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Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen, and Stable Diffusion. However, their use in medicine, where imaging data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic images may play a crucial role in privacy-preserving artificial intelligence and can also be used to augment small datasets. We show that diffusion probabilistic models can synthesize high-quality medical data for magnetic resonance imaging (MRI) and computed tomography (CT). For quantitative evaluation, two radiologists rated the quality of the synthesized images regarding \"realistic image appearance\", \"anatomical correctness\", and \"consistency between slices\". Furthermore, we demonstrate that synthetic images can be used in self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (Dice scores, 0.91 [without synthetic data], 0.95 [with synthetic data]).

Predicting vertebral height is complex due to individual factors. AI-based medical imaging analysis offers new opportunities for vertebral assessment. Thereby, these novel methods may contribute to sex-adapted nomograms and vertebral height prediction models, aiding in diagnosing spinal conditions like compression fractures and supporting individualized, sex-specific medicine. In this study an AI-based CT-imaging spine analysis of 262 subjects (mean age 32.36 years, range 20–54 years) was conducted, including a total of 3117 vertebrae, to assess sex-associated anatomical variations. Automated segmentations provided anterior, central, and posterior vertebral heights. Regression analysis with a cubic spline linear mixed-effects model was adapted to age, sex, and spinal segments. Measurement reliability was confirmed by two readers with an intraclass correlation coefficient (ICC) of 0.94–0.98. Female vertebral heights were consistently smaller than males ( p  < 0.05). The largest differences were found in the upper thoracic spine (T1–T6), with mean differences of 7.9–9.0%. Specifically, T1 and T2 showed differences of 8.6% and 9.0%, respectively. The strongest height increase between consecutive vertebrae was observed from T9 to L1 (mean slope of 1.46; 6.63% for females and 1.53; 6.48% for males). This study highlights significant sex-based differences in vertebral heights, resulting in sex-adapted nomograms that can enhance diagnostic accuracy and support individualized patient assessments.

Federated learning is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often face challenges like partially labeled datasets, where only a few locations have certain expert annotations, leaving large portions of unlabeled data unused. Leveraging these could enhance transformer architectures’ ability in regimes with small and diversely annotated sets. We conduct the largest federated cardiac CT analysis to date ( n = 8, 104) in a real-world setting across eight hospitals. Our two-step semi-supervised strategy distills knowledge from task-specific CNNs into a transformer. First, CNNs predict on unlabeled data per label type and then the transformer learns from these predictions with label-specific heads. This improves predictive accuracy and enables simultaneous learning of all partial labels across the federation, and outperforms UNet-based models in generalizability on downstream tasks. Code and model weights are made openly available for leveraging future cardiac CT analysis.

ObjectivesThis study aims to investigate how radiomics analysis can help understand the association between plaque texture, epicardial adipose tissue (EAT), and cardiovascular risk. Working with a Photon-counting CT, which exhibits enhanced feature stability, offers the potential to advance radiomics analysis and enable its integration into clinical routines.MethodsCoronary plaques were manually segmented in this retrospective, single-centre study and radiomic features were extracted using pyradiomics. The study population was divided into groups according to the presence of high-risk plaques (HRP), plaques with at least 50% stenosis, plaques with at least 70% stenosis, or triple-vessel disease. A combined group with patients exhibiting at least one of these risk factors was formed. Random forest feature selection identified differentiating features for the groups. EAT thickness and density were measured and compared with feature selection results.ResultsA total number of 306 plaques from 61 patients (mean age 61 years +/− 8.85 [standard deviation], 13 female) were analysed. Plaques of patients with HRP features or relevant stenosis demonstrated a higher presence of texture heterogeneity through various radiomics features compared to patients with only an intermediate stenosis degree. While EAT thickness did not significantly differ, affected patients showed significantly higher mean densities in the 50%, HRP, and combined groups, and insignificantly higher densities in the 70% and triple-vessel groups.ConclusionThe combination of a higher EAT density and a more heterogeneous plaque texture might offer an additional tool in identifying patients with an elevated risk of cardiovascular events.Clinical relevance statementCardiovascular disease is the leading cause of mortality globally. Plaque composition and changes in the EAT are connected to cardiac risk. A better understanding of the interrelation of these risk indicators can lead to improved cardiac risk prediction.Key PointsCardiac plaque composition and changes in the EAT are connected to cardiac risk.Higher EAT density and more heterogeneous plaque texture are related to traditional risk indicators.Radiomics texture analysis conducted on PCCT scans can help identify patients with elevated cardiac risk.

Maladaptive remodelling mechanisms occur in patients with repaired tetralogy of Fallot (rToF) resulting in a cycle of metabolic and structural changes. Biventricular shape analysis may indicate mechanisms associated with adverse events independent of pulmonary regurgitant volume index (PRVI). We aimed to determine novel remodelling patterns associated with adverse events in patients with rToF using shape and function analysis. Biventricular shape and function were studied in 192 patients with rToF (median time from TOF repair to baseline evaluation 13.5 years). Linear discriminant analysis (LDA) and principal component analysis (PCA) were used to identify shape differences between patients with and without adverse events. Adverse events included death, arrhythmias, and cardiac arrest with median follow-up of 10 years. LDA and PCA showed that shape characteristics pertaining to adverse events included a more circular left ventricle (LV) (decreased eccentricity), dilated (increased sphericity) LV base, increased right ventricular (RV) apical sphericity, and decreased RV basal sphericity. Multivariate LDA showed that the optimal discriminative model included only RV apical ejection fraction and one PCA mode associated with a more circular and dilated LV base (AUC = 0.77). PRVI did not add value, and shape changes associated with increased PRVI were not predictive of adverse outcomes. Pathological remodelling patterns in patients with rToF are significantly associated with adverse events, independent of PRVI. Mechanisms related to incident events include LV basal dilation with a reduced RV apical ejection fraction.

