Precise and rapid identification of knee osteoarthritis (OA) is essential for efficient management and therapy planning. Conventional diagnostic techniques frequently depend on subjective interpretation, which have shortcomings, particularly during the first phases of the illness. In this study, magnetic resonance imaging (MRI) was used to create knee datasets as novel techniques for evaluating knee OA. This methodology utilizes artificial intelligence (AI) algorithms to identify and evaluate important indications of knee osteoarthritis, including osteophytes, eburnation, bone marrow lesions (BMLs), and cartilage thickness. We conducted training and evaluation on multiple deep learning models, including ResNet50, DenseNet121, VGG16 and ResNet101 utilizing annotated MRI data. By conducting thorough statistical analysis and validation, we have proven the efficacy of our models in precisely diagnosing and grading knee OA. This research presents a new grading method, verified by experienced radiologists, that uses eburnation as a significant indicator of the severity of knee OA. This study provides a new method for an AI-powered automated system designed to diagnose knee OA. This system will simplify the diagnostic process, minimize mistakes made by humans, and enhance the effectiveness of clinical treatment. Through the integration of AI-ML (machine learning) technologies, our goal is to improve patient outcomes, optimize the utilization of healthcare resources, and enable personalized knee OA therapy.

Metabolomics has been associated with cognitive decline and dementia, but the relationship between metabolites and brain aging remains unclear. We aimed to investigate the associations of metabolomics with brain age assessed by neuroimaging and to explore whether these relationships vary according to apolipoprotein E (APOE) ε4. This study included 17,770 chronic brain disorder-free participants aged 40-69 years from UK Biobank who underwent neuroimaging scans an average of 9 years after baseline. A total of 249 plasma metabolites were measured using nuclear magnetic resonance spectroscopy at baseline. Brain age was estimated using LASSO regression and 1079 brain MRI phenotypes and brain age gap (BAG; i.e., brain age minus chronological age) was calculated. Data were analyzed using linear regression. We identified 64 and 77 metabolites associated with brain age and BAG, respectively, of which 55 overlapped. Lipids (including cholesterol, cholesteryl esters, free cholesterol, phospholipids, and total lipids) in S/M-HDL, as well as phospholipids and triglycerides as a percentage of total lipids in different-density lipoproteins, were associated with larger BAG. The percentages of cholesterol, cholesteryl esters, and free cholesterol to total lipids in VLDL, LDL, and HDL of different particle sizes were associated with smaller BAG. The associations of LA/FA, omega-6/FA, SFA/FA, and phospholipids to total lipids in L-HDL with brain age were consistent across APOE ε4 carriers and non-carriers (all p for interaction > 0.05). Plasma metabolites show remarkably widespread associations with brain aging regardless of APOE ε4 genetic risk. Metabolic profiles could serve as an early indicator of accelerated brain aging.

