Novel artificial intelligence tools have the potential to significantly enhance productivity in medicine, while also maintaining or even improving treatment quality. In this study, we aimed to evaluate the current capability of ChatGPT-4.0 to accurately interpret multimodal musculoskeletal tumor cases.We created 25 cases, each containing images from X-ray, computed tomography, magnetic resonance imaging, or scintigraphy. ChatGPT-4.0 was tasked with classifying each case using a six-option, two-choice question, where both a primary and a secondary diagnosis were allowed. For performance evaluation, human raters also assessed the same cases.When only the primary diagnosis was taken into account, the accuracy of human raters was greater than that of ChatGPT-4.0 by a factor of nearly 2 (87% vs. 44%). However, in a setting that also considered secondary diagnoses, the performance gap shrank substantially (accuracy: 94% vs. 71%). Power analysis relying on Cohen's w confirmed the adequacy of the sample set size (n: 25).The tested artificial intelligence tool demonstrated lower performance than human raters. Considering factors such as speed, constant availability, and potential future improvements, it appears plausible that artificial intelligence tools could serve as valuable assistance systems for doctors in future clinical settings. · ChatGPT-4.0 classifies musculoskeletal cases using multimodal imaging inputs.. · Human raters outperform AI in primary diagnosis accuracy by a factor of nearly two.. · Including secondary diagnoses improves AI performance and narrows the gap.. · AI demonstrates potential as an assistive tool in future radiological workflows.. · Power analysis confirms robustness of study findings with the current sample size.. · Bosbach WA, Schoeni L, Beisbart C et al. Evaluating the Diagnostic Accuracy of ChatGPT-4.0 for Classifying Multimodal Musculoskeletal Masses: A Comparative Study with Human Raters. Rofo 2025; DOI 10.1055/a-2594-7085.

This paper describes PARADIM, a digital infrastructure designed to support research at the interface of data science and medical imaging, with a focus on Research Data Management best practices. The platform is built from open-source components and rooted in the FAIR principles through strict compliance with the DICOM standard. It addresses key needs in data curation, governance, privacy, and scalable resource management. Supporting every stage of the data science discovery cycle, the platform offers robust functionalities for user identity and access management, data de-identification, storage, annotation, as well as model training and evaluation. Rich metadata are generated all along the research lifecycle to ensure the traceability and reproducibility of results. PARADIM hosts several medical image collections and allows the automation of large-scale, computationally intensive pipelines (e.g., automatic segmentation, dose calculations, AI model evaluation). The platform fills a gap at the interface of data science and medical imaging, where digital infrastructures are key in the development, evaluation, and deployment of innovative solutions in the real world.

Diffusion models, while trained for image generation, have emerged as powerful foundational feature extractors for downstream tasks. We find that off-the-shelf diffusion models, trained exclusively to generate natural RGB images, can identify semantically meaningful correspondences in medical images. Building on this observation, we propose to leverage diffusion model features as a similarity measure to guide deformable image registration networks. We show that common intensity-based similarity losses often fail in challenging scenarios, such as when certain anatomies are visible in one image but absent in another, leading to anatomically inaccurate alignments. In contrast, our method identifies true semantic correspondences, aligning meaningful structures while disregarding those not present across images. We demonstrate superior performance of our approach on two tasks: multimodal 2D registration (DXA to X-Ray) and monomodal 3D registration (brain-extracted to non-brain-extracted MRI). Code: https://github.com/uncbiag/dgir

The incidence of Alzheimer's disease is rising with the increasing elderly population worldwide. While no cure exists, early diagnosis can significantly slow disease progression. Computer-aided diagnostic systems are becoming critical tools for assisting in the early detection of Alzheimer's disease. In this systematic review, we aim to evaluate recent advancements in computer-aided decision support systems for Alzheimer's disease diagnosis, focusing on data modalities, machine learning methods, and performance metrics. We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies published between 2021 and 2024 were retrieved from PubMed, IEEEXplore and Web of Science, using search terms related to Alzheimer's disease classification, neuroimaging, machine learning, and diagnostic performance. A total of 39 studies met the inclusion criteria, focusing on the use of Magnetic Resonance Imaging, Positron Emission Tomography, and biomarkers for Alzheimer's disease classification using machine learning models. Multimodal approaches, combining Magnetic Resonance Imaging with Positron Emission Tomography and Cognitive assessments, outperformed single-modality studies in diagnostic accuracy reliability. Convolutional Neural Networks were the most commonly used machine learning models, followed by hybrid models and Random Forest. The highest accuracy reported for binary classification was 100%, while multi-class classification achieved up to 99.98%. Techniques like Synthetic Minority Over-sampling Technique and data augmentation were frequently employed to address data imbalance, improving model generalizability. Our review highlights the advantages of using multimodal data in computer-aided decision support systems for more accurate Alzheimer's disease diagnosis. However, we also identified several limitations, including data imbalance, small sample sizes, and the lack of external validation in most studies. Future research should utilize larger, more diverse datasets, incorporate longitudinal data, and validate models in real-world clinical trials. Additionally, there is a growing need for explainability in machine learning models to ensure they are interpretable and trusted in clinical settings. While computer-aided decision support systems show great promise in improving the early diagnosis of Alzheimer's disease, further work is needed to enhance their robustness, generalizability, and clinical applicability. By addressing these challenges, computer-aided decision support systems could play a pivotal role in the early detection and management of Alzheimer's disease, potentially improving patient outcomes and reducing healthcare costs.

