The assessment of resting energy expenditure (REE) is a challenging task with the current existing methods. The reference method, indirect calorimetry (IC), is not widely available, and other surrogates, such as equations and bioimpedance (BIA) show poor agreement with IC. Body composition (BC), in particular muscle mass, plays an important role in REE. In recent years, computed tomography (CT) has emerged as a reliable tool for BC assessment, but its usefulness for the REE evaluation has not been examined. In the present study we have explored the usefulness of CT-scan imaging to assess the REE using AI machine-learning models. Single-centre observational cross-sectional pilot study from January to June 2022, including 90 fasting, clinically stable adults (≥18 years) with no contraindications for indirect calorimetry (IC), bioimpedance (BIA), or abdominal CT-scan. REE was measured using classical predictive equations, IC, BIA and skeletal CT-scan. The proposed model was based on a second-order linear regression with different input parameters, and the output corresponds to the estimated REE. The model was trained and tested using a cross-validation one-vs-all strategy including subjects with different characteristics. Data from 90 subjects were included in the final analysis. Bland-Altman plots showed that the CT-based estimation model had a mean bias of 0 kcal/day (LoA: -508.4 to 508.4) compared with IC, indicating better agreement than most predictive equations and similar agreement to BIA (bias 53.4 kcal/day, LoA: -475.7 to 582.4). Surprisingly, gender and BMI, ones of the mains variables included in all the BIA algorithms and mathematical equations were not relevant variables for REE calculated by means of AI coupled to skeletal CT scan. These findings were consistent with the results of other performance metrics, including mean absolute error (MAE), root mean square error (RMSE), and Lin's concordance correlation coefficient (CCC), which also favored the CT-based method over conventional equations. Our results suggest that the analysis of a CT-scan image by means of machine learning model is a reliable tool for the REE estimation. These findings have the potential to significantly change the paradigm and guidelines for nutritional assessment.

Medical specialists need to perform precise MRI analysis for accurate diagnosis of brain tumors. Current research has developed multiple artificial intelligence (AI) techniques for the process automation of brain tumor identification. However, existing approaches often depend on singular datasets, limiting their generalization capabilities across diverse clinical scenarios. The research introduces SCR-1DResNet as a new diagnostic tool for brain tumor detection that incorporates self-calibrated Random Forest along with one-dimensional residual networks. The research starts with MRI image acquisition from multiple Kaggle datasets then proceeds through stepwise processing that eliminates noise, enhances images, and performs resizing and normalization and conducts skull stripping operations. After data collection the WaveSegNet mode l extracts important attributes from tumors at multiple scales. Components of Random Forest classifier together with One-Dimensional Residual Network form the SCR-1DResNet model via self-calibration optimization to improve prediction reliability. Tests show the proposed system produces classification precision of 98.50% accompanied by accuracy of 98.80% and recall reaching 97.80% respectively. The SCR-1DResNet model demonstrates superior diagnostic capability and enhanced performance speed which shows strong prospects towards clinical decision support systems and improved neurological and oncological patient treatments.

In multi-center neuroimaging studies, the technical variability caused by the batch differences could hinder the ability to aggregate data across sites, and negatively impact the reliability of study-level results. Recent efforts in neuroimaging harmonization have aimed to minimize these technical gaps and reduce technical variability across batches. While Generative Adversarial Networks (GAN) has been a prominent method for addressing harmonization tasks, GAN-harmonized images suffer from artifacts or anatomical distortions. Given the advancements of denoising diffusion probabilistic model which produces high-fidelity images, we have assessed the efficacy of the diffusion model for neuroimaging harmonization. While GAN-based methods intrinsically transform imaging styles between two domains per model, we have demonstrated the diffusion model's superior capability in harmonizing images across multiple domains with single model. Our experiments highlight that the learned domain invariant anatomical condition reinforces the model to accurately preserve the anatomical details while differentiating batch differences at each diffusion step. Our proposed method has been tested using T1-weighted MRI images from two public neuroimaging datasets of ADNI1 and ABIDE II, yielding harmonization results with consistent anatomy preservation and superior FID score compared to the GAN-based methods. We have conducted multiple analyses including extensive quantitative and qualitative evaluations against the baseline models, ablation study showcasing the benefits of the learned domain invariant conditions, and improvements in the consistency of perivascular spaces segmentation analysis and volumetric analysis through harmonization.

