Intracerebral haemorrhage (ICH) is a common form of stroke that affects millions of people worldwide. The incidence is associated with a high rate of mortality and morbidity. Accurate diagnosis using brain non-contrast computed tomography (NCCT) is crucial for decision-making on potentially life-saving surgery. Limited access to expert readers and inter-observer variability imposes barriers to timeous and accurate ICH diagnosis. We proposed a hybrid deep learning model for automated ICH diagnosis using NCCT images, which comprises a convolutional autoencoder (CAE) to extract features with reduced data dimensionality and a dense neural network (DNN) for classification. In order to ensure that the model generalizes to new data, we trained it using tenfold cross-validation and holdout methods. Principal component analysis (PCA) based dimensionality reduction and classification is systematically implemented for comparison. The study dataset comprises 1645 ("ICH" class) and 1648 ("Normal" class belongs to patients with non-hemorrhagic stroke) labelled images obtained from 108 patients, who had undergone CT examination on a 64-slice computed tomography scanner at Kalinga Institute of Medical Sciences between 2020 and 2023. Our developed CAE-DNN hybrid model attained 99.84% accuracy, 99.69% sensitivity, 100% specificity, 100% precision, and 99.84% F1-score, which outperformed the comparator PCA-DNN model as well as the published results in the literature. In addition, using saliency maps, our CAE-DNN model can highlight areas on the images that are closely correlated with regions of ICH, which have been manually contoured by expert readers. The CAE-DNN model demonstrates the proof-of-concept for accurate ICH detection and localization, which can potentially be implemented to prioritize the treatment using NCCT images in clinical settings.

Accurately predicting hematoma enlargement (HE) is crucial for improving the prognosis of patients with cerebral haemorrhage. Artificial intelligence (AI) is a potentially reliable assistant for medical image recognition. This study systematically reviews medical imaging articles on the predictive performance of AI in HE. Retrieved relevant studies published before October, 2024 from Embase, Institute of Electrical and Electronics Engineers (IEEE), PubMed, Web of Science, and Cochrane Library databases. The diagnostic test of predicting hematoma enlargement based on CT image training artificial intelligence model, and reported 2 × 2 contingency tables or provided sensitivity (SE) and specificity (SP) for calculation. Two reviewers independently screened the retrieved citations and extracted data. The methodological quality of studies was assessed using the QUADAS-AI, and Preferred Reporting Items for Systematic reviews and Meta-Analyses was used to ensure standardised reporting of studies. Subgroup analysis was performed based on sample size, risk of bias, year of publication, ratio of training set to test set, and number of centres involved. 36 articles were included in this Systematic review to qualitative analysis, of which 23 have sufficient information for further quantitative analysis. Among these articles, there are a total of 7 articles used deep learning (DL) and 16 articles used machine learning (ML). The comprehensive SE and SP of ML are 78% (95% CI: 69-85%) and 85% (78-90%), respectively, while the AUC is 0.89 (0.86-0.91). The SE and SP of DL was 87% (95% CI: 80-92%) and 75% (67-81%), respectively, with an AUC of 0.88 (0.85-0.91). The subgroup analysis found that when the ratio of the training set to the test set is 7:3, the sensitivity is 0.77(0.62-0.91), <i>p</i> = 0.03; In terms of specificity, the group with sample size more than 200 has higher specificity, which is 0.83 (0.75-0.92), <i>p</i> = 0.02; among the risk groups in the study design, the specificity of the risk group was higher, which was 0.83 (0.76-0.89), <i>p</i> = 0.02. The group specificity of articles published before 2021 was higher, 0.84 (0.77-0.90); and the specificity of data from a single research centre was higher, which was 0.85 (0.80-0.91), <i>p</i> < 0.001. Artificial intelligence algorithms based on imaging have shown good performance in predicting HE.

The accurate assessment of computed tomography (CT) image quality is crucial for ensuring diagnostic reliability while minimizing radiation dose. Radiologists' evaluations are time-consuming and labor-intensive. Existing automated approaches often require large CT datasets with predefined image quality assessment (IQA) scores, which often do not align well with clinical evaluations. We aim to develop a reference-free, automated method for CT IQA that closely reflects radiologists' evaluations, reducing the dependency on large annotated datasets. We propose Task-Focused Knowledge Transfer (TFKT), a deep learning-based IQA method leveraging knowledge transfer from task-similar natural image datasets. TFKT incorporates a hybrid convolutional neural network-transformer model, enabling accurate quality predictions by learning from natural image distortions with human-annotated mean opinion scores. The model is pre-trained on natural image datasets and fine-tuned on low-dose computed tomography perceptual image quality assessment data to ensure task-specific adaptability. Extensive evaluations demonstrate that the proposed TFKT method effectively predicts IQA scores aligned with radiologists' assessments on in-domain datasets and generalizes well to out-of-domain clinical pediatric CT exams. The model achieves robust performance without requiring high-dose reference images. Our model is capable of assessing the quality of <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mo>∼</mo> <mn>30</mn></mrow> </math> CT image slices in a second. The proposed TFKT approach provides a scalable, accurate, and reference-free solution for CT IQA. The model bridges the gap between traditional and deep learning-based IQA, offering clinically relevant and computationally efficient assessments applicable to real-world clinical settings.

