regions. With the above technologies, the proposed method enjoys a better speed-accuracy trade-off compared to current methods. BSA-Net attains Dice scores of 92.55%, 91.42%, and 84.67% on the LA, Utah, and Waikato datasets, respectively, with a mere 2.16 M parameters-approximately 80% fewer than other contemporary state-of-the-art models. Extensive experimental results on three benchmark datasets have demonstrated that BSA-Net, consistently and significantly outperforms existing state-of-the-art methods.

We present MInDI-3D (Medical Inversion by Direct Iteration in 3D), the first 3D conditional diffusion-based model for real-world sparse-view Cone Beam Computed Tomography (CBCT) artefact removal, aiming to reduce imaging radiation exposure. A key contribution is extending the "InDI" concept from 2D to a full 3D volumetric approach for medical images, implementing an iterative denoising process that refines the CBCT volume directly from sparse-view input. A further contribution is the generation of a large pseudo-CBCT dataset (16,182) from chest CT volumes of the CT-RATE public dataset to robustly train MInDI-3D. We performed a comprehensive evaluation, including quantitative metrics, scalability analysis, generalisation tests, and a clinical assessment by 11 clinicians. Our results show MInDI-3D's effectiveness, achieving a 12.96 (6.10) dB PSNR gain over uncorrected scans with only 50 projections on the CT-RATE pseudo-CBCT (independent real-world) test set and enabling an 8x reduction in imaging radiation exposure. We demonstrate its scalability by showing that performance improves with more training data. Importantly, MInDI-3D matches the performance of a 3D U-Net on real-world scans from 16 cancer patients across distortion and task-based metrics. It also generalises to new CBCT scanner geometries. Clinicians rated our model as sufficient for patient positioning across all anatomical sites and found it preserved lung tumour boundaries well.

Low-dose CT (LDCT) protocols reduce radiation exposure but increase image noise, compromising diagnostic confidence. Diffusion-based generative models have shown promise for LDCT denoising by learning image priors and performing iterative refinement. In this work, we introduce AST-n, an accelerated inference framework that initiates reverse diffusion from intermediate noise levels, and integrate high-order ODE solvers within conditioned models to further reduce sampling steps. We evaluate two acceleration paradigms--AST-n sampling and standard scheduling with high-order solvers -- on the Low Dose CT Grand Challenge dataset, covering head, abdominal, and chest scans at 10-25 % of standard dose. Conditioned models using only 25 steps (AST-25) achieve peak signal-to-noise ratio (PSNR) above 38 dB and structural similarity index (SSIM) above 0.95, closely matching standard baselines while cutting inference time from ~16 seg to under 1 seg per slice. Unconditional sampling suffers substantial quality loss, underscoring the necessity of conditioning. We also assess DDIM inversion, which yields marginal PSNR gains at the cost of doubling inference time, limiting its clinical practicality. Our results demonstrate that AST-n with high-order samplers enables rapid LDCT reconstruction without significant loss of image fidelity, advancing the feasibility of diffusion-based methods in clinical workflows.

This study aimed to determine if an ensemble (stacking) model that integrates three independently developed base models can reliably predict patients' neurological outcomes following out-of-hospital cardiac arrest (OHCA) within 3 h of arrival and outperform each individual model. This retrospective study included patients with OHCA (≥ 18 years) admitted directly to Nara Medical University between April 2015 and March 2024 who remained comatose for ≥ 3 h after arrival and had suitable head computed tomography (CT) images. The area under the receiver operating characteristic curve (AUC) and Briers scores were used to evaluate the performance of four models (resuscitation-related background OHCA score factors, bilateral pupil diameter, single-slice head CT within 3 h of arrival, and an ensemble stacked model combining these three models) in predicting favourable neurological outcomes at hospital discharge or 1 month, as defined by a Cerebral Performance Category scale of 1-2. Among 533 patients, 82 (15%) had favourable outcomes. The OHCA, pupil, and head CT models yielded AUCs of 0.76, 0.65, and 0.68 with Brier scores of 0.11, 0.13, and 0.12, respectively. The ensemble model outperformed the other models (AUC, 0.82; Brier score, 0.10), thereby supporting its application for early clinical decision-making and optimising resource allocation.

Segmenting individual instances of mitochondria from imaging datasets can provide rich quantitative information, but is prohibitively time-consuming when done manually, prompting interest in the development of automated algorithms using deep neural networks. Existing solutions for various segmentation tasks are optimized for either: high-resolution three-dimensional imaging, relying on well-defined object boundaries (e.g., whole neuron segmentation in volumetric electron microscopy datasets); or low-resolution two-dimensional imaging, boundary-invariant but poorly suited to large 3D objects (e.g., whole-cell segmentation of light microscopy images). Mitochondria in whole-cell 3D electron microscopy datasets often lie in the middle ground - large, yet with ambiguous borders, challenging current segmentation tools. To address this, we developed skeleton-oriented object segmentation (SKOOTS) - a novel approach that efficiently segments large, densely packed mitochondria. SKOOTS accurately and efficiently segments mitochondria in previously difficult contexts and can also be applied to segment other objects in 3D light microscopy datasets. This approach bridges a critical gap between existing segmentation approaches, improving the utility of automated analysis of three-dimensional biomedical imaging data. We demonstrate the utility of SKOOTS by applying it to segment over 15,000 cochlear hair cell mitochondria across experimental conditions in under 2 hours on a consumer-grade PC, enabling downstream morphological analysis that revealed subtle structural changes following aminoglycoside exposure - differences not detectable using analysis approaches currently used in the field.

