Alzheimer's disease (AD) is one of the most known causes of dementia which can be characterized by continuous deterioration in the cognitive skills of elderly people. It is a non-reversible disorder that can only be cured if detected early, which is known as mild cognitive impairment (MCI). The most common biomarkers to diagnose AD are structural atrophy and accumulation of plaques and tangles, which can be detected using magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. Therefore, the present paper proposes wavelet transform-based multimodality fusion of MRI and PET scans to incorporate structural and metabolic information for the early detection of this life-taking neurodegenerative disease. Further, the deep learning model, ResNet-50, extracts the fused images' features. The random vector functional link (RVFL) with only one hidden layer is used to classify the extracted features. The weights and biases of the original RVFL network are being optimized by using an evolutionary algorithm to get optimum accuracy. All the experiments and comparisons are performed over the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to demonstrate the suggested algorithm's efficacy.

Transrectal ultrasound, particularly in the combination of high-frequency ultrasound and MR-TRUS fusion technologies, provides a highly precise and effective method for correlation and targeted biopsy of suspicious intraprostatic lesions detected by MRI. Advances in imaging technology, driven by 29 Mhz micro-ultrasound transducers, robotic-assisted systems, and the integration of AI-based analyses, promise further improvements in diagnostic accuracy and a reduction in unnecessary biopsies. Further technological advancements and improved TRUS training could contribute to a decentralized and cost-effective diagnostic evaluation of prostate cancer in the future.

The objective of this study was to evaluate a combination of deep learning (DL)-reconstructed parallel acquisition technique (PAT) and simultaneous multislice (SMS) acceleration imaging in comparison to conventional knee imaging. Adults undergoing knee magnetic resonance imaging (MRI) with DL-enhanced acquisitions were prospectively analyzed from December 2023 to April 2024. The participants received T1 without fat saturation and fat-suppressed PD-weighted TSE pulse sequences using conventional two-fold PAT (P2) and either DL-enhanced four-fold PAT (P4) or a combination of DL-enhanced four-fold PAT with two-fold SMS acceleration (P4S2). Three independent readers assessed image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and radiomics features. 34 participants (mean age 45±17years; 14 women) were included who underwent P4S2, P4, and P2 imaging. Both P4S2 and P4 demonstrated higher CNR and SNR values compared to P2 (P<.001). P4 was diagnostically inferior to P2 only in the visualization of cartilage damage (P<.005), while P4S2 consistently outperformed P2 in anatomical delineation across all evaluated structures and raters (P<.05). Radiomics analysis revealed significant differences in contrast and gray-level characteristics among P2, P4, and P4S2 (P<.05). P4 reduced time by 31% and P4S2 by 41% compared to P2 (P<.05). P4S2 DL acceleration offers significant advancements over P4 and P2 in knee MRI, combining superior image quality and improved anatomical delineation at significant time reduction. Its improvements in anatomical delineation, energy consumption, and workforce optimization make P4S2 a significant step forward.

To demonstrate that a deep learning (DL) model can be employed as a teaching tool to improve radiologists' ability to perform a subsequent imaging task without additional artificial intelligence (AI) assistance at time of image interpretation. Three human readers were tasked to categorize 50 frontal knee radiographs by male and female sex before and after reviewing data derived from our DL model. The model's high accuracy in performing this task was revealed to the human subjects, who were also supplied the DL model's resultant occlusion interpretation maps ("heat maps") to serve as a teaching tool for study before final testing. Two weeks later, the three human readers performed the same task with a new set of 50 radiographs. The average accuracy of the three human readers was initially 0.59 (95%CI: 0.59-0.65), not statistically different than guessing given our sample skew. The DL model categorized sex with 0.96 accuracy. After study of AI-derived "heat maps" and associated radiographs, the average accuracy of the human readers, without the direct help of AI, on the new set of radiographs increased to 0.80 (95%CI: 0.73-0.86), a significant improvement (p=0.0270). AI-derived data can be used as a teaching tool to improve radiologists' own ability to perform an imaging task. This is an idea that we have not before seen advanced in the radiology literature. AI can be used as a teaching tool to improve the intrinsic accuracy of radiologists, even without the concurrent use of AI.

This study aims to develop a deep learning model for a robust diagnosis of Carpal Tunnel Syndrome (CTS) based on comparative classification leveraging the ultrasound images of the thenar and hypothenar muscles. We recruited 152 participants, both patients with varying severities of CTS and healthy individuals. The enrolled patients underwent ultrasonography, which provided ultrasound image data of the thenar and hypothenar muscles from the median and ulnar nerves. These images were used to train a deep learning model. We compared the performance of our model with previous comparative methods using echo intensity ratio or machine learning, and non-comparative methods based on deep learning. During the training process, comparative guidance based on cosine similarity was used so that the model learns to automatically identify the abnormal differences in echotexture between the ultrasound images of the thenar and hypothenar muscles. The proposed deep learning model with comparative guidance showed the highest performance. The comparison of Receiver operating characteristic (ROC) curves between models demonstrated that the Comparative guidance was effective in autonomously identifying complex features within the CTS dataset. The proposed deep learning model with comparative guidance was shown to be effective in automatically identifying important features for CTS diagnosis from the ultrasound images. The proposed comparative approach was found to be robust to the traditional problems in ultrasound image analysis such as different cut-off values and anatomical variation of patients. Proposed deep learning methodology facilitates accurate and efficient diagnosis of CTS from ultrasound images.

This study aimed to explore the feasibility of using habitat radiomics based on magnetic resonance imaging (MRI) to predict metachronous liver metastasis (MLM) in locally advanced rectal cancer (LARC) patients. A nomogram was developed by integrating multiple factors to enhance predictive accuracy. Retrospective data from 385 LARC patients across two centers were gathered. The data from Center 1 were split into a training set of 203 patients and an internal validation set of 87 patients, while Center 2 provided an external test set of 95 patients. K - means clustering was used on T2 - weighted images, and the region of interest was extended at different thicknesses. After feature extraction and selection, four machine - learning algorithms were utilized to build radiomics models. A nomogram was created by combining habitat radiomics, conventional radiomics, and clinical independent predictors. Model performance was evaluated by the AUC, and clinical utility was assessed through calibration curve and DCA. Habitat radiomics outperformed other single models in predicting MLM, with AUCs of 0.926, 0.864, and 0.851 in respective sets. The integrated nomogram achieved even higher AUCs of 0.959, 0.925, and 0.889. DCA and calibration curve analysis showed its high net benefit and good calibration. MRI - based habitat radiomics can effectively predict MLM in LARC patients. The integrated nomogram has optimal predictive performance and improves model accuracy significantly.

Routine knee MRI protocols for 1.5 T and 3 T scanners, do not include T2*-w gradient echo (T2*W) images, which are useful in several clinical scenarios such as the assessment of cartilage, synovial blooming (deposition of hemosiderin), chondrocalcinosis and the evaluation of the physis in pediatric patients. Herein, we aimed to develop an open-source deep learning model that creates synthetic T2*W images of the knee using fat-suppressed intermediate-weighted images. A cycleGAN model was trained with 12,118 sagittal knee MR images and tested on an independent set of 2996 images. Diagnostic interchangeability of synthetic T2*W images was assessed against a series of findings. Voxel intensity of four tissues was evaluated with Bland-Altman plots. Image quality was assessed with the use of root mean squared error (NRMSE), structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). Code, model and a standalone executable file are provided on github. The model achieved a median NRMSE, PSNR and SSIM of 0.5, 17.4, and 0.5, respectively. Images were found interchangeable with an intraclass correlation coefficient >0.95 for all findings. Mean voxel intensity was equal between synthetic and conventional images. Four types of artifacts were identified: geometrical distortion (86/163 cases), object insertion/omission (11/163 cases), a wrap-around-like (26/163 cases) and an incomplete fat-suppression artifact (120/163 cases), which had a median 0 impact (no impact) on the diagnosis. In conclusion, the developed open-source GAN model creates synthetic T2*W images of the knee of high diagnostic value and quality. The identified artifacts had no or minor effect on the diagnostic value of the images.

Adolescent major depressive disorder (MDD) is a serious mental health condition that has been linked to abnormal functional connectivity (FC) patterns within the brain. However, whether FC could be used as a potential biomarker for diagnosis of adolescent MDD is still unclear. The aim of our study was to investigate the potential diagnostic value of whole-brain FC in adolescent MDD. Resting-state functional magnetic resonance imaging data were obtained from 94 adolescents with MDD and 78 healthy adolescents. The whole brain was segmented into 90 regions of interest (ROIs) using the automated anatomical labeling atlas. FC was assessed by calculating the Pearson correlation coefficient of the average time series between each pair of ROIs. A multivariate pattern analysis was employed to classify patients from controls using the whole-brain FC as input features. The linear support vector machine classifier achieved an accuracy of 69.18% using the optimal functional connection features. The consensus functional connections were mainly located within and between large-scale brain networks. The top 10 nodes with the highest weight in the classification model were mainly located in the default mode, salience, auditory, and sensorimotor networks. Our findings highlighted the importance of functional network connectivity in the neurobiology of adolescent MDD, and suggested the possibility of altered FC and high-weight regions as complementary diagnostic markers in adolescents with depression.

There is an urgent need for the precise prediction of cerebral amyloidosis using noninvasive and accessible indicators to facilitate the early diagnosis of individuals with the preclinical stage of Alzheimer's disease (AD). Two hundred and four individuals with subjective cognitive decline (SCD) were enrolled in this study. All subjects completed neuropsychological assessments and underwent 18F-florbetapir PET, structural MRI, and functional MRI. A total of 315 features were extracted from the MRI, demographics, and neuropsychological scales and selected using the least absolute shrinkage and selection operator (LASSO). The logistic regression (LR) model, based on machine learning, was trained to classify SCD as either β-amyloid (Aβ) positive or negative. A nomogram was established using a multivariate LR model to predict the risk of Aβ+. The performance of the prediction model and nomogram was assessed with area under the curve (AUC) and calibration. The final model was based on the right rostral anterior cingulate thickness, the grey matter volume of the right inferior temporal, the ReHo of the left posterior cingulate gyrus and right superior temporal gyrus, as well as MoCA-B and AVLT-R. In the training set, the model achieved a good AUC of 0.78 for predicting Aβ+, with an accuracy of 0.72. The validation of the model also yielded a favorable discriminatory ability with an AUC of 0.88 and an accuracy of 0.83. We have established and validated a model based on cognitive, sMRI, and fMRI data that exhibits adequate discrimination. This model has the potential to predict amyloid status in the SCD group and provide a noninvasive, cost-effective way that might facilitate early screening, clinical diagnosis, and drug clinical trials.