Ischemic changes are not visible on non-contrast head CT until several hours after infarction, though deep convolutional neural networks have shown promise in the detection of subtle imaging findings. This study aims to assess if dual-energy CT (DECT) acquisition can improve early infarct visibility for machine learning. The retrospective dataset consisted of 330 DECTs acquired up to 48 h prior to confirmation of a DWI positive infarct on MRI between 2016 and 2022. Infarct segmentation maps were generated from the MRI and co-registered to the CT to serve as ground truth for segmentation. A self-configuring 3D nnU-Net was trained for segmentation on (1) standard 120 kV mixed-images (2) 190 keV virtual monochromatic images and (3) 120 kV + 190 keV images as dual channel inputs. Algorithm performance was assessed with Dice scores with paired t-tests on a test set. Global aggregate Dice scores were 0.616, 0.645, and 0.665 for standard 120 kV images, 190 keV, and combined channel inputs respectively. Differences in overall Dice scores were statistically significant with highest performance for combined channel inputs (p < 0.01). Small but statistically significant differences were observed for infarcts between 6 and 12 h from last-known-well with higher performance for larger infarcts. Volumetric accuracy trended higher with combined inputs but differences were not statistically significant (p = 0.07). Supplementation of standard head CT images with dual-energy data provides earlier and more accurate segmentation of infarcts for machine learning particularly between 6 and 12 h after last-known-well.

Urolithiasis, a common and painful urological condition, is influenced by factors such as lifestyle, genetics, and medication. Differentiating between different types of kidney stones is crucial for personalized therapy. The purpose of this study is to investigate the use of photon-counting computed tomography (PCCT) in combination with radiomics and machine learning to develop a method for automated and detailed characterization of kidney stones. This approach aims to enhance the accuracy and detail of stone classification beyond what is achievable with conventional computed tomography (CT) and dual-energy CT (DECT). In this ex vivo study, 135 kidney stones were first classified using infrared spectroscopy. All stones were then scanned in a PCCT embedded in a phantom. Various monoenergetic reconstructions were generated, and radiomics features were extracted. Statistical analysis was performed using Random Forest (RF) classifiers for both individual reconstructions and a combined model. The combined model, using radiomics features from all monoenergetic reconstructions, significantly outperformed individual reconstructions and SPP parameters, with an AUC of 0.95 and test accuracy of 0.81 for differentiating all six stone types. Feature importance analysis identified key parameters, including NGTDM_Strength and wavelet-LLH_firstorder_Variance. This ex vivo study demonstrates that radiomics-driven PCCT analysis can improve differentiation between kidney stone subtypes. The combined model outperformed individual monoenergetic levels, highlighting the potential of spectral profiling in PCCT to optimize treatment through image-based strategies. Question How can photon-counting computed tomography (PCCT) combined with radiomics improve the differentiation of kidney stone types beyond conventional CT and dual-energy CT, enhancing personalized therapy? Findings Our ex vivo study demonstrates that a combined spectral-driven radiomics model achieved 95% AUC and 81% test accuracy in differentiating six kidney stone types. Clinical relevance Implementing PCCT-based spectral-driven radiomics allows for precise non-invasive differentiation of kidney stone types, leading to improved diagnostic accuracy and more personalized, effective treatment strategies, potentially reducing the need for invasive procedures and recurrence.

