To construct a pathomics-based machine learning model to enhance the diagnostic efficacy of LungPro navigational bronchoscopy for peripheral pulmonary lesions and to optimize the management strategy for LungPro-diagnosed negative lesions. Clinical data and hematoxylin and eosin (H&E)-stained whole slide images (WSIs) were collected from 144 consecutive patients undergoing LungPro virtual bronchoscopy at a single institution between January 2022 and December 2023. Patients were stratified into diagnosis-positive and diagnosis-negative cohorts based on histopathological or etiological confirmation. An artificial intelligence (AI) model was developed and validated using 94 diagnosis-positive cases. Logistic regression (LR) identified associations between clinical/imaging characteristics and malignant pulmonary lesion risk factors. We implemented a convolutional neural network (CNN) with weakly supervised learning to extract image-level features, followed by multiple instance learning (MIL) for patient-level feature aggregation. Multiple machine learning (ML) algorithms were applied to model the extracted features. A multimodal diagnostic framework integrating clinical, imaging, and pathomics data were subsequently developed and evaluated on 50 LungPro-negative patients to assess the framework's diagnostic performance and predictive validity. Univariable and multivariable logistic regression analyses identified that age, lesion boundary and mean computed tomography (CT) attenuation were independent risk factors for malignant peripheral pulmonary lesions (P < 0.05). A histopathological model using a MIL fusion strategy showed strong diagnostic performance for lung cancer, with area under the curve (AUC) values of 0.792 (95% CI 0.680-0.903) in the training cohort and 0.777 (95% CI 0.531-1.000) in the test cohort. Combining predictive clinical features with pathological characteristics enhanced diagnostic yield for peripheral pulmonary lesions to 0.848 (95% CI 0.6945-1.0000). In patients with initially negative LungPro biopsy results, the model identified 20 of 28 malignant lesions (sensitivity: 71.43%) and 15 of 22 benign lesions (specificity: 68.18%). Class activation mapping (CAM) validated the model by highlighting key malignant features, including conspicuous nucleoli and nuclear atypia. The fusion diagnostic model that incorporates clinical and pathomic features markedly enhances the diagnostic accuracy of LungPro in this retrospective cohort. This model aids in the detection of subtle malignant characteristics, thereby offering evidence to support precise and targeted therapeutic interventions for lesions that LungPro classifies as negative in clinical settings.

Identifying objective neuroimaging biomarkers to forecast Alzheimer's disease (AD) progression is crucial for timely intervention. However, this task remains challenging due to the complex dysfunctions in the spatio-temporal characteristics of underlying brain networks, which are often overlooked by existing methods. To address these limitations, we develop an interpretable spatio-temporal graph neural network framework to predict future AD progression, leveraging dual Stochastic Differential Equations (SDEs) to model the irregularly-sampled longitudinal functional magnetic resonance imaging (fMRI) data. We validate our approach on two independent cohorts, including the Open Access Series of Imaging Studies (OASIS-3) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our framework effectively learns sparse regional and connective importance probabilities, enabling the identification of key brain circuit abnormalities associated with disease progression. Notably, we detect the parahippocampal cortex, prefrontal cortex, and parietal lobule as salient regions, with significant disruptions in the ventral attention, dorsal attention, and default mode networks. These abnormalities correlate strongly with longitudinal AD-related clinical symptoms. Moreover, our interpretability strategy reveals both established and novel neural systems-level and sex-specific biomarkers, offering new insights into the neurobiological mechanisms underlying AD progression. Our findings highlight the potential of spatio-temporal graph-based learning for early, individualized prediction of AD progression, even in the context of irregularly-sampled longitudinal imaging data.

Tuberculosis (TB) is a serious infectious disease that remains a global health challenge. While chest X-rays (CXRs) are widely used for TB detection, manual interpretation can be subjective and time-consuming. Automated classification of CXRs into TB and non-TB cases can significantly support healthcare professionals in timely and accurate diagnosis. This paper introduces a hybrid deep learning approach for classifying CXR images. The solution is based on the CoAtNet framework, which combines the strengths of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The model is pre-trained on the large-scale ImageNet dataset to ensure robust generalization across diverse images. The evaluation is conducted on the IN-CXR tuberculosis dataset from ICMR-NIRT, which contains a comprehensive collection of CXR images of both normal and abnormal categories. The hybrid model achieves a binary classification accuracy of 86.39% and an ROC-AUC score of 93.79%, outperforming tested baseline models that rely exclusively on either CNNs or ViTs when trained on this dataset. Furthermore, the integration of Local Interpretable Model-agnostic Explanations (LIME) enhances the interpretability of the model's predictions. This combination of reliable performance and transparent, interpretable results strengthens the model's role in AI-driven medical imaging research. Code will be made available upon request.

Neoadjuvant chemotherapy (NAC) is a crucial therapeutic approach for treating breast cancer, yet accurately predicting treatment response remains a significant clinical challenge. Conventional ultrasound plays a vital role in assessing tumor morphology but lacks the ability to quantitatively capture intratumoral heterogeneity. Ultrasound radiomics, which extracts high-throughput quantitative imaging features, offers a novel approach to enhance NAC response prediction. This study aims to evaluate the predictive efficacy of ultrasound radiomics models based on pre-treatment, post-treatment, and combined imaging features for assessing the NAC response in patients with HER2-low breast cancer. This retrospective multicenter study included 359 patients with HER2-low breast cancer who underwent NAC between January 1, 2016, and December 31, 2020. A total of 488 radiomic features were extracted from pre- and post-treatment ultrasound images. Feature selection was conducted in two stages: first, Pearson correlation analysis (threshold: 0.65) was applied to remove highly correlated features and reduce redundancy; then, Recursive Feature Elimination with Cross-Validation (RFECV) was employed to identify the optimal feature subset for model construction. The dataset was divided into a training set (244 patients) and an external validation set (115 patients from independent centers). Model performance was assessed via the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score. Three models were initially developed: (1) a pre-treatment model (AUC = 0.716), (2) a post-treatment model (AUC = 0.772), and (3) a combined pre- and post-treatment model (AUC = 0.762).To enhance feature selection, Recursive Feature Elimination with Cross-Validation was applied, resulting in optimized models with reduced feature sets: (1) the pre-treatment model (AUC = 0.746), (2) the post-treatment model (AUC = 0.712), and (3) the combined model (AUC = 0.759). Ultrasound radiomics is a non-invasive and promising approach for predicting response to neoadjuvant chemotherapy in HER2-low breast cancer. The pre-treatment model yielded reliable performance after feature selection. While the combined model did not substantially enhance predictive accuracy, its stable performance suggests that longitudinal ultrasound imaging may help capture treatment-induced phenotypic changes. These findings offer preliminary support for individualized therapeutic decision-making.

To develop a hybrid fusion model-deep learning radiomics nomograms (DLRN), integrating radiomics and transfer learning for assisting sonographers differentiate benign and malignant parotid gland tumors. This study retrospectively analyzed a total of 328 patients with pathologically confirmed parotid gland tumors from two centers. Radiomics features extracted from ultrasound images were input into eight machine learning classifiers to construct Radiomics (Rad) model. Additionally, images were also input into seven transfer learning networks to construct deep transfer learning (DTL) model. The prediction probabilities from these two models were combined through decision fusion to construct a DLR model. Clinical features were further integrated with the prediction probabilities of the DLR model to develop the DLRN model. The performance of these models was evaluated using receiver operating characteristic curve analysis, calibration curve, decision curve analysis and the Hosmer-Lemeshow test. In the internal and external validation cohorts, compared with Clinic (AUC = 0.891 and 0.734), Rad (AUC = 0.809 and 0.860), DTL (AUC = 0.905 and 0.782) and DLR (AUC = 0.932 and 0.828), the DLRN model demonstrated the greatest discriminative ability (AUC = 0.931 and 0.934), showing the best discriminative power. With the assistance of DLR, the diagnostic accuracy of resident, attending and chief physician increased by 6.6%, 6.5% and 1.2%, respectively. The hybrid fusion model DLRN significantly enhances the diagnostic performance for benign and malignant tumors of the parotid gland. It can effectively assist sonographers in making more accurate diagnoses.

Insomnia disorder is a prevalent condition associated with significant negative impacts on health and daily functioning. Transcranial direct current stimulation (tDCS) has emerged as a potential technique for improving sleep. However, questions remain regarding its clinical efficacy, and there is a lack of standardized individualized stimulation protocols. This study aims to evaluate the efficacy of model-driven, individualized tDCS for treating insomnia disorder through a randomized, double-blind, sham-controlled trial. A total of 40 patients diagnosed with insomnia disorder will be recruited and randomly assigned to either an active tDCS group or a sham stimulation group. Individualized stimulation parameters will be determined through machine learning-based electric field modeling incorporating structural MRI and EEG data. Participants will undergo 10 sessions of tDCS (5 days/week for 2 consecutive weeks), with follow-up assessments conducted at 2 and 4 weeks after treatment. The primary outcome is the reduction in the Insomnia Severity Index (ISI) score at two weeks post-treatment. Secondary outcomes include changes in sleep parameters, anxiety, and depression scores. This study is expected to provide evidence for the effectiveness of individualized tDCS in improving sleep quality and reducing insomnia symptoms. This integrative approach, combining advanced neuroimaging and electrophysiological biomarkers, has the potential to establish an evidence-based framework for individualized brain stimulation, optimizing therapeutic outcomes. This study is registered at ClinicalTrials.gov (Identifier: NCT06671457) and was registered on 4 November 2024. The online version contains supplementary material available at 10.1186/s12888-025-07347-5.

QUS spectral parametric imaging offers a fast and accurate method for breast lesion characterization. This study explored using deep CNNs to classify breast lesions from QUS spectral parametric images, aiming to enhance radiomics and conventional machine learning. Predictive models were developed using transfer learning with pre-trained CNNs to distinguish malignant from benign lesions. The dataset included 276 participants: 184 malignant (median age, 51 years [IQR: 27-81 years]) and 92 benign cases (median age, 46 years [IQR: 18-75 years]). QUS spectral parametric imaging was applied to the US RF data and resulted in 1764 images of QUS spectral (MBF, SS, and SI), along with QUS scattering parameters (ASD and AAC). The data were randomly split into 60% training, 20% validation, and 20% test sets, stratified by lesion subtype, and repeated five times. The number of convolutional blocks was optimized, and the final convolutional layer was fine-tuned. Models tested included ResNet, Inception-v3, Xception, and EfficientNet. Xception-41 achieved a recall of 86 ± 3%, specificity of 87 ± 5%, balanced accuracy of 87 ± 3%, and an AUC of 0.93 ± 0.02 on test sets. EfficientNetV2-M showed similar performance with a recall of 91 ± 1%, specificity of 81 ± 7%, balanced accuracy of 86 ± 3%, and an AUC of 0.92 ± 0.02. CNN models outperformed radiomics and conventional machine learning (p-values < 0.05). This study demonstrated the capability of end-to-end CNN-based models for the accurate characterization of breast masses from QUS spectral parametric images.

Pneumonia is a prevalent and serious respiratory disease, responsible for a significant number of cases globally. With advancements in deep learning, the automatic diagnosis of pneumonia has attracted significant research attention in medical image classification. However, current methods still face several challenges. First, since lesions are often visible in only a few slices, slice-based classification algorithms may overlook critical spatial contextual information in CT sequences, and slice-level annotations are labor-intensive. Moreover, chest CT sequence-based pneumonia classification algorithms that rely solely on sequence-level coarse-grained labels remain limited, especially in integrating multi-modal information. To address these challenges, we propose a Multi-modal Text-Guided Network (MTGNet) for pneumonia classification using chest CT sequences. In this model, we design a sequential graph pooling network to encode the CT sequences by gradually selecting important slice features to obtain a sequence-level representation. Additionally, a CT description encoder is developed to learn representations from textual reports. To simulate the clinical diagnostic process, we employ multi-modal training and single-modal testing. A modal transfer module is proposed to generate simulated textual features from CT sequences. Cross-modal attention is then employed to fuse the sequence-level and simulated textual representations, thereby enhancing feature learning within the CT sequences by incorporating semantic information from textual descriptions. Furthermore, contrastive learning is applied to learn discriminative features by maximizing the similarity of positive sample pairs and minimizing the similarity of negative sample pairs. Extensive experiments on a self-constructed pneumonia CT sequences dataset demonstrate that the proposed model significantly improves classification performance.

Magnetic resonance imaging (MRI) serves as the clinical gold standard for diagnosing lumbar disc herniation (LDH). This multicenter study was to develop and clinically validate a deep learning (DL) model utilizing axial T2-weighted lumbar MRI sequences to automate LDH detection, following the Michigan State University (MSU) morphological classification criteria. A total of 8428 patients (100000 axial lumbar MRIs) with spinal surgeons annotating the datasets per MSU criteria, which classifies LDH into 11 subtypes based on morphology and neural compression severity, were analyzed. A DL architecture integrating adaptive multi-scale feature fusion titled as AFFM-YOLOv8 was developed. Model performance was validated against radiologists' annotations using accuracy, precision, recall, F1-score, and Cohen's κ (95% confidence intervals). The proposed model demonstrated superior diagnostic performance with a 91.01% F1-score (3.05% improvement over baseline) and 3% recall enhancement across all evaluation metrics. For surgical indication prediction, strong inter-rater agreement was achieved with senior surgeons (κ = 0.91, 95% CI 90.6-91.4) and residents (κ = 0.89, 95% CI 88.5-89.4), reaching consensus levels comparable to expert-to-expert agreement (senior surgeons: κ = 0.89; residents: κ = 0.87). This study establishes a DL framework for automated LDH diagnosis using large-scale axial MRI datasets. The model achieves clinician-level accuracy in MUS-compliant classification, addressing key limitations of prior binary classification systems. By providing granular spatial and morphological insights, this tool holds promise for standardizing LDH assessment and reducing diagnostic delays in resource-constrained settings.

Precise assessment of Graves` orbitopathy (GO) predicts therapeutic strategies. Various imaging techniques and different measurement methods are used, but there is a lack of standardization. Traditionally, the Clinical Activity Score (CAS) has been used for assessing GO, especially for evaluating disease activity to predict response to glucocorticoid (GC) therapy, but technological developments have led to a shift towards more objective imaging methods that offer accuracy. Imaging methods for Graves' orbitopathy assessment include ultrasonography (USG), computed tomography (CT), magnetic resonance imaging (MRI), and single photon emission computed tomography (SPECT). These can be divided into those that assess disease activity (MRI, SPECT) and those that assess disease severity (USG, CT, MRI, SPECT). USG is the accessible first-aid tool that provides non-invasive imaging of orbital structures, with a short time of examination making it highly suitable for initial evaluation and monitoring of GO. It does have limitations, particularly in visualizing the apex of the orbit. Initially, orbital CT was thought to provide more accurate morphological information, particularly in extraocular muscles, and superior visualization of bone structures compared to MRI, making it the imaging modality of choice prior to planned orbital decompression; however, it has difficulty in accurately assessing the inflammatory activity stages of GO. Although CT offers a better view of deeper-lying tissue, it is limited by radiation exposure. MRI is best suited for follow-up examinations because it offers superior soft tissue visualization and precise tissue differentiation. However, it is not specific for orbital changes, the examination is very expensive, and it is rarely available. Recent literature proposes that nuclear medicine imaging techniques may be the best discipline for assessing GO. SPECT fused with low-dose CT scans is now used to increase the diagnostic value of the investigation. It provides functional information on top of the anatomical images. The use of cost-effective radioisotope - technetium-99m (99mTc)-DTPA - gives great diagnostic results with short examination time, low radiation exposure, and satisfactory spatial resolution. Nowadays, 36 years after CAS development and with technological improvement, researchers aim to integrate artificial intelligence tools with SPECT/CT imaging to diagnose and stage GO activity more effectively.