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.

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.

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.

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.

Brain tumors pose a severe health risk, often leading to fatal outcomes if not detected early. While most studies focus on improving classification accuracy, this research emphasizes prediction certainty, quantified through loss values. Traditional metrics like accuracy and precision do not capture confidence in predictions, which is critical for medical applications. This study establishes a correlation between lower loss values and higher prediction certainty, ensuring more reliable tumor classification. We evaluate CNN, ResNet50, XceptionNet, and a Proposed Model (VGG19 with customized classification layers) using accuracy, precision, recall, and loss. Results show that while accuracy remains comparable across models, the Proposed Model achieves the best performance (96.95 % accuracy, 0.087 loss), outperforming others in both precision and recall. These findings demonstrate that certainty-aware AI models are essential for reliable clinical decision-making. This study highlights the potential of AI to bridge the shortage of medical professionals by integrating reliable diagnostic tools in healthcare. AI-powered systems can enhance early detection and improve patient outcomes, reinforcing the need for certainty-driven AI adoption in medical imaging.

Although there are notable structural abnormalities in the brain associated with psychotic diseases, it is still unclear how these abnormalities relate to clinical presentation. However, the fractal dimension (FD), which offers details on the complexity and irregularity of brain microstructures, may be a promising feature, as demonstrated by neuropsychiatric disorders such as Parkinson's and Alzheimer's. It may offer a possible biomarker for the detection and prognosis of psychosis when paired with machine learning. The purpose of this study is to investigate FD as a structural magnetic resonance imaging (sMRI) feature from individuals with a high clinical risk of psychosis who did not transit to psychosis (CHR_NT), clinical high risk who transit to psychosis (CHR_T), patients with first-episode psychosis (FEP) and healthy controls (HC). Using a machine learning approach that ultimately classifies sMRI images, the goals are (a) to evaluate FD as a potential biomarker and (b) to investigate its ability to predict a subsequent transition to psychosis from the high-risk clinical condition. We obtained sMRI images from 194 subjects, including 44 HCs, 77 FEPs, 16 CHR_Ts, and 57 CHR_NTs. We extracted the FD features and analyzed them using machine learning methods under five classification schemas (a) FEP vs. HC, (b) FEP vs. CHR_NT, (c) FEP vs. CHR_T, (d) CHR_NT vs. CHR_T, (d) CHR_NT vs. HC and (e) CHR_T vs. HC. In addition, the CHR_T group was used as external validation in (a), (b) and (d) comparisons to examine whether the progression of the disorder followed the FEP or CHR_NT patterns. The proposed algorithm resulted in a balanced accuracy greater than 0.77. This study has shown that FD can function as a predictive neuroimaging marker, providing fresh information on the microstructural alterations triggered throughout the course of psychosis. The effectiveness of FD in the detection of psychosis and transition to psychosis should be established by further research using larger datasets.

This study aimed to predict and classify MRI PI-RADs scores using different machine learning algorithms and to detect the concordance of PI-RADs scoring with the outcome target of prostate biopsy. Machine learning (ML) algorithms were used to develop best-fitting models for the prediction and classification of MRI PI-RAD. The Random Forest and Extra Trees models achieved the best performance compared to the other methods. The accuracy of both models was 91.95%. The AUC was 0.9329 for the Random Forest model and 0.9404 for the Extra Trees model. PSA level, PSA density, and diameter of the largest lesion were the most important features for the importance of outcome classification. ML prediction enhanced the PI-RAD classification, where clinically significant prostate cancer (csPCa) cases increased from 0% to 1.9% in the low-risk PI-RAD class, this showed that the model identified some previously missed cases. Predictive machine learning models showed an excellent ability to predict MRI Pi-RAD scores and discriminate between low- and high-risk scores. However, caution should be exercised, as a high percentage of negative biopsy cases were assigned Pi-RAD 4 and Pi-RAD 5 scores. ML integration may enhance PI-RAD's utility by reducing unnecessary biopsies in low-risk patients (via better csPCa detection) and refining the high-risk categorization. Combining such PI-RAD scores with significant parameters, such as PSA density, lesion diameter, number of lesions, and age, in decision curve analysis and utility paradigms would assist physicians' clinical decisions.

