Deep learning has shown significant potential in diagnosing neurodegenerative diseases from MRI data. However, most existing methods rely heavily on large volumes of labeled data and often yield representations that lack interpretability. To address both challenges, we propose a novel self-supervised cross-encoder framework that leverages the temporal continuity in longitudinal MRI scans for supervision. This framework disentangles learned representations into two components: a static representation, constrained by contrastive learning, which captures stable anatomical features; and a dynamic representation, guided by input-gradient regularization, which reflects temporal changes and can be effectively fine-tuned for downstream classification tasks. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our method achieves superior classification accuracy and improved interpretability. Furthermore, the learned representations exhibit strong zero-shot generalization on the Open Access Series of Imaging Studies (OASIS) dataset and cross-task generalization on the Parkinson Progression Marker Initiative (PPMI) dataset. The code for the proposed method will be made publicly available.

Single photon emission computed tomography (SPECT) remains the most widely used modality for the assessment of coronary artery disease (CAD) owing to its diagnostic and prognostic value, cost-effectiveness, broad availability, and ability to be performed with exercise testing. However, major cardiology guidelines recommend positron emission tomography (PET) over SPECT when available, largely due to its superior accuracy and ability to provide absolute myocardial blood flow quantification. A key limitation of SPECT is its reliance on relative perfusion imaging, which may overlook diffuse flow reductions, such as those seen in balanced ischemia, diffuse atherosclerosis, and microvascular dysfunction. With the shifting paradigm of CAD toward non-obstructive disease, the need for absolute quantification has become increasingly critical. This review highlights the strengths and limitations of SPECT and explores strategies to preserve its clinical relevance in the PET era. These include the adoption of CZT-SPECT technology for quantification, the use of hybrid systems for attenuation correction and calcium scoring, the adoption of stress-only protocols, the integration of quantitative data and calcium scoring into reporting, and the emerging applications of artificial intelligence (AI) among others.

Brain imaging with MRI or CT is standard in screening for intracranial disease among ambulatory cancer patients. Although MRI offers greater sensitivity, CT is frequently employed due to its accessibility, affordability, and faster acquisition time. However, the necessity of routinely performing a non-contrast CT with the contrast-enhanced study is unknown. This study evaluates the clinical and economic utility of the non-contrast portion of the brain CT examination. A board-certified neuroradiologist reviewed 737 brain CT reports from outpatients at MD Anderson Cancer Center who underwent contrast and non-contrast CT for cancer staging (October 2014 to March 2016) to assess if significant findings were identified only on non-contrast CT. A GPT-3 model was then fine-tuned to extract reports with a high likelihood of unique and significant non-contrast findings from 1,980 additional brain CT reports (January 2017 to April 2022). These reports were manually reviewed by two neuroradiologists, with adjudication by a third reviewer if needed. The incremental cost-effectiveness ratio of non-contrast CT inclusion was then calculated based on Medicare reimbursement and the 95% confidence interval of the proportion of all reports in which non-contrast CT was necessary for identifying significant findings RESULTS: Seven of 737 reports in the initial dataset revealed significant findings unique to the non-contrast CT, all of which were hemorrhage. The GPT-3 model identified 145 additional reports with a high unique non-contrast CT finding likelihood for manual review from the second dataset of 1,980 reports. 19 of these reports were found to have unique and significant non-contrast CT findings. In total, 0.96% (95% CI: 0.63% -1.40%) of reports had significant findings identified only on non-contrast CT. The incremental cost-effectiveness ratio for identification of a single significant finding on non-contrast CT missed on the contrast-enhanced study was $1,855 to $4,122. In brain CT for ambulatory screening for intracranial disease in cancer patients, non-contrast CT offers limited additional diagnostic value compared to contrast-enhanced CT alone. Considering the associated financial cost, workload, and patient radiation exposure associated with performing a non-contrast CT, contrast-enhanced brain CT alone is sufficient for cancer staging in asymptomatic cancer patients. GPT-3＝ Generative Pretrained Transformers 3.

Tumor deposits (TDs) are an important prognostic factor in rectal cancer. However, integrated models combining clinical, habitat radiomics, and deep learning (DL) features for preoperative TDs detection remain unexplored. To investigate fusion models based on MRI for preoperative TDs identification and prognosis in rectal cancer. Retrospective. Surgically diagnosed rectal cancer patients (n = 635): training (n = 259) and internal validation (n = 112) from center 1; center 2 (n = 264) for external validation. 1.5/3T, T2-weighted image (T2WI) using fast spin echo sequence. Four models (clinical, habitat radiomics, DL, fusion) were developed for preoperative TDs diagnosis (184 TDs positive). T2WI was segmented using nnUNet, and habitat radiomics and DL features were extracted separately. Clinical parameters were analyzed independently. The fusion model integrated selected features from all three approaches through two-stage selection. Disease-free survival (DFS) analysis was used to assess the models' prognostic performance. Intraclass correlation coefficient (ICC), logistic regression, Mann-Whitