The aim of this study was to develop and externally validate a machine-learning model that retrospectively identifies patients with acute respiratory distress syndrome (acute respiratory distress syndrome [ARDS]) using electronic health record (EHR) data. In this retrospective cohort study, ARDS was identified via physician-adjudication in three cohorts of patients with hypoxemic respiratory failure (training, internal validation, and external validation). Machine-learning models were trained to classify ARDS using vital signs, respiratory support, laboratory data, medications, chest radiology reports, and clinical notes. The best-performing models were assessed and internally and externally validated using the area under receiver-operating curve (AUROC), area under precision-recall curve, integrated calibration index (ICI), sensitivity, specificity, positive predictive value (PPV), and ARDS timing. Patients with hypoxemic respiratory failure undergoing mechanical ventilation within two distinct health systems. None. There were 1,845 patients in the training cohort, 556 in the internal validation cohort, and 199 in the external validation cohort. ARDS prevalence was 19%, 17%, and 31%, respectively. Regularized logistic regression models analyzing structured data (EHR model) and structured data and radiology reports (EHR-radiology model) had the best performance. During internal and external validation, the EHR-radiology model had AUROC of 0.91 (95% CI, 0.88-0.93) and 0.88 (95% CI, 0.87-0.93), respectively. Externally, the ICI was 0.13 (95% CI, 0.08-0.18). At a specified model threshold, sensitivity and specificity were 80% (95% CI, 75%-98%), PPV was 64% (95% CI, 58%-71%), and the model identified patients with a median of 2.2 hours (interquartile range 0.2-18.6) after meeting Berlin ARDS criteria. Machine-learning models analyzing EHR data can retrospectively identify patients with ARDS across different institutions.

The prevalence of coronary artery disease (CAD) has become the major cause of death across the world in recent years. The accurate segmentation of coronary artery is important in clinical diagnosis and treatment of coronary artery disease (CAD) such as stenosis detection and plaque analysis. Deep learning techniques have been shown to assist medical experts in diagnosing diseases using biomedical imaging. There are many methods which employ 2D DL models for medical image segmentation. The 2D Pyramid Scene Parsing Neural Network (PSPNet) has potential in this domain but not explored for the segmentation of coronary arteries from 3D Coronary Computed Tomography Angiography (CCTA) images. The contribution of present research work is to propose the modification of 2D PSPNet into 3D PSPNet for segmenting the coronary arteries from 3D CCTA images. The innovative factor is to evaluate the network performance by employing Global processing and Patch based processing methods. The experimental results achieved a Dice Similarity Coefficient (DSC) of 0.76 for Global process method and 0.73 for Patch based method using a subset of 200 images from the ImageCAS dataset.

Effective intervention for mild cognitive impairment (MCI) is key for preventing dementia. As a neuroprotective agent, butylphthalide has the potential to treat MCI due to Alzheimer disease (AD). However, the pharmacological mechanism of butylphthalide from the brain network perspective is not clear. Therefore, we aimed to investigate the multimodal brain network changes associated with butylphthalide treatment in MCI due to AD. A total of 270 patients with MCI due to AD received either butylphthalide or placebo at a ratio of 1:1 for 1 year. Effective treatment was defined as a decrease in the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-cog) > 2.5. Brain networks were constructed using T1-magnetic resonance imaging and fluorodeoxyglucose positron emission tomography. A support vector machine was applied to develop predictive models. Both treatment (drug vs. placebo)-time interactions and efficacy (effective vs. ineffective)-time interactions were detected on some overlapping structural network metrics. Simple effects analyses revealed a significantly increased global efficiency in the structural network under both treatment and effective treatment of butylphthalide. Among the overlapping metrics, an increased degree centrality of left paracentral lobule was significantly related to poorer cognitive improvement. The predictive model based on baseline multimodal network metrics exhibited high accuracy (88.93%) of predicting butylphthalide's efficacy. Butylphthalide may restore abnormal organization in structural networks of patients with MCI due to AD, and baseline network metrics could be predictive markers for therapeutic efficacy of butylphthalide. This study was registered in the Chinese Clinical Trial Registry (Registration Number: ChiCTR1800018362, Registration Date: 2018-09-13).