In radiotherapy, auto-segmentation tools using deep learning assist in contouring organs-at-risk (OARs). We developed a segmentation model for head and neck (HN) OARs dedicated to contrast-enhanced (CE) computed tomography (CT) using the segmentation software, Ai-Seg, and compared the performance between CE and non-CE (nCE) CT. The retrospective study recruited 321 patients with HN cancers and trained a segmentation model using CE CT (CE model). The CE model was installed in Ai-Seg and applied to additional 25 patients with CE and nCE CT. The Dice similarity coefficient (DSC) and average Hausdorff distance (AHD) were calculated between the ground truth and Ai-Seg contours for brain, brainstem, chiasm, optic nerves, cochleae, oral cavity, parotid glands, pharyngeal constrictor muscle, and submandibular glands (SMGs). We compared the CE model and the existing model trained with nCE CT available in Ai-Seg for 6 OARs. The CE model obtained significantly higher DSCs on CE CT for parotid and SMGs compared to the existing model. The CE model provided significantly lower DSC values and higher AHD values on nCE CT for SMGs than on CE CT, but comparable values for other OARs. The CE model achieved significantly better performance than the existing model and can be used on nCE CT images without significant performance difference, except SMGs. Our results may facilitate the adoption of segmentation tools in clinical practice. We developed a segmentation model for HN OARs dedicated to CE CT using Ai-Seg and evaluated its usability on nCE CT.

Current study aimed to investigate radiomics features derived from two-center diffusion-MRI to differentiate benign and hepatocellular carcinoma (HCC) liver nodules. A total of 328 patients with 517 LI-RADS 2-5 nodules were included. MR images were retrospectively collected from 3 T and 1.5 T MRI vendors. Lesions were categorized into 242 benign and 275 HCC based on follow-up imaging for LR-2,3 and pathology results for LR4,5 nodules, and randomly divided into training (80%) and test (20%) sets. Preprocessing included resampling and normalization. Radiomics features were extracted from lesion volume-of-interest (VOI) on diffusion Images. Scanner variability was corrected using ComBat harmonization method followed by High-correlation filter, PCA filter, and LASSO to select important features. Best classifier model was selected by 10-fold cross-validation, and accuracy was assessed on the test dataset. 1,434 features were extracted, and subsequent classifiers were constructed based on the 16 most important selected features. Notably, support-vector machine (SVM) demonstrated better performance in the test dataset in distinguishing between benign and HCC nodules, achieving an accuracy of 0.92, sensitivity of 0.94, and specificity of 0.86. Utilizing diffusion-MRI radiomics, our study highlights the performance of SVM, trained on lesions' diffusivity characteristics, in distinguishing benign and HCC nodules, ensuring clinical potential. It is suggested that further evaluations be conducted on multi-center datasets to address harmonization challenges. Integration of diffusion radiomics, for monitoring water restriction patterns as tumor histopathological index, with machine learning models demonstrates potential for achieving a reliable noninvasive method to improve the current diagnosis criteria.

Developmental Dysplasia of the Hip (DDH) stands as one of the preeminent hip disorders prevalent in pediatric orthopedics. Automated diagnostic instruments, driven by artificial intelligence methodologies, are capable of providing substantial assistance to clinicians in the diagnosis of DDH. We have developed a dataset designated as Multitasking DDH (MTDDH), which is composed of two sub-datasets. Dataset 1 encompasses 1,250 pelvic X-ray images, with annotations demarcating four discrete regions for the evaluation of pelvic X-ray quality, in tandem with eight pivotal points serving as support for DDH diagnosis. Dataset 2 contains 906 pelvic X-ray images, and each image has been annotated with eight key points for assisting in the diagnosis of DDH. Notably, MTDDH represents the pioneering dataset engineered for the comprehensive evaluation of pelvic X-ray quality while concurrently offering the most exhaustive set of eight key points to bolster DDH diagnosis, thus fulfilling the exigency for enhanced diagnostic precision. Ultimately, we presented the elaborate process of constructing the MTDDH and furnished a concise introduction regarding its application.