To investigate the prediction of a model constructed by combining machine learning (ML) with clinical features and ultrasound radiomics in the clinical staging of cervical cancer. General clinical and ultrasound data of 227 patients with cervical cancer who received transvaginal ultrasonography were retrospectively analyzed. The region of interest (ROI) radiomics profiles of the original image and derived image were retrieved and profile screening was performed. The chosen profiles were employed in radiomics model and Radscore formula construction. Prediction models were developed utilizing several ML algorithms by Python based on an integrated dataset of clinical features and ultrasound radiomics. Model performances were evaluated via AUC. Plot calibration curves and clinical decision curves were used to assess model efficacy. The model developed by support vector machine (SVM) emerged as the superior model. Integrating clinical characteristics with ultrasound radiomics, it showed notable performance metrics in both the training and validation datasets. Specifically, in the training set, the model obtained an AUC of 0.88 (95% Confidence Interval (CI): 0.83-0.93), alongside a 0.84 accuracy, 0.68 sensitivity, and 0.91 specificity. When validated, the model maintained an AUC of 0.77 (95% CI: 0.63-0.88), with 0.77 accuracy, 0.62 sensitivity, and 0.83 specificity. The calibration curve aligned closely with the perfect calibration line. Additionally, based on the clinical decision curve analysis, the model offers clinical utility over wide-ranging threshold possibilities. The clinical- and radiomics-based SVM model provides a noninvasive tool for predicting cervical cancer stage, integrating ultrasound radiomics and key clinical factors (age, abortion history) to improve risk stratification. This approach could guide personalized treatment (surgery vs. chemoradiation) and optimize staging accuracy, particularly in resource-limited settings where advanced imaging is scarce.

Machine learning technology has been extensively applied in the medical field, particularly in the context of disease prediction and patient rehabilitation assessment. Acute spinal cord injury (ASCI) is a sudden trauma that frequently results in severe neurological deficits and a significant decline in quality of life. Early prediction of neurological recovery is crucial for the personalized treatment planning. While extensively explored in other medical fields, this study is the first to apply multiple machine learning methods and Shapley Additive Explanations (SHAP) analysis specifically to ASCI for predicting neurological recovery. A total of 387 ASCI patients were included, with clinical, imaging, and laboratory data collected. Key features were selected using univariate analysis, Lasso regression, and other feature selection techniques, integrating clinical, radiomics, and laboratory data. A range of machine learning models, including XGBoost, Logistic Regression, KNN, SVM, Decision Tree, Random Forest, LightGBM, ExtraTrees, Gradient Boosting, and Gaussian Naive Bayes, were evaluated, with Gaussian Naive Bayes exhibiting the best performance. Radiomics features extracted from T2-weighted fat-suppressed MRI scans, such as original_glszm_SizeZoneNonUniformity and wavelet-HLL_glcm_SumEntropy, significantly enhanced predictive accuracy. SHAP analysis identified critical clinical features, including IMLL, INR, BMI, Cys C, and RDW-CV, in the predictive model. The model was validated and demonstrated excellent performance across multiple metrics. The clinical utility and interpretability of the model were further enhanced through the application of patient clustering and nomogram analysis. This model has the potential to serve as a reliable tool for clinicians in the formulation of personalized treatment plans and prognosis assessment.

This study presents a multimodal AI framework designed for precisely classifying medical diagnostic images. Utilizing publicly available datasets, the proposed system compares the strengths of convolutional neural networks (CNNs) and different large language models (LLMs). This in-depth comparative analysis highlights key differences in diagnostic performance, execution efficiency, and environmental impacts. Model evaluation was based on accuracy, F1-score, average execution time, average energy consumption, and estimated $CO_2$ emission. The findings indicate that although CNN-based models can outperform various multimodal techniques that incorporate both images and contextual information, applying additional filtering on top of LLMs can lead to substantial performance gains. These findings highlight the transformative potential of multimodal AI systems to enhance the reliability, efficiency, and scalability of medical diagnostics in clinical settings.

This study presents a multimodal AI framework designed for precisely classifying medical diagnostic images. Utilizing publicly available datasets, the proposed system compares the strengths of convolutional neural networks (CNNs) and different large language models (LLMs). This in-depth comparative analysis highlights key differences in diagnostic performance, execution efficiency, and environmental impacts. Model evaluation was based on accuracy, F1-score, average execution time, average energy consumption, and estimated $CO_2$ emission. The findings indicate that although CNN-based models can outperform various multimodal techniques that incorporate both images and contextual information, applying additional filtering on top of LLMs can lead to substantial performance gains. These findings highlight the transformative potential of multimodal AI systems to enhance the reliability, efficiency, and scalability of medical diagnostics in clinical settings.