Acute ischemic stroke (AIS) is a major cause of mortality and morbidity, with hemorrhagic transformation (HT) as a severe complication. Accurate prediction of HT is essential for optimizing treatment strategies. This review assesses the accuracy and utility of deep learning (DL) and radiomics in predicting HT through imaging, regarding clinical decision-making for AIS patients. A literature search was conducted across five databases (Pubmed, Scopus, Web of Science, Embase, IEEE) up to January 23, 2025. Studies involving DL or radiomics-based ML models for predicting HT in AIS patients were included. Data from training, validation, and clinical-combined models were extracted and analyzed separately. Pooled sensitivity, specificity, and AUC were calculated with a random-effects bivariate model. For the quality assessment of studies, the Methodological Radiomics Score (METRICS) and QUADAS-2 tool were used. 16 studies consisting of 3,083 individual participants were included in the meta-analysis. The pooled AUC for training cohorts was 0.87, sensitivity 0.80, and specificity 0.85. For validation cohorts, AUC was 0.87, sensitivity 0.81, and specificity 0.86. Clinical-combined models showed an AUC of 0.93, sensitivity 0.84, and specificity 0.89. Moderate to severe heterogeneity was noted and addressed. Deep-learning models outperformed radiomics models, while clinical-combined models outperformed deep learning-only and radiomics-only models. The average METRICS score was 62.85%. No publication bias was detected. DL and radiomics models showed great potential in predicting HT in AIS patients. However, addressing methodological issues-such as inconsistent reference standards and limited external validation-is essential for the clinical implementation of these models.

Vision Transformer shows great superiority in medical image segmentation due to the ability to learn long-range dependency. For medical image segmentation from 3-D data, such as computed tomography (CT), existing methods can be broadly classified into 2-D-based and 3-D-based methods. One key limitation in 2-D-based methods is that the intraslice information is ignored, while the limitation in 3-D-based methods is the high computation cost and memory consumption, resulting in a limited feature representation for inner slice information. During the clinical examination, radiologists primarily use the axial plane and then routinely review both axial and coronal planes to form a 3-D understanding of anatomy. Motivated by this fact, our key insight is to design a hybrid model that can first learn fine-grained inner slice information and then generate a 3-D understanding of anatomy by incorporating 3-D information. We present a novel Hybrid Residual TransFormer (HResFormer) for 3-D medical image segmentation. Building upon standard 2-D and 3-D Transformer backbones, HResFormer involves two novel key designs: 1) a Hybrid Local-Global fusion Module (HLGM) to effectively and adaptively fuse inner slice information from 2-D Transformers and intraslice information from 3-D volumes for 3-D Transformers with local fine-grained and global long-range representation and 2) residual learning of the hybrid model, which can effectively leverage the inner slice and intraslice information for better 3-D understanding of anatomy. Experiments show that our HResFormer outperforms prior art on widely used medical image segmentation benchmarks. This article sheds light on an important but neglected way to design Transformers for 3-D medical image segmentation.

Dynamic functional connectivity (DFC) extracted from resting-state functional magnetic resonance imaging (fMRI) has been widely used for neuropsychiatric disorder classifications. However, serious information redundancy within DFC matrices can significantly undermine the performance of classification models based on them. Moreover, traditional deep models cannot adapt well to connectivity-like data, and insufficient training samples further hinder their effective training. In this study, we proposed a novel region-state masked autoencoder (RS-MAE) for proficient representation learning based on DFC matrices and ultimately neuropsychiatric disorder classifications based on fMRI. Three strategies were taken to address the aforementioned limitations. First, masked autoencoder (MAE) was introduced to reduce redundancy within DFC matrices and learn effective representations of human brain function simultaneously. Second, region-state (RS) patch embedding was proposed to replace space-time patch embedding in video MAE to adapt to DFC matrices, in which only topological locality, rather than spatial locality, exists. Third, random state concatenation (RSC) was introduced as a DFC matrix augmentation approach, to alleviate the problem of training sample insufficiency. Neuropsychiatric disorder classifications were attained by fine-tuning the pretrained encoder included in RS-MAE. The performance of the proposed RS-MAE was evaluated on four publicly available datasets, achieving accuracies of 76.32%, 77.25%, 88.87%, and 76.53% for the attention deficit and hyperactivity disorder (ADHD), autism spectrum disorder (ASD), Alzheimer's disease (AD), and schizophrenia (SCZ) classification tasks, respectively. These results demonstrate the efficacy of the RS-MAE as a proficient deep learning model for neuropsychiatric disorder classifications.

The escalating demand for medical image interpretation underscores the critical need for advanced artificial intelligence solutions to enhance the efficiency and accuracy of radiological diagnoses. This paper introduces CXR-PathFinder, a novel Large Language Model (LLM)-centric foundation model specifically engineered for automated chest X-ray (CXR) report generation. We propose a unique training paradigm, Clinician-Guided Adversarial Fine-Tuning (CGAFT), which meticulously integrates expert clinical feedback into an adversarial learning framework to mitigate factual inconsistencies and improve diagnostic precision. Complementing this, our Knowledge Graph Augmentation Module (KGAM) acts as an inference-time safeguard, dynamically verifying generated medical statements against authoritative knowledge bases to minimize hallucinations and ensure standardized terminology. Leveraging a comprehensive dataset of millions of paired CXR images and expert reports, our experiments demonstrate that CXR-PathFinder significantly outperforms existing state-of-the-art medical vision-language models across various quantitative metrics, including clinical accuracy (Macro F1 (14): 46.5, Micro F1 (14): 59.5). Furthermore, blinded human evaluation by board-certified radiologists confirms CXR-PathFinder's superior clinical utility, completeness, and accuracy, establishing its potential as a reliable and efficient aid for radiological practice. The developed method effectively balances high diagnostic fidelity with computational efficiency, providing a robust solution for automated medical report generation.

Medical professionals, especially those in training, often depend on visual reference materials to support an accurate diagnosis and develop pattern recognition skills. However, existing resources may lack the diversity and accessibility needed for broad and effective clinical learning. This paper presents a system designed to generate realistic medical images from textual descriptions and validate their accuracy through a classification model. A pretrained stable diffusion model was fine-tuned using Low-Rank Adaptation (LoRA) on the PathMNIST dataset, consisting of nine colorectal histopathology tissue types. The generative model was trained multiple times using different training parameter configurations, guided by domain-specific prompts to capture meaningful features. To ensure quality control, a ResNet-18 classification model was trained on the same dataset, achieving 99.76% accuracy in detecting the correct label of a colorectal histopathological medical image. Generated images were then filtered using the trained classifier and an iterative process, where inaccurate outputs were discarded and regenerated until they were correctly classified. The highest performing version of the generative model from experimentation achieved an F1 score of 0.6727, with precision and recall scores of 0.6817 and 0.7111, respectively. Some types of tissue, such as adipose tissue and lymphocytes, reached perfect classification scores, while others proved more challenging due to structural complexity. The self-validating approach created demonstrates a reliable method for synthesizing domain-specific medical images because of high accuracy in both the generation and classification portions of the system, with potential applications in both diagnostic support and clinical education. Future work includes improving prompt-specific accuracy and extending the system to other areas of medical imaging.

The clinical utility of body composition in predicting the severity of acute pancreatitis (AP) remains unclear. We aimed to measure body composition using artificial intelligence (AI) to predict severe AP in hospitalized patients. We performed a retrospective study of patients hospitalized with AP at three tertiary care centers in 2018. Patients with computer tomography (CT) imaging of the abdomen at admission were included. A fully automated and validated abdominal segmentation algorithm was used for body composition analysis. The primary outcome was severe AP, defined as having persistent single- or multi-organ failure as per the revised Atlanta classification. 352 patients were included. Severe AP occurred in 35 patients (9.9%). In multivariable analysis, adjusting for male sex and first episode of AP, intermuscular adipose tissue (IMAT) was associated with severe AP, OR = 1.06 per 5 cm², p = 0.0207. Subcutaneous adipose tissue (SAT) area approached significance, OR = 1.05, p = 0.17. Neither visceral adipose tissue (VAT) nor skeletal muscle (SM) was associated with severe AP. In obese patients, a higher SM was associated with severe AP in unadjusted analysis (86.7 vs 75.1 and 70.3 cm² in moderate and mild, respectively p = 0.009). In this multi-site retrospective study using AI to measure body composition, we found elevated IMAT to be associated with severe AP. Although SAT was non-significant for severe AP, it approached statistical significance. Neither VAT nor SM were significant. Further research in larger prospective studies may be beneficial.

Accurately identifying individuals who are at high risk of lung cancer is critical to optimize lung cancer screening with low-dose CT (LDCT). We sought to compare the performance of traditional regression models and artificial intelligence (AI)-based models in predicting future lung cancer risk. A systematic review and meta-analysis were conducted with reporting according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE, Embase, Scopus, and the Cumulative Index to Nursing and Allied Health Literature databases for studies reporting the performance of AI or traditional regression models for predicting lung cancer risk. Two researchers screened articles, and a third researcher resolved conflicts. Model characteristics and predictive performance metrics were extracted. The quality of studies was assessed using the Prediction model Risk of Bias Assessment Tool. A meta-analysis assessed the discrimination performance of models, based on area under the receiver operating characteristic curve (AUC). One hundred forty studies met inclusion criteria and included 185 traditional and 64 AI-based models. Of these, 16 AI models and 65 traditional models have been externally validated. The pooled AUC of external validations of AI models was 0.82 (95% confidence interval [CI], 0.80-0.85), and the pooled AUC for traditional regression models was 0.73 (95% CI, 0.72-0.74). In a subgroup analysis, AI models that included LDCT had a pooled AUC of 0.85 (95% CI, 0.82-0.88). Overall risk of bias was high for both AI and traditional models. AI-based models, particularly those using imaging data, show promise for improving lung cancer risk prediction over traditional regression models. Future research should focus on prospective validation of AI models and direct comparisons with traditional methods in diverse populations.