To examine how vertebral attenuation changes with aging, and to establish age-adjusted CT attenuation value cutoffs for diagnosing osteoporosis. This multi-center retrospective study included 11,246 patients (mean age ± standard deviation, 50 ± 13 years; 7139 men) who underwent CT and dual-energy X-ray absorptiometry (DXA) in six health-screening centers between 2022 and 2023. Using deep-learning-based software, attenuation values of L1 vertebral bodies were measured. Segmented linear regression in women and simple linear regression in men were used to assess how attenuation values change with aging. A multivariable linear regression analysis was performed to determine whether age is associated with CT attenuation values independently of the DXA T-score. Age-adjusted cutoffs targeting either 90% sensitivity or 90% specificity were derived using quantile regression. Performance of both age-adjusted and age-unadjusted cutoffs was measured, where the target sensitivity or specificity was considered achieved if a 95% confidence interval encompassed 90%. While attenuation values declined consistently with age in men, they declined abruptly in women aged > 42 years. Such decline occurred independently of the DXA T-score (p < 0.001). Age adjustment seemed critical for age ≥ 65 years, where the age-adjusted cutoffs achieved the target (sensitivity of 91.5% (86.3-95.2%) when targeting 90% sensitivity and specificity of 90.0% (88.3-91.6%) when targeting 90% specificity), but age-unadjusted cutoffs did not (95.5% (91.2-98.0%) and 73.8% (71.4-76.1%), respectively). Age-adjusted cutoffs provided a more reliable diagnosis of osteoporosis than age-unadjusted cutoffs since vertebral attenuation values decrease with age, regardless of DXA T-scores. Question How does vertebral CT attenuation change with age? Findings Independent of dual-energy X-ray absorptiometry T-score, vertebral attenuation values on CT declined at a constant rate in men and abruptly in women over 42 years of age. Clinical relevance Age adjustments are needed in opportunistic osteoporosis screening, especially among the elderly.

AI tools in radiology are revolutionising the diagnosis, evaluation, and management of patients. However, there is a major gap between the large number of developed AI tools and those translated into daily clinical practice, which can be primarily attributed to limited usefulness and trust in current AI tools. Instead of technically driven development, little effort has been put into value-based development to ensure AI tools will have a clinically relevant impact on patient care. An iterative comprehensive value evaluation process covering the complete AI tool lifecycle should be part of radiology AI development. For value assessment of health technologies, health technology assessment (HTA) is an extensively used and comprehensive method. While most aspects of value covered by HTA apply to radiology AI, additional aspects, including transparency, explainability, and robustness, are unique to radiology AI and crucial in its value assessment. Additionally, value assessment should already be included early in the design stage to determine the potential impact and subsequent requirements of the AI tool. Such early assessment should be systematic, transparent, and practical to ensure all stakeholders and value aspects are considered. Hence, early value-based development by incorporating early HTA will lead to more valuable AI tools and thus facilitate translation to clinical practice. CLINICAL RELEVANCE STATEMENT: This paper advocates for the use of early value-based assessments. These assessments promote a comprehensive evaluation on how an AI tool in development can provide value in clinical practice and thus help improve the quality of these tools and the clinical process they support. KEY POINTS: Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains. Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools. Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption.

Diagnosis of lesions of the parapharyngeal space (PPS) often poses a diagnostic and therapeutic challenge due to its deep location. As a result of the topographical relationship to nearby neck spaces, a very precise differential diagnosis is possible based on imaging criteria. When in doubt, imaging-guided - usually CT-guided - biopsy and even drainage remain options.Through a precise analysis of the literature including the most recent publications, this review precisely describes the basic and most recent imaging applications for various PPS pathologies and the differential diagnostic scheme for assigning the respective lesions in addition to the possibilities of using interventional radiology.The different pathologies of PPS from congenital malformations and inflammation to tumors are discussed according to frequency. Characteristic criteria and, more recently, the use of advanced imaging procedures and the introduction of artificial intelligence (AI) allow a very precise differential diagnosis and support further diagnosis and therapy. After precise access planning, almost all pathologies of the PPS can be biopsied or, if necessary, drained using CT-assisted procedures.Radiological procedures play an important role in the diagnosis and treatment planning of PPS pathologies. · Lesions of the PPS account for about 1-2% of all pathologies of the head and neck region. The majority are benign lesions and inflammatory processes.. · If differential diagnostic questions remain unanswered, material can - if necessary - be obtained via a CT-guided biopsy. Exclusion criteria are hypervascularized processes, especially paragangliomas and angiomas.. · The use of artificial intelligence (AI) in head and neck imaging of various pathologies, such as tumor segmentation, pathological TMN classification, detection of lymph node metastases, and extranodal extension, has significantly increased in recent years.. · Vogl TJ, Burck I, Stöver T et al. Parapharyngeal Space: Diagnostic Imaging and Intervention. Rofo 2025; 197: 638-646.