Medical image translation has become an essential tool in modern radiotherapy, providing complementary information for target delineation and dose calculation. However, current approaches are constrained by their modality-specific nature, requiring separate model training for each pair of imaging modalities. This limitation hinders the efficient deployment of comprehensive multimodal solutions in clinical practice. To develop a unified image translation method using variational autoencoder (VAE) latent space mapping, which enables flexible conversion between different medical imaging modalities to meet clinical demands. We propose a three-stage approach to construct a unified image translation model. Initially, a VAE is trained to learn a shared latent space for various medical images. A stacked bidirectional transformer is subsequently utilized to learn the mapping between different modalities within the latent space under the guidance of the image modality. Finally, the VAE decoder is fine-tuned to improve image quality. Our internal dataset collected paired imaging data from 87 head and neck cases, with each case containing cone beam computed tomography (CBCT), computed tomography (CT), MR T1c, and MR T2W images. The effectiveness of this strategy is quantitatively evaluated on our internal dataset and a public dataset by the mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Additionally, the dosimetry characteristics of the synthetic CT images are evaluated, and subjective quality assessments of the synthetic MR images are conducted to determine their clinical value. The VAE with the Kullback‒Leibler (KL)-16 image tokenizer demonstrates superior image reconstruction ability, achieving a Fréchet inception distance (FID) of 4.84, a PSNR of 32.80 dB, and an SSIM of 92.33%. In synthetic CT tasks, the model shows greater accuracy in intramodality translations than in cross-modality translations, as evidenced by an MAE of 21.60 ± 8.80 Hounsfield unit (HU) in the CBCT-to-CT task and 45.23 ± 13.21 HU/47.55 ± 13.88 in the MR T1c/T2w-to-CT tasks. For the cross-contrast MR translation tasks, the results are very close, with mean PSNR and SSIM values of 26.33 ± 1.36 dB and 85.21% ± 2.21%, respectively, for the T1c-to-T2w translation and 26.03 ± 1.67 dB and 85.73% ± 2.66%, respectively, for the T2w-to-T1c translation. Dosimetric results indicate that all the gamma pass rates for synthetic CTs are higher than 99% for photon intensity-modulated radiation therapy (IMRT) planning. However, the subjective quality assessment scores for synthetic MR images are lower than those for real MR images. The proposed three-stage approach successfully develops a unified image translation model that can effectively handle a wide range of medical image translation tasks. This flexibility and effectiveness make it a valuable tool for clinical applications.

BackgroundAcute appendicitis remains a challenging diagnosis in pediatric populations, with high rates of misdiagnosis and negative appendectomies despite advances in imaging modalities. Current diagnostic tools, including clinical scoring systems like Alvarado and Pediatric Appendicitis Score (PAS), lack sufficient sensitivity and specificity, while reliance on CT scans raises concerns about radiation exposure, contrast hazards and sedation in children. Moreover, no established tool effectively predicts progression from uncomplicated to complicated appendicitis, creating a critical gap in clinical decision-making. ObjectiveTo develop and evaluate a machine learning model that integrates clinical, laboratory, and radiological findings for accurate diagnosis and complication prediction in pediatric appendicitis and to deploy this model as an interpretable web-based tool for clinical decision support. MethodsWe analyzed data from 780 pediatric patients (ages 0-18) with suspected appendicitis admitted to Childrens Hospital St. Hedwig, Regensburg, between 2016 and 2021. For severity prediction, our dataset was augmented with 430 additional cases from published literature and only the confirmed cases of acute appendicitis(n=602) were used. After feature selection using statistical methods and recursive feature elimination, we developed a Random Forest model named Dharma, optimized through hyperparameter tuning and cross-validation. Model performance was evaluated on independent test sets and compared with conventional diagnostic tools. ResultsDharma demonstrated superior diagnostic performance with an AUC-ROC of 0.96 ({+/-}0.02 SD) in cross-validation and 0.97-0.98 on independent test sets. At an optimal threshold of 64%, the model achieved specificity of 88%-98%, sensitivity of 89%-95%, and positive predictive value of 93%-99%. For complication prediction, Dharma attained a sensitivity of 93% ({+/-}0.05 SD) in cross-validation and 96% on the test set, with a negative predictive value of 98%. The model maintained strong performance even in cases where the appendix could not be visualized on ultrasonography (AUC-ROC 0.95, sensitivity 89%, specificity 87% at the threshold of 30%). ConclusionDharma is a novel, interpretable machine learning based clinical decision support tool designed to address the diagnostic challenges of pediatric appendicitis by integrating easily obtainable clinical, laboratory, and radiological data into a unified, real-time predictive framework. Unlike traditional scoring systems and imaging modalities, which may lack specificity or raise safety concerns in children, Dharma demonstrates high accuracy in diagnosing appendicitis and predicting progression from uncomplicated to complicated cases, potentially reducing unnecessary surgeries and CT scans. Its robust performance, even with incomplete imaging data, underscores its utility in resource-limited settings. Delivered through an intuitive, transparent, and interpretable web application, Dharma supports frontline providers--particularly in low- and middle-income settings--in making timely, evidence-based decisions, streamlining patient referrals, and improving clinical outcomes. By bridging critical gaps in current diagnostic and prognostic tools, Dharma offers a practical and accessible 21st-century solution tailored to real-world pediatric surgical care across diverse healthcare contexts. Furthermore, the underlying framework and concepts of Dharma may be adaptable to other clinical challenges beyond pediatric appendicitis, providing a foundation for broader applications of machine learning in healthcare. Author SummaryAccurate diagnosis of pediatric appendicitis remains challenging, with current clinical scores and imaging tests limited by sensitivity, specificity, predictive values, and safety concerns. We developed Dharma, an interpretable machine learning model that integrates clinical, laboratory, and radiological data to assist in diagnosing appendicitis and predicting its severity in children. Evaluated on a large dataset supplemented by published cases, Dharma demonstrated strong diagnostic and prognostic performance, including in cases with incomplete imaging--making it potentially especially useful in resource-limited settings for early decision-making and streamlined referrals. Available as a web-based tool, it provides real-time support to healthcare providers in making evidence-based decisions that could reduce negative appendectomies while avoiding hazards associated with advanced imaging modalities such as sedation, contrast, or radiation exposure. Furthermore, the open-access concepts and framework underlying Dharma have the potential to address diverse healthcare challenges beyond pediatric appendicitis.

