Intraoperative ultrasound scanning is a demanding visuotactile task. It requires operators to simultaneously localise the ultrasound perspective and manually perform slight adjustments to the pose of the probe, making sure not to apply excessive force or breaking contact with the tissue, while also characterising the visible tissue. To analyse the probe-tissue contact, an iterative filtering and topological method is proposed to identify the underlying visible tissue, which can be used to detect acoustic shadow and construct confidence maps of perceptual salience. For evaluation, datasets containing both in vivo and medical phantom data are created. A suite of evaluations is performed, including an evaluation of acoustic shadow classification. Compared to an ablation, deep learning, and statistical method, the proposed approach achieves superior classification on in vivo data, achieving an <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>F</mi> <mi>β</mi></msub> </math> score of 0.864, in comparison with 0.838, 0.808, and 0.808. A novel framework for evaluating the confidence estimation of probe-tissue contact is created. The phantom data are captured specifically for this, and comparison is made against two established methods. The proposed method produced the superior response, achieving an average normalised root-mean-square error of 0.168, in comparison with 1.836 and 4.542. Evaluation is also extended to determine the algorithm's robustness to parameter perturbation, speckle noise, data distribution shift, and capability for guiding a robotic scan. The results of this comprehensive set of experiments justify the potential clinical value of the proposed algorithm, which can be used to support clinical training and robotic ultrasound automation.

Radiomics is transforming medical imaging by extracting complex features that enhance disease diagnosis, prognosis, and treatment evaluation. However, traditional approaches face significant challenges, such as the need for manual feature engineering, high dimensionality, and limited sample sizes. This paper presents Radio DINO, a novel family of deep learning foundation models that leverage self-supervised learning (SSL) techniques from DINO and DINOV2, pretrained on the RadImageNet dataset. The novelty of our approach lies in (1) developing Radio DINO to capture rich semantic embeddings, enabling robust feature extraction without manual intervention, (2) demonstrating superior performance across various clinical tasks on the MedMNISTv2 dataset, surpassing existing models, and (3) enhancing the interpretability of the model by providing visualizations that highlight its focus on clinically relevant image regions. Our results show that Radio DINO has the potential to democratize advanced radiomics tools, making them accessible to healthcare institutions with limited resources and ultimately improving diagnostic and prognostic outcomes in radiology.

This retrospective study aims to develop a deep learning-based approach to whole-body CT segmentation out of standard PSMA-PET-CT to assess body composition in metastatic castration resistant prostate cancer (mCRPC) patients prior to [¹⁷⁷Lu]Lu-PSMA radioligand therapy (RLT). Our goal is to go beyond standard PSMA-PET-based pretherapeutic assessment and identify additional body composition metrics out of the CT-component, with potential prognostic value. We used a deep learning segmentation model to perform fully automated segmentation of different tissue compartments, including visceral- (VAT), subcutaneous- (SAT), intra/intermuscular- adipose tissue (IMAT) from [⁶⁸ Ga]Ga-PSMA-PET-CT scans of n = 86 prostate cancer patients before RLT. The proportions of different adipose tissue compartments to total adipose tissue (TAT) assessed on a 3D CT-volume of the abdomen or on a 2D single slice basis (centered at third lumbal vertebra (L3)) were compared for their prognostic value. First, univariate and multivariate Cox proportional hazards regression analyses were performed. Subsequently, the subjects were dichotomized at the median tissue composition, and these subgroups were evaluated by Kaplan-Meier analysis with the log-rank test. The automated segmentation model was useful for delineating different adipose tissue compartments and skeletal muscle across different patient anatomies. Analyses revealed significant correlations between lower SAT and higher IMAT ratios and poorer therapeutic outcomes in Cox regression analysis (SAT/TAT: p = 0.038; IMAT/TAT: p < 0.001) in the 3D model. In the single slice approach only IMAT/SAT was significantly associated with survival in Cox regression analysis (p < 0.001; SAT/TAT: p > 0.05). IMAT ratio remained an independent predictor of survival in multivariate analysis when including PSMA-PET and blood-based prognostic factors. In this proof-of-principle study the implementation of a deep learning-based whole-body analysis provides a robust and detailed CT-based assessment of body composition in mCRPC patients undergoing RLT. Potential prognostic parameters have to be corroborated in larger prospective datasets.

