Artificial intelligence (AI) is a revolutionary tool yet to be fully integrated into several health care sectors, including medical imaging. AI can transform how medical imaging is conducted and interpreted, especially in cardio-oncology. This study aims to systematically review the available literature on the use of AI in cardio-oncology imaging to predict cardiotoxicity and describe the possible improvement of different imaging modalities that can be achieved if AI is successfully deployed to routine practice. We conducted a database search in PubMed, Ovid MEDLINE, Cochrane Library, CINAHL, and Google Scholar from inception to 2023 using the AI research assistant tool (Elicit) to search for original studies reporting AI outcomes in adult patients diagnosed with any cancer and undergoing cardiotoxicity assessment. Outcomes included incidence of cardiotoxicity, left ventricular ejection fraction, risk factors associated with cardiotoxicity, heart failure, myocardial dysfunction, signs of cancer therapy-related cardiovascular toxicity, echocardiography, and cardiac magnetic resonance imaging. Descriptive information about each study was recorded, including imaging technique, AI model, outcomes, and limitations. The systematic search resulted in 7 studies conducted between 2018 and 2023, which are included in this review. Most of these studies were conducted in the United States (71%), included patients with breast cancer (86%), and used magnetic resonance imaging as the imaging modality (57%). The quality assessment of the studies had an average of 86% compliance in all of the tool's sections. In conclusion, this systematic review demonstrates the potential of AI to enhance cardio-oncology imaging for predicting cardiotoxicity in patients with cancer. Our findings suggest that AI can enhance the accuracy and efficiency of cardiotoxicity assessments. However, further research through larger, multicenter trials is needed to validate these applications and refine AI technologies for routine use, paving the way for improved patient outcomes in cancer survivors at risk of cardiotoxicity.

Shortcut learning has been identified as a source of algorithmic unfairness in medical imaging artificial intelligence (AI), but its impact on photoacoustic tomography (PAT), particularly concerning sex bias, remains underexplored. This study investigates this issue using peripheral artery disease (PAD) diagnosis as a specific clinical application. To examine the potential for sex bias due to shortcut learning in convolutional neural network (CNNs) and assess how such biases might affect diagnostic predictions, we created training and test datasets with varying PAD prevalence between sexes. Using these datasets, we explored (1) whether CNNs can classify the sex from imaging data, (2) how sex-specific prevalence shifts impact PAD diagnosis performance and underdiagnosis disparity between sexes, and (3) how similarly CNNs encode sex and PAD features. Our study with 147 individuals demonstrates that CNNs can classify the sex from calf muscle PAT images, achieving an AUROC of 0.75. For PAD diagnosis, models trained on data with imbalanced sex-specific disease prevalence experienced significant performance drops (up to 0.21 AUROC) when applied to balanced test sets. Additionally, greater imbalances in sex-specific prevalence within the training data exacerbated underdiagnosis disparities between sexes. Finally, we identify evidence of shortcut learning by demonstrating the effective reuse of learned feature representations between PAD diagnosis and sex classification tasks. CNN-based models trained on PAT data may engage in shortcut learning by leveraging sex-related features, leading to biased and unreliable diagnostic predictions. Addressing demographic-specific prevalence imbalances and preventing shortcut learning is critical for developing models in the medical field that are both accurate and equitable across diverse patient populations.

This study introduces APD-FFNet, a novel, explainable deep learning architecture for automated periodontitis diagnosis using panoramic radiographs. A total of 337 panoramic radiographs, annotated by a periodontist, served as the dataset. APD-FFNet combines custom convolutional and transformer-based layers within a deep feature fusion framework that captures both local and global contextual features. Performance was evaluated using accuracy, the F1 score, the area under the receiver operating characteristic curve, the Jaccard similarity coefficient, and the Matthews correlation coefficient. McNemar's test confirmed statistical significance, and SHapley Additive exPlanations provided interpretability insights. APD-FFNet achieved 94% accuracy, a 93.88% F1 score, 93.47% area under the receiver operating characteristic curve, 88.47% Jaccard similarity coefficient, and 88.46% Matthews correlation coefficient, surpassing comparable approaches. McNemar's test validated these findings (p < 0.05). Explanations generated by SHapley Additive exPlanations highlighted important regions in each radiograph, supporting clinical applicability. By merging convolutional and transformer-based layers, APD-FFNet establishes a new benchmark in automated, interpretable periodontitis diagnosis, with low hyperparameter sensitivity facilitating its integration into regular dental practice. Its adaptable design suggests broader relevance to other medical imaging domains. This is the first feature fusion method specifically devised for periodontitis diagnosis, supported by an expert-curated dataset and advanced explainable artificial intelligence. Its robust accuracy, low hyperparameter sensitivity, and transparent outputs set a new standard for automated periodontal analysis.

