The bone volume drilled by surgeons during mastoidectomy is determined by the need to localize the position, optimize the view, and reach the surgical endpoint while avoiding critical structures. Predicting the volume of bone removed before an operation can significantly enhance surgical training by providing precise, patient-specific guidance and enable the development of more effective computer-assisted and robotic surgical interventions. Single institution, cross-sectional. VR simulation. We developed a deep learning pipeline to automate the prediction of bone volume removed during mastoidectomy using data from virtual reality mastoidectomy simulations. The data set included 15 deidentified temporal bone computed tomography scans. The network was evaluated using fivefold cross-validation, comparing predicted and actual bone removal with metrics such as the Dice score (DSC) and Hausdorff distance (HD). Our method achieved a median DSC of 0.775 (interquartile range [IQR]: 0.725-0.810) and a median HD of 0.492 mm (IQR: 0.298-0.757 mm). Predictions reached the mastoidectomy endpoint of visualizing the horizontal canal and incus in 80% (12/15) of temporal bones. Qualitative analysis indicated that predictions typically produced realistic mastoidectomy endpoints, though some cases showed excessive or insufficient bone removal, particularly at the temporal bone cortex and tegmen mastoideum. This study establishes a foundational step in using deep learning to predict bone volume removal during mastoidectomy. The results indicate that learning-based methods can reasonably approximate the surgical endpoint of mastoidectomy. Further refinement with larger, more diverse data sets and improved model architectures will be essential for enhancing prediction accuracy.

Achieving clinical level performance and widespread deployment for generating radiology impressions encounters a giant challenge for conventional artificial intelligence models tailored to specific diseases and organs. Concurrent with the increasing accessibility of radiology reports and advancements in modern general AI techniques, the emergence and potential of deployable radiology AI exploration have been bolstered. Here, we present ChatRadio-Valuer, the first general radiology diagnosis large language model for localized deployment within hospitals and being close to clinical use for multi-institution and multi-system diseases. ChatRadio-Valuer achieved 15 state-of-the-art results across five human systems and six institutions in clinical-level events (n=332,673) through rigorous and full-spectrum assessment, including engineering metrics, clinical validation, and efficiency evaluation. Notably, it exceeded OpenAI's GPT-3.5 and GPT-4 models, achieving superior performance in comprehensive disease diagnosis compared to the average level of radiology experts. Besides, ChatRadio-Valuer supports zero-shot transfer learning, greatly boosting its effectiveness as a radiology assistant, while ensuring adherence to privacy standards and being readily utilized for large-scale patient populations. Our expeditions suggest the development of localized LLMs would become an imperative avenue in hospital applications.

This study aims to integrate CT imaging with occupational health surveillance data to construct a multimodal model for preclinical CWP identification and individualized risk evaluation. CT images and occupational health surveillance data were retrospectively collected from 874 coal workers, including 228 Stage I and 4 Stage II pneumoconiosis patients, along with 600 healthy and 42 subcategory 0/1 coal workers. First, the YOLOX was employed for automated 3D lung extraction to extract radiomics features. Second, two feature selection algorithms were applied to select critical features from both CT radiomics and occupational health data. Third, three distinct feature sets were constructed for model training: CT radiomics features, occupational health data, and their multimodal integration. Finally, five machine learning models were implemented to predict the preclinical stage of CWP. The model's performance was evaluated using the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity. SHapley Additive exPlanation (SHAP) values were calculated to determine the prediction role of each feature in the model with the highest predictive performance. The YOLOX-based lung extraction demonstrated robust performance, achieving an Average Precision (AP) of 0.98. 8 CT radiomic features and 4 occupational health surveillance data were selected for the multimodal model. The optimal occupational health surveillance feature subset comprised the Length of service. Among 5 machine learning algorithms evaluated, the Decision Tree-based multimodal model showed superior predictive capacity on the test set of 142 samples, with an AUC of 0.94 (95% CI 0.88-0.99), accuracy 0.95, specificity 1.00, and Youden's index 0.83. SHAP analysis indicated that Total Protein Results, original shape Flatness, diagnostics Image original Mean were the most influential contributors. Our study demonstrated that the multimodal model demonstrated strong predictive capability for the preclinical stage of CWP by integrating CT radiomic features with occupational health data.