Infant meningitis can be a life-threatening disease and requires prompt and accurate diagnosis to prevent severe outcomes or death. Gold-standard diagnosis requires lumbar puncture (LP) to obtain and analyze cerebrospinal fluid (CSF). Despite being standard practice, LPs are invasive, pose risks for the patient and often yield negative results, either due to contamination with red blood cells from the puncture itself or because LPs are routinely performed to rule out a life-threatening infection, despite the disease's relatively low incidence. Furthermore, in low-income settings where incidence is the highest, LPs and CSF exams are rarely feasible, and suspected meningitis cases are generally treated empirically. There is a growing need for non-invasive, accurate diagnostic methods. We developed a three-stage deep learning framework using Neosonics ultrasound technology for 30 infants with suspected meningitis and a permeable fontanelle at three Spanish University Hospitals (from 2021 to 2023). In stage 1, 2194 images were processed for quality control using a vessel/non-vessel model, with a focus on vessel identification and manual removal of images exhibiting artifacts such as poor coupling and clutter. This refinement process resulted in a final cohort comprising 16 patients-6 cases (336 images) and 10 controls (445 images), yielding 781 images for the second stage. The second stage involved the use of a deep learning model to classify images based on a white blood cell count threshold (set at 30 cells/mm³) into control or meningitis categories. The third stage integrated explainable artificial intelligence (XAI) methods, such as Grad-CAM visualizations, alongside image statistical analysis, to provide transparency and interpretability of the model's decision-making process in our artificial intelligence-driven screening tool. Our approach achieved 96% accuracy in quality control and 93% precision and 92% accuracy in image-level meningitis detection, with an overall patient-level accuracy of 94%. It identified 6 meningitis cases and 10 controls with 100% sensitivity and 90% specificity, demonstrating only a single misclassification. The use of gradient-weighted class activation mapping-based XAI significantly enhanced diagnostic interpretability, and to further refine our insights we incorporated a statistics-based XAI approach. By analyzing image metrics such as entropy and standard deviation, we identified texture variations in the images attributable to the presence of cells, which improved the interpretability of our diagnostic tool. This study supports the efficacy of a multi-stage deep learning model for non-invasive screening of infant meningitis and its potential to guide the need for LPs. It also highlights the transformative potential of artificial intelligence in medical diagnostic screening for neonatal health care, paving the way for future research and innovations.

Transcranial Doppler (TCD) ultrasound has significant clinical value for assessing cerebral hemodynamics, but its reliance on operator expertise limits broader clinical adoption. In this work, we present a lightweight real-time deep learning-based approach capable of automatically identifying the middle cerebral artery (MCA) in TCD Color Doppler images. Two state-of-the-art object detection models, YOLOv10 and Real-Time Detection Transformers (RT-DETR), were investigated for automated MCA detection in real-time. TCD Color Doppler data (41 subjects; 365 videos; 61,611 frames) were collected from neurologically healthy individuals (n = 31) and stroke patients (n = 10). MCA bounding box annotations were performed by clinical experts on all frames. Model training consisted of pretraining utilizing a large abdominal ultrasound dataset followed by subsequent fine-tuning on acquired TCD data. Detection performance at the instance and frame levels, and inference speed were assessed through four-fold cross-validation. Inter-rater agreement between model and two human expert readers was assessed using distance between bounding boxes and inter-rater variability was quantified using the individual equivalence coefficient (IEC) metric. Both YOLOv10 and RT-DETR models showed comparable frame level accuracy for MCA presence, with F1 scores of 0.884 ± 0.023 and 0.884 ± 0.019 respectively. YOLOv10 outperformed RT-DETR for instance-level localization accuracy (AP: 0.817 vs. 0.780) and had considerably faster inference speed on a desktop CPU (11.6 ms vs. 91.14 ms). Furthermore, YOLOv10 showed an average inference time of 36 ms per frame on a tablet device. The IEC was -1.08 with 95 % confidence interval: [-1.45, -0.19], showing that the AI predictions deviated less from each reader than the readers' annotations deviated from each other. Real-time automated detection of the MCA is feasible and can be implemented on mobile platforms, potentially enabling wider clinical adoption by less-trained operators in point-of-care settings.