The 3-dimensional (3D) double echo steady state (DESS) magnetic resonance imaging sequence can image knee cartilage with high, isotropic resolution, particularly at high and ultra-high field strengths. Advanced undersampling techniques with high acceleration factors can provide the short acquisition times required for clinical use. However, the optimal undersampling scheme and its limits are unknown. High-resolution isotropic (reconstructed voxel size: 0.3 × 0.3 × 0.3 mm 3 ) 3D DESS images of 40 knees in 20 volunteers were acquired at 7 T with varying undersampling factors (R = 4-30) and schemes (regular: GRAPPA, CAIPIRINHA; incoherent: compressed sensing [CS]), whereas the remaining imaging parameters were kept constant. All imaging data were reconstructed with deep learning (DL) algorithms. Three readers rated image quality on a 4-point Likert scale. Four-fold accelerated GRAPPA was used as reference standard. Incidental cartilage lesions were graded on a modified Whole-Organ Magnetic Resonance Imaging Score (WORMS). Friedman's analysis of variance characterized rating differences. The interreader agreement was assessed using κ statistics. The quality of 16-fold accelerated CS images was not rated significantly different from that of 4-fold accelerated GRAPPA and 8-fold accelerated CAIPIRINHA images, whereas the corresponding data were acquired 4.5 and 2 times faster (01:12 min:s) than in 4-fold accelerated GRAPPA (5:22 min:s) and 8-fold accelerated CAIPIRINHA (2:22 min:s) acquisitions, respectively. Interreader agreement for incidental cartilage lesions was almost perfect for 4-fold accelerated GRAPPA (κ = 0.91), 8-fold accelerated CAIPIRINHA (κ = 0.86), and 8- to 16-fold accelerated CS (κ = 0.91). Our results suggest significant advantages of incoherent versus regular undersampling patterns for high-resolution 3D DESS cartilage imaging with high acceleration factors. The combination of CS undersampling with DL reconstruction enables fast, isotropic, high-resolution acquisitions without apparent impairment of image quality. Since DESS specific absorption rate values tend to be moderate, CS DESS with DL reconstruction promises potential for high-resolution assessment of cartilage morphology and other musculoskeletal anatomies at 7 T.

Transplantation of fresh osteochondral allografts is a possible biological resurfacing option to substitute massive bone loss and provide proper gliding surfaces for extended and deep osteochondral lesions of weight-bearing articular surfaces. Limited chondrocyte survival and technical difficulties may compromise the efficacy of osteochondral transfers. As experimental data suggest that minimizing the time between graft harvest and implantation may improve chondrocyte survival rate a <48 hours donor to recipient time was used to repair massive osteochondral defects. For optimal graft congruency, a magnetic resonance-based artificial intelligence algorithm was also developed to provide proper technical support. Based on 3 years of experience, increased survival rate of transplanted chondrocytes and improved clinical outcomes were observed.

Evaluation of artificial intelligence (AI) algorithms for prostate segmentation is challenging because ground truth is lacking. We aimed to: (1) create a reference standard data set with precise prostate contours by expert consensus, and (2) evaluate various AI tools against this standard. We obtained prostate magnetic resonance imaging cases from six institutions from the Qualitative Prostate Imaging Consortium. A panel of 4 experts (2 genitourinary radiologists and 2 prostate radiation oncologists) meticulously developed consensus prostate segmentations on axial T₂-weighted series. We evaluated the performance of 6 AI tools (3 commercially available and 3 academic) using Dice scores, distance from reference contour, and volume error. The panel achieved consensus prostate segmentation on each slice of all 68 patient cases included in the reference data set. We present 2 patient examples to serve as contouring guides. Depending on the AI tool, median Dice scores (across patients) ranged from 0.80 to 0.94 for whole prostate segmentation. For a typical (median) patient, AI tools had a mean error over the prostate surface ranging from 1.3 to 2.4 mm. They maximally deviated 3.0 to 9.4 mm outside the prostate and 3.0 to 8.5 mm inside the prostate for a typical patient. Error in prostate volume measurement for a typical patient ranged from 4.3% to 31.4%. We established an expert consensus benchmark for prostate segmentation. The best-performing AI tools have typical accuracy greater than that reported for radiation oncologists using computed tomography scans (the most common clinical approach for radiation therapy planning). Physician review remains essential to detect occasional major errors.