The composition of urinary calculi serves as a critical determinant for personalized surgical strategies; however, such compositional data are often unavailable preoperatively. This study aims to develop a machine learning-based preoperative prediction model for stone composition and evaluate its clinical utility. A retrospective cohort study design was employed to include patients with urinary calculi admitted to the Department of Urology at the Second Affiliated Hospital of Zhengzhou University from 2019 to 2024. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression combined with multivariate logistic regression, and a binary prediction model for urinary calculi was subsequently constructed. Model validation was conducted using metrics such as the area under the curve (AUC), while Shapley Additive Explanations(SHAP) values were applied to interpret the predictive outcomes. Among 708 eligible patients, distinct prediction models were established for four stone types: calcium oxalate stones: Logistic regression achieved optimal performance (AUC = 0.845), with maximum stone CT value, 24-hour urinary oxalate, and stone size as top predictors (SHAP-ranked); infection stones: Logistic regression (AUC = 0.864) prioritized stone size, urinary pH, and recurrence history; uric acid stones: LASSO-ridge-elastic net model demonstrated exceptional accuracy (AUC = 0.961), driven by maximum CT value, 24-hour oxalate, and urinary calcium; calcium-containing stones: Logistic regression attained better prediction (AUC = 0.953), relying on CT value, 24-hour calcium, and stone size. This study developed a machine learning prediction model based on multi-algorithm integration, achieving accurate preoperative discrimination of urinary stone composition. The integration of key imaging features with metabolic indicators enhanced the model's predictive performance.

Body surface area (BSA)-based chemotherapy dosing remains standard despite its limitations in predicting toxicity. Variations in body composition, particularly skeletal muscle and adipose tissue, influence drug metabolism and toxicity risk. This study aims to investigate the mediating role of body composition in the relationship between BSA-based dosing and dose-limiting toxicities (DLTs) in colorectal cancer patients receiving oxaliplatin-based chemotherapy. We retrospectively analyzed 483 stage III colorectal cancer patients treated at National Cheng Kung University Hospital (2013-2021). An artificial intelligence (AI)-driven algorithm quantified skeletal muscle and adipose tissue compartments from lumbar 3 (L3) vertebral-level computed tomography (CT) scans. Mediation analysis evaluated body composition's role in chemotherapy-related toxicities. Among the cohort, 18.2% (n = 88) experienced DLTs. While BSA alone was not significantly associated with DLTs (OR = 0.473, p = 0.376), increased intramuscular adipose tissue (IMAT) significantly predicted higher DLT risk (OR = 1.047, p = 0.038), whereas skeletal muscle area was protective. Mediation analysis confirmed that IMAT partially mediated the relationship between BSA and DLTs (indirect effect: 0.05, p = 0.040), highlighting adipose infiltration's role in chemotherapy toxicity. BSA-based dosing inadequately accounts for interindividual variations in chemotherapy tolerance. AI-assisted body composition analysis provides a precision oncology framework for identifying high-risk patients and optimizing chemotherapy regimens. Prospective validation is warranted to integrate body composition into routine clinical decision-making.

Generative artificial intelligence models facilitate open-data sharing by proposing synthetic data as surrogates of real patient data. Despite the promise for healthcare, some of these models are susceptible to patient data memorization, where models generate patient data copies instead of novel synthetic samples, resulting in patient re-identification. Here we assess memorization in unconditional latent diffusion models by training them on a variety of datasets for synthetic data generation and detecting memorization with a self-supervised copy detection approach. We show a high degree of patient data memorization across all datasets, with approximately 37.2% of patient data detected as memorized and 68.7% of synthetic samples identified as patient data copies. Latent diffusion models are more susceptible to memorization than autoencoders and generative adversarial networks, and they outperform non-diffusion models in synthesis quality. Augmentation strategies during training, small architecture size and increasing datasets can reduce memorization, while overtraining the models can enhance it. These results emphasize the importance of carefully training generative models on private medical imaging datasets and examining the synthetic data to ensure patient privacy.

