The study aimed to review the applicability and performance of various Convolutional Neural Network (CNN) models for the identification of periodontal bone loss (PBL) in digital periapical radiographs achieved through classification, detection, and segmentation approaches. We searched the PubMed, IEEE Xplore, and SCOPUS databases for articles published up to June 2024. After the selection process, a total of 11 studies were included in this review. The reviewed studies demonstrated that CNNs have a significant potential application for automatic identification of PBL on periapical radiographs through classification and segmentation approaches. CNN architectures can be utilized to classify the presence or absence of PBL, the severity or degree of PBL, and PBL area segmentation. CNN showed a promising performance for PBL identification on periapical radiographs. Future research should focus on dataset preparation, proper selection of CNN architecture, and robust performance evaluation to improve the model. Utilizing an optimized CNN architecture is expected to assist dentists by providing accurate and efficient identification of PBL.

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.

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.

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.

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.

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.

Brucella spondylitis (BS) and tuberculous spondylitis (TS) are prevalent spinal infections with distinct treatment protocols. Rapid and accurate differentiation between these two conditions is crucial for effective clinical management; however, current imaging and pathogen-based diagnostic methods fall short of fully meeting clinical requirements. This study explores the feasibility of employing deep learning (DL) models based on conventional magnetic resonance imaging (MRI) to differentiate BS and TS. A total of 310 subjects were enrolled in our hospital, comprising 209 with BS, 101 with TS. The participants were randomly divided into a training set (n = 217) and a test set (n = 93). And 74 with other hospital was external validation set. Integrating Convolutional Block Attention Module (CBAM) into the ResNeXt-50 architecture and training the model using sagittal T2-weighted images (T2WI). Classification performance was evaluated using the area under the receiver operating characteristic (AUC) curve, and diagnostic accuracy was compared against general models such as ResNet50, GoogleNet, EfficientNetV2, and VGG16. The CBAM-ResNeXt model revealed superior performance, with accuracy, precision, recall, F1-score, and AUC from 0.942, 0.940, 0.928, 0.934, 0.953, respectively. These metrics outperformed those of the general models. The proposed model offers promising potential for the diagnosis of BS and TS using conventional MRI. It could serve as an invaluable tool in clinical practice, providing a reliable reference for distinguishing between these two diseases.

<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.

Accurate prognosis of non-small cell lung cancer (NSCLC) patients undergoing immunotherapy is essential for personalized treatment planning, enabling informed patient decisions, and improving both treatment outcomes and quality of life. However, the lack of large, relevant datasets and effective multi-modal feature fusion strategies pose significant challenges in this domain. To address these challenges, we present a large-scale dataset and introduce a novel framework for multi-modal feature fusion aimed at enhancing the accuracy of survival prediction. The dataset comprises 3D CT images and corresponding clinical records from NSCLC patients treated with immune checkpoint inhibitors (ICI), along with progression-free survival (PFS) and overall survival (OS) data. We further propose a cross-modality masked learning approach for medical feature fusion, consisting of two distinct branches, each tailored to its respective modality: a Slice-Depth Transformer for extracting 3D features from CT images and a graph-based Transformer for learning node features and relationships among clinical variables in tabular data. The fusion process is guided by a masked modality learning strategy, wherein the model utilizes the intact modality to reconstruct missing components. This mechanism improves the integration of modality-specific features, fostering more effective inter-modality relationships and feature interactions. Our approach demonstrates superior performance in multi-modal integration for NSCLC survival prediction, surpassing existing methods and setting a new benchmark for prognostic models in this context.

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.