Accurate staging of hepatic fibrosis is critical for prognostication and management among patients with chronic liver disease, and noninvasive, efficient alternatives to biopsy are urgently needed. This study aimed to evaluate the performance of an automated deep learning (DL) algorithm for fibrosis staging and for differentiating patients with hepatic fibrosis from healthy individuals via magnetic resonance (MR) images with and without additional clinical data. A total of 500 patients from two medical centers were retrospectively analyzed. DL models were developed based on delayed-phase MR images to predict fibrosis stages. Additional models were constructed by integrating the DL algorithm with nonimaging variables, including serologic biomarkers [aminotransferase-to-platelet ratio index (APRI) and fibrosis index based on four factors (FIB-4)], viral status (hepatitis B and C), and MR scanner parameters. Diagnostic performance, was assessed via the area under the receiver operating characteristic curve (AUROC), and comparisons were through use of the DeLong test. Sensitivity and specificity of the DL and full models (DL plus all clinical features) were compared with those of experienced radiologists and serologic biomarkers via the McNemar test. In the test set, the full model achieved AUROC values of 0.99 [95% confidence interval (CI): 0.94-1.00], 0.98 (95% CI: 0.93-0.99), 0.90 (95% CI: 0.83-0.95), 0.81 (95% CI: 0.73-0.88), and 0.84 (95% CI: 0.76-0.90) for staging F0-4, F1-4, F2-4, F3-4, and F4, respectively. This model significantly outperformed the DL model in early-stage classification (F0-4 and F1-4). Compared with expert radiologists, it showed superior specificity for F0-4 and higher sensitivity across the other four classification tasks. Both the DL and full models showed significantly greater specificity than did the biomarkers for staging advanced fibrosis (F3-4 and F4). The proposed DL algorithm provides a noninvasive method for hepatic fibrosis staging and screening, outperforming both radiologists and conventional biomarkers, and may facilitate improved clinical decision-making.

Currently, there is no fully automated tool available for evaluating the degree of cervical spinal stenosis. The aim of this study was to develop and validate the use of artificial intelligence (AI) algorithms for the assessment of cervical spinal stenosis. In this retrospective multi-center study, cervical spine magnetic resonance imaging (MRI) scans obtained from July 2020 to June 2023 were included. Studies of patients with spinal instrumentation or studies with suboptimal image quality were excluded. Sagittal T2-weighted images were used. The training data from the Fourth People's Hospital of Shanghai (Hos. 1) and Shanghai Changzheng Hospital (Hos. 2) were annotated by two musculoskeletal (MSK) radiologists following Kang's system as the standard reference. First, a convolutional neural network (CNN) was trained to detect the region of interest (ROI), with a second Transformer for classification. The performance of the deep learning (DL) model was assessed on an internal test set from Hos. 2 and an external test set from Shanghai Changhai Hospital (Hos. 3), and compared among six readers. Metrics such as detection precision, interrater agreement, sensitivity (SEN), and specificity (SPE) were calculated. Overall, 795 patients were analyzed (mean age ± standard deviation, 55±14 years; 346 female), with 589 in the training (75%) and validation (25%) sets, 206 in the internal test set, and 95 in the external test set. Four tasks with different clinical application scenarios were trained, and their accuracy (ACC) ranged from 0.8993 to 0.9532. When using a Kang system score of ≥2 as a threshold for diagnosing central cervical canal stenosis in the internal test set, both the algorithm and six readers achieved similar areas under the receiver operating characteristic curve (AUCs) of 0.936 [95% confidence interval (CI): 0.916-0.955], with a SEN of 90.3% and SPE of 93.8%; the AUC of the DL model was 0.931 (95% CI: 0.917-0.946), with a SEN in the external test set of 100%, and a SPE of 86.3%. Correlation analysis comparing the DL method, the six readers, and MRI reports between the reference standard showed a moderate correlation, with R values ranging from 0.589 to 0.668. The DL model produced approximately the same upgrades (9.2%) and downgrades (5.1%) as the six readers. The DL model could fully automatically and reliably assess cervical canal stenosis using MRI scans.