Periprosthetic acetabular fractures following primary total hip arthroplasty (THA) using a cementless acetabular component range from occult to severe fractures. The aims of this study were to evaluate the perioperative periprosthetic acetabular fracture rate and patient-specific risks of a modular cementless acetabular component. In this study, we included 7,016 primary THAs (61.4% women, 38.6% men; age, 67 years; interquartile-range, 58 to 74) that received a cementless-hydroxyapatite-coated modular-titanium press-fit acetabular component from a single manufacturer between January 2013 and September 2022. All perioperative radiographs and CT (computer tomography) scans were analyzed for all causes. Patient-specific data and the revision rate were retrieved, and radiographic measurements were performed using artificial intelligence-based software. Following matching based on patients' demographics, a comparison was made between patients who had and did not have periacetabular fractures in order to identify patient-specific and radiographic risk factors for periacetabular fractures. The fracture rate was 0.8% (56 of 7,016). Overall, 33.9% (19 of 56) were small occult fractures solely visible on CT. Additionally, there were 21 of 56 (37.5%) with a stable small fracture. Both groups (40 of 56 (71.4%)) were treated nonoperatively. Revision THA was necessary in 16 of 56, resulting in an overall revision rate of 0.2% (16 of 7,016). Patient-specific risk factors were small acetabular-component size (≤ 50), a low body mass index (BMI) (< 24.5), a higher age (> 68 years), women, a low lateral-central-age-angle (< 24°), a high Extrusion-index (> 20%), a high sharp-angle (> 38°), and a high Tönnis-angle (> 10°). A wide range of periprosthetic acetabular fractures were observed following primary cementless THA. In total, 71.4% of acetabular fractures were small cracks that did not necessitate revision surgery. By identifying patient-specific risk factors, such as advanced age, women, low BMI, and dysplastic hips, future complications may be reduced.

The automatic screening of thyroid nodules using computer-aided diagnosis holds great promise in reducing missed and misdiagnosed cases in clinical practice. However, most current research focuses on single-modal images and does not fully leverage the comprehensive information from multimodal medical images, limiting model performance. To enhance screening accuracy, this study uses a deep learning framework that integrates high-dimensional convolutions of B-mode ultrasound (BMUS) and strain elastography (SE) images to predict the malignancy of TI-RADS 4 thyroid nodules with high-risk features. First, we extract nodule regions from the images and expand the boundary areas. Then, adaptive particle swarm optimization (APSO) and contrast limited adaptive histogram equalization (CLAHE) algorithms are applied to enhance ultrasound image contrast. Finally, deep learning techniques are used to extract and fuse high-dimensional features from both ultrasound modalities to classify benign and malignant thyroid nodules. The proposed model achieved an AUC of 0.937 (95 % CI 0.917-0.949) and 0.927 (95 % CI 0.907-0.948) in the test and external validation sets, respectively, demonstrating strong generalization ability. When compared with the diagnostic performance of three groups of radiologists, the model outperformed them significantly. Meanwhile, with the model's assistance, all three radiologist groups showed improved diagnostic performance. Furthermore, heatmaps generated by the model show a high alignment with radiologists' expertise, further confirming its credibility. The results indicate that our model can assist in clinical thyroid nodule diagnosis, reducing the risk of missed and misdiagnosed diagnoses, particularly for high-risk populations, and holds significant clinical value.

The fetal standard plane acquisition task aims to detect an Ultrasound (US) image characterized by specified anatomical landmarks and appearance for assessing fetal growth. However, in practice, due to variability in human operator skill and possible fetal motion, it can be challenging for a human operator to acquire a satisfactory standard plane. To support a human operator with this task, this paper first describes an approach to automatically predict the fetal head standard plane from a video segment approaching the standard plane. A transformer-based image predictor is proposed to produce a high-quality standard plane by understanding diverse scales of head anatomy within the US video frame. Because of the visual gap between the video frames and standard plane image, the predictor is equipped with an offset adaptor that performs domain adaption to translate the off-plane structures to the anatomies that would usually appear in a standard plane view. To enhance the anatomical details of the predicted US image, the approach is extended by utilizing a second modality, US probe movement, that provides 3D location information. Quantitative and qualitative studies conducted on two different head biometry planes demonstrate that the proposed US image predictor produces clinically plausible standard planes with superior performance to comparative published methods. The results of dual-modality solution show an improved visualization with enhanced anatomical details of the predicted US image. Clinical evaluations are also conducted to demonstrate the consistency between the predicted echo textures and the expected echo patterns seen in a typical real standard plane, which indicates its clinical feasibility for improving the standard plane acquisition process.