ObjectiveTo develop and validate machine learning (ML) models for objective and comprehensive quantification of sagittal craniosynostosis (SCS) severity, enhancing clinical assessment, management, and research.DesignA cross-sectional study that combined the analysis of computed tomography (CT) scans and expert ratings.SettingThe study was conducted at a children's hospital and a major computer imaging institution. Our survey collected expert ratings from participating surgeons.ParticipantsThe study included 195 patients with nonsyndromic SCS, 221 patients with nonsyndromic metopic craniosynostosis (CS), and 178 age-matched controls. Fifty-four craniofacial surgeons participated in rating 20 patients head CT scans.InterventionsComputed tomography scans for cranial morphology assessment and a radiographic diagnosis of nonsyndromic SCS.Main OutcomesAccuracy of the proposed Sagittal Severity Score (SSS) in predicting expert ratings compared to cephalic index (CI). Secondary outcomes compared Likert ratings with SCS status, the predictive power of skull-based versus skin-based landmarks, and assessments of an unsupervised ML model, the Cranial Morphology Deviation (CMD), as an alternative without ratings.ResultsThe SSS achieved significantly higher accuracy in predicting expert responses than CI (<i>P</i> < .05). Likert ratings outperformed SCS status in supervising ML models to quantify within-group variations. Skin-based landmarks demonstrated equivalent predictive power as skull landmarks (<i>P</i> < .05, threshold 0.02). The CMD demonstrated a strong correlation with the SSS (Pearson coefficient: 0.92, Spearman coefficient: 0.90, <i>P</i> < .01).ConclusionsThe SSS and CMD can provide accurate, consistent, and comprehensive quantification of SCS severity. Implementing these data-driven ML models can significantly advance CS care through standardized assessments, enhanced precision, and informed surgical planning.

Early detection of glaucoma represents a vital factor in securing vision while the disease retains its position as one of the central causes of blindness worldwide. The current glaucoma screening strategies with expert interpretation depend on complex and time-consuming procedures which slow down both diagnosis processes and intervention timing. This research adopts a complex automated glaucoma diagnostic system that combines optimized segmentation solutions together with classification platforms. The proposed segmentation approach implements an enhanced version of U-Net++ using dynamic parameter control provided by GWO to segment optic disc and cup regions in retinal fundus images. Through the implementation of GWO the algorithm uses wolf-pack hunting strategies to adjust parameters dynamically which enables it to locate diverse textural patterns inside images. The system uses a CapsNet capsule network for classification because it maintains visual spatial organization to detect glaucoma-related patterns precisely. The developed system secures an evaluation accuracy of 95.1% in segmentation and classification tasks better than typical approaches. The automated system eliminates and enhances clinical diagnostic speed as well as diagnostic precision. The tool stands out because of its supreme detection accuracy and reliability thus making it an essential clinical early-stage glaucoma diagnostic system and a scalable healthcare deployment solution. To develop an advanced automated glaucoma diagnostic system by integrating an optimized U-Net++ segmentation model with a Capsule Network (CapsNet) classifier, enhanced through Grey Wolf Optimization Algorithm (GWOA), for precise segmentation of optic disc and cup regions and accurate glaucoma classification from retinal fundus images. This study proposes a two-phase computer-assisted diagnosis (CAD) framework. In the segmentation phase, an enhanced U-Net++ model, optimized by GWOA, is employed to accurately delineate the optic disc and cup regions in fundus images. The optimization dynamically tunes hyperparameters based on grey wolf hunting behavior for improved segmentation precision. In the classification phase, a CapsNet architecture is used to maintain spatial hierarchies and effectively classify images as glaucomatous or normal based on segmented outputs. The performance of the proposed model was validated using the ORIGA retinal fundus image dataset, and evaluated against conventional approaches. The proposed GWOA-UNet++ and CapsNet framework achieved a segmentation and classification accuracy of 95.1%, outperforming existing benchmark models such as MTA-CS, ResFPN-Net, DAGCN, MRSNet and AGCT. The model demonstrated robustness against image irregularities, including variations in optic disc size and fundus image quality, and showed superior performance across accuracy, sensitivity, specificity, precision, and F1-score metrics. The developed automated glaucoma detection system exhibits enhanced diagnostic accuracy, efficiency, and reliability, offering significant potential for early-stage glaucoma detection and clinical decision support. Future work will involve large-scale multi-ethnic dataset validation, integration with clinical workflows, and deployment as a mobile or cloud-based screening tool.

