In this study, we attempted to identify the subtypes of autism spectrum disorder (ASD) with the help of anatomical alterations found in structural magnetic resonance imaging (sMRI) data of the ASD brain and machine learning tools. Initially, the sMRI data was preprocessed using the FreeSurfer toolbox. Further, the brain regions were segmented into 148 regions of interest using the Destrieux atlas. Features such as volume, thickness, surface area, and mean curvature were extracted for each brain region. We performed principal component analysis independently on the volume, thickness, surface area, and mean curvature features and identified the top 10 features. Further, we applied k-means clustering on these top 10 features and validated the number of clusters using Elbow and Silhouette method. Our study identified two clusters in the dataset which significantly shows the existence of two subtypes in ASD. We identified the features such as volume of scaled lh_G_front middle, thickness of scaled rh_S_temporal transverse, area of scaled lh_S_temporal sup, and mean curvature of scaled lh_G_precentral as the significant features discriminating the two clusters with statistically significant p-value (p<0.05). Thus, our proposed method is effective for the identification of ASD subtypes and can also be useful for the screening of other similar neurological disorders.

Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification. We developed a highly reproducible, personalized prognostication, and clinical subgrouping system using machine learning (ML) on routine clinical data, magnetic resonance imaging (MRI), and molecular measures from 2838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, and III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]). The ML model stratified patients into distinct prognostic subgroups with HRs between subgroups I-II and I-III of 1.62 (95% CI: 1.43-1.84, P < .001) and 3.48 (95% CI: 2.94-4.11, P < .001), respectively. Analysis of imaging features revealed several tumor properties contributing unique prognostic value, supporting the feasibility of a generalizable prognostic classification system in a diverse cohort. Our ML model demonstrates extensive reproducibility and online accessibility, utilizing routine imaging data rather than complex imaging protocols. This platform offers a unique approach to personalized patient management and clinical trial stratification in GBM.

We developed and validated a magnetic resonance imaging (MRI)-based radiomics model for the classification of high-grade glioma (HGG) and determined the optimal machine learning (ML) approach. This retrospective analysis included 184 patients (59 grade III lesions and 125 grade IV lesions). Radiomics features were extracted from MRI with T1-weighted imaging (T1WI). The least absolute shrinkage and selection operator (LASSO) feature selection method and seven classification methods including logistic regression, XGBoost, Decision Tree, Random Forest (RF), Adaboost, Gradient Boosting Decision Tree, and Stacking fusion model were used to differentiate HGG. Performance was compared on AUC, sensitivity, accuracy, precision and specificity. In the non-fusion models, the best performance was achieved by using the XGBoost classifier, and using SMOTE to deal with the data imbalance to improve the performance of all the classifiers. The Stacking fusion model performed the best, with an AUC = 0.95 (sensitivity of 0.84; accuracy of 0.85; F1 score of 0.85). MRI-based quantitative radiomics features have good performance in identifying the classification of HGG. The XGBoost method outperforms the classifiers in the non-fusion model and the Stacking fusion model outperforms the non-fusion model.

Exploring the early stages of Alzheimer's disease (AD) is crucial for timely intervention to help manage symptoms and set expectations for affected individuals and their families. However, the study of the early stages of AD involves analysing heterogeneous disease cohorts which may present challenges for some modelling techniques. This heterogeneity stems from the diverse nature of AD itself, as well as the inclusion of undiagnosed or 'at-risk' AD individuals or the presence of comorbidities which differentially affect AD biomarkers within the cohort. Normative modelling is an emerging technique for studying heterogeneous disorders that can quantify how brain imaging-based measures of individuals deviate from a healthy population. The normative model provides a statistical description of the 'normal' range that can be used at subject level to detect deviations, which may relate to pathological effects. In this work, we applied a deep learning-based normative model, pre-trained on MRI scans in the UK Biobank, to investigate ageing and identify abnormal age-related decline. We calculated deviations, relative to the healthy population, in multi-modal MRI data of non-demented individuals in the external EPAD (ep-ad.org) cohort and explored these deviations with the aim of determining whether normative modelling could detect AD-relevant subtle differences between individuals. We found that aggregate measures of deviation based on the entire brain correlated with measures of cognitive ability and biological phenotypes, indicating the effectiveness of a general deviation metric in identifying AD-related differences among individuals. We then explored deviations in individual imaging features, stratified by cognitive performance and genetic risk, across different brain regions and found that the brain regions showing deviations corresponded to those affected by AD such as the hippocampus. Finally, we found that 'at-risk' individuals in the EPAD cohort exhibited increasing deviation over time, with an approximately 6.4 times greater t-statistic in a pairwise t-test compared to a 'super-healthy' cohort. This study highlights the capability of deep normative modelling approaches to detect subtle differences in brain morphology among individuals at risk of developing AD in a non-demented population. Our findings allude to the potential utility of normative deviation metrics in monitoring disease progression.

