DSC-MRI perfusion is a medical imaging technique for diagnosing and prognosing brain tumors and strokes. Its analysis relies on mathematical deconvolution, but noise or motion artifacts in a clinical environment can disrupt this process, leading to incorrect estimate of perfusion parameters. Although deep learning approaches have shown promising results, their calibration typically rely on third-party deconvolution algorithms to generate reference outputs and are bound to reproduce their limitations. To adress this problem, we propose a physics-informed autoencoder that leverages an analytical model to decode the perfusion parameters and guide the learning of the encoding network. This autoencoder is trained in a self-supervised fashion without any third-party software and its performance is evaluated on a database with glioma patients. Our method shows reliable results for glioma grading in accordance with other well-known deconvolution algorithms despite a lower computation time. It also achieved competitive performance even in the presence of high noise which is critical in a medical environment.

Positron Emission Tomography (PET) myocardial perfusion imaging (MPI) is a powerful tool for predicting coronary artery disease (CAD). Coronary artery calcium (CAC) provides incremental risk stratification to PET-MPI and enhances diagnostic accuracy. We assessed additive value of CAC score, derived from PET/CT attenuation maps to stress TPD results using the novel 18F-flurpiridaz tracer in detecting significant CAD. Patients from 18F-flurpiridaz phase III clinical trial who underwent PET/CT MPI with 18F-flurpiridaz tracer, had available CT attenuation correction (CTAC) scans for CAC scoring, and underwent invasive coronary angiography (ICA) within a 6-month period between 2011 and 2013, were included. Total perfusion deficit (TPD) was quantified automatically, and CAC scores from CTAC scans were assessed using artificial intelligence (AI)-derived segmentation and manual scoring. Obstructive CAD was defined as ≥50% stenosis in Left Main (LM) artery, or 70% or more stenosis in any of the other major epicardial vessels. Prediction performance for CAD was assessed by comparing the area under receiver operating characteristic curve (AUC) for stress TPD alone and in combination with CAC score. Among 498 patients (72% males, median age 63 years) 30.1% had CAD. Incorporating CAC score resulted in a greater AUC: manual scoring (AUC=0.87, 95% Confidence Interval [CI] 0.34-0.90; p=0.015) and AI-based scoring (AUC=0.88, 95%CI 0.85-0.90; p=0.002) compared to stress TPD alone (AUC 0.84, 95% CI 0.80-0.92). Combining automatically derived TPD and CAC score enhances 18F-flurpiridaz PET MPI accuracy in detecting significant CAD, offering a method that can be routinely used with PET/CT scanners without additional scanning or technologist time.

Low muscle mass is a risk factor for chronic kidney disease. In this study, we examined the relationship between muscle mass and mortality, as well as end-stage kidney disease (ESKD), in patients with autosomal dominant polycystic kidney disease (ADPKD). Retrospective cohort study. 1,443 patients with ADPKD from eight tertiary-care hospitals in Korea between 2006 and 2020. Computed tomography images were obtained at the third lumbar vertebra to measure the skeletal muscle area (SMA) using an artificial intelligence system. SMA indexed for w a height² s classified as low-attenuation muscle area (LAMA) or normal-attenuation muscle area (NAMA) based on muscle quality. All-cause mortality and ESKD. Cox proportional hazards regression, adjusted for sex, age, creatinine, glucose, and height-adjusted total kidney volume, was used to investigate the associations of muscle indices with all-cause mortality and ESKD. Subgroup analyses were conducted based on body mass index categories: low or normal (<25 kg/m²) and overweight or obese (≥25 kg/m²). The study population included more than half female patients, and the mean estimated glomerular filtration rate was 68.4 ml/min/1.73m². Mean follow-up was 5.14 years. Greater SMA/height² and NAMA/height² were associated with a lower risk of mortality (HRs 0.58 (95% CI 0.39-0.88) and 0.55 (95% CI, 0.39-0.79), respectively). Greater NAMA/height² was associated with a 26% lower ESKD incidence (0.74 (0.59,0.92), but a greater LAMA/height² was associated with a lower ESKD incidence (HR 1.18, 95% CI 1.01-1.37). A higher NAMA/LAMA ratio was associated with a lower ESKD incidence (HR 0.74, 95% CI 0.60-0.92). Greater muscle mass was associated with a lower risk of mortality among overweight individuals and a lower risk of ESKD in underweight individuals. Lack of details about muscle strength and performance. Among individuals with ADPKD, greater and higher-quality muscle mass were associated with lower risk of mortality and progression of CKD to ESKD.

Structural brain deficits associated with generalized anxiety disorder (GAD), panic disorder (PD), and obsessive-compulsive disorder (OCD) have been documented, but their integration within a unified framework remains unexplored. This study investigates, in anxiety and anxiety-related disorders, whether they share neurophysiological bases, whether structural changes (SCs) are linked to common genes, whether shared SCs co-occur with similar functional brain impairments, and whether brain morphometry can serve as biomarkers for diagnosis and treatment prediction. Participants included 100 individuals with GAD, 58 with PD, 45 with OCD, and 85 healthy controls, all drug-free. Structural and resting-state functional magnetic resonance imaging scans and clinical assessments were conducted before and after 4 weeks of paroxetine monotherapy. Analyses included voxel-based and surface-based morphometry; functional connectivity (FC) and Granger causality analysis (GCA) with shared SCs as regions of interest; associations between clinical assessments and neuroimaging metrics; associations between gene expression profiles and SCs; and machine learning. Cingulate atrophy (CA) emerged as a common SC, with disorder-specific atrophy in gray matter volume (GMV) and cortical surface. Transcriptome-neuroimaging correlations identified shared genetic associations with GMV alterations, with negatively correlated genes enriched in neurodevelopment and cellular growth regulation (ND-CGR). Cingulate GMV was positively correlated with cognitive performance in GAD and PD patients. FC and GCA showed CA disrupted networks governing emotional regulation and cognitive control, characterized by overactive top-down influence and reduced bottom-up feedback. Machine learning demonstrated strong performance in classification and treatment response prediction, with cingulate morphometry contributing significantly. CA is a shared neural substrate in GAD, PD, and OCD, linked to genetic disruptions in ND-CGR, cognitive impairments, and functional brain deficits. Cingulate morphometry holds promise as a biomarker for diagnosis and treatment response in these conditions.

Actionable findings requiring follow-up with additional imaging or other diagnostic procedures are frequently reported for a wide variety of radiology exams. Completion of recommended follow-up can lead to new diagnoses including cancer. However, recommended follow-up completion is inconsistent, particularly when follow-up is for findings unrelated to the initial reason for the exam. Follow-up recommendation tracking systems, using a combination of information technology tools and human navigators, can facilitate completion of recommended follow-up, but often require significant effort for manual chart review and direct communication with providers and patients. Artificial intelligence (AI), including large language models (LLMs) able to process vast and diverse unstructured text data, offer the opportunity to improve efficiency with data extraction and aggregation tasks, like those required for follow-up recommendation management. In this review article, we will review the key components of follow-up recommendation management systems: (1) identification of follow-up recommendations within radiology reports, (2) communication of these recommendations, (3) tracking of follow-up recommendations to completion, and (4) outcomes tracking. For each component, we will explore how AI can improve efficiency and expand capabilities of robust management systems that ensure the loop is closed for follow-up recommendations.

Breast cancer remains a significant global health concern, and machine learning algorithms and computer-aided detection systems have shown great promise in enhancing the accuracy and efficiency of mammography image analysis. However, there is a critical need for large, benchmark datasets for training deep learning models for breast cancer detection. In this work we developed Mammo-Bench, a large-scale benchmark dataset of mammography images, by collating data from six well-curated resources, viz., DDSM, INbreast, KAU-BCMD, CMMD, CDD-CESM and DMID. To ensure consistency across images from diverse sources while preserving clinically relevant features, a preprocessing pipeline that includes breast segmentation, pectoral muscle removal, and intelligent cropping is proposed. The dataset consists of 19,731 high-quality mammographic images from 6,500 patients across 6 countries and is one of the largest open-source mammography databases to the best of our knowledge. To show the efficacy of training on the large dataset, performance of ResNet101 architecture was evaluated on Mammo-Bench and the results compared by training independently on a few member datasets and an external dataset, VinDr-Mammo. An accuracy of 78.8% (with data augmentation of the minority classes) and 77.8% (without data augmentation) was achieved on the proposed benchmark dataset, compared to the other datasets for which accuracy varied from 25 - 69%. Noticeably, improved prediction of the minority classes is observed with the Mammo-Bench dataset. These results establish baseline performance and demonstrate Mammo-Benchs utility as a comprehensive resource for developing and evaluating mammography analysis systems.

PURPOSETo prospectively evaluate the feasibility and performance of expedited screening mammogram interpretation for women identified as high-risk by a deep learning risk model. METHODS AND MATERIALSThis HIPAA-compliant, IRB-approved prospective controlled study was conducted at an urban safety-net facility. The Mirai breast cancer risk model was retrospectively calibrated on 114,229 local mammograms (2006-2023) to identify the top 10% of 1-year breast cancer risk scores. During the prospective study (12/2024-6/2025), Mirai 1-year risk scores were generated in real time. On enrollment days, high-risk women were approached for consent and offered immediate interpretation of their screening exam. Patients assessed as BI-RADS 0 were offered same-day diagnostic evaluation when feasible. Outcomes included feasibility of immediate interpretation, time to screening result (Ts), diagnostic evaluation (Td), and biopsy (Tb), as well as cancer detection rate (CDR). Comparisons were made with high-risk controls on non-enrollment days. RESULTSAmong 4,145 screening mammograms, Mirai flagged 525 (12.7%) as high-risk; 973 (23.5%) were performed on enrollment days with 115 (11.8%) flagged as high-risk. Of 100 women who consented, 94% received immediate reads. Thirty-one were assessed as BI-RADS 0; 30 underwent diagnostic imaging (26 same day). Thirteen biopsies yielded 6 malignant (4 invasive, 2 DCIS), 2 high-risk, and 5 benign lesions. The CDR in high-risk expedited women was 60/1,000 (95% CI, 22.3-126.0) compared with 2.3/1,000 (95% CI, 0.3-8.4) in non-high-risk women (odds ratio 27.1; p<0.001). Median Ts, Td, and Tb were significantly shorter in expedited patients versus high-risk controls (13.0 min vs 191.9 min; 1.3 hrs vs 852.8 hrs; 20.1 vs 59.0 days; all p<0.001). For screen-detected cancers, expedited interpretation reduced mean Ts, Td, and Tb by 99.1%, 99.1%, and 87.2%, respectively. CONCLUSIONIntegrating an AI risk model into mammography workflow is feasible and enables same-day evaluation for high-risk women. This approach markedly shortens time to diagnostic imaging and biopsy to provide timely breast cancer care.

