Early identification of individuals who progress from normal cognition (NC) to mild cognitive impairment (MCI) may help prevent cognitive decline. We aimed to build predictive models using radiomic features of the bilateral hippocampus in combination with scores from neuropsychological assessments. We utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study 175 NC individuals, identifying 50 who progressed to MCI within seven years. Employing the Least Absolute Shrinkage and Selection Operator (LASSO) on T1-weighted images, we extracted and refined hippocampal features. Classification models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and light gradient boosters (LightGBM), were built based on significant neuropsychological scores. Model validation was conducted using 5-fold cross-validation, and hyperparameters were optimized with Scikit-learn, using an 80:20 data split for training and testing. We found that the LightGBM model achieved an area under the receiver operating characteristic (ROC) curve (AUC) value of 0.89 and an accuracy of 0.79 in the training set, and an AUC value of 0.80 and an accuracy of 0.74 in the test set. The study identified that T1-weighted magnetic resonance imaging radiomic of the hippocampus would be used to predict the progression to MCI at the normal cognitive stage, which might provide a new insight into clinical research.

Integrating deep learning into clinical workflows for medical image analysis holds promise for improving diagnostic accuracy. However, strict data privacy regulations and the sensitivity of clinical IT infrastructure limit the deployment of cloud-based solutions. This paper introduces WebIPred, a web-based application that loads deep learning models directly within the client's web browser, protecting patient privacy while maintaining compatibility with clinical IT environments. WebIPred supports the application of pre-trained models published on Zenodo and other repositories, allowing clinicians to apply these models to real patient data without the need for extensive technical knowledge. This paper outlines WebIPred's model integration system, prediction workflow, and privacy features. Our results show that WebIPred offers a privacy-protecting and flexible application for image classification, only relying on client-side processing. WebIPred combines its strong commitment to data privacy and security with a user-friendly interface that makes it easy for clinicians to integrate AI into their workflows.

Recently, combining the strategy of consistency regularization with uncertainty estimation has shown promising performance on semi-supervised medical image segmentation tasks. However, most existing methods estimate the uncertainty solely based on the outputs of a single neural network, which results in imprecise uncertainty estimations and eventually degrades the segmentation performance. In this paper, we propose a novel Uncertainty Co-estimator (UnCo) framework to deal with this problem. Inspired by the co-training technique, UnCo establishes two different mean-teacher modules (i.e., two pairs of teacher and student models), and estimates three types of uncertainty from the multi-source predictions generated by these models. Through combining these uncertainties, their differences will help to filter out incorrect noise in each estimate, thus allowing the final fused uncertainty maps to be more accurate. These resulting maps are then used to enhance a cross-consistency regularization imposed between the two modules. In addition, UnCo also designs an internal consistency regularization within each module, so that the student models can aggregate diverse feature information from both modules, thus promoting the semi-supervised segmentation performance. Finally, an adversarial constraint is introduced to maintain the model diversity. Experimental results on four medical image datasets indicate that UnCo can achieve new state-of-the-art performance on both 2D and 3D semi-supervised segmentation tasks. The source code will be available at https://github.com/z1010x/UnCo.

This paper addresses the problem of detecting possible serious bacterial infection (pSBI) of infancy, i.e. a clinical presentation consistent with bacterial sepsis in newborn infants using cranial ultrasound (cUS) images. The captured image set for each patient enables multiview imagery: coronal and sagittal, with geometric overlap. To exploit this geometric relation, we develop a new learning framework, called the intersection-guided Crossview Local- and Image-level Fusion Network (CLIF-Net). Our technique employs two distinct convolutional neural network branches to extract features from coronal and sagittal images with newly developed multi-level fusion blocks. Specifically, we leverage the spatial position of these images to locate the intersecting region. We then identify and enhance the semantic features from this region across multiple levels using cross-attention modules, facilitating the acquisition of mutually beneficial and more representative features from both views. The final enhanced features from the two views are then integrated and projected through the image-level fusion layer, outputting pSBI and non-pSBI class probabilities. We contend that our method of exploiting multi-view cUS images enables a first of its kind, robust 3D representation tailored for pSBI detection. When evaluated on a dataset of 302 cUS scans from Mbale Regional Referral Hospital in Uganda, CLIF-Net demonstrates substantially enhanced performance, surpassing the prevailing state-of-the-art infection detection techniques.