Recent advancements in multimodal Large Language Models (LLMs) have significantly enhanced the automation of medical image analysis, particularly in generating radiology reports from chest X-rays (CXR). However, these models still suffer from hallucinations and clinically significant errors, limiting their reliability in real-world applications. In this study, we propose Look & Mark (L&M), a novel grounding fixation strategy that integrates radiologist eye fixations (Look) and bounding box annotations (Mark) into the LLM prompting framework. Unlike conventional fine-tuning, L&M leverages in-context learning to achieve substantial performance gains without retraining. When evaluated across multiple domain-specific and general-purpose models, L&M demonstrates significant gains, including a 1.2% improvement in overall metrics (A.AVG) for CXR-LLaVA compared to baseline prompting and a remarkable 9.2% boost for LLaVA-Med. General-purpose models also benefit from L&M combined with in-context learning, with LLaVA-OV achieving an 87.3% clinical average performance (C.AVG)-the highest among all models, even surpassing those explicitly trained for CXR report generation. Expert evaluations further confirm that L&M reduces clinically significant errors (by 0.43 average errors per report), such as false predictions and omissions, enhancing both accuracy and reliability. These findings highlight L&M's potential as a scalable and efficient solution for AI-assisted radiology, paving the way for improved diagnostic workflows in low-resource clinical settings.

Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has significant potential to advance the capabilities of nuclear neuroimaging. The current and emerging applications of ML and DL in the processing, analysis, enhancement and interpretation of SPECT and PET imaging are explored for brain imaging. Key developments include automated image segmentation, disease classification, and radiomic feature extraction, including lower dimensionality first and second order radiomics, higher dimensionality third order radiomics and more abstract fourth order deep radiomics. DL-based reconstruction, attenuation correction using pseudo-CT generation, and denoising of low-count studies have a role in enhancing image quality. AI has a role in sustainability through applications in radioligand design and preclinical imaging while federated learning addresses data security challenges to improve research and development in nuclear cerebral imaging. There is also potential for generative AI to transform the nuclear cerebral imaging space through solutions to data limitations, image enhancement, patient-centered care, workflow efficiencies and trainee education. Innovations in ML and DL are re-engineering the nuclear neuroimaging ecosystem and reimagining tomorrow's precision medicine landscape.

The integration of artificial intelligence (AI) with radiology signifies a transformative era in medicine. Vision foundation models have been adopted to enhance radiologic imaging analysis. However, the inherent complexities of 2D and 3D radiologic data present unique challenges that existing models, which are typically pretrained on general nonmedical images, do not adequately address. To bridge this gap and harness the diagnostic precision required in radiologic imaging, we introduce radiologic contrastive language-image pretraining (RadCLIP): a cross-modal vision-language foundational model that utilizes a vision-language pretraining (VLP) framework to improve radiologic image analysis. Building on the contrastive language-image pretraining (CLIP) approach, RadCLIP incorporates a slice pooling mechanism designed for volumetric image analysis and is pretrained using a large, diverse dataset of radiologic image-text pairs. This pretraining effectively aligns radiologic images with their corresponding text annotations, resulting in a robust vision backbone for radiologic imaging. Extensive experiments demonstrate RadCLIP's superior performance in both unimodal radiologic image classification and cross-modal image-text matching, underscoring its significant promise for enhancing diagnostic accuracy and efficiency in clinical settings. Our key contributions include curating a large dataset featuring diverse radiologic 2D/3D image-text pairs, pretraining RadCLIP as a vision-language foundation model on this dataset, developing a slice pooling adapter with an attention mechanism for integrating 2D images, and conducting comprehensive evaluations of RadCLIP on various radiologic downstream tasks.