To train and test an AI-based algorithm for automated detection of focal bone marrow lesions (FL) from MRI. This retrospective feasibility study included 444 patients with monoclonal plasma cell disorders. For this feasibility study, only FLs in the left pelvis were included. Using the nnDetection framework, the algorithm was trained based on 334 patients with 494 FLs from center 1, and was tested on an internal test set (36 patients, 89 FLs, center 1) and a multicentric external test set (74 patients, 262 FLs, centers 2-11). Mean average precision (mAP), F1-score, sensitivity, positive predictive value (PPV), and Spearman correlation coefficient between automatically determined and actual number of FLs were calculated. On the internal/external test set, the algorithm achieved a mAP of 0.44/0.34, F1-Score of 0.54/0.44, sensitivity of 0.49/0.34, and a PPV of 0.61/0.61, respectively. In two subsets of the external multicentric test set with high imaging quality, the performance nearly matched that of the internal test set, with mAP of 0.45/0.41, F1-Score of 0.50/0.53, sensitivity of 0.44/0.43, and a PPV of 0.60/0.71, respectively. There was a significant correlation between the automatically determined and actual number of FLs on both the internal (r=0.51, p=0.001) and external multicentric test set (r=0.59, p<0.001). This study demonstrates that the automated detection of FLs from MRI, and thereby the automated assessment of the number of FLs, is feasible.

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.

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.

Stroke is a leading cause of mortality and disability worldwide, with approximately 15 million people suffering strokes annually. Machine learning (ML) techniques have emerged as powerful tools for stroke prediction, enabling early identification of risk factors through data-driven approaches. However, the clinical utility and performance characteristics of these approaches require systematic evaluation. To systematically review and analyze ML techniques used for stroke prediction, systematically synthesize performance metrics across different prediction targets and data sources, evaluate their clinical applicability, and identify research trends focusing on patient population characteristics and stroke prevalence patterns. A systematic review was conducted following PRISMA guidelines. Five databases (Google Scholar, Lens, PubMed, ResearchGate, and Semantic Scholar) were searched for open-access publications on ML-based stroke prediction published between January 2013 and December 2024. Data were extracted on publication characteristics, datasets, ML methodologies, evaluation metrics, prediction targets (stroke occurrence vs. outcomes), data sources (EHR, imaging, biosignals), patient demographics, and stroke prevalence. Descriptive synthesis was performed due to substantial heterogeneity precluding quantitative meta-analysis. Fifty-eight studies were included, with peak publication output in 2021 (21 articles). Studies targeted three main prediction objectives: stroke occurrence prediction (n = 52, 62.7 %), stroke outcome prediction (n = 19, 22.9 %), and stroke type classification (n = 12, 14.4 %). Data sources included electronic health records (n = 48, 57.8 %), medical imaging (n = 21, 25.3 %), and biosignals (n = 14, 16.9 %). Systematic analysis revealed ensemble methods consistently achieved highest accuracies for stroke occurrence prediction (range: 90.4-97.8 %), while deep learning excelled in imaging-based applications. African populations, despite highest stroke mortality rates globally, were represented in fewer than 4 studies. ML techniques show promising results for stroke prediction. However, significant gaps exist in representation of high-risk populations and real-world clinical validation. Future research should prioritize population-specific model development and clinical implementation frameworks.

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.

To develop a deep learning model to detect apparent and occult scaphoid fractures from plain wrist radiographs and to compare the model's diagnostic performance with that of a group of experts. A dataset comprising 408 patients, 410 wrists, and 1011 radiographs was collected. 718 of these radiographs contained a scaphoid fracture, verified by magnetic resonance imaging or computed tomography scans. 58 of these fractures were occult. The images were divided into training, test, and occult fracture test sets. The images were annotated by marking the scaphoid bone and the possible fracture area. The performance of the developed DL model was compared with the ground truth and the assessments of three clinical experts. The DL model achieved a sensitivity of 0.86 (95 % CI: 0.75-0.93) and a specificity of 0.83 (0.64-0.94). The model's accuracy was 0.85 (0.76-0.92), and the area under the receiver operating characteristics curve was 0.92 (0.86-0.97). The clinical experts' sensitivity ranged from 0.77 to 0.89, and specificity from 0.83 to 0.97. The DL model detected 24 of 58 (41 %) occult fractures, compared to 10.3 %, 13.7 %, and 6.8 % by the clinical experts. Detecting scaphoid fractures using a segmentation-based DL model is feasible and comparable to previously developed DL models. The model performed similarly to a group of experts in identifying apparent scaphoid fractures and demonstrated higher diagnostic accuracy in detecting occult fractures. The improvement in occult fracture detection could enhance patient care.

Cardiac sarcoidosis is a form of inflammatory cardiomyopathy that varies in its clinical presentation. It is associated with significant clinical complications such as high-degree atrioventricular block, ventricular tachycardia, heart failure and sudden cardiac death. It is challenging to diagnose clinically, and its increasing detection rate may represent increasing awareness of the disease by clinicians as well as a rising incidence. Prompt diagnosis and risk stratification reduces morbidity and mortality from cardiac sarcoidosis. Noninvasive diagnostic modalities such as ECG, echocardiography, PET/computed tomography (PET/CT) and cardiac MRI (cMRI) are increasingly playing important roles in cardiac sarcoidosis diagnosis. Artificial intelligence driven applications are increasingly being applied to these diagnostic modalities to improve the detection of cardiac sarcoidosis. Review of the recent literature suggests artificial intelligence based algorithms in PET/CT and cMRIs can predict cardiac sarcoidosis as accurately as trained experts, however, there are few published studies on artificial intelligence based algorithms in ECG and echocardiography. The impressive advances in artificial intelligence have the potential to transform patient screening in cardiac sarcoidosis, aid prompt diagnosis and appropriate risk stratification and change clinical practice.