Peritoneal metastasis is a key factor in the poor prognosis of advanced gastrointestinal cancer patients. Traditional radiological diagnostic faces challenges such as insufficient sensitivity. Through technologies like radiomics and deep learning, artificial intelligence can deeply analyze the tumor heterogeneity and microenvironment features in medical images, revealing markers of peritoneal metastasis and constructing high-precision predictive models. These technologies have demonstrated advantages in tasks such as predicting peritoneal metastasis, assessing the risk of peritoneal recurrence, and identifying small metastatic foci during surgery. This paper summarizes the representative progress and application prospects of medical imaging artificial intelligence in the diagnosis and treatment of peritoneal metastasis, and discusses potential development directions such as multimodal data fusion and large model. The integration of medical imaging artificial intelligence with clinical practice is expected to advance personalized and precision medicine in the diagnosis and treatment of peritoneal metastasis in gastrointestinal cancers.

To investigate whether pulse pressure or mean arterial pressure mediates the relationship between age and white matter hyperintensity load and to examine the mediating effect of white matter hyperintensities on cognition. Demographic information, blood pressure, current medication lists, and Montreal Cognitive Assessment scores for 231 stroke- and dementia-free adults were retrospectively obtained from the Aging Brain Cohort study. Total WMH load was determined from T2-FLAIR magnetic resonance scans using the TrUE-Net deep learning tool for white matter segmentation. In separate models, we used mediation analysis to assess whether pulse pressure or MAP mediates the relationship between age and total white matter hyperintensity load, controlling for cardiovascular confounds. We also assessed whether white matter hyperintensity load mediated the relationship between age and cognitive scores. Pulse pressure, but not mean arterial pressure, significantly mediated the relationship between age and white matter hyperintensity load. White matter hyperintensity load partially mediated the relationship between age and Montreal Cognitive Assessment score. Our results indicate that pulse pressure, but not mean arterial pressure, is mechanistically associated with age-related accumulation of white matter hyperintensities, independent of other cardiovascular risk factors. White matter hyperintensity load was a mediator of cognitive scores across the adult lifespan. Effective management of pulse pressure may be especially important for maintenance of brain health and cognition.

'Brain age' is a biological clock typically used to describe brain health with one number, but its relationship with established gradients of cortical organization remains unclear. We address this gap by leveraging a data-driven, region-specific brain age approach in 335 neurologically intact adults, using a convolutional neural network (volBrain) to estimate regional brain ages directly from structural MRI without a predefined set of morphometric properties. Six distinct gradients of brain aging are replicated in two independent cohorts. Spatial patterns of accelerated brain aging in older adults quantitatively align with the archetypal sensorimotor-to-association axis of cortical organization. Other brain aging gradients reflect neurobiological hierarchies such as gene expression and externopyramidization. Participant-level correspondences to brain age gradients are associated with cognitive and sensorimotor performance and explained behavioral variance more effectively than global brain age. These results suggest that regional brain age patterns reflect fundamental principles of cortical organization and behavior.

Breast cancer poses a real and immense threat to humankind, thus a need to develop a way of diagnosing this devastating disease early, accurately, and in a simpler manner. Thus, while substantial progress has been made in developing machine learning algorithms, deep learning, and transfer learning models, issues with diagnostic accuracy and minimizing diagnostic errors persist. This paper introduces MobNAS, a model that uses MobileNetV2 and NASNetLarge to sort breast cancer images into benign, malignant, or normal classes. The study employs a multi-class classification design and uses a publicly available dataset comprising 1,578 ultrasound images, including 891 benign, 421 malignant, and 266 normal cases. By deploying MobileNetV2, it is easy to work well on devices with less computational capability than is used by NASNetLarge, which enhances its applicability and effectiveness in other tasks. The performance of the proposed MobNAS model was tested on the breast cancer image dataset, and the accuracy level achieved was 97%, the Mean Absolute Error (MAE) was 0.05, and the Matthews Correlation Coefficient (MCC) was 95%. From the findings of this research, it is evident that MobNAS can enhance diagnostic accuracy and reduce existing shortcomings in breast cancer detection.