People living with human immunodeficiency virus (PLWH) are at risk of human immunodeficiency virus (HIV)-associated neurocognitive disorders (HAND). The mildest disease stage of HAND is asymptomatic neurocognitive impairment (ANI), and the accurate diagnosis of this stage can facilitate timely clinical interventions. The aim of this study was to mine features related to the diagnosis of ANI based on resting-state functional magnetic resonance imaging (rs-fMRI) and to establish classification models. A total of 74 patients with 74 ANI and 78 with PLWH but no neurocognitive disorders (PWND) were enrolled. Basic clinical, T1-weighted imaging, and rs-fMRI data were obtained. The rs-fMRI signal values and radiomics features of 116 brain regions designated by the Anatomical Automatic Labeling template were collected, and the features were selected via the least absolute shrinkage and selection operator. rs-fMRI, radiomics, and combined models were constructed with five machine learning classifiers, respectively. Model performance was evaluated via the mean area under the curve (AUC), accuracy, sensitivity, and specificity. Twenty-one rs-fMRI signal values and 28 radiomics features were selected to construct models. The performance of the combined models was exceptional, with the standout random forest (RF) model delivering an AUC value of 0.902 [95% confidence interval (CI): 0.813-0.990] in the validation set and 1.000 (95% CI: 1.000-1.000) in the training set. Further analysis of the 49 features revealed significantly overlapping brain regions for both feature types. Three key features demonstrating significant differences between ANI and PWND were identified (all P values <0.001). These features correlated with cognitive test performance (r>0.3). The RF combined model exhibited high classification performance in ANI, enabling objective and reliable individual diagnosis in clinical practice. It thus represents a novel method for characterizing the brain functional impairment and pathophysiology of patients with ANI. Greater attention should be paid to the frontoparietal and striatum in the research and clinical work related to ANI.

The use of multiparametric magnetic resonance imaging (MRI) in predicting lymphovascular invasion (LVI) in breast cancer has been well-documented in the literature. However, the majority of the related studies have primarily focused on intratumoral characteristics, overlooking the potential contribution of peritumoral features. The aim of this study was to evaluate the effectiveness of multiparametric MRI in predicting LVI by analyzing both intratumoral and peritumoral radiomics features and to assess the added value of incorporating both regions in LVI prediction. A total of 366 patients underwent preoperative breast MRI from two centers and were divided into training (n=208), validation (n=70), and test (n=88) sets. Imaging features were extracted from intratumoral and peritumoral T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced MRI. Five models were developed for predicting LVI status based on logistic regression: the tumor area (TA) model, peritumoral area (PA) model, tumor-plus-peritumoral area (TPA) model, clinical model, and combined model. The combined model was created incorporating the highest radiomics score and clinical factors. Predictive efficacy was evaluated via the receiver operating characteristic (ROC) curve and area under the curve (AUC). The Shapley additive explanation (SHAP) method was used to rank the features and explain the final model. The performance of the TPA model was superior to that of the TA and PA models. A combined model was further developed via multivariable logistic regression, with the TPA radiomics score (radscore), MRI-assessed axillary lymph node (ALN) status, and peritumoral edema (PE) being incorporated. The combined model demonstrated good calibration and discrimination performance across the training, validation, and test datasets, with AUCs of 0.888 [95% confidence interval (CI): 0.841-0.934], 0.856 (95% CI: 0.769-0.943), and 0.853 (95% CI: 0.760-0.946), respectively. Furthermore, we conducted SHAP analysis to evaluate the contributions of TPA radscore, MRI-ALN status, and PE in LVI status prediction. The combined model, incorporating clinical factors and intratumoral and peritumoral radscore, effectively predicts LVI and may potentially aid in tailored treatment planning.

Accurately predicting early recurrence in brain tumor patients following surgical resection remains a clinical challenge. This study proposes a multi-modal machine learning framework that integrates structural MRI features with clinical biomarkers to improve postoperative recurrence prediction. We employ four machine learning algorithms -- Gradient Boosting Machine (GBM), Random Survival Forest (RSF), CoxBoost, and XGBoost -- and validate model performance using concordance index (C-index), time-dependent AUC, calibration curves, and decision curve analysis. Our model demonstrates promising performance, offering a potential tool for risk stratification and personalized follow-up planning.