Organ segmentation using ¹⁸F-FDG PET images alone has not been extensively explored. Segmentation based methods based on deep learning (DL) have traditionally relied on CT or MRI images, which are vulnerable to alignment issues and artifacts. This study aimed to develop a DL approach for segmenting the entire liver based solely on ¹⁸F-FDG PET images. We analyzed data from 120 patients who were assessed using ¹⁸F-FDG PET. A three-dimensional (3D) U-Net model from nnUNet and preprocessed PET images served as DL and input images, respectively, for the model. The model was trained with 5-fold cross-validation on data from 100 patients, and segmentation accuracy was evaluated on an independent test set of 20 patients. Accuracy was assessed using Intersection over Union (IoU), Dice coefficient, and liver volume. Image quality was evaluated using mean (SUVmean) and maximum (SUVmax) standardized uptake value and signal-to-noise ratio (SNR). The model achieved an average IoU of 0.89 and an average Dice coefficient of 0.94 based on test data from 20 patients, indicating high segmentation accuracy. No significant discrepancies in image quality metrics were identified compared with ground truth. Liver regions were accurately extracted from ¹⁸F-FDG PET images which allowed rapid and stable evaluation of liver uptake in individual patients without the need for CT or MRI assessments.

Cervical vertebrae fractures pose a significant risk to a patient's health. The accurate diagnosis and prompt treatment need to be provided for effective treatment. Moreover, the automated analysis of the cervical vertebrae fracture is of utmost important, as deep learning models have been widely used and play significant role in identification and classification. In this paper, we propose a novel hybrid transfer learning approach for the identification and classification of fractures in axial CT scan slices of the cervical spine. We utilize the publicly available RSNA (Radiological Society of North America) dataset of annotated cervical vertebrae fractures for our experiments. The CT scan slices undergo preprocessing and analysis to extract features, employing four distinct pre-trained transfer learning models to detect abnormalities in the cervical vertebrae. The top-performing model, Inception-ResNet-v2, is combined with the upsampling component of U-Net to form a hybrid architecture. The hybrid model demonstrates superior performance over traditional deep learning models, achieving an overall accuracy of 98.44% on 2,984 test CT scan slices, which represents a 3.62% improvement over the 95% accuracy of predictions made by radiologists. This study advances clinical decision support systems, equipping medical professionals with a powerful tool for timely intervention and accurate diagnosis of cervical vertebrae fractures, thereby enhancing patient outcomes and healthcare efficiency.

Major depressive disorder (MDD) is a highly prevalent mental health condition with significant public health implications. Early detection is crucial for timely intervention, but current diagnostic methods often rely on subjective clinical assessments, leading to delayed or inaccurate diagnoses. Advances in neuroimaging and machine learning (ML) offer the potential for objective and accurate early detection. This study aimed to develop and validate ML models using multisite functional magnetic resonance imaging data for the early detection of MDD, compare their performance, and evaluate their clinical applicability. We used functional magnetic resonance imaging data from 1200 participants (600 with early-stage MDD and 600 healthy controls) across 3 public datasets. In total, 4 ML models-support vector machine, random forest, gradient boosting machine, and deep neural network-were trained and evaluated using a 5-fold cross-validation framework. Models were assessed for accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve. Shapley additive explanations values and activation maximization techniques were applied to interpret model predictions. The deep neural network model demonstrated superior performance with an accuracy of 89% (95% CI 86%-92%) and an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97), outperforming traditional diagnostic methods by 15% (P<.001). Key predictive features included altered functional connectivity between the dorsolateral prefrontal cortex, anterior cingulate cortex, and limbic regions. The model achieved 78% sensitivity (95% CI 71%-85%) in identifying individuals who developed MDD within a 2-year follow-up period, demonstrating good generalizability across datasets. Our findings highlight the potential of artificial intelligence-driven approaches for the early detection of MDD, with implications for improving early intervention strategies. While promising, these tools should complement rather than replace clinical expertise, with careful consideration of ethical implications such as patient privacy and model biases.

To test the performance of a DL model developed and validated for screen-detected pulmonary nodules on incidental nodules detected in a clinical setting. A retrospective dataset of incidental pulmonary nodules sized 5-15 mm was collected, and a subset of size-matched solid nodules was selected. The performance of the DL model was compared to the Brock model. AUCs with 95% CIs were compared using the DeLong method. Sensitivity and specificity were determined at various thresholds, using a 10% threshold for the Brock model as reference. The model's calibration was visually assessed. The dataset included 49 malignant and 359 benign solid or part-solid nodules, and the size-matched dataset included 47 malignant and 47 benign solid nodules. In the complete dataset, AUCs [95% CI] were 0.89 [0.85, 0.93] for the DL model and 0.86 [0.81, 0.92] for the Brock model (p = 0.27). In the size-matched subset, AUCs of the DL and Brock models were 0.78 [0.69, 0.88] and 0.58 [0.46, 0.69] (p < 0.01), respectively. At a 10% threshold, the Brock model had a sensitivity of 0.49 [0.35, 0.63] and a specificity of 0.92 [0.89, 0.94]. At a threshold of 17%, the DL model matched the specificity of the Brock model at the 10% threshold, but had a higher sensitivity (0.57 [0.43, 0.71]). Calibration analysis revealed that the DL model overestimated the malignancy probability. The DL model demonstrated good discriminatory performance in a dataset of incidental nodules and outperformed the Brock model, but may need recalibration for clinical practice. Question What is the performance of a DL model for pulmonary nodule malignancy risk estimation developed on screening data in a dataset of incidentally detected nodules? Findings The DL model performed well on a dataset of nodules from clinical routine care and outperformed the Brock model in a size-matched subset. Clinical relevance This study provides further evidence about the potential of DL models for risk stratification of incidental nodules, which may improve nodule management in routine clinical practice.

The development of medical imaging techniques has made a significant contribution to clinical decision-making. However, the existence of suboptimal imaging quality, as indicated by irregular illumination or imbalanced intensity, presents significant obstacles in automating disease screening, analysis, and diagnosis. Existing approaches for natural image enhancement are mostly trained with numerous paired images, presenting challenges in data collection and training costs, all while lacking the ability to generalize effectively. Here, we introduce a pioneering training-free Diffusion Model for Universal Medical Image Enhancement, named UniMIE. UniMIE demonstrates its unsupervised enhancement capabilities across various medical image modalities without the need for any fine-tuning. It accomplishes this by relying solely on a single pre-trained model from ImageNet. We conduct a comprehensive evaluation on 13 imaging modalities and over 15 medical types, demonstrating better qualities, robustness, and accuracy than other modality-specific and data-inefficient models. By delivering high-quality enhancement and corresponding accuracy downstream tasks across a wide range of tasks, UniMIE exhibits considerable potential to accelerate the advancement of diagnostic tools and customized treatment plans. UniMIE represents a transformative approach to medical image enhancement, offering a versatile and robust solution that adapts to diverse imaging conditions. By improving image quality and facilitating better downstream analyses, UniMIE has the potential to revolutionize clinical workflows and enhance diagnostic accuracy across a wide range of medical applications.

Enhance mammography quality to increase cancer detection by implementing continuous AI-driven feedback mechanisms, ensuring reliable, consistent, and high-quality screening by the 'Perfect', 'Good', 'Moderate', and 'Inadequate' (PGMI) criteria. To assess the impact of the AI software 'b-box^TM' on mammography quality, we conducted a comparative analysis of PGMI scores. We evaluated scores 50 days before (A) and after the software's implementation in 2021 (B), along with assessments made in the first week of August 2022 (C1) and 2023 (C2), comparing them to evaluations conducted by two readers. Except for postsurgical patients, we included all diagnostic and screening mammograms from one tertiary hospital. A total of 4577 mammograms from 1220 women (mean age: 59, range: 21-94, standard deviation: 11.18) were included. 1728 images were obtained before (A) and 2330 images after the 2021 software implementation (B), along with 269 images in 2022 (C1) and 250 images in 2023 (C2). The results indicated a significant improvement in diagnostic image quality (p < 0.01). The percentage of 'Perfect' examinations rose from 22.34% to 32.27%, while 'Inadequate' images decreased from 13.31% to 5.41% in 2021, continuing the positive trend with 4.46% and 3.20% 'inadequate' images in 2022 and 2023, respectively (p < 0.01). Using a reliable software platform to perform AI-driven quality evaluation in real-time has the potential to make lasting improvements in image quality, support radiographers' professional growth, and elevate institutional quality standards and documentation simultaneously. Question How can AI-powered quality assessment reduce inadequate mammographic quality, which is known to impact sensitivity and increase the risk of interval cancers? Findings AI implementation decreased 'inadequate' mammograms from 13.31% to 3.20% and substantially improved parenchyma visualization, with consistent subgroup trends. Clinical relevance By reducing 'inadequate' mammograms and enhancing imaging quality, AI-driven tools improve diagnostic reliability and support better outcomes in breast cancer screening.

This work uses T1, STIR, and T2 MRI sequences of the lumbar vertebrae and BMD measurements to identify osteoporosis using deep learning. An analysis of 1350 MRI images from 50 individuals who had simultaneous BMD and MRI scans was performed. The accuracy of a custom convolution neural network for osteoporosis categorization was assessed using deep learning. T2-weighted MRIs were most diagnostic. The suggested model outperformed T1 and STIR sequences with 88.5% accuracy, 88.9% sensitivity, and 76.1% F1-score. Modern deep learning models like GoogleNet, EfficientNet-B3, ResNet50, InceptionV3, and InceptionResNetV2 were compared to its performance. These designs performed well, but our model was more sensitive and accurate. This research shows that T2-weighted MRI is the best sequence for osteoporosis diagnosis and that deep learning overcomes BMD-based approaches by reducing ionizing radiation. These results support clinical use of deep learning with MRI for safe, accurate, and quick osteoporosis diagnosis.

To develop and validate artificial intelligence models based on contrast-enhanced CT(CECT) images of venous phase using deep learning (DL) and Radiomics approaches to predict lymphovascular invasion in gastric cancer prior to surgery. We retrospectively analyzed data from 351 gastric cancer patients, randomly splitting them into two cohorts (training cohort, n = 246; testing cohort, n = 105) in a 7:3 ratio. The tumor region of interest (ROI) was outlined on venous phase CT images as the input for the development of radiomics, 2D and 3D DL models (DL2D and DL3D). Of note, by centering the analysis on the tumor's maximum cross-section and incorporating seven adjacent 2D images, we generated stable 2.5D data to establish a multi-instance learning (MIL) model. Meanwhile, the clinical and feature-combined models which integrated traditional CT enhancement parameters (Ratio), radiomics, and MIL features were also constructed. Models' performance was evaluated by the area under the curve (AUC), confusion matrices, and detailed metrics, such as sensitivity and specificity. A nomogram based on the combined model was established and applied to clinical practice. The calibration curve was used to evaluate the consistency between the predicted LVI of each model and the actual LVI of gastric cancer, and the decision curve analysis (DCA) was used to evaluate the net benefit of each model. Among the developed models, 2.5D MIL and combined models exhibited the superior performance in comparison to the clinical model, the radiomics model, the DL2D model, and the DL3D model as evidenced by the AUC values of 0.820, 0.822, 0.748, 0.725, 0.786, and 0.711 on testing set, respectively. Additionally, the 2.5D MIL and combined models also showed good calibration for LVI prediction, and could provide a net clinical benefit when the threshold probability ranged from 0.31 to 0.98, and from 0.28 to 0.84, indicating their clinical usefulness. The MIL and combined models highlight their performance in predicting preoperative lymphovascular invasion in gastric cancer, offering valuable insights for clinicians in selecting appropriate treatment options for gastric cancer patients.

Epicardial Adipose Tissue (EAT) is a recognized risk factor for cardiovascular diseases and plays a pivotal role in the pathophysiology of Atrial Fibrillation (AF). Accurate automatic segmentation of the EAT around the Left Atrium (LA) from Magnetic Resonance Imaging (MRI) data remains challenging. While Convolutional Neural Networks excel at multi-scale feature extraction using stacked convolutions, they struggle to capture long-range self-similarity and hierarchical relationships, which are essential in medical image segmentation. In this study, we present and validate PoinUNet, a deep learning model that integrates a Poincaré embedding layer into a 3D UNet to enhance LA wall and fat segmentation from Dixon MRI data. By using hyperbolic space learning, PoinUNet captures complex LA and EAT relationships and addresses class imbalance and fat geometry challenges using a new loss function. Sixty-six participants, including forty-eight AF patients, were scanned at 1.5T. The first network identified fat regions, while the second utilized Poincaré embeddings and convolutional layers for precise segmentation, enhanced by fat fraction maps. PoinUNet achieved a Dice Similarity Coefficient of 0.87 and a Hausdorff distance of 9.42 on the test set. This performance surpasses state-of-the-art methods, providing accurate quantification of the LA wall and LA EAT.

To evaluate the performance of magnetic resonance imaging (MRI)-based artificial intelligence (AI) in the preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). A systematic search of PubMed, Embase, and Web of Science was conducted up to May 2025, following PRISMA guidelines. Studies using MRI-based AI models with histopathologically confirmed MVI were included. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. Statistical synthesis used bivariate random-effects models. Twenty-nine studies were included, totaling 2838 internal and 1161 external validation cases. Pooled internal validation showed a sensitivity of 0.81 (95% CI: 0.76-0.85), specificity of 0.82 (95% CI: 0.78-0.85), diagnostic odds ratio (DOR) of 19.33 (95% CI: 13.15-28.42), and area under the curve (AUC) of 0.88 (95% CI: 0.85-0.91). External validation yielded a comparable AUC of 0.85. Traditional machine learning methods achieved higher sensitivity than deep learning approaches in both internal and external validation cohorts (both P < 0.05). Studies incorporating both radiomics and clinical features demonstrated superior sensitivity and specificity compared to radiomics-only models (P < 0.01). MRI-based AI demonstrates high performance for preoperative prediction of MVI in HCC, particularly for MRI-based models that combine multimodal imaging and clinical variables. However, substantial heterogeneity and low GRADE levels may affect the strength of the evidence, highlighting the need for methodological standardization and multicenter prospective validation to ensure clinical applicability.