In recent years, hip arthroscopy has made great progress and has been extended to the treatment of intra-articular or periarticular diseases. However, the complex structure of the hip joint, high technical operation requirements and relatively long learning curve have hindered the popularization and development of hip arthroscopy in China. Therefore, on the one hand, it is necessary to promote the research and training of standardized techniques for the diagnosis of hip disease and the treatment of arthroscopic surgery, so as to improve the safety, effectiveness and popularization of the technology. On the other hand, our organization proactively leverages cutting-edge digitalization and intelligentization technologies, including medical image digitalization, medical big data analytics, artificial intelligence, surgical navigation and robotic control, virtual reality, telemedicine, and 5G communication technology. We conduct a range of innovative research and development initiatives such as intelligent-assisted diagnosis of hip diseases, digital preoperative planning, surgical intelligent navigation and robotic procedures, and smart rehabilitation solutions. These efforts aim to facilitate a digitalization and intelligentization leap in technology and continuously enhance the precision of diagnosis and treatment. In conclusion, standardization promotes the homogenization of diagnosis and treatment, while digitalization and intelligentization facilitate the precision of operations. The synergy of the two lays the foundation for personalized diagnosis and treatment and continuous innovation, ultimately driving the rapid development of hip arthroscopy technology.

Privacy-preserving large language models (PP-LLMs) hold potential for assisting clinicians with documentation. We evaluated a PP-LLM to improve the clinical information on radiology request forms for musculoskeletal magnetic resonance imaging (MRI) and to automate protocoling, which ensures that the most appropriate imaging is performed. The present retrospective study included musculoskeletal MRI radiology request forms that had been randomly collected from June to December 2023. Studies without electronic medical record (EMR) entries were excluded. An institutional PP-LLM (Claude Sonnet 3.5) augmented the original radiology request forms by mining EMRs, and, in combination with rule-based processing of the LLM outputs, suggested appropriate protocols using institutional guidelines. Clinical information on the original and PP-LLM radiology request forms were compared with use of the RI-RADS (Reason for exam Imaging Reporting and Data System) grading by 2 musculoskeletal (MSK) radiologists independently (MSK1, with 13 years of experience, and MSK2, with 11 years of experience). These radiologists established a consensus reference standard for protocoling, against which the PP-LLM and of 2 second-year board-certified radiologists (RAD1 and RAD2) were compared. Inter-rater reliability was assessed with use of the Gwet AC1, and the percentage agreement with the reference standard was calculated. Overall, 500 musculoskeletal MRI radiology request forms were analyzed for 407 patients (202 women and 205 men with a mean age [and standard deviation] of 50.3 ± 19.5 years) across a range of anatomical regions, including the spine/pelvis (143 MRI scans; 28.6%), upper extremity (169 scans; 33.8%) and lower extremity (188 scans; 37.6%). Two hundred and twenty-two (44.4%) of the 500 MRI scans required contrast. The clinical information provided in the PP-LLM-augmented radiology request forms was rated as superior to that in the original requests. Only 0.4% to 0.6% of PP-LLM radiology request forms were rated as limited/deficient, compared with 12.4% to 22.6% of the original requests (p < 0.001). Almost-perfect inter-rater reliability was observed for LLM-enhanced requests (AC1 = 0.99; 95% confidence interval [CI], 0.99 to 1.0), compared with substantial agreement for the original forms (AC1 = 0.62; 95% CI, 0.56 to 0.67). For protocoling, MSK1 and MSK2 showed almost-perfect agreement on the region/coverage (AC1 = 0.96; 95% CI, 0.95 to 0.98) and contrast requirement (AC1 = 0.98; 95% CI, 0.97 to 0.99). Compared with the consensus reference standard, protocoling accuracy for the PP-LLM was 95.8% (95% CI, 94.0% to 97.6%), which was significantly higher than that for both RAD1 (88.6%; 95% CI, 85.8% to 91.4%) and RAD2 (88.2%; 95% CI, 85.4% to 91.0%) (p < 0.001 for both). Musculoskeletal MRI request form augmentation with an institutional LLM provided superior clinical information and improved protocoling accuracy compared with clinician requests and non-MSK-trained radiologists. Institutional adoption of such LLMs could enhance the appropriateness of MRI utilization and patient care. Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Biomedical image classification is of paramount importance in enhancing diagnostic precision and improving patient outcomes across diverse medical disciplines. In recent years, the advent of deep learning methodologies has significantly transformed this domain by facilitating notable advancements in image analysis and classification endeavors. This paper provides a thorough overview of the application of deep learning techniques in biomedical image classification, encompassing various types of healthcare data, including medical images derived from modalities such as mammography, histopathology, and radiology. A detailed discourse on deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and advanced models such as generative adversarial networks (GANs), is presented. Additionally, we delineate the distinctions between supervised, unsupervised, and reinforcement learning approaches, along with their respective roles within the context of biomedical imaging. This study systematically investigates 50 deep learning methodologies employed in the healthcare sector, elucidating their effectiveness in various tasks, including disease detection, image segmentation, and classification. It particularly emphasizes models that have been trained on publicly available datasets, thereby highlighting the significant role of open-access data in fostering advancements in AI-driven healthcare innovations. Furthermore, this review accentuates the transformative potential of deep learning in the realm of biomedical image analysis and delineates potential avenues for future research within this rapidly evolving field.