Diagnostic imaging is an integral part of identifying spondyloarthropathies (SpA), yet the interpretation of these images can be challenging. This review evaluated the use of deep learning models to enhance the diagnostic accuracy of SpA imaging. Following PRISMA guidelines, we systematically searched major databases up to February 2024, focusing on studies that applied deep learning to SpA imaging. Performance metrics, model types, and diagnostic tasks were extracted and analyzed. Study quality was assessed using QUADAS-2. We analyzed 21 studies employing deep learning in SpA imaging diagnosis across MRI, CT, and X-ray modalities. These models, particularly advanced CNNs and U-Nets, demonstrated high accuracy in diagnosing SpA, differentiating arthritis forms, and assessing disease progression. Performance metrics frequently surpassed traditional methods, with some models achieving AUCs up to 0.98 and matching expert radiologist performance. This systematic review underscores the effectiveness of deep learning in SpA imaging diagnostics across MRI, CT, and X-ray modalities. The studies reviewed demonstrated high diagnostic accuracy. However, the presence of small sample sizes in some studies highlights the need for more extensive datasets and further prospective and external validation to enhance the generalizability of these AI models. Question How can deep learning models improve diagnostic accuracy in imaging for spondyloarthropathies (SpA), addressing challenges in early detection and differentiation from other forms of arthritis? Findings Deep learning models, especially CNNs and U-Nets, showed high accuracy in SpA imaging across MRI, CT, and X-ray, often matching or surpassing expert radiologists. Clinical relevance Deep learning models can enhance diagnostic precision in SpA imaging, potentially reducing diagnostic delays and improving treatment decisions, but further validation on larger datasets is required for clinical integration.

To investigate the behavior of artificial intelligence (AI) CT-based body composition biomarkers at different virtual monoenergetic imaging (VMI) levels using dual-energy CT (DECT). This retrospective study included 88 contrast-enhanced abdominopelvic CTs acquired with rapid-kVp switching DECT. Images were reconstructed into five VMI levels (40, 55, 70, 85, 100 keV). Fully automated algorithms for quantifying CT number (HU) in abdominal fat (subcutaneous and visceral), skeletal muscle, bone, calcium (abdominal Agatston score), and organ size (area or volume) were applied. Biomarker median difference relative to 70 keV and interquartile range were reported by energy level to characterize variation. Linear regression was performed to calibrate non-70 keV data and to estimate their equivalent 70 keV biomarker attenuation values. Relative to 70 keV, absolute median differences in attenuation-based biomarkers (excluding Agatston score) ranged 39-358, 12-102, 5-48, 9-75 HU for 40, 55, 85, 100 keV, respectively. For area-based biomarkers, differences ranged 6-15, 3-4, 2-7, 0-5 cm² for 40, 55, 85, 100 keV. For volume-based biomarkers, differences ranged 12-34, 8-68, 12-52, 1-57 cm³ for 40, 55, 85, 100 keV. Agatston score behavior was more spurious with median differences ranging 70-204 HU. In general, VMI < 70 keV showed more variation in median biomarker measurement than VMI > 70 keV. This study characterized the behavior of a fully automated AI CT biomarker toolkit across varying VMI levels obtained with DECT. The data showed relatively little biomarker value change when measured at or greater than 70 keV. Lower VMI datasets should be avoided due to larger deviations in measured value as compared to 70 keV, a level considered equivalent to conventional 120 kVp exams.