This investigation proposes a machine learning framework leveraging preoperative MRI and CT imaging data to predict postoperative complications related to distal instrumentation (DIP) in degenerative lumbar scoliosis patients undergoing long-segment fusion procedures. We retrospectively analyzed 136 patients, categorizing based on the development of DIP. Preoperative MRI and CT scans provided muscle function and bone density data, including the relative gross cross-sectional area and relative functional cross-sectional area of the multifidus, erector spinae, paraspinal extensor, psoas major muscles, the gross muscle fat index and functional muscle fat index, Hounsfield unit values of the lumbosacral region and the lower instrumented vertebra. Predictive factors for DIP were selected through stepwise LASSO regression. The filtered and all factors were incorporated into six machine learning algorithms twice, namely k-nearest neighbors, decision tree, support vector machine, random forest, multilayer perceptron (MLP), and Naïve Bayes, with tenfold cross-validation. Among patients, 16.9% developed DIP, with the multifidus' functional cross-sectional area and lumbosacral region's Hounsfield unit value as significant predictors. The MLP model exhibited superior performance when all predictive factors were input, with an average AUC of 0.98 and recall rate of 0.90. We compared various machine learning algorithms and constructed, trained, and validated predictive models based on muscle function and bone density-related variables obtained from preoperative CT and MRI, which could identify patients with high risk of DIP after long-segment spinal fusion surgery.

Compound figures, which are multi-panel composites containing diverse subfigures, are ubiquitous in biomedical literature, yet large-scale subfigure extraction remains largely unaddressed. Prior work on subfigure extraction has been limited in both dataset size and generalizability, leaving a critical open question: How does high-fidelity image-text alignment via large-scale subfigure extraction impact representation learning in vision-language models? We address this gap by introducing a scalable subfigure extraction pipeline based on transformer-based object detection, trained on a synthetic corpus of 500,000 compound figures, and achieving state-of-the-art performance on both ImageCLEF 2016 and synthetic benchmarks. Using this pipeline, we release OPEN-PMC-18M, a large-scale high quality biomedical vision-language dataset comprising 18 million clinically relevant subfigure-caption pairs spanning radiology, microscopy, and visible light photography. We train and evaluate vision-language models on our curated datasets and show improved performance across retrieval, zero-shot classification, and robustness benchmarks, outperforming existing baselines. We release our dataset, models, and code to support reproducible benchmarks and further study into biomedical vision-language modeling and representation learning.

Traditional guidance for intracranial aneurysm (IA) management is dichotomized by rupture status. Fundamental to the management of ruptured aneurysm is the detection and treatment of SAH, along with securing the aneurysm by the safest technique. On the other hand, unruptured aneurysms first require a careful assessment of their natural history versus treatment risk, including an imaging assessment of aneurysm size, location, and morphology, along with additional evidence-based risk factors such as smoking, hypertension, and family history. Unfortunately, a large proportion of ruptured aneurysms are in the lower risk size category (<7 mm), putting a premium on discovering a more refined noninvasive biomarker to detect and stratify aneurysm instability before rupture. In this review of aneurysm work-up, we cover the gamut of established imaging modalities (eg, CT, CTA, DSA, FLAIR, 3D TOF-MRA, contrast-enhanced-MRA) as well as more novel MR techniques (MR vessel wall imaging, dynamic contrast-enhanced MRI, computational fluid dynamics). Additionally, we evaluate the current landscape of artificial intelligence software and its integration into diagnostic and risk-stratification pipelines for IAs. These advanced MR techniques, increasingly complemented with artificial intelligence models, offer a paradigm shift by evaluating factors beyond size and morphology, including vessel wall inflammation, permeability, and hemodynamics. Additionally, we provide our institution's scan parameters for many of these modalities as a reference. Ultimately, this review provides an organized, up-to-date summary of the array of available modalities/sequences for IA imaging to help build protocols focused on IA characterization.