Artificial intelligence (AI) algorithms can provide rapid and precise radiographic bone age (BA) assessment. This study assessed the effects of an AI algorithm on the BA assessment performance of radiologists, and evaluated how automation bias could affect radiologists. In this prospective randomized crossover study, six radiologists with varying levels of experience (senior, mi-level, and junior) assessed cases from a test set of 200 standard BA radiographs. The test set was equally divided into two subsets: datasets A and B. Each radiologist assessed BA independently without AI assistance (A- B-) and with AI assistance (A+ B+). We used the mean of assessments made by two experts as the ground truth for accuracy assessment; subsequently, we calculated the mean absolute difference (MAD) between the radiologists' BA predictions and ground-truth BA and evaluated the proportion of estimates for which the MAD exceeded one year. Additionally, we compared the radiologists' performance under conditions of early AI assistance with their performance under conditions of delayed AI assistance; the radiologists were allowed to reject AI interpretations. The overall accuracy of senior, mid-level, and junior radiologists improved significantly with AI assistance than without AI assistance (MAD: 0.74 vs. 0.46 years, p < 0.001; proportion of assessments for which MAD exceeded 1 year: 24.0% vs. 8.4%, p < 0.001). The proportion of improved BA predictions with AI assistance (16.8%) was significantly higher than that of less accurate predictions with AI assistance (2.3%; p < 0.001). No consistent timing effect was observed between conditions of early and delayed AI assistance. Most disagreements between radiologists and AI occurred over images for patients aged ≤8 years. Senior radiologists had more disagreements than other radiologists. The AI algorithm improved the BA assessment accuracy of radiologists with varying experience levels. Automation bias was prone to affect less experienced radiologists.

Huntington's disease (HD) is a chronic neurodegenerative ailment that affects cognitive decline, motor impairment, and psychiatric symptoms. However, the existing HD detection methods are struggle with limited annotated datasets that restricts their generalization performance. This research work proposes a novel MIMI-ONET for primary detection of HD using augmented multi-modal brain MRI images. The two-dimensional stationary wavelet transform (2DSWT) decomposes the MRI images into different frequency wavelet sub-bands. These sub-bands are enhanced with Contract Stretching Adaptive Histogram Equalization (CSAHE) and Multi-scale Adaptive Retinex (MSAR) by reducing the irrelevant distortions. The proposed MIMI-ONET introduces a Hepta Generative Adversarial Network (Hepta-GAN) to generates different noise-free HD images based on hepta azimuth angles (45°, 90°, 135°, 180°, 225°, 270°, 315°). Hepta-GAN incorporates Affine Estimation Module (AEM) to extract the multi-scale features using dilated convolutional layers for efficient HD image generation. Moreover, Hepta-GAN is normalized with Butterfly Optimization (BO) algorithm for enhancing augmentation performance by balancing the parameters. Finally, the generated images are given to Deep neural network (DNN) for the classification of normal control (NC), Adult-Onset HD (AHD) and Juvenile HD (JHD) cases. The ability of the proposed MIMI-ONET is evaluated with precision, specificity, f1 score, recall, and accuracy, PSNR and MSE. From the experimental results, the proposed MIMI-ONET attains the accuracy of 98.85% and reaches PSNR value of 48.05 based on the gathered Image-HD dataset. The proposed MIMI-ONET increases the overall accuracy of 9.96%, 1.85%, 5.91%, 13.80% and 13.5% for 3DCNN, KNN, FCN, RNN and ML framework respectively.

