Knee osteoarthritis (KOA) is a leading cause of disability globally. Early and accurate diagnosis is paramount in preventing its progression and improving patients' quality of life. However, the inconsistency in radiologists' expertise and the onset of visual fatigue during prolonged image analysis often compromise diagnostic accuracy, highlighting the need for automated diagnostic solutions. In this study, we present an advanced deep learning model, OA-HybridCNN (OHC), which integrates ResNet and DenseNet architectures. This integration effectively addresses the gradient vanishing issue in DenseNet and augments prediction accuracy. To evaluate its performance, we conducted a thorough comparison with other deep learning models using five-fold cross-validation and external tests. The OHC model outperformed its counterparts across all performance metrics. In external testing, OHC exhibited an accuracy of 91.77%, precision of 92.34%, and recall of 91.36%. During the five-fold cross-validation, its average AUC and ACC were 86.34% and 87.42%, respectively. Deep learning, particularly exemplified by the OHC model, has greatly improved the efficiency and accuracy of KOA imaging diagnosis. The adoption of such technologies not only alleviates the burden on radiologists but also significantly enhances diagnostic precision.

This study proposes a novel deep learning-based approach for aneurysm wall characteristics, including thin-walled (TW) and hyperplastic-remodeling (HR) regions. We analyzed fifty-two unruptured cerebral aneurysms employing 4D-computed tomography angiography (4D-CTA) and intraoperative recordings. The TW and HR regions were identified in intraoperative images. The 3D trajectories of observation points on aneurysm walls were processed to compute a time series of 3D speed, acceleration, and smoothness of motion, aiming to evaluate the aneurysm wall characteristics. To facilitate point-level risk evaluation using the time-series data, we developed a convolutional neural network (CNN)-long- short-term memory (LSTM)-based regression model enriched with attention layers. In order to accommodate patient heterogeneity, a patient-independent feature extraction mechanism was introduced. Furthermore, unlabeled data were incorporated to enhance the data-intensive deep model. The proposed method achieved an average diagnostic accuracy of 92%, significantly outperforming a simpler model lacking attention. These results underscore the significance of patient-independent feature extraction and the use of unlabeled data. This study demonstrates the efficacy of a fine-grained deep learning approach in predicting aneurysm wall characteristics using 4D-CTA. Notably, incorporating an attention-based network structure proved to be particularly effective, contributing to enhanced performance.

The locus coeruleus (LC) is linked to the development and pathophysiology of neurodegenerative diseases such as Alzheimer's disease (AD). Magnetic resonance imaging-based LC features have shown potential to assess LC integrity in vivo. We present a deep learning-based LC segmentation and feature extraction method called Ensemble-based Locus Coeruleus Segmentation Network (ELSI-Net) and apply it to healthy aging and AD dementia datasets. Agreement to expert raters and previously published LC atlases were assessed. We aimed to reproduce previously reported differences in LC integrity in aging and AD dementia and correlate extracted features to cerebrospinal fluid (CSF) biomarkers of AD pathology. ELSI-Net demonstrated high agreement to expert raters and published atlases. Previously reported group differences in LC integrity were detected and correlations to CSF biomarkers were found. Although we found excellent performance, further evaluations on more diverse datasets from clinical cohorts are required for a conclusive assessment of ELSI-Net's general applicability. We provide a thorough evaluation of a fully automatic locus coeruleus (LC) segmentation method termed Ensemble-based Locus Coeruleus Segmentation Network (ELSI-Net) in aging and Alzheimer's disease (AD) dementia.ELSI-Net outperforms previous work and shows high agreement with manual ratings and previously published LC atlases.ELSI-Net replicates previously shown LC group differences in aging and AD.ELSI-Net's LC mask volume correlates with cerebrospinal fluid biomarkers of AD pathology.

Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide. Comorbidities in patients with COPD significantly increase morbidity, mortality, and healthcare costs, posing a significant burden on the management of COPD. Given the complex clinical manifestations and varying severity of COPD comorbidities, accurate diagnosis and evaluation are particularly important in selecting appropriate treatment options. With the development of medical imaging technology, AI-based chest CT, as a noninvasive imaging modality, provides a detailed assessment of COPD comorbidities. Recent studies have shown that certain radiographic features on chest CT can be used as alternative markers of comorbidities in COPD patients. CT-based radiomics features provided incremental predictive value than clinical risk factors only, predicting an AUC of 0.73 for COPD combined with CVD. However, AI has inherent limitations such as lack of interpretability, and further research is needed to improve them. This review evaluates the progress of AI technology combined with chest CT imaging in COPD comorbidities, including lung cancer, cardiovascular disease, osteoporosis, sarcopenia, excess adipose depots, and pulmonary hypertension, with the aim of improving the understanding of imaging and the management of COPD comorbidities for the purpose of improving disease screening, efficacy assessment, and prognostic evaluation.

This study evaluates the impact of artificial intelligence (AI) assistance on the diagnostic performance of radiologists with varying levels of experience in interpreting mammograms in a Malaysian tertiary referral center, particularly in women with dense breasts. A retrospective study including 434 digital mammograms interpreted by two general radiologists (12 and 6 years of experience) and two trainees (2 years of experience). Diagnostic performance was assessed with and without AI assistance (Lunit INSIGHT MMG), using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Inter-reader agreement was measured using kappa statistics. AI assistance significantly improved the diagnostic performance of all reader groups across all metrics (p < 0.05). The senior radiologist consistently achieved the highest sensitivity (86.5% without AI, 88.0% with AI) and specificity (60.5% without AI, 59.2% with AI). The junior radiologist demonstrated the highest PPV (56.9% without AI, 74.6% with AI) and NPV (90.3% without AI, 92.2% with AI). The trainees showed the lowest performance, but AI significantly enhanced their accuracy. AI assistance was particularly beneficial in interpreting mammograms of women with dense breasts. AI assistance significantly enhances the diagnostic accuracy and consistency of radiologists in mammogram interpretation, with notable benefits for less experienced readers. These findings support the integration of AI into clinical practice, particularly in resource-limited settings where access to specialized breast radiologists is constrained.