This study aims to compare the diagnostic efficiency of the American College of Radiology-Thyroid Imaging, Reporting, and Data System (ACR-TIRADS), fine-needle aspiration (FNA) cytopathology alone, and the dynamic artificial intelligence (AI) diagnostic system. A total of 1035 patients from three hospitals were included in the study. Of these, 590 were from the retrospective dataset and 445 cases were from the prospective dataset. The diagnostic accuracy of the dynamic AI system in the thyroid nodules was evaluated in comparison to the gold standard of postoperative pathology. The sensitivity, specificity, ROC, and diagnostic differences in the κ-factor relative to the gold standard were analyzed for the AI system and the FNA. The dynamic AI diagnostic system showed good diagnostic stability in different ages and sexes and nodules of different sizes. The diagnostic AUC of the dynamic AI system showed a significant improvement from 0.89 to 0.93 compared to ACR TI-RADS. Compared to that of FNA cytopathology, the diagnostic efficacy of the dynamic AI system was found to be no statistical difference in both the retrospective cohort and the prospective cohort. The dynamic AI diagnostic system enhances the accuracy of ACR TI-RADS-based diagnoses and has the potential to replace biopsies, thus reducing the necessity for invasive procedures in patients.

Lung cancer is the leading cause of patient mortality in the world. Early diagnosis of malignant pulmonary nodules in CT images can have a significant impact on reducing disease mortality and morbidity. In this work, we propose LMLCC-Net, a novel deep learning framework for classifying nodules from CT scan images using a 3D CNN, considering Hounsfield Unit (HU)-based intensity filtering. Benign and malignant nodules have significant differences in their intensity profile of HU, which was not exploited in the literature. Our method considers the intensity pattern as well as the texture for the prediction of malignancies. LMLCC-Net extracts features from multiple branches that each use a separate learnable HU-based intensity filtering stage. Various combinations of branches and learnable ranges of filters were explored to finally produce the best-performing model. In addition, we propose a semi-supervised learning scheme for labeling ambiguous cases and also developed a lightweight model to classify the nodules. The experimental evaluations are carried out on the LUNA16 dataset. Our proposed method achieves a classification accuracy (ACC) of 91.96%, a sensitivity (SEN) of 92.04%, and an area under the curve (AUC) of 91.87%, showing improved performance compared to existing methods. The proposed method can have a significant impact in helping radiologists in the classification of pulmonary nodules and improving patient care.

Medical disease prediction, particularly through imaging, remains a challenging task due to the complexity and variability of medical data, including noise, ambiguity, and differing image quality. Recent deep learning models, including Knowledge Distillation (KD) methods, have shown promising results in brain tumor image identification but still face limitations in handling uncertainty and generalizing across diverse medical conditions. Traditional KD methods often rely on a context-unaware temperature parameter to soften teacher model predictions, which does not adapt effectively to varying uncertainty levels present in medical images. To address this issue, we propose a novel framework that integrates Ant Colony Optimization (ACO) for optimal teacher-student model selection and a novel context-aware predictor approach for temperature scaling. The proposed context-aware framework adjusts the temperature based on factors such as image quality, disease complexity, and teacher model confidence, allowing for more robust knowledge transfer. Additionally, ACO efficiently selects the most appropriate teacher-student model pair from a set of pre-trained models, outperforming current optimization methods by exploring a broader solution space and better handling complex, non-linear relationships within the data. The proposed framework is evaluated using three publicly available benchmark datasets, each corresponding to a distinct medical imaging task. The results demonstrate that the proposed framework significantly outperforms current state-of-the-art methods, achieving top accuracy rates: 98.01% on the MRI brain tumor (Kaggle) dataset, 92.81% on the Figshare MRI dataset, and 96.20% on the GastroNet dataset. This enhanced performance is further evidenced by the improved results, surpassing existing benchmarks of 97.24% (Kaggle), 91.43% (Figshare), and 95.00% (GastroNet).

Recent advancements in foundation models, such as the Segment Anything Model (SAM), have shown strong performance in various vision tasks, particularly image segmentation, due to their impressive zero-shot segmentation capabilities. However, effectively adapting such models for medical image classification is still a less explored topic. In this paper, we introduce a new framework to adapt SAM for medical image classification. First, we utilize the SAM image encoder as a feature extractor to capture segmentation-based features that convey important spatial and contextual details of the image, while freezing its weights to avoid unnecessary overhead during training. Next, we propose a novel Spatially Localized Channel Attention (SLCA) mechanism to compute spatially localized attention weights for the feature maps. The features extracted from SAM's image encoder are processed through SLCA to compute attention weights, which are then integrated into deep learning classification models to enhance their focus on spatially relevant or meaningful regions of the image, thus improving classification performance. Experimental results on three public medical image classification datasets demonstrate the effectiveness and data-efficiency of our approach.