To develop a deep learning model based on Residual Networks (ResNet) for the automated and accurate identification of contrast phases in abdominal CT images. A dataset of 1175 abdominal contrast-enhanced CT scans was retrospectively collected for the model development, and another independent dataset of 215 scans from five hospitals was collected for external testing. Each contrast phase was independently annotated by two radiologists. A ResNet-based model was developed to automatically classify phases into the early arterial phase (EAP) or late arterial phase (LAP), portal venous phase (PVP), and delayed phase (DP). Strategy A identified EAP or LAP, PVP, and DP in one step. Strategy B used a two-step approach: first classifying images as arterial phase (AP), PVP, and DP, then further classifying AP images into EAP or LAP. Model performance and strategy comparison were evaluated. In the internal test set, the overall accuracy of the two-step strategy was 98.3% (283/288; p < 0.001), significantly higher than that of the one-step strategy (91.7%, 264/288; p < 0.001). In the external test set, the two-step model achieved an overall accuracy of 99.1% (639/645), with sensitivities of 95.1% (EAP), 99.4% (LAP), 99.5% (PVP), and 99.5% (DP). The proposed two-step ResNet-based model provides highly accurate and robust identification of contrast phases in abdominal CT images, outperforming the conventional one-step strategy. Automated and accurate identification of contrast phases in abdominal CT images provides a robust tool for improving image quality control and establishes a strong foundation for AI-driven applications, particularly those leveraging contrast-enhanced abdominal imaging data. Accurate identification of contrast phases is crucial in abdominal CT imaging. The two-step ResNet-based model achieved superior accuracy across internal and external datasets. Automated phase classification strengthens imaging quality control and supports precision AI applications.

Chest radiography requires effective quality control (QC) to reduce high retake rates. However, existing QC measures are all retrospective and implemented after exposure, often necessitating retakes when image quality fails to meet standards and thereby increasing radiation exposure to patients. To address this issue, we proposed a 3D human body (3D-HB) reconstruction algorithm to realize prospective QC. Our objective was to investigate the feasibility of using the reconstructed 3D-HB for prospective QC in chest radiography and evaluate its impact on retake rates.&#xD;Approach: This prospective study included patients indicated for posteroanterior (PA) and lateral (LA) chest radiography in May 2024. A 3D-HB reconstruction algorithm integrating the SMPL-X model and the HybrIK-X algorithm was proposed to convert patients' 2D images into 3D-HBs. QC metrics regarding patient positioning and collimation were assessed using chest radiographs (reference standard) and 3D-HBs, with results compared using ICCs, linear regression, and receiver operating characteristic curves. For retake rate evaluation, a real-time 3D-HB visualization interface was developed and chest radiography was conducted in two four-week phases: the first without prospective QC and the second with prospective QC. Retake rates between the two phases were compared using chi-square tests. &#xD;Main results: 324 participants were included (mean age, 42 years±19 [SD]; 145 men; 324 PA and 294 LA examinations). The ICCs for the clavicle and midaxillary line angles were 0.80 and 0.78, respectively. Linear regression showed good relation for clavicle angles (R2: 0.655) and midaxillary line angles (R2: 0.616). In PA chest radiography, the AUCs of 3D-HBs were 0.89, 0.87, 0.91 and 0.92 for assessing scapula rotation, lateral tilt, centered positioning and central X-ray alignment respectively, with 97% accuracy in collimation assessment. In LA chest radiography, the AUCs of 3D-HBs were 0.87, 0.84, 0.87 and 0.88 for assessing arms raised, chest rotation, centered positioning and central X-ray alignment respectively, with 94% accuracy in collimation assessment. In retake rate evaluation, 3995 PA and 3295 LA chest radiographs were recorded. The implementation of prospective QC based on the 3D-HB reduced retake rates from 8.6% to 3.5% (PA) and 19.6% to 4.9% (LA) (p < .001).&#xD;Significance: The reconstructed 3D-HB is a feasible tool for prospective QC in chest radiography, providing real-time feedback on patient positioning and collimation before exposure. Prospective QC based on the reconstructed 3D-HB has the potential to reshape the future of radiography QC by significantly reducing retake rates and improving clinical standardization.