Only in recent years it has been demonstrated that the thoracolumbar fascia is involved in low back pain (LBP), thus highlighting its implications for treatments. Furthermore, an easily accessible and non-invasive way to investigate the fascia in real time is the ultrasound examination, which to be reliable as is, it must overcome the challenges related to the configuration of the machine and the experience of the operator. Therefore, the lack of a clear understanding of the fascial system combined with the penalty related to the setting of the ultrasound acquisition has generated a gap that makes its effective evaluation difficult during clinical routine. The aim of the present work is to fill this gap by investigating the effectiveness of using a deep learning approach to segment the thoracolumbar fascia from ultrasound imaging. A total of 538 ultrasound images of the thoracolumbar fascia of LBP subjects were finally used to train and test a deep learning network. An additional test set (so-called Test set 2) was collected from another center, operator, machine manufacturer, patient cohort, and protocol to improve the generalizability of the study. A U-Net-based architecture was demonstrated to be able to segment these structures with a final training accuracy of 0.99 and a validation accuracy of 0.91. The accuracy of the prediction computed on a test set (87 images not included in the training set) reached the 0.94, with a mean intersection over union index of 0.82 and a Dice-score of 0.76. These latter metrics were outperformed by those in Test set 2. The validity of the predictions was also verified and confirmed by two expert clinicians. Automatic identification of the thoracolumbar fascia has shown promising results to thoroughly investigate its alteration and target a personalized rehabilitation intervention based on each patient-specific scenario.

Due to differences in subjective experience and professional level among doctors, as well as inconsistent diagnostic criteria, there are issues with the accuracy and reliability of single imaging diagnosis results for knee joint injuries. To address these issues, magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US) are adopted in this article for ensemble learning, and deep learning (DL) is combined for automatic analysis. By steps such as image enhancement, noise elimination, and tissue segmentation, the quality of image data is improved, and then convolutional neural networks (CNN) are used to automatically identify and classify injury types. The experimental results show that the DL model exhibits high sensitivity and specificity in the diagnosis of different types of injuries, such as anterior cruciate ligament tear, meniscus injury, cartilage injury, and fracture. The diagnostic accuracy of anterior cruciate ligament tear exceeds 90%, and the highest diagnostic accuracy of cartilage injury reaches 95.80%. In addition, compared with traditional manual image interpretation, the DL model has significant advantages in time efficiency, with a significant reduction in average interpretation time per case. The diagnostic consistency experiment shows that the DL model has high consistency with doctors' diagnosis results, with an overall error rate of less than 2%. The model has high accuracy and strong generalization ability when dealing with different types of joint injuries. These data indicate that combining multiple imaging technologies and the DL algorithm can effectively improve the accuracy and efficiency of diagnosing sports injuries of knee joints.

Evaluating large artery atherosclerosis is critical for predicting and preventing ischemic strokes. Ultrasonographic assessment of the carotid arteries is the preferred first-line examination due to its ease of use, noninvasive, and absence of radiation exposure. This study proposed an automated classification model for the common carotid artery (CCA), carotid bulb, internal carotid artery (ICA), and external carotid artery (ECA) to enhance the quantification of carotid artery examinations.&#xD;Approach: A total of 2,943 B-mode ultrasound images (CCA: 1,563; bulb: 611; ICA: 476; ECA: 293) from 288 patients were collected. Three distinct sets of embedding features were extracted from artificial intelligence networks including pre-trained DenseNet201, vision Transformer (ViT), and echo contrastive language-image pre-training (EchoCLIP) models using deep learning architectures for pattern recognition. These features were then combined in a support vector machine (SVM) classifier to interpret the anatomical structures in B-mode images.&#xD;Main results: After ten-fold cross-validation, the model achieved an accuracy of 82.3%, which was significantly better than using individual feature sets, with a p-value of <0.001.&#xD;Significance: The proposed model could make carotid artery examinations more accurate and consistent with the achieved classification accuracy. The source code is available at https://github.com/buddykeywordw/Artery-Segments-Recognition&#xD.