Computed tomography imaging, a non-invasive tool, is used around the globe by medical professionals to identify and diagnose lung cancer; a lethal disease with high rates of occurrence and mortality globally. Radiomics extracted from medical images, including computed tomography, in tandem with machine learning frameworks has received considerable focus and research for lung nodule identification.This investigation can help out clinicians to reach radiomics-based better and quicker decision support system for treatments and early diagnosis. However, it is still foggy and unclear which radiomics feature(s) to use for the prediction of pulmonary nodule. Consequently, this work is offered with an endeavor to efficiently apply machine learning techniques and radiomics to classify CT pulmonary nodules. Lung Image Data Consortium (LIDC), containing 1018 CT cancer cases, is put to use. The Wavelet Packet Transform is used in conjunction with geometrical features, gray level run length matrix, gray level co-occurrence method and gray level difference method techniques to extract radiomics. Two techniques, boosted and bagged ensemble classification trees, are employed to choose an apposite set of features. The categorization of nodules as malignant or benign is assessed by the utilization of cutting-edge machine learning models: Support Vector Machines, Boosted Classification Ensemble Tree, Decision Trees, Bagged Classification Ensemble Tree, RUSBoosted Ensemble Trees, Subspace Discriminant Ensemble and Subspace KNN Ensemble. The findings reveal that the Ensemble Subspace KNN gives best AUROC (93.4%), accuracy (88.3%) and F1-score (85.2%) using BACET feature selection method. The best sensitivity is produced by FGSVM (97.1%). RUSBOCET gives best precision and specificity of 93.4% and 83.1% respectively. Lung Cancer remains the most common and deadly type of cancer. Early detection of lung lesions and nodules is crucial in the fight against lung cancer. The purpose of this study was to investigate radiomics based on geometrical, texture, and Daubechies WPT texture features for quantitative CT image analysis. The LIDC database was used in this study. Geometrical features, texture features based on three statistical methodologies (GLCM, GLDM GLRLM) and Daubechies WPT texture features are retrieved from the nodules. Using the ensemble EFS, BOCET and BACET, pertinent features were identified. Lastly, various cutting-edge ML classifiers were used to classify LC as malignant or benign. The out-turn shows that, using BACET EFS, Ensemble Subspace KNN gives best AUROC (93.4%), accuracy (88.3%) and F1-score (85.2%). FGSVM yields the best sensitivity of 97.1%. RUSBOCET gives best precision and best specificity of 93.4% and 83.1% respectively. Therefore, the methodology can be applied with efficacy to the CT based PN classification. Thus, the result can assist medical professionals in making better decisions and interventions.

Airway foreign body aspiration remains a potentially life-threatening emergency, predominantly affecting children under 5 years and adults over 65 years. This review synthesizes current evidence on diagnostic strategies, bronchoscopic extraction techniques, procedural outcomes, complication management, and emerging technologies in airway foreign body management. Multidetector computed tomography with three-dimensional reconstruction has significantly improved diagnostic accuracy, achieving sensitivity of 98-99% for radiopaque objects and 85-92% for radiolucent materials. Flexible bronchoscopy has evolved from a diagnostic tool to a first-line therapeutic modality, with recent pediatric meta-analyses demonstrating 87% success rates and adult series showing comparable outcomes to rigid bronchoscopy for appropriately selected cases. Rigid bronchoscopy maintains superiority in asphyxiating presentations, and for large (>1.5 cm), sharp, or severely impacted foreign bodies. Technological innovations including robotic-assisted bronchoscopy, electromagnetic navigation systems, and artificial intelligence-powered imaging analysis are enhancing procedural precision and safety. Successful airway foreign body management requires individualized, multidisciplinary approaches integrating patient clinical status, foreign body characteristics, and institutional expertise. Success depends on appropriate bronchoscopic modality selection, comprehensive preprocedural planning, availability of specialized retrieval instruments, and readiness to manage potential complications. Integration of advanced imaging, simulation-based training protocols, and telemedicine consultation are becoming essential components of contemporary practice.

Given the global rise of MASLD, which impacts approximately one-third of the population, there is a need for earlier diagnosis and effective treatment strategies to avoid long-term hepatic cardiovascular and renal complications. This review summarizes the recent advances in noninvasive diagnosis and new pharmacological agents approved for MASLD. The main step forward in diagnostics is a step away from invasive biopsy and emphasis on noninvasive methods including serum biomarkers (e.g. CK-18 and FGF21), imaging (e.g. MRI-PDFF and US-FLI), combination of the two and use of artificial intelligence and machine learning models, for early detection and risk stratification of MASLD and MASH. Multiomics approaches, such as metabolomics and lipidomics, reveal disease-specific signatures, and may help with phenotypic classification of MASLD. Personalized management for MASLD include gut microbiota modulation and point-of-care devices for rapid diagnosis. Novel therapies include THR β agonists, GLP-1/dual GLP-1/GIP agonists, FXR agonists and FGF analogues, which show promise in reducing hepatic fat and fibrosis. These findings enable earlier MASLD diagnosis and tailored interventions, improving clinical outcomes in primary care and resource-limited settings. Future research should focus on validating cost-effective tools, and developing combination therapies to address the multifaceted nature of MASLD.