Objectives Structured reporting enhances comparability, readability, and content detail. Large language models (LLMs) could convert free text into structured data without disrupting radiologists’ reporting workflow. This study evaluated an on-premise, privacy-preserving LLM for automatically structuring free-text radiology reports. Materials and methods We developed an approach to controlling the LLM output, ensuring the validity and completeness of structured reports produced by a locally hosted Llama-2-70B-chat model. A dataset with de-identified narrative chest radiograph (CXR) reports was compiled retrospectively. It included 202 English reports from a publicly available MIMIC-CXR dataset and 197 German reports from our university hospital. Senior radiologist prepared a detailed, fully structured reporting template with 48 question-answer pairs. All reports were independently structured by the LLM and two human readers. Bayesian inference (Markov chain Monte Carlo sampling) was used to estimate the distributions of Matthews correlation coefficient (MCC), with [−0.05, 0.05] as the region of practical equivalence (ROPE). Results The LLM generated valid structured reports in all cases, achieving an average MCC of 0.75 (94% HDI: 0.70–0.80) and F1 score of 0.70 (0.70–0.80) for English, and 0.66 (0.62–0.70) and 0.68 (0.64–0.72) for German reports, respectively. The MCC differences between LLM and humans were within ROPE for both languages: 0.01 (−0.05 to 0.07), 0.01 (−0.05 to 0.07) for English, and −0.01 (−0.07 to 0.05), 0.00 (−0.06 to 0.06) for German, indicating approximately comparable performance. Conclusion Locally hosted, open-source LLMs can automatically structure free-text radiology reports with approximately human accuracy. However, the understanding of semantics varied across languages and imaging findings. Key Points Question Why has structured reporting not been widely adopted in radiology despite clear benefits and how can we improve this? Findings A locally hosted large language model successfully structured narrative reports, showing variation between languages and findings. Critical relevance Structured reporting provides many benefits, but its integration into the clinical routine is limited. Automating the extraction of structured information from radiology reports enables the capture of structured data while allowing the radiologist to maintain their reporting workflow.

PurposeGiven the multitude of challenges surgeons face during mitral valve repair surgery, they should have a high confidence in handling of instruments and in the application of surgical techniques before they enter the operating room. Unfortunately, opportunities for surgical training of minimally invasive repair are very limited, leading to a situation where most surgeons undergo a steep learning curve while operating the first patients.MethodsIn order to provide a realistic tool for surgical training, a commercial simulator was augmented by flexible patient-specific mitral valve replica. In an elaborated production pipeline, finalized after many optimization cycles, models were segmented from 3D ultrasound and then 3D-printable molds were computed automatically and printed in rigid material, the lower part being water-soluble. After silicone injection, the silicone model was dissolved from the mold and anchored in the simulator.ResultsTo our knowledge, our models are the first to comprise the full mitral valve apparatus, i.e., the annulus, leaflets, chordae tendineae and papillary muscles. Nine different valve molds were automatically created according to the proposed workflow (seven prolapsed valves and two valves with functional mitral insufficiency). From these mold geometries, 16 replica were manufactured. A material test revealed that EcoflexTM 00-30 is the most suitable material for leaflet-mimicking tissue out of seven mixtures. Production time was around 36 h per valve. Twelve surgeons performed various surgical techniques, e.g., annuloplasty, neo-chordae implantation, triangular leaflet resection, and assessed the realism of the valves very positively.ConclusionThe standardized production process guarantees a high anatomical recapitulation of the silicone valves to the segmented models and the ultrasound data. Models are of unprecedented quality and maintain a high realism during haptic interaction with instruments and suture material.

Recent progress in artificial intelligence (AI) includes generative models, multimodal foundation models, and federated learning, which enable a wide spectrum of novel exciting applications and scenarios for cardiac image analysis and cardiovascular interventions. The disruptive nature of these novel technologies enables concurrent text and image analysis by so-called vision–language transformer models. They not only allow for automatic derivation of image reports, synthesis of novel images conditioned on certain textual properties, and visual questioning and answering in an oral or written dialogue style, but also for the retrieval of medical images from a large database based on a description of the pathology or specifics of the dataset of interest. Federated learning is an additional ingredient in these novel developments, facilitating multi-centric collaborative training of AI approaches and therefore access to large clinical cohorts. In this review paper, we provide an overview of the recent developments in the field of cardiovascular imaging and intervention and offer a future outlook.

Purpose Machine learning approaches can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes. Surgical workflow and instrument recognition are two tasks that are complicated in this manner, because of heavy data imbalances resulting from different length of phases and their potential erratic occurrences. Furthermore, sub-properties like instrument (co-)occurrence are usually not particularly considered when defining the split. Methods We present a publicly available data visualization tool that enables interactive exploration of dataset partitions for surgical phase and instrument recognition. The application focuses on the visualization of the occurrence of phases, phase transitions, instruments, and instrument combinations across sets. Particularly, it facilitates assessment of dataset splits, especially regarding identification of sub-optimal dataset splits. Results We performed analysis of the datasets Cholec80, CATARACTS, CaDIS, M2CAI-workflow, and M2CAI-tool using the proposed application. We were able to uncover phase transitions, individual instruments, and combinations of surgical instruments that were not represented in one of the sets. Addressing these issues, we identify possible improvements in the splits using our tool. A user study with ten participants demonstrated that the participants were able to successfully solve a selection of data exploration tasks. Conclusion In highly unbalanced class distributions, special care should be taken with respect to the selection of an appropriate dataset split because it can greatly influence the assessments of machine learning approaches. Our interactive tool allows for determination of better splits to improve current practices in the field. The live application is available at https://cardio-ai.github.io/endovis-ml/ .