The progress of artificial intelligence (AI) research in dental medicine is hindered by data acquisition challenges and imbalanced distributions. These problems are especially apparent when planning to develop AI-based diagnostic or analytic tools for various lesions, such as maxillary sinus lesions (MSL) including mucosal thickening and polypoid lesions. Traditional unsupervised generative models struggle to simultaneously control the image realism, diversity, and lesion-type specificity. This study establishes an expert-guided framework to overcome these limitations to elevate AI-based diagnostic accuracy. A StyleGAN2 framework was developed for generating clinically relevant MSL images (such as mucosal thickening and polypoid lesion) under expert control. The generated images were then integrated into training datasets to evaluate their effect on ResNet50's diagnostic performance. Here we show: 1) Both lesion subtypes achieve satisfactory fidelity metrics, with structural similarity indices (SSIM > 0.996) and maximum mean discrepancy values (MMD < 0.032), and clinical validation scores close to those of real images; 2) Integrating baseline datasets with synthetic images significantly enhances diagnostic accuracy for both internal and external test sets, particularly improving area under the precision-recall curve (AUPRC) by approximately 8% and 14% for mucosal thickening and polypoid lesions in the internal test set, respectively. The StyleGAN2-based image generation tool effectively addressed data scarcity and imbalance through high-quality MSL image synthesis, consequently boosting diagnostic model performance. This work not only facilitates AI-assisted preoperative assessment for maxillary sinus lift procedures but also establishes a methodological framework for overcoming data limitations in medical image analysis.

Platinum resistance of high-grade serous ovarian cancer (HGSOC) cannot currently be recognized by specific molecular biomarkers. We aimed to compare the predictive capacity of various models integrating MRI habitat, whole slide images (WSIs), and clinical parameters to predict platinum sensitivity in HGSOC patients. A retrospective study involving 998 eligible patients from four hospitals was conducted. MRI habitats were clustered using K-means algorithm on multi-parametric MRI. Following feature extraction and selection, a Habitat model was developed. Vision Transformer (ViT) and multi-instance learning were trained to derive the patch-level prediction and WSI-level prediction on hematoxylin and eosin (H&E)-stained WSIs, respectively, forming a Pathology model. Logistic regression (LR) was used to create a Clinic model. A multi-modal model integrating Clinic, Habitat, and Pathology (CHP) was constructed using Multi-Head Attention (MHA) and compared with the unimodal models and Ensemble multi-modal models. The area under the curve (AUC) and integrated discrimination improvement (IDI) value were used to assess model performance and gains. In the internal validation cohort and the external test cohort, the Habitat model showed the highest AUCs (0.722 and 0.685) compared to the Clinic model (0.683 and 0.681) and the Pathology model (0.533 and 0.565), respectively. The AUCs (0.789 and 0.807) of the multi-modal model interating CHP based on MHA were highest than those of any unimodal models and Ensemble multi-modal models, with positive IDI values. MRI-based habitat imaging showed potentials to predict platinum sensitivity in HGSOC patients. Multi-modal integration of CHP based on MHA was helpful to improve prediction performance.

This study centers around the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in the domain of computer vision, delving deeply into the issues pertaining to parameters and computational complexity that stem from the utilization of large convolutional kernels. Even though the size of the convolutional kernels has been extended up to 51×51, the enhancement of performance has hit a plateau, and moreover, striped convolution incurs a performance degradation. Enlightened by the hierarchical visual processing mechanism inherent in humans, this research innovatively incorporates a shared parameter mechanism for large convolutional kernels. It synergizes the expansion of the receptive field enabled by large convolutional kernels with the extraction of fine-grained features facilitated by small convolutional kernels. To address the surging number of parameters, a meticulously designed parameter sharing mechanism is employed, featuring fine-grained processing in the central region of the convolutional kernel and wide-ranging parameter sharing in the periphery. This not only curtails the parameter count and mitigates the model complexity but also sustains the model's capacity to capture extensive spatial relationships. Additionally, in light of the problems of spatial feature information loss and augmented memory access during the 1×1 convolutional channel compression phase, this study further puts forward a dynamic channel sampling approach, which markedly elevates the accuracy of tumor subregion segmentation. To authenticate the efficacy of the proposed methodology, a comprehensive evaluation has been conducted on three brain tumor segmentation datasets, namely BraTs2020, BraTs2024, and Medical Segmentation Decathlon Brain 2018. The experimental results evince that the proposed model surpasses the current mainstream ConvNet and Transformer architectures across all performance metrics, proffering novel research perspectives and technical stratagems for the realm of medical image segmentation.

Artificial intelligence (AI) has emerged as a transformative force in orthopedic surgery. Potentially encompassing pre-, intra-, and postoperative processes, it can process complex medical imaging, provide real-time surgical guidance, and analyze large datasets for outcome prediction and optimization. AI has shown improvements in surgical precision, efficiency, and patient outcomes across orthopedic subspecialties, and large language models and agentic AI systems are expanding AI utility beyond surgical applications into areas such as clinical documentation, patient education, and autonomous decision support. The successful implementation of AI in orthopedic surgery requires careful attention to validation, regulatory compliance, and healthcare system integration. As these technologies continue to advance, maintaining the balance between innovation and patient safety remains crucial, with the ultimate goal of achieving more personalized, efficient, and equitable healthcare delivery while preserving the essential role of human clinical judgment. This review examines the current landscape and future trajectory of AI applications in orthopedic surgery, highlighting both technological advances and their clinical impact. Studies have suggested that AI-assisted procedures achieve higher accuracy and better functional outcomes compared to conventional methods, while reducing operative times and complications. However, these technologies are designed to augment rather than replace clinical expertise, serving as sophisticated tools to enhance surgeons' capabilities and improve patient care.

To summarize recent advancements in artificial intelligence-driven lesion segmentation and novel neuroimaging modalities that enhance the identification and characterization of multiple sclerosis (MS) lesions, emphasizing their implications for clinical use and research. Artificial intelligence, particularly deep learning approaches, are revolutionizing MS lesion assessment and segmentation, improving accuracy, reproducibility, and efficiency. Artificial intelligence-based tools now enable automated detection not only of T2-hyperintense white matter lesions, but also of specific lesion subtypes, including gadolinium-enhancing, central vein sign-positive, paramagnetic rim, cortical, and spinal cord lesions, which hold diagnostic and prognostic value. Novel neuroimaging techniques such as quantitative susceptibility mapping (QSM), χ-separation imaging, and soma and neurite density imaging (SANDI), together with PET, are providing deeper insights into lesion pathology, better disentangling their heterogeneities and clinical relevance. Artificial intelligence-powered lesion segmentation tools hold great potential for improving fast, accurate and reproducible lesional assessment in the clinical scenario, thus improving MS diagnosis, monitoring, and treatment response assessment. Emerging neuroimaging modalities may contribute to advance the understanding MS pathophysiology, provide more specific markers of disease progression, and novel potential therapeutic targets.

Sinonasal pathology can be complex and requires a systematic and meticulous approach. Artificial Intelligence (AI) has the potential to improve diagnostic accuracy and efficiency in sinonasal imaging, but its clinical applicability remains an area of ongoing research. This systematic review evaluates the methodologies and clinical relevance of AI in detecting sinonasal pathology through radiological imaging. Key search terms included "artificial intelligence," "deep learning," "machine learning," "neural network," and "paranasal sinuses,". Abstract and full-text screening was conducted using predefined inclusion and exclusion criteria. Data were extracted on study design, AI architectures used (e.g., Convolutional Neural Networks (CNN), Machine Learning classifiers), and clinical characteristics, such as imaging modality (e.g., Computed Tomography (CT), Magnetic Resonance Imaging (MRI)). A total of 53 studies were analyzed, with 85% retrospective, 68% single-center, and 92.5% using internal databases. CT was the most common imaging modality (60.4%), and chronic rhinosinusitis without nasal polyposis (CRSsNP) was the most studied condition (34.0%). Forty-one studies employed neural networks, with classification as the most frequent AI task (35.8%). Key performance metrics included Area Under the Curve (AUC), accuracy, sensitivity, specificity, precision, and F1-score. Quality assessment based on CONSORT-AI yielded a mean score of 16.0 ± 2. AI shows promise in improving sinonasal imaging interpretation. However, as existing research is predominantly retrospective and single-center, further studies are needed to evaluate AI's generalizability and applicability. More research is also required to explore AI's role in treatment planning and post-treatment prediction for clinical integration.

This study aimed to develop an automated method for pulmonary artery and vein segmentation in both left and right lungs from computed tomography (CT) images using artificial intelligence (AI). The segmentations were evaluated using PulmoSR software, which provides 3D visualizations of patient-specific anatomy, potentially enhancing a surgeon's understanding of the lung structure. A dataset of 125 CT scans from lung segmentectomy patients at Erasmus MC was used. Manual annotations for pulmonary arteries and veins were created with 3D Slicer. nnU-Net models were trained for both lungs, assessed using Dice score, sensitivity, and specificity. Intraoperative recordings demonstrated clinical applicability. A paired t-test evaluated statistical significance of the differences between automatic and manual segmentations. The nnU-Net model, trained at full 3D resolution, achieved a mean Dice score between 0.91 and 0.92. The mean sensitivity and specificity were: left artery: 0.86 and 0.99, right artery: 0.84 and 0.99, left vein: 0.85 and 0.99, right vein: 0.85 and 0.99. The automatic method reduced segmentation time from ∼1.5 hours to under 5 min. Five cases were evaluated to demonstrate how the segmentations support lung segmentectomy procedures. P-values for Dice scores were all below 0.01, indicating statistical significance. The nnU-Net models successfully performed automatic segmentation of pulmonary arteries and veins in both lungs. When integrated with visualization tools, these automatic segmentations can enhance preoperative and intraoperative planning by providing detailed 3D views of patients anatomy.

Pancreatic Cancer (PC) is a highly aggressive tumor that is mainly diagnosed at later stages. Various imaging technologies, such as CT, MRI, and EUS, possess limitations in early PC diagnosis. Therefore, this review article explores the various innovative biomarkers for PC detection, such as DNA methylation, Noncoding RNAs, and proteomic biomarkers, and the role of AI in PC detection at early stages. Innovative biomarkers, such as DNA methylation genes, show higher specificity and sensitivity in PC diagnosis. Additionally, various non-coding RNAs, such as long non-coding RNAs (lncRNAs) and microRNAs, show high diagnostic accuracy and serve as diagnostic and prognostic biomarkers. Additionally, proteomic biomarkers retain higher diagnostic accuracy in different body fluids. Apart from this, the utilization of AI showed that AI surpassed the radiologist's diagnostic performance in PC detection. The combination of AI and advanced biomarkers can revolutionize early PC detection. However, large-scale, prospective studies are needed to validate its clinical utility. Further. standardization of biomarker panels and AI algorithms is a vital step toward their reliable applications in early PC detection, ultimately improving patient outcomes.

Thymomas, though rare, present a wide range of clinical behaviors, from indolent to aggressive forms, making accurate risk stratification crucial for treatment planning. Traditional methods such as histopathology and radiological assessments often lack the ability to capture tumor heterogeneity, which can impact prognosis. Radiomics, combined with machine learning, provides a method to extract and analyze quantitative imaging features, offering the potential to improve tumor classification and risk prediction. By segmenting tumors into distinct habitat zones, it becomes possible to assess intratumoral heterogeneity more effectively. This study employs radiomics and machine learning techniques to enhance thymoma risk prediction, aiming to improve diagnostic consistency and reduce variability in radiologists' assessments. This study aims to identify different habitat zones within thymomas through CT imaging feature analysis and to establish a predictive model to differentiate between high and low-risk thymomas. Additionally, the study explores how this model can assist radiologists. We obtained CT imaging data from 133 patients with thymoma who were treated at the Affiliated Hospital of Guangdong Medical University from 2015 to 2023. Images from the plain scan phase, venous phase, arterial phase, and their differential images (subtracted images) were used. Tumor regions were segmented into three habitat zones using K-Means clustering. Imaging features from each habitat zone were extracted using the PyRadiomics (van Griethuysen, 2017) library. The 28 most distinguishing features were selected through Mann-Whitney U tests (Mann, 1947) and Spearman's correlation analysis (Spearman, 1904). Five predictive models were built using the same machine learning algorithm (Support Vector Machine [SVM]): Habitat1, Habitat2, Habitat3 (trained on features from individual tumor habitat regions), Habitat All (trained on combined features from all regions), and Intra (trained on intratumoral features), and their performances were evaluated for comparison. The models' diagnostic outcomes were compared with the diagnoses of four radiologists (two junior and two experienced physicians). The AUC (area under curve) for habitat zone 1 was 0.818, for habitat zone 2 was 0.732, and for habitat zone 3 was 0.763. The comprehensive model, which combined data from all habitat zones, achieved an AUC of 0.960, outperforming the model based on traditional radiomic features (AUC of 0.720). The model significantly improved the diagnostic accuracy of all four radiologists. The AUCs for junior radiologists 1 and 2 increased from 0.747 and 0.775 to 0.932 and 0.972, respectively, while for experienced radiologists 1 and 2, the AUCs increased from 0.932 and 0.859 to 0.977 and 0.972, respectively. This study successfully identified distinct habitat zones within thymomas through CT imaging feature analysis and developed an efficient predictive model that significantly improved diagnostic accuracy. This model offers a novel tool for risk assessment of thymomas and can aid in guiding clinical decision-making.

Cone-beam CT (CBCT) is crucial for patient alignment and target verification in radiation therapy (RT). However, for non-coplanar beams, potential collisions between the treatment couch and the on-board imaging system limit the range that the gantry can be rotated. Limited-angle measurements are often insufficient to generate high-quality volumetric images for image-domain registration, therefore limiting the use of CBCT for position verification. An alternative to image-domain registration is to use a few 2D projections acquired by the onboard kV imager to register with the 3D planning CT for patient position verification, which is referred to as 2D-3D registration. The 2D-3D registration involves converting the 3D volume into a set of digitally reconstructed radiographs (DRRs) expected to be comparable to the acquired 2D projections. The domain gap between the generated DRRs and the acquired projections can happen due to the inaccurate geometry modeling in DRR generation and artifacts in the actual acquisitions. We aim to improve the efficiency and accuracy of the challenging 2D-3D registration problem in non-coplanar RT with limited-angle CBCT scans. We designed an accelerated, dataset-free, and patient-specific 2D-3D registration framework based on an implicit neural representation (INR) network and a composite similarity measure. The INR network consists of a lightweight three-layer multilayer perception followed by average pooling to calculate rigid motion parameters, which are used to transform the original 3D volume to the moving position. The Radon transform and imaging specifications at the moving position are used to generate DRRs with higher accuracy. We designed a composite similarity measure consisting of pixel-wise intensity difference and gradient differences between the generated DRRs and acquired projections to further reduce the impact of their domain gap on registration accuracy. We evaluated the proposed method on both simulation data and real phantom data acquired from a Varian TrueBeam machine. Comparisons with a conventional non-deep-learning registration approach and ablation studies on the composite similarity measure were conducted to demonstrate the efficacy of the proposed method. In the simulation data experiments, two X-ray projections of a head-and-neck image with <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><msup><mn>45</mn> <mo>∘</mo></msup> <annotation>${45}^\circ$</annotation></semantics> </math> discrepancy were used for the registration. The accuracy of the registration results was evaluated on experiments set up at four different moving positions with ground-truth moving parameters. The proposed method achieved sub-millimeter accuracy in translations and sub-degree accuracy in rotations. In the phantom experiments, a head-and-neck phantom was scanned at three different positions involving couch translations and rotations. We achieved translation errors of <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mo><</mo> <mn>2</mn> <mspace></mspace> <mi>mm</mi></mrow> <annotation>$< 2\nobreakspace {\rm mm}$</annotation></semantics> </math> and subdegree accuracy for pitch and roll. Experiments on registration using different numbers of projections with varying angle discrepancies demonstrate the improved accuracy and robustness of the proposed method, compared to both the conventional registration approach and the proposed approach without certain components of the composite similarity measure. We proposed a dataset-free lightweight INR-based registration with a composite similarity measure for the challenging 2D-3D registration problem with limited-angle CBCT scans. Comprehensive evaluations of both simulation data and experimental phantom data demonstrated the efficiency, accuracy, and robustness of the proposed method.