Functional brain lateralization, a key feature of the human brain that shows alterations in various mental disorders, remains poorly understood in panic disorder (PD), and its investigation may provide valuable insights into the neurobiological underpinnings of psychiatric conditions. This study investigates hemispheric lateralization in drug-naive patients with PD before and after treatment, explores its associations with gene expression and neurotransmitter profiles, and examines its utility for diagnosis and treatment outcome prediction. Fifty-eight patients and 85 healthy controls (HCs) were enrolled. Clinical assessments and resting-state functional magnetic resonance imaging scans were conducted before and after a 4-week paroxetine monotherapy. Intra-hemispheric functional connectivity strength (FCS), inter-hemispheric FCS, and parameter of asymmetry (PAS) were calculated. Imaging-transcriptomic and imaging-neurotransmitter correlation analyses were conducted. PAS was used in machine learning models for classification and treatment outcome prediction. Compared with HCs, patients exhibited enhanced intra-hemispheric FCS and decreased PAS in the caudate nucleus/pallidum and thalamus, with associated genes, dopamine and serotonin receptor densities, and vesicular acetylcholine transporter densities linking these lateralization alterations to neural signaling and synaptic function. FCS and PAS results were consistent across different correlation thresholds (0.15, 0.2, and 0.25). No significant changes in FCS or PAS were observed following treatment. PAS demonstrated excellent performance in classification (accuracy = 75.52 %) and treatment outcomes prediction (r = 0.763). Hemispheric lateralization in the cortico-basal ganglia-thalamic network was significantly altered in patients with PD, with these changes linked to disruptions in genes and neurotransmitter profiles which are associated with neural signal transduction and synaptic function. PAS shows promise as a biomarker for PD diagnosis and treatment outcome prediction.

<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To improve the generalizability of pathologic complete response (pCR) prediction following neoadjuvant chemotherapy using deep learning (DL)-based retrospective pharmacokinetic quantification (RoQ) of early-treatment dynamic contrast-enhanced (DCE) MRI. Materials and Methods This multicenter retrospective study included breast MRI data from four publicly available datasets of patients with breast cancer acquired from May 2002 to November 2016. RoQ was performed using a previously developed DL model for clinical multiphasic DCE-MRI datasets. Radiomic analysis was performed on RoQ maps and conventional enhancement maps. These data, together with clinicopathologic variables and shape-based radiomic analysis, were subsequently applied in pCR prediction using logistic regression. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC). Results A total of 1073 female patients with breast cancer were included. The proposed method showed improved consistency and generalizability compared with the reference method, achieving higher AUCs across external datasets (0.82 [CI: 0.72-0.91], 0.75 [CI: 0.71-0.79], and 0.77 [CI: 0.66-0.86] for Datasets A2, B, and C, respectively). On Dataset A2 (from the same study as the training dataset), there was no significant difference in performance between the proposed method and reference method (<i>P</i> = .80). Notably, on the combined external datasets, the proposed method significantly outperformed the reference method (AUC: 0.75 [CI: 0.72- 0.79] vs 0.71 [CI: 0.68-0.76], <i>P</i> = .003). Conclusion This work offers a novel approach to improve the generalizability and predictive accuracy of pCR response in breast cancer across diverse datasets, achieving higher and more consistent AUC scores than existing methods. ©RSNA, 2025.

Localized drug delivery can enhance therapeutic efficacy while minimizing systemic side effects, making sustained-release ophthalmic inserts an attractive alternative to traditional eye drops. Such inserts offer improved patient compliance through prolonged therapeutic effects and a reduced need for frequent administration. This study focuses on dexamethasone-containing ophthalmic inserts. These inserts utilize a key excipient, polyethylene glycol (PEG), which forms a hydrogel upon contact with tear fluid. Developing generic equivalents of PEG-based inserts is challenging due to difficulties in characterizing inactive ingredients and the absence of standardized physicochemical characterization methods to demonstrate similarity. To address this gap, a suite of analytical approaches was applied to both PEG precursor materials sourced from different vendors and manufactured inserts. ¹H NMR, FTIR, MALDI, and SEC revealed variations in end-group functionalization, impurity content, and molecular weight distribution of the excipient. These differences led to changes in the finished insert network properties such as porosity, pore size and structure, gel mechanical strength, and crystallinity, which were corroborated by X-ray microscopy, AI-based image analysis, thermal, mechanical, and density measurements. In vitro release testing revealed distinct drug release profiles across formulations, with swelling rate correlated to release rate (i.e., faster release with rapid swelling). The use of non-micronized and micronized dexamethasone also contributed to release profile differences. Through comprehensive characterization of these PEG-based dexamethasone inserts, correlations between polymer quality, hydrogel microstructure, and release kinetics were established. The study highlights how excipient differences can alter product performance, emphasizing the importance of thorough analysis in developing generic equivalents of complex drug products.