Compound figures, which are multi-panel composites containing diverse subfigures, are ubiquitous in biomedical literature, yet large-scale subfigure extraction remains largely unaddressed. Prior work on subfigure extraction has been limited in both dataset size and generalizability, leaving a critical open question: How does high-fidelity image-text alignment via large-scale subfigure extraction impact representation learning in vision-language models? We address this gap by introducing a scalable subfigure extraction pipeline based on transformer-based object detection, trained on a synthetic corpus of 500,000 compound figures, and achieving state-of-the-art performance on both ImageCLEF 2016 and synthetic benchmarks. Using this pipeline, we release OPEN-PMC-18M, a large-scale high quality biomedical vision-language dataset comprising 18 million clinically relevant subfigure-caption pairs spanning radiology, microscopy, and visible light photography. We train and evaluate vision-language models on our curated datasets and show improved performance across retrieval, zero-shot classification, and robustness benchmarks, outperforming existing baselines. We release our dataset, models, and code to support reproducible benchmarks and further study into biomedical vision-language modeling and representation learning.

Medical image reconstruction aims to generate high-quality images from sparsely sampled raw sensor data, which poses an ill-posed inverse problem. Traditional iterative reconstruction methods rely on prior information to empirically construct regularization terms, a process that is not trivial. While deep learning (DL)-based supervised reconstruction has made significant progress in improving image quality, it requires large-scale training data, which is difficult to obtain in medical imaging. Recently, implicit neural representation (INR) has emerged as a promising approach, offering a flexible and continuous representation of images by modeling the underlying signal as a function of spatial coordinates. This allows INR to capture fine details and complex structures more effectively than conventional discrete methods. This paper provides a comprehensive review of INR-based medical image reconstruction techniques, highlighting its growing impact on the field. The benefits of INR in both image and measurement domains are presented, and its advantages, limitations, and future research directions are discussed.&#xD.

We investigated pharmacokinetics, dosimetric patterns, and absorbed dose (AD)-effect correlations in [¹⁷⁷Lu]Lu-DOTATATE peptide receptor radionuclide therapy (PRRT) for metastatic neuroendocrine tumors (NETs) to develop strategies for future personalized dosimetry-guided treatments. <b>Methods:</b> Patients treated with standard [¹⁷⁷Lu]Lu-DOTATATE PRRT were recruited for serial SPECT/CT imaging. Kidneys were segmented on CT using a deep learning algorithm, and tumors were segmented at each cycle using a SPECT gradient-based tool, guided by radiologist-defined contours on baseline CT/MRI. Dosimetry was performed using an automated workflow that included contour intensity-based SPECT-SPECT registration, generation of Monte Carlo dose-rate maps, and dose-rate fitting. Lesion-level response at first follow-up was evaluated using both radiologic (RECIST and modified RECIST) and [⁶⁸Ga]Ga-DOTATATE PET-based criteria. Kidney toxicity was evaluated based on the estimated glomerular filtration rate (eGFR) at 9 mo after PRRT. <b>Results:</b> Dosimetry was performed after cycle 1 in 30 patients and after all cycles in 22 of 30 patients who completed SPECT/CT imaging after each cycle. Median cumulative tumor (<i>n</i> = 78) AD was 2.2 Gy/GBq (range, 0.1-20.8 Gy/GBq), whereas median kidney AD was 0.44 Gy/GBq (range, 0.25-0.96 Gy/GBq). The tumor-to-kidney AD ratio decreased with each cycle (median, 6.4, 5.7, 4.7, and 3.9 for cycles 1-4) because of a decrease in tumor AD, while kidney AD remained relatively constant. Higher-grade (grade 2) and pancreatic NETs showed a significantly larger drop in AD with each cycle, as well as significantly lower AD and effective half-life (T_eff), than did low-grade (grade 1) and small intestinal NETs, respectively. T_eff remained relatively constant with each cycle for both tumors and kidneys. Kidney T_eff and AD were significantly higher in patients with low eGFR than in those with high eGFR. Tumor AD was not significantly associated with response measures. There was no nephrotoxicity higher than grade 2; however, a significant negative association was found in univariate analyses between eGFR at 9 mo and AD to the kidney, which improved in a multivariable model that also adjusted for baseline eGFR (cycle 1 AD, <i>P</i> = 0.020, adjusted <i>R</i> ² = 0.57; cumulative AD, <i>P</i> = 0.049, adjusted <i>R</i> ² = 0.65). The association between percentage change in eGFR and AD to the kidney was also significant in univariate analysis and after adjusting for baseline eGFR (cycle 1 AD, <i>P</i> = 0.006, adjusted <i>R</i> ² = 0.21; cumulative AD, <i>P</i> = 0.019, adjusted <i>R</i> ² = 0.21). <b>Conclusion:</b> The dosimetric behavior we report over different cycles and for different NET subgroups can be considered when optimizing PRRT to individual patients. The models we present for the relationship between eGFR and AD have potential for clinical use in predicting renal function early in the treatment course. Furthermore, reported pharmacokinetics for patient subgroups allow more appropriate selection of population parameters to be used in protocols with fewer imaging time points that facilitate more widespread adoption of dosimetry.