Classical biomedical data science models are trained on a single modality and aimed at one specific task. However, the exponential increase in the size and capabilities of the foundation models inside and outside medicine shows a shift toward task-agnostic models using large-scale, often internet-based, data. Recent research into smaller foundation models trained on specific literature, such as programming textbooks, demonstrated that they can display capabilities similar to or superior to large generalist models, suggesting a potential middle ground between small task-specific and large foundation models. This study attempts to introduce a domain-specific multimodal model, Congress of Neurological Surgeons (CNS)-Contrastive Language-Image Pretraining (CLIP), developed for neurosurgical applications, leveraging data exclusively from Neurosurgery Publications. We constructed a multimodal data set of articles from Neurosurgery Publications through PDF data collection and figure-caption extraction using an artificial intelligence pipeline for quality control. Our final data set included 24 021 figure-caption pairs. We then developed a fine-tuning protocol for the OpenAI CLIP model. The model was evaluated on tasks including neurosurgical information retrieval, computed tomography imaging classification, and zero-shot ImageNet classification. CNS-CLIP demonstrated superior performance in neurosurgical information retrieval with a Top-1 accuracy of 24.56%, compared with 8.61% for the baseline. The average area under receiver operating characteristic across 6 neuroradiology tasks achieved by CNS-CLIP was 0.95, slightly superior to OpenAI's Contrastive Language-Image Pretraining at 0.94 and significantly outperforming a vanilla vision transformer at 0.62. In generalist classification, CNS-CLIP reached a Top-1 accuracy of 47.55%, a decrease from the baseline of 52.37%, demonstrating a catastrophic forgetting phenomenon. This study presents a pioneering effort in building a domain-specific multimodal model using data from a medical society publication. The results indicate that domain-specific models, while less globally versatile, can offer advantages in specialized contexts. This emphasizes the importance of using tailored data and domain-focused development in training foundation models in neurosurgery and general medicine.

Tear size and shape are known to prognosticate the efficacy of surgical rotator cuff (RC) repair; however, current manual measurements on magnetic resonance images (MRIs) exhibit high interobserver variabilities and exclude 3-dimensional (3D) morphologic information. This study aimed to develop algorithms for automatic 3D analyses of posterosuperior full-thickness RC tear to enable efficient and precise tear evaluation and 3D tear visualization. A deep-learning network for automatic segmentation of the tear region in coronal and sagittal multicenter MRI was trained with manually segmented (consensus of 3 experts) proton density- and T2-weighted MRI of shoulders with full-thickness posterosuperior tears (n = 200). Algorithms for automatic measurement of tendon retraction, tear width, tear area, and automatic Patte classification considering the 3D morphology of the shoulder were implemented and evaluated against manual segmentation (n = 59). Automatic Patte classification was calculated using automatic segmented humerus and scapula on T1-weighted MRI of the same shoulders. Tears were automatically segmented, enabling 3D visualization of the tear, with a mean Dice coefficient of 0.58 ± 0.21 compared to an interobserver variability of 0.46 ± 0.21. The mean absolute error of automatic tendon retraction and tear width measurements (4.98 ± 4.49 mm and 3.88 ± 3.18 mm) were lower than the interobserver variabilities (5.42 ± 7.09 mm and 5.92 ± 1.02 mm). The correlations of all measurements performed on automatic tear segmentations compared with those on consensus segmentations were higher than the interobserver correlation. Automatic Patte classification achieved a Cohen kappa value of 0.62, compared with the interobserver variability of 0.56. Retraction calculated using standard linear measures underestimated the tear size relative to measurements considering the curved shape of the humeral head, especially for larger tears. Even on highly heterogeneous data, the proposed algorithms showed the feasibility to successfully automate tear size analysis and to enable automatic 3D visualization of the tear situation. The presented algorithms standardize cross-center tear analyses and enable the calculation of additional metrics, potentially improving the predictive power of image-based tear measurements for the outcome of surgical treatments, thus aiding in RC tear diagnosis, treatment decision, and planning.