Considerable socioeconomic disparities exist among pediatric traumatic brain injury (TBI) patients. This study aims to analyze the effects of social determinants of health on head injury outcomes and to create a novel machine-learning algorithm (MLA) that incorporates socioeconomic factors to predict the likelihood of a positive or negative trauma-related finding on head computed tomography (CT). A cohort of blunt trauma patients under age 15 who presented to the largest safety net hospital in New England between January 2006 and December 2013 (n=211) was included in this study. Patient socioeconomic data such as race, language, household income, and insurance type were collected alongside other parameters like Injury Severity Score (ISS), age, sex, and mechanism of injury. Multivariable analysis was performed to identify significant factors in predicting a positive head CT outcome. The cohort was split into 80% training (168 samples) and 20% testing (43 samples) datasets using stratified sampling. Twenty-two multi-parametric MLAs were trained with 5-fold cross-validation and hyperparameter tuning via GridSearchCV, and top-performing models were evaluated on the test dataset. Significant factors associated with pediatric head CT outcome included ISS, age, and insurance type (p<0.05). The age of the subjects with a clinically relevant trauma-related head CT finding (median= 1.8 years) was significantly different from the age of patients without such findings (median= 9.1 years). These predictors were utilized to train the machine learning models. With ISS, the Fine Gaussian SVM achieved the highest test AUC (0.923), with accuracy=0.837, sensitivity=0.647, and specificity=0.962. The Coarse Tree yielded accuracy=0.837, AUC=0.837, sensitivity=0.824, and specificity=0.846. Without ISS, the Narrow Neural Network performed best with accuracy=0.837, AUC=0.857, sensitivity=0.765, and specificity=0.885. Key predictors of clinically relevant head CT findings in pediatric TBI include ISS, age, and social determinants of health, with children under 5 at higher risk. A novel Fine Gaussian SVM model outperformed other MLA, offering high accuracy in predicting outcomes. This tool shows promise for improving clinical decisions while minimizing radiation exposure in children. TBI = Traumatic Brain Injury; ISS = Injury Severity Score; MLA = Machine Learning Algorithm; CT = Computed Tomography; AUC = Area Under the Curve.

The core role of medical images in disease diagnosis makes their quality directly affect the accuracy of clinical judgment. However, due to factors such as low-dose scanning, equipment limitations and imaging artifacts, medical images are often accompanied by non-uniform noise interference, which seriously affects structure recognition and lesion detection. This paper proposes a medical image adaptive denoising model (MI-ND) that integrates multi-scale convolutional and Transformer architecture, introduces a noise level estimator (NLE) and a noise adaptive attention module (NAAB), and realizes channel-spatial attention regulation and cross-modal feature fusion driven by noise perception. Systematic testing is carried out on multimodal public datasets. Experiments show that this method significantly outperforms the comparative methods in image quality indicators such as PSNR, SSIM, and LPIPS, and improves the F1 score and ROC-AUC in downstream diagnostic tasks, showing strong prac-tical value and promotional potential. The model has outstanding benefits in structural recovery, diagnostic sensitivity, and cross-modal robustness, and provides an effective solution for medical image enhancement and AI-assisted diagnosis and treatment.

In medical image processing, accurate diagnosis is of paramount importance. Leveraging machine learning techniques, particularly top-rank learning, shows significant promise by focusing on the most crucial instances. However, challenges arise from noisy labels and class-ambiguous instances, which can severely hinder the top-rank objective, as they may be erroneously placed among the top-ranked instances. To address these, we propose a novel approach that enhances toprank learning by integrating a rejection module. Cooptimized with the top-rank loss, this module identifies and mitigates the impact of outliers that hinder training effectiveness. The rejection module functions as an additional branch, assessing instances based on a rejection function that measures their deviation from the norm. Through experimental validation on a medical dataset, our methodology demonstrates its efficacy in detecting and mitigating outliers, improving the reliability and accuracy of medical image diagnoses.

The volume of image data generated in the medical field is continuously increasing. Manual annotation is both costly and prone to human error. Additionally, deep learning-based medical image algorithms rely on large, accurately annotated training datasets, which are expensive to produce and often result in instability. This study introduces LR-COBRAS, an interactive computer-aided data annotation algorithm designed for medical experts. LR-COBRAS aims to assist healthcare professionals in achieving more precise annotation outcomes through interactive processes, thereby optimizing medical image annotation tasks. The algorithm enhances must-link and cannot-link constraints during interactions through a logic reasoning module. It automatically generates potential constraint relationships, reducing the frequency of user interactions and improving clustering accuracy. By utilizing rules such as symmetry, transitivity, and consistency, LR-COBRAS effectively balances automation with clinical relevance. Experimental results based on the MedMNIST＋ dataset and ChestX-ray8 dataset demonstrate that LR-COBRAS significantly outperforms existing methods in clustering accuracy, efficiency, and interactive burden, showcasing superior robustness and applicability. This algorithm provides a novel solution for intelligent medical image analysis. The source code for our implementation is available on https://github.com/cjw-bbxc/MILR-COBRAS.