Magnetic resonance imaging of the lumbar spine is a key technique for clarifying the cause of disease. The greatest challenges today are the repetitive and time-consuming process of interpreting these complex MR images and the problem of unequal diagnostic results from physicians with different levels of experience. To address these issues, in this study, an improved YOLOv8 model (GE-YOLOv8) that combines a gradient search module and efficient channel attention was developed. To address the difficulty of intervertebral disc feature extraction, the GS module was introduced into the backbone network, which enhances the feature learning ability for the key structures through the gradient splitting strategy, and the number of parameters was reduced by 2.1%. The ECA module optimizes the weights of the feature channels and enhances the sensitivity of detection for small-target lesions, and the mAP50 was improved by 4.4% compared with that of YOLOv8. GE-YOLOv8 demonstrated the significance of this innovation on the basis of a P value <.001, with YOLOv8 as the baseline. The experimental results on a dataset from the Pingtan Branch of Union Hospital of Fujian Medical University and an external test dataset show that the model has excellent accuracy.

Dementia is a degenerative and chronic disorder, increasingly prevalent among older adults, posing significant challenges in providing appropriate care. As the number of dementia cases continues to rise, delivering optimal care becomes more complex. Machine learning (ML) plays a crucial role in addressing this challenge by utilizing medical data to enhance care planning and management for individuals at risk of various types of dementia. Magnetic resonance imaging (MRI) is a commonly used method for analyzing neurological disorders. Recent evidence highlights the benefits of integrating artificial intelligence (AI) techniques with MRI, significantly enhancing the diagnostic accuracy for different forms of dementia. This paper explores the use of AI in the automated detection and classification of dementia, aiming to streamline early diagnosis and improve patient outcomes. Integrating ML models into clinical practice can transform dementia care by enabling early detection, personalized treatment plans, and more effectual monitoring of disease progression. In this study, an Enhancing Automated Detection and Classification of Dementia in Thinking Inability Persons using Artificial Intelligence Techniques (EADCD-TIPAIT) technique is presented. The goal of the EADCD-TIPAIT technique is for the detection and classification of dementia in individuals with cognitive impairment using MRI imaging. The EADCD-TIPAIT method performs preprocessing to scale the input data using z-score normalization to obtain this. Next, the EADCD-TIPAIT technique performs a binary greylag goose optimization (BGGO)-based feature selection approach to efficiently identify relevant features that distinguish between normal and dementia-affected brain regions. In addition, the wavelet neural network (WNN) classifier is employed to detect and classify dementia. Finally, the improved salp swarm algorithm (ISSA) is implemented to choose the WNN technique's hyperparameters optimally. The stimulation of the EADCD-TIPAIT technique is examined under a Dementia prediction dataset. The performance validation of the EADCD-TIPAIT approach portrayed a superior accuracy value of 95.00% under diverse measures.

Critical retained foreign objects (RFOs), including surgical instruments like sponges and needles, pose serious patient safety risks and carry significant financial and legal implications for healthcare institutions. Detecting critical RFOs using artificial intelligence remains challenging due to their rarity and the limited availability of chest X-ray datasets that specifically feature critical RFOs cases. Existing datasets only contain non-critical RFOs, like necklace or zipper, further limiting their utility for developing clinically impactful detection algorithms. To address these limitations, we introduce "Hopkins RFOs Bench", the first and largest dataset of its kind, containing 144 chest X-ray images of critical RFO cases collected over 18 years from the Johns Hopkins Health System. Using this dataset, we benchmark several state-of-the-art object detection models, highlighting the need for enhanced detection methodologies for critical RFO cases. Recognizing data scarcity challenges, we further explore image synthetic methods to bridge this gap. We evaluate two advanced synthetic image methods, DeepDRR-RFO, a physics-based method, and RoentGen-RFO, a diffusion-based method, for creating realistic radiographs featuring critical RFOs. Our comprehensive analysis identifies the strengths and limitations of each synthetic method, providing insights into effectively utilizing synthetic data to enhance model training. The Hopkins RFOs Bench and our findings significantly advance the development of reliable, generalizable AI-driven solutions for detecting critical RFOs in clinical chest X-rays.