To develop a CT-based deep learning radiomics nomogram (DLRN) for the preoperative prediction of peritoneal metastasis (PM) in patients with ovarian cancer (OC). A total of 296 patients with OCs were randomly divided into training dataset (N = 207) and test dataset (N = 89). The radiomics features and DL features were extracted from CT images of each patient. Specifically, radiomics features were extracted from the 3D tumor regions, while DL features were extracted from the 2D slice with the largest tumor region of interest (ROI). The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomics and DL features, and the radiomics score (Radscore) and DL score (Deepscore) were calculated. Multivariate logistic regression was employed to construct clinical model. The important clinical factors, radiomics and DL features were integrated to build the DLRN. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and DeLong's test. Nine radiomics features and 10 DL features were selected. Carbohydrate antigen 125 (CA-125) was the independent clinical predictor. In the training dataset, the AUC values of the clinical, radiomics and DL models were 0.618, 0.842, and 0.860, respectively. In the test dataset, the AUC values of these models were 0.591, 0.819 and 0.917, respectively. The DLRN showed better performance than other models in both training and test datasets with AUCs of 0.943 and 0.951, respectively. Decision curve analysis and calibration curve showed that the DLRN provided relatively high clinical benefit in both the training and test datasets. The DLRN demonstrated superior performance in predicting preoperative PM in patients with OC. This model offers a highly accurate and noninvasive tool for preoperative prediction, with substantial clinical potential to provide critical information for individualized treatment planning, thereby enabling more precise and effective management of OC patients.

In this review, a comprehensive overview of the current state of artificial intelligence (AI) research in Inflammatory Bowel Disease (IBD) diagnostics in the domains of endoscopy, radiology and histology is presented. Moreover, key considerations for development of AI algorithms in medical image analysis are discussed. AI presents a potential breakthrough in real-time, objective and rapid endoscopic assessment, with implications for predicting disease progression. It is anticipated that, by harmonising multimodal data, AI will transform patient care through early diagnosis, accurate patient profiling and therapeutic response prediction. The ability of AI in cross-sectional medical imaging to improve diagnostic accuracy, automate and enable objective assessment of disease activity and predict clinical outcomes highlights its transformative potential. AI models have consistently outperformed traditional methods of image interpretation, particularly in complex areas such as differentiating IBD subtypes, identifying disease progression and complications. The use of AI in histology is a particularly dynamic research field. Implementation of AI algorithms in clinical practice is still lagging, a major hurdle being the lack of a digital workflow in many pathology institutes. Adoption is likely to start with implementation of automatic disease activity scoring. Beyond matching pathologist performance, algorithms may teach us more about IBD pathophysiology. While AI is set to substantially advance IBD diagnostics, various challenges such as heterogeneous datasets, retrospective designs and assessment of different endpoints must be addressed. Implementation of novel standards of reporting may drive an increase in research quality and overcome these obstacles.

Computed tomography angiography (CTA) imaging is essential to evaluate and analyse complex abdominal and thoraco-abdominal aortic aneurysms. However, CTA analyses are labour intensive, time consuming, and prone to interphysician variability. Fully automatic volume segmentation (FAVS) using artificial intelligence with deep learning has been validated for infrarenal aorta imaging but requires further testing for thoracic and visceral aorta segmentation. This study assessed FAVS accuracy against physician controlled manual segmentation (PCMS) in the descending thoracic aorta, visceral abdominal aorta, and visceral vasculature. This was a retrospective, multicentre, observational cohort study. Fifty pre-operative CTAs of patients with abdominal aortic aneurysm were randomly selected. Comparisons between FAVS and PCMS and assessment of inter- and intra-observer reliability of PCMS were performed. Volumetric segmentation performance was evaluated using sensitivity, specificity, Dice similarity coefficient (DSC), and Jaccard index (JI). Visceral vessel identification was compared by analysing branchpoint coordinates. Bland-Altman limits of agreement (BA-LoA) were calculated for proximal visceral diameters (excluding duplicate renals). FAVS demonstrated performance comparable with PCMS for volumetric segmentation, with a median DSC of 0.93 (interquartile range [IQR] 0.03), JI of 0.87 (IQR 0.05), sensitivity of 0.99 (IQR 0.01), and specificity of 1.00 (IQR 0.00). These metrics are similar to interphysician comparisons: median DSC 0.93 (IQR 0.07), JI 0.87 (IQR 0.12), sensitivity 0.90 (IQR 0.08), and specificity 1.00 (IQR 0.00). FAVS correctly identified 99.5% (183/184) of visceral vessels. Branchpoint coordinates for FAVS and PCMS were within the limits of CTA spatial resolution (Δx -0.33 [IQR 2.82], Δy 0.61 [IQR 4.85], Δz 2.10 [IQR 4.69] mm). BA-LoA for proximal visceral diameter measurements showed reasonable agreement: FAVS vs. PCMS mean difference -0.11 ± 5.23 mm compared with interphysician variability of 0.03 ± 5.27 mm. FAVS provides accurate, efficient segmentation of the thoracic and visceral aorta, delivering performance comparable with manual segmentation by expert physicians. This technology may enhance clinical workflows for monitoring and planning treatments for complex abdominal and thoraco-abdominal aortic aneurysms.