This study evaluates the performance of Iterative and AI-based Reconstruction algorithms in CT imaging for brain, chest, and upper abdomen assessments. Using a 320-slice CT scanner, phantom images were analysed through quantitative metrics such as Noise, Contrast-to-Noise-Ratio and Target Transfer Function. Additionally, five radiologists performed subjective evaluations on real patient images by scoring clinical parameters related to anatomical structures across the three body sites. The study aimed to relate results obtained with the typical approach related to parameters involved in medical physics using a Catphan physical phantom, with the evaluations assigned by the radiologists to the clinical parameters chosen in this study, and to determine whether the physical approach alone can ensure the implementation of new procedures and the optimization in clinical practice. AI-based algorithms demonstrated superior performance in chest and abdominal imaging, enhancing parenchymal and vascular detail with notable reductions in noise. However, their performance in brain imaging was less effective, as the aggressive noise reduction led to excessive smoothing, which affected diagnostic interpretability. Iterative reconstruction methods provided balanced results for brain imaging, preserving structural details and maintaining diagnostic clarity. The findings emphasize the need for region-specific optimization of reconstruction protocols. While AI-based methods can complement traditional IR techniques, they should not be assumed to inherently improve outcomes. A critical and cautious introduction of AI-based techniques is essential, ensuring radiologists adapt effectively without compromising diagnostic accuracy.

IntroductionPoint-of-care ultrasonography (POCUS) enables clinicians to obtain critical diagnostic information at the bedside especially in resource limited settings. This information may include 2D cardiac quantitative data, although measuring the data manually can be time-consuming and subject to user experience. Artificial intelligence (AI) can potentially automate this quantification. This study assessed the interpretation of key cardiac measurements on POCUS images by an AI-enabled device (AISAP Cardio V1.0). MethodsThis retrospective diagnostic accuracy study included 200 POCUS cases from four hospitals (two in Israel and two in the United States). Each case was independently interpreted by three cardiologists and the device for seven measurements (left ventricular (LV) ejection fraction, inferior vena cava (IVC) maximal diameter, left atrial (LA) area, right atrial (RA) area, LV end diastolic diameter, right ventricular (RV) fractional area change and aortic root diameter). The endpoints were the root mean square error (RMSE) of the device compared to the average cardiologist measurement (LV ejection fraction and IVC maximal diameter were primary endpoints; the other measurements were secondary endpoints). Predefined passing criteria were based on the upper bounds of the RMSE 95% confidence intervals (CIs). The inter-cardiologist RMSE was also calculated for reference. ResultsThe device achieved the passing criteria for six of the seven measurements. While not achieving the passing criterion for RV fractional area change, it still achieved a better RMSE than the inter-cardiologist RMSE. The RMSE was 6.20% (95% CI: 5.57 to 6.83; inter-cardiologist RMSE of 8.23%) for LV ejection fraction, 0.25cm (95% CI: 0.20 to 0.29; 0.36cm) for IVC maximal diameter, 2.39cm2 (95% CI: 1.96 to 2.82; 4.39cm2) for LA area, 2.11cm2 (95% CI: 1.75 to 2.47; 3.49cm2) for RA area, 5.06mm (95% CI: 4.58 to 5.55; 4.67mm) for LV end diastolic diameter, 10.17% (95% CI: 9.01 to 11.33; 14.12%) for RV fractional area change and 0.19cm (95% CI: 0.16 to 0.21; 0.24cm) for aortic root diameter. DiscussionThe device accurately calculated these cardiac measurements especially when benchmarked against inter-cardiologist variability. Its use could assist clinicians who utilize POCUS and better enable their clinical decision-making.