Temporal lobe epilepsy (TLE) is a progressive brain network disorder. Elucidating network reorganization and identifying disease progression-associated biomarkers are crucial for understanding pathological mechanisms, quantifying disease burden, and optimizing clinical strategies. This study aimed to investigate progressive changes in TLE by constructing a novel individualized morphological brain network based on T1-weighted structural magnetic resonance imaging (MRI). MRI data were collected from 34 postoperative seizure-free TLE patients and 28 age- and sex-matched healthy controls (HC), with patients divided into LONG-TERM and SHORT-TERM groups. Individualized morphological networks were constructed using the Morphometric INverse Divergence (MIND) framework by integrating cortical thickness and volume features (CTV-MIND). Network properties were then calculated and compared across groups to identify features potentially associated with disease progression. Results revealed progressive hub-node reorganization in CTV-MIND networks, with the LONG-TERM group showing increased connectivity in the lesion-side temporal lobe compared to SHORT-TERM and HC groups. The altered network node properties showed a significant correlation with local cortical atrophy. Incorporating identified network features into a machine learning-based brain age prediction model further revealed significantly elevated brain age in TLE. Notably, duration-related brain regions exerted a more significant and specific impact on premature brain aging in TLE than other regional combinations. Thus, prolonged duration may serve as an important contributor to the pathological aging observed in TLE. Our findings could help clinicians better identify abnormal brain trajectories in TLE and have the potential to facilitate the optimization of personalized treatment strategies.

Tonsilloliths can be seen on panoramic radiographs (PRs) as deposits located on the middle portion of the ramus of the mandible. Although tonsilloliths are clinically harmless, the high risk of misdiagnosis leads to unnecessary advanced examinations and interventions, thus jeopardizing patient safety and increasing unnecessary resource use in the healthcare system. Therefore, this study aims to meet an important clinical need by providing accurate and rapid diagnostic support. The dataset consisted of a total of 275 PRs, with 125 PRs lacking tonsillolith and 150 PRs having tonsillolith. ResNet and EfficientNet CNN models were assessed during the model selection process. An evaluation was conducted to analyze the learning capacity, intricacy, and compatibility of each model with the problem at hand. The effectiveness of the models was evaluated using accuracy, recall, precision, and F1 score measures following the training phase. Both the ResNet18 and EfficientNetB0 models were able to differentiate between tonsillolith-present and tonsillolith-absent conditions with an average accuracy of 89%. ResNet101 demonstrated underperformance when contrasted with other models. EfficientNetB1 exhibits satisfactory accuracy in both categories. The EfficientNetB0 model exhibits a 93% precision, 87% recall, 90% F1 score, and 89% accuracy. This study indicates that implementing AI-powered deep learning techniques would significantly improve the clinical diagnosis of tonsilloliths.

Image denoising is a fundamental task in computer vision, particularly in medical ultrasound (US) imaging, where speckle noise significantly degrades image quality. Although recent advancements in deep neural networks have led to substantial improvements in denoising for natural images, these methods cannot be directly applied to US speckle noise, as it is not purely random. Instead, US speckle arises from complex wave interference within the body microstructure, making it tissue-dependent. This dependency means that obtaining two independent noisy observations of the same scene, as required by pioneering Noise2Noise, is not feasible. Additionally, blind-spot networks also cannot handle US speckle noise due to its high spatial dependency. To address this challenge, we introduce Speckle2Self, a novel self-supervised algorithm for speckle reduction using only single noisy observations. The key insight is that applying a multi-scale perturbation (MSP) operation introduces tissue-dependent variations in the speckle pattern across different scales, while preserving the shared anatomical structure. This enables effective speckle suppression by modeling the clean image as a low-rank signal and isolating the sparse noise component. To demonstrate its effectiveness, Speckle2Self is comprehensively compared with conventional filter-based denoising algorithms and SOTA learning-based methods, using both realistic simulated US images and human carotid US images. Additionally, data from multiple US machines are employed to evaluate model generalization and adaptability to images from unseen domains. \textit{Code and datasets will be released upon acceptance.

The early detection and treatment of osteoporosis and prevention of fragility fractures are urgent societal issues. We developed an artificial intelligence-assisted diagnostic system that estimated not only lumbar bone mineral density but also femoral bone mineral density from anteroposterior lumbar X-ray images. We evaluated the performance of lumbar and femoral bone mineral density estimations and the osteoporosis classification accuracy of an artificial intelligence-assisted diagnostic system using lumbar X-ray images from a population-based cohort. The artificial neural network consisted of a deep neural network for estimating lumbar and femoral bone mineral density values and classifying lumbar X-ray images into osteoporosis categories. The deep neural network was built by training dual-energy X-ray absorptiometry-derived lumbar and femoral bone mineral density values as the ground truth of the training data and preprocessed X-ray images. Five-fold cross-validation was performed to evaluate the accuracy of the estimated BMD. A total of 1454 X-ray images from 1454 participants were analyzed using the artificial neural network. For the bone mineral density estimation performance, the mean absolute errors were 0.076 g/cm² for the lumbar and 0.071 g/cm² for the femur between dual-energy X-ray absorptiometry-derived and artificial intelligence-estimated bone mineral density values. The classification performances for the lumbar and femur of patients with osteopenia, in terms of sensitivity, were 86.4% and 80.4%, respectively, and the respective specificities were 84.1% and 76.3%. CLINICAL SIGNIFICANCE: The system was able to estimate the bone mineral density and classify the osteoporosis category of not only patients in clinics or hospitals but also of general inhabitants.