To explore whether a CT-based AI framework, leveraging multi-scale features, can offer a non-invasive approach to accurately predict pathological grade and Ki67 index in clear cell renal cell carcinoma (ccRCC). In this multicenter retrospective study, a total of 1073 pathologically confirmed ccRCC patients from seven cohorts were split into internal cohorts (training and validation sets) and an external test set. The AI framework comprised an image processor, a 3D-kidney and tumor segmentation model by 3D-UNet, a multi-scale features extractor built upon unsupervised learning, and a multi-task classifier utilizing XGBoost. A quantitative model interpretation technique, known as SHapley Additive exPlanations (SHAP), was employed to explore the contribution of multi-scale features. The 3D-UNet model showed excellent performance in segmenting both the kidney and tumor regions, with Dice coefficients exceeding 0.92. The proposed multi-scale features model exhibited strong predictive capability for pathological grading and Ki67 index, with AUROC values of 0.84 and 0.87, respectively, in the internal validation set, and 0.82 and 0.82, respectively, in the external test set. The SHAP results demonstrated that features from radiomics, the 3D Auto-Encoder, and dimensionality reduction all made significant contributions to both prediction tasks. The proposed AI framework, leveraging multi-scale features, accurately predicts the pathological grade and Ki67 index of ccRCC. The CT-based AI framework leveraging multi-scale features offers a promising avenue for accurately predicting the pathological grade and Ki67 index of ccRCC preoperatively, indicating a direction for non-invasive assessment. Non-invasively determining pathological grade and Ki67 index in ccRCC could guide treatment decisions. The AI framework integrates segmentation, classification, and model interpretation, enabling fully automated analysis. The AI framework enables non-invasive preoperative detection of high-risk tumors, assisting clinical decision-making.

The application of artificial intelligence (AI) technology has realized the transformation of people's production and lifestyle, and also promoted the rapid development of the medical field. At present, the application of intelligence in the medical field is increasing. Using its advanced methods and technologies of AI, this paper aims to realize the integration of medical imaging-aided diagnosis system and AI, which is helpful to analyze and solve the loopholes and errors of traditional artificial diagnosis in the diagnosis of pulmonary nodules. Drawing on the principles and rules of image segmentation methods, the construction and optimization of a medical image-aided diagnosis system is carried out to realize the precision of the diagnosis system in the diagnosis of pulmonary nodules. In the diagnosis of pulmonary nodules carried out by traditional artificial and medical imaging-assisted diagnosis systems, 231 nodules with pathology or no change in follow-up for more than two years were also tested in 200 cases. The results showed that the AI software detected a total of 881 true nodules with a sensitivity of 99.10% (881/889). The radiologists detected 385 true nodules with a sensitivity of 43.31% (385/889). The sensitivity of AI software in detecting non-calcified nodules was significantly higher than that of radiologists (99.01% vs 43.30%, P < 0.001), and the difference was statistically significant.

This study aimed to develop a BI-RADS network (DL-UM) via integrating ultrasound (US) and mammography (MG) images and explore its performance in improving breast lesion diagnosis and management when collaborating with radiologists, particularly in cases with discordant US and MG Breast Imaging Reporting and Data System (BI-RADS) classifications. We retrospectively collected image data from 1283 women with breast lesions who underwent both US and MG within one month at two medical centres and categorised them into concordant and discordant BI-RADS classification subgroups. We developed a DL-UM network via integrating US and MG images, and DL networks using US (DL-U) or MG (DL-M) alone, respectively. The performance of DL-UM network for breast lesion diagnosis was evaluated using ROC curves and compared to DL-U and DL-M networks in the external testing dataset. The diagnostic performance of radiologists with different levels of experience under the assistance of DL-UM network was also evaluated. In the external testing dataset, DL-UM outperformed DL-M in sensitivity (0.962 vs. 0.833, P = 0.016) and DL-U in specificity (0.667 vs. 0.526, P = 0.030), respectively. In the discordant BI-RADS classification subgroup, DL-UM achieved an AUC of 0.910. The diagnostic performance of four radiologists improved when collaborating with the DL-UM network, with AUCs increased from 0.674-0.772 to 0.889-0.910, specificities from 52.1%-75.0 to 81.3-87.5% and reducing unnecessary biopsies by 16.1%-24.6%, particularly for junior radiologists. Meanwhile, DL-UM outputs and heatmaps enhanced radiologists' trust and improved interobserver agreement between US and MG, with weighted kappa increased from 0.048 to 0.713 (P < 0.05). The DL-UM network, integrating complementary US and MG features, assisted radiologists in improving breast lesion diagnosis and management, potentially reducing unnecessary biopsies.