BackgroundInadequate pharmacologic stress may limit the diagnostic and prognostic accuracy of myocardial perfusion imaging (MPI). The splenic ratio (SR), a measure of stress adequacy, has emerged as a potential imaging biomarker. ObjectivesTo evaluate the prognostic value of artificial intelligence (AI)-derived SR in a large multicenter 82Rb-PET cohort undergoing regadenoson stress testing. MethodsWe retrospectively analyzed 10,913 patients from three sites in the REFINE PET registry with clinically indicated MPI and linked clinical outcomes. SR was calculated using fully automated algorithms as the ratio of splenic uptake at stress versus rest. Patients were stratified by SR into high ([&ge;]90th percentile) and low (<90th percentile) groups. The primary outcome was major adverse cardiovascular events (MACE). Survival analysis was conducted using Kaplan-Meier and Cox proportional hazards models adjusted for clinical and imaging covariates, including myocardial flow reserve (MFR [&ge;]2 vs. <2). ResultsThe cohort had a median age of 68 years, with 57% male patients. Common risk factors included hypertension (84%), dyslipidemia (76%), diabetes (33%), and prior coronary artery disease (31%). Median follow-up was 4.6 years. Patients with high SR (n=1,091) had an increased risk of MACE (HR 1.18, 95% CI 1.06-1.31, p=0.002). Among patients with preserved MFR ([&ge;]2; n=7,310), high SR remained independently associated with MACE (HR 1.44, 95% CI 1.24-1.67, p<0.0001). ConclusionsElevated AI-derived SR was independently associated with adverse cardiovascular outcomes, including among patients with preserved MFR. These findings support SR as a novel, automated imaging biomarker for risk stratification in 82Rb PET MPI. Condensed AbstractAI-derived splenic ratio (SR), a marker of pharmacologic stress adequacy, was independently associated with increased cardiovascular risk in a large 82Rb PET cohort, even among patients with preserved myocardial flow reserve (MFR). High SR identified individuals with elevated MACE risk despite normal perfusion and flow findings, suggesting unrecognized physiologic vulnerability. Incorporating automated SR into PET MPI interpretation may enhance risk stratification and identify patients who could benefit from intensified preventive care, particularly when traditional imaging markers appear reassuring. These findings support SR as a clinically meaningful, easily integrated biomarker in stress PET imaging.

This review was written to inform practicing clinical rheumatologists about recent advances in artificial intelligence (AI) based research in rheumatoid arthritis (RA), using accessible and practical language. We highlight developments from 2023 to early 2025 across diagnostic imaging, treatment prediction, drug discovery, and patient-facing tools. Given the increasing clinical interest in AI and its potential to augment care delivery, this article aims to bridge the gap between technical innovation and real-world rheumatology practice. Several AI models have demonstrated high accuracy in early RA detection using imaging modalities such as thermal imaging and nuclear scans. Predictive models for treatment response have leveraged routinely collected electronic health record (EHR) data, moving closer to practical application in clinical workflows. Patient-facing tools like mobile symptom checkers and large language models (LLMs) such as ChatGPT show promise in enhancing education and engagement, although accuracy and safety remain variable. AI has also shown utility in identifying novel biomarkers and accelerating drug discovery. Despite these advances, as of early 2025, no AI-based tools have received FDA approval for use in rheumatology, in contrast to other specialties. Artificial intelligence holds tremendous promise to enhance clinical care in RA-from early diagnosis to personalized therapy. However, clinical adoption remains limited due to regulatory, technical, and implementation challenges. A streamlined regulatory framework and closer collaboration between clinicians, researchers, and industry partners are urgently needed. With thoughtful integration, AI can serve as a valuable adjunct in addressing clinical complexity and workforce shortages in rheumatology.

Transperineal ultrasound (TPUS) is a widely used tool for evaluating female pelvic organ prolapse (POP), but its accurate interpretation relies on experience, causing diagnostic variability. This study aims to develop and validate a multi-task deep learning model to automate POP assessment using TPUS images. TPUS images from 1340 female patients (January-June 2023) were evaluated by two experienced physicians. The presence and severity of cystocele, uterine prolapse, rectocele, and excessive mobility of perineal body (EMoPB) were documented. After preprocessing, 1072 images were used for training and 268 for validation. The model used ResNet34 as the feature extractor and four parallel fully connected layers to predict the conditions. Model performance was assessed using confusion matrix and area under the curve (AUC). Gradient-weighted class activation mapping (Grad-CAM) visualized the model's focus areas. The model demonstrated strong diagnostic performance, with accuracies and AUC values as follows: cystocele, 0.869 (95% CI, 0.824-0.905) and 0.947 (95% CI, 0.930-0.962); uterine prolapse, 0.799 (95% CI, 0.746-0.842) and 0.931 (95% CI, 0.911-0.948); rectocele, 0.978 (95% CI, 0.952-0.990) and 0.892 (95% CI, 0.849-0.927); and EMoPB, 0.869 (95% CI, 0.824-0.905) and 0.942 (95% CI, 0.907-0.967). Grad-CAM heatmaps revealed that the model's focus areas were consistent with those observed by human experts. This study presents a multi-task deep learning model for automated POP assessment using TPUS images, showing promising efficacy and potential to benefit a broader population of women.