The generalization of deep learning-based low-dose computed tomography (CT) reconstruction models to doses unseen in the training data is important and remains challenging. Previous efforts heavily rely on paired data to improve the generalization performance and robustness through collecting either diverse CT data for re-training or a few test data for fine-tuning. Recently, diffusion models have shown promising and generalizable performance in low-dose CT (LDCT) reconstruction, however, they may produce unrealistic structures due to the CT image noise deviating from Gaussian distribution and imprecise prior information from the guidance of noisy LDCT images. In this paper, we propose a noise-inspired diffusion model for generalizable LDCT reconstruction, termed NEED, which tailors diffusion models for noise characteristics of each domain. First, we propose a novel shifted Poisson diffusion model to denoise projection data, which aligns the diffusion process with the noise model in pre-log LDCT projections. Second, we devise a doubly guided diffusion model to refine reconstructed images, which leverages LDCT images and initial reconstructions to more accurately locate prior information and enhance reconstruction fidelity. By cascading these two diffusion models for dual-domain reconstruction, our NEED requires only normal-dose data for training and can be effectively extended to various unseen dose levels during testing via a time step matching strategy. Extensive qualitative, quantitative, and segmentation-based evaluations on two datasets demonstrate that our NEED consistently outperforms state-of-the-art methods in reconstruction and generalization performance. Source code is made available at https://github.com/qgao21/NEED.

To evaluate the depiction capability of fine lung nodules and airways using high-resolution settings on ultra-high-resolution energy-integrating detector CT (UHR-CT), incorporating large matrix sizes, thin-slice thickness, and iterative reconstruction (IR)/deep-learning reconstruction (DLR), using cadaveric human lungs and corresponding histological images. Images of 20 lungs were acquired using conventional CT (CCT), UHR-CT, and photon-counting detector CT (PCD-CT). CCT images were reconstructed with a 512 matrix and IR (CCT-512-IR). UHR-CT images were reconstructed with four settings by varying the matrix size and the reconstruction method: UHR-512-IR, UHR-1024-IR, UHR-2048-IR, and UHR-1024-DLR. Two imaging settings of PCD-CT were used: PCD-512-IR and PCD-1024-IR. CT images were visually evaluated and compared with histology. Overall, 6769 nodules (median: 1321 µm) and 92 airways (median: 851 µm) were evaluated. For nodules, UHR-2048-IR outperformed CCT-512-IR, UHR-512-IR, and UHR-1024-IR (p < 0.001). UHR-1024-DLR showed no significant difference from UHR-2048-IR in the overall nodule score after Bonferroni correction (uncorrected p = 0.043); however, for nodules > 1000 μm, UHR-2048-IR demonstrated significantly better scores than UHR-1024-DLR (p = 0.003). For airways, UHR-1024-IR and UHR-512-IR showed significant differences (p < 0.001), with no notable differences among UHR-1024-IR, UHR-2048-IR, and UHR-1024-DLR. UHR-2048-IR detected nodules and airways with median diameters of 604 µm and 699 µm, respectively. No significant difference was observed between UHR-512-IR and PCD-512-IR (p > 0.1). PCD-1024-IR outperformed UHR-CTs for nodules > 1000 μm (p ≤ 0.001), while UHR-1024-DLR outperformed PCD-1024-IR for airways > 1000 μm (p = 0.005). UHR-2048-IR demonstrated the highest scores among the evaluated EID-CT images. UHR-CT showed potential for detecting submillimeter nodules and airways. With the 512 matrix, UHR-CT demonstrated performance comparable to PCD-CT. Question There are scarce data evaluating the depiction capabilities of ultra-high-resolution energy-integrating detector CT (UHR-CT) for fine structures, nor any comparisons with photon-counting detector CT (PCD-CT). Findings UHR-CT depicted nodules and airways with median diameters of 604 µm and 699 µm, showing no significant difference from PCD-CT with the 512 matrix. Clinical relevance High-resolution imaging is crucial for lung diagnosis. UHR-CT has the potential to contribute to pulmonary nodule diagnosis and airway disease evaluation by detecting fine opacities and airways.

Multimodal Large Language Models (MLLMs) have emerged as a promising way to automate Radiology Report Generation (RRG). In this work, we systematically investigate the design space of 3D MLLMs, including visual input representation, projectors, Large Language Models (LLMs), and fine-tuning techniques for 3D CT report generation. We also introduce two knowledge-based report augmentation methods that improve performance on the GREEN score by up to 10\%, achieving the 2nd place on the MICCAI 2024 AMOS-MM challenge. Our results on the 1,687 cases from the AMOS-MM dataset show that RRG is largely independent of the size of LLM under the same training protocol. We also show that larger volume size does not always improve performance if the original ViT was pre-trained on a smaller volume size. Lastly, we show that using a segmentation mask along with the CT volume improves performance. The code is publicly available at https://github.com/bowang-lab/AMOS-MM-Solution