Radiogenomics holds promise in identifying molecular alterations in nonsmall cell lung cancer (NSCLC) using imaging features. Previously, we developed a radiogenomics model to predict epidermal growth factor receptor (EGFR) mutations based on contrast-enhanced computed tomography (CECT) in NSCLC patients. The current study aimed to externally validate this model using a publicly available National Institutes of Health (NIH)-based NSCLC dataset and assess the effect of EGFR mutation prevalence on model performance through synthetic minority oversampling technique (SMOTE). The original radiogenomics model was validated on an independent NIH cohort (n=140). For assessing the influence of disease prevalence, six SMOTE-augmented datasets were created, simulating EGFR mutation prevalence from 25% to 50%. Seven models were developed (one from original data, six SMOTE-augmented), each undergoing rigorous cross-validation, feature selection, and logistic regression modeling. Models were tested against the NIH cohort. Performance was compared using area under the receiver operating characteristic curve (Area Under the Curve [AUC]), and differences between radiomic-only, clinical-only, and combined models were statistically assessed. External validation revealed poor diagnostic performance for both our model and a previously published EGFR radiomics model (AUC ∼0.5). The clinical model alone achieved higher diagnostic accuracy (AUC 0.74). SMOTE-augmented models showed increased sensitivity but did not improve overall AUC compared to the clinical-only model. Changing EGFR mutation prevalence had minimal impact on AUC, challenging previous assumptions about the influence of sample imbalance on model performance. External validation failed to reproduce prior radiogenomics model performance, while clinical variables alone retained strong predictive value. SMOTE-based oversampling did not improve diagnostic accuracy, suggesting that, in EGFR prediction, radiomics may offer limited value beyond clinical data. Emphasis on robust external validation and data-sharing is essential for future clinical implementation of radiogenomic models.

Radiotherapy plays a critical role in treating esophageal cancer, but individual responses vary significantly, impacting patient outcomes. This study integrates machine learning-driven multimodal radiomics and transcriptomics to develop predictive models for radiotherapy sensitivity and prognosis in esophageal cancer. We applied the SEResNet101 deep learning model to imaging and transcriptomic data from the UCSC Xena and TCGA databases, identifying prognosis-associated genes such as STUB1, PEX12, and HEXIM2. Using Lasso regression and Cox analysis, we constructed a prognostic risk model that accurately stratifies patients based on survival probability. Notably, STUB1, an E3 ubiquitin ligase, enhances radiotherapy sensitivity by promoting the ubiquitination and degradation of SRC, a key oncogenic protein. In vitro and in vivo experiments confirmed that STUB1 overexpression or SRC silencing significantly improves radiotherapy response in esophageal cancer models. These findings highlight the predictive power of multimodal data integration for individualized radiotherapy planning and underscore STUB1 as a promising therapeutic target for enhancing radiotherapy efficacy in esophageal cancer.

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.

Advances in medical imaging and deep learning have propelled progress in whole slide image (WSI) analysis, with multiple instance learning (MIL) showing promise for efficient and accurate diagnostics. However, conventional MIL models often lack adaptability to evolving datasets, as they rely on static training that cannot incorporate new information without extensive retraining. Applying continual learning (CL) to MIL models is a possible solution, but often sees limited improvements. In this paper, we analyze CL in the context of attention MIL models and find that the model forgetting is mainly concentrated in the attention layers of the MIL model. Using the results of this analysis we propose two components for improving CL on MIL: Attention Knowledge Distillation (AKD) and the Pseudo-Bag Memory Pool (PMP). AKD mitigates catastrophic forgetting by focusing on retaining attention layer knowledge between learning sessions, while PMP reduces the memory footprint by selectively storing only the most informative patches, or ``pseudo-bags'' from WSIs. Experimental evaluations demonstrate that our method significantly improves both accuracy and memory efficiency on diverse WSI datasets, outperforming current state-of-the-art CL methods. This work provides a foundation for CL in large-scale, weakly annotated clinical datasets, paving the way for more adaptable and resilient diagnostic models.

While the acute phase of the COVID-19 pandemic has subsided, its long-term effects persist through Post-Acute Sequelae of COVID-19 (PASC), commonly known as Long COVID. There remains substantial uncertainty regarding both its duration and optimal management strategies. PASC manifests as a diverse array of persistent or newly emerging symptoms--ranging from fatigue, dyspnea, and neurologic impairments (e.g., brain fog), to cardiovascular, pulmonary, and musculoskeletal abnormalities--that extend beyond the acute infection phase. This heterogeneous presentation poses substantial challenges for clinical assessment, diagnosis, and treatment planning. In this paper, we focus on imaging findings that may suggest fibrotic damage in the lungs, a critical manifestation characterized by scarring of lung tissue, which can potentially affect long-term respiratory function in patients with PASC. This study introduces a novel multi-center chest CT analysis framework that combines deep learning and radiomics for fibrosis prediction. Our approach leverages convolutional neural networks (CNNs) and interpretable feature extraction, achieving 82.2% accuracy and 85.5% AUC in classification tasks. We demonstrate the effectiveness of Grad-CAM visualization and radiomics-based feature analysis in providing clinically relevant insights for PASC-related lung fibrosis prediction. Our findings highlight the potential of deep learning-driven computational methods for early detection and risk assessment of PASC-related lung fibrosis--presented for the first time in the literature.

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.

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.