In knee osteoarthritis (KOA) treatment, preventive measures to reduce its onset risk are a key factor. Among individuals with radiographically healthy knees, however, future knee joint integrity and condition cannot be predicted by clinically applicable methods. We investigated if knee joint morphology derived from widely accessible and cost-effective radiographs could be helpful in predicting future knee joint integrity and condition. We combined knee joint morphology with known risk predictors such as age, height, and weight. Baseline data were utilized as predictors, and the maximal severity of KOA after 8 years served as a target variable. The three KOA categories in this study were based on Kellgren-Lawrence grading: healthy, moderate, and severe. We employed a two-stage machine learning model that utilized two random forest algorithms. We trained three models: the subject demographics (SD) model utilized only SD; the image model utilized only knee joint morphology from radiographs; the merged model utilized combined predictors. The training data comprised an 8-year follow-up of 1222 knees from 683 individuals. The SD- model obtained a weighted F1 score (WF1) of 77.2% and a balanced accuracy (BA) of 65.6%. The Image-model performance metrics were lowest, with a WF1 of 76.5% and BA of 63.8%. The top-performing merged model achieved a WF1 score of 78.3% and a BA of 68.2%. Our two-stage prediction model provided improved results based on performance metrics, suggesting potential for application in clinical settings.

Due to population aging, the increasing prevalence of Alzheimer's Disease (AD) and related dementias are major public health concerns. Dietary consumption of antioxidant nutrients, in particular the carotenoid β-carotene, has been associated with lower age-related neurocognitive decline. What is unclear, however, is the extent to which antioxidant nutrients may exert neuroprotective effects via their influence on established indicators of age-related changes in brain tissue. This study thus tested associations of circulating β-carotene and other nutrients with a structural neuroimaging indicator of brain age derived from cross-validated machine learning models trained to predict chronological age from brain tissue morphology in independent cohorts. Midlife adults (N=132, aged 30.4 to 50.8 years, 59 female at birth) underwent a structural magnetic resonance imaging (MRI) protocol and fasting phlebotomy to assess plasma concentrations of β-carotene, retinol, γ-tocopherol, ⍺-tocopherol, and β-cryptoxanthin. In regression analyses adjusting for chronological age, sex at birth, smoking status, MRI image quality, season of testing, annual income, and education, greater circulating levels of β-carotene were associated with a lower (i.e., younger) predicted brain age (β=-0.23, 95% CI=-0.40 to -0.07, P=0.006). Other nutrients were not statistically associated with brain age, and results persisted after additional covariate control for body mass index, cortical volume, and cortical thickness. These cross-sectional findings are consistent with the possibility that dietary intake of β-carotene may be associated with slower biological aging at the level of the brain, as reflected by a neuroimaging indicator of brain age.

Retroperitoneal sarcoma (RPS) is highly heterogeneous, leading to different risks of distant metastasis (DM) among patients with the same clinical stage. This study aims to develop a quantitative method for assessing intratumoral heterogeneity (ITH) using preoperative contrast-enhanced CT (CECT) scans and evaluate its ability to predict DM risk. We conducted a retrospective analysis of 274 PRS patients who underwent complete surgical resection and were monitored for ≥ 36 months at two centers. Conventional radiomics (C-radiomics), ITH radiomics, and deep-learning (DL) features were extracted from the preoperative CECT scans and developed single-modality models. Clinical indicators and high-throughput CECT features were integrated to develop a combined model for predicting DM. The performance of the models was evaluated by measuring the receiver operating characteristic curve and Harrell's concordance index (C-index). Distant metastasis-free survival (DMFS) was also predicted to further assess survival benefits. The ITH model demonstrated satisfactory predictive capability for DM in internal and external validation cohorts (AUC: 0.735, 0.765; C-index: 0.691, 0.729). The combined model that combined clinicoradiological variables, ITH-score, and DL-score achieved the best predictive performance in internal and external validation cohorts (AUC: 0.864, 0.801; C-index: 0.770, 0.752), successfully stratified patients into high- and low-risk groups for DM (p < 0.05). The combined model demonstrated promising potential for accurately predicting the DM risk and stratifying the DMFS risk in RPS patients undergoing complete surgical resection, providing a valuable tool for guiding treatment decisions and follow-up strategies. The intratumoral heterogeneity analysis facilitates the identification of high-risk retroperitoneal sarcoma patients prone to distant metastasis and poor prognoses, enabling the selection of candidates for more aggressive surgical and post-surgical interventions. Preoperative identification of retroperitoneal sarcoma (RPS) with a high potential for distant metastasis (DM) is crucial for targeted interventional strategies. Quantitative assessment of intratumoral heterogeneity achieved reasonable performance for predicting DM. The integrated model combining clinicoradiological variables, ITH radiomics, and deep-learning features effectively predicted distant metastasis-free survival.