The present study followed the reporting guidelines for systematic reviews and meta-analyses. We conducted this study to review the diagnostic value of artificial intelligence (AI) for various types of lumbar spinal stenosis (LSS) and the level of stenosis, offering evidence-based support for the development of smart diagnostic tools. AI is currently being utilized for image processing in clinical practice. Some studies have explored AI techniques for identifying the severity of LSS in recent years. Nevertheless, there remains a shortage of structured data proving its effectiveness. Four databases (PubMed, Cochrane, Embase, and Web of Science) were searched until March 2024, including original studies that utilized deep learning (DL) and machine learning (ML) models to diagnose LSS. The risk of bias of included studies was assessed using Quality Assessment of Diagnostic Accuracy Studies is a quality evaluation tool for diagnostic research (diagnostic tests). Computed Tomography. PROSPERO is an international database of prospectively registered systematic reviews. Summary Receiver Operating Characteristic. Magnetic Resonance. Central canal stenosis. three-dimensional magnetic resonance myelography. The accuracy in the validation set was extracted for a meta-analysis. The meta-analysis was completed in R4.4.0. A total of 48 articles were included, with an overall accuracy of 0.885 (95% CI: 0.860-0907) for dichotomous tasks. Among them, the accuracy was 0.892 (95% CI: 0.867-0915) for DL and 0.833 (95% CI: 0.760-0895) for ML. The overall accuracy for LSS was 0.895 (95% CI: 0.858-0927), with an accuracy of 0.912 (95% CI: 0.873-0.944) for DL and 0.843 (95% CI: 0.766-0.907) for ML. The overall accuracy for central canal stenosis was 0.875 (95% CI: 0.821-0920), with an accuracy of 0.881 (95% CI: 0.829-0.925) for DL and 0.733 (95% CI: 0.541-0.877) for ML. The overall accuracy for neural foramen stenosis was 0.893 (95% CI: 0.851-0.928). In polytomous tasks, the accuracy was 0.936 (95% CI: 0.895-0.967) for no LSS, 0.503 (95% CI: 0.391-0.614) for mild LSS, 0.512 (95% CI: 0.336-0.688) for moderate LSS, and 0.860 for severe LSS (95% CI: 0.733-0.954). AI is highly valuable for diagnosing LSS. However, further external validation is necessary to enhance the analysis of different stenosis categories and improve the diagnostic accuracy for mild to moderate stenosis levels.

Vestibular schwannoma (VS) is the most prevalent intracranial schwannoma. Surgery is one of the options for the treatment of VS, with the preservation of facial nerve (FN) function being the primary objective. Therefore, postoperative FN function prediction is essential. However, achieving automation for such a method remains a challenge. In this study, we proposed a fully automatic deep learning approach based on multi-sequence magnetic resonance imaging (MRI) to predict FN function after surgery in VS patients. We first developed a segmentation network 2.5D Trans-UNet, which combined Transformer and U-Net to optimize contour segmentation for radiomic feature extraction. Next, we built a deep learning network based on the integration of 1DConvolutional Neural Network (1DCNN) and Gated Recurrent Unit (GRU) to predict postoperative FN function using the extracted features. We trained and tested the 2.5D Trans-UNet segmentation network on public and private datasets, achieving accuracies of 89.51% and 90.66%, respectively, confirming the model's strong performance. Then Feature extraction and selection were performed on the private dataset's segmentation results using 2.5D Trans-UNet. The selected features were used to train the 1DCNN-GRU network for classification. The results showed that our proposed fully automatic radiomics pipeline outperformed the traditional radiomics pipeline on the test set, achieving an accuracy of 88.64%, demonstrating its effectiveness in predicting the postoperative FN function in VS patients. Our proposed automatic method has the potential to become a valuable decision-making tool in neurosurgery, assisting neurosurgeons in making more informed decisions regarding surgical interventions and improving the treatment of VS patients.

Cerebral vascular occlusion is a serious condition that can lead to stroke and permanent neurological damage due to insufficient oxygen and nutrients reaching brain tissue. Early diagnosis and accurate segmentation are critical for effective treatment planning. Due to its high soft tissue contrast, Magnetic Resonance Imaging (MRI) is commonly used for detecting these occlusions such as ischemic stroke. However, challenges such as low contrast, noise, and heterogeneous lesion structures in MRI images complicate manual segmentation and often lead to misinterpretations. As a result, deep learning-based Computer-Aided Diagnosis (CAD) systems are essential for faster and more accurate diagnosis and treatment methods, although they can sometimes face challenges such as high computational costs and difficulties in segmenting small or irregular lesions. This study proposes a novel U-Net architecture enhanced with ConvNeXtV2 blocks and GRN-based Multi-Layer Perceptrons (MLP) to address these challenges in cerebral vascular occlusion segmentation. This is the first application of ConvNeXtV2 in this domain. The proposed model significantly improves segmentation accuracy, even in low-contrast regions, while maintaining high computational efficiency, which is crucial for real-world clinical applications. To reduce false positives and improve overall accuracy, small lesions (≤5 pixels) were removed in the preprocessing step with the support of expert clinicians. Experimental results on the ISLES 2022 dataset showed superior performance with an Intersection over Union (IoU) of 0.8015 and a Dice coefficient of 0.8894. Comparative analyses indicate that the proposed model achieves higher segmentation accuracy than existing U-Net variants and other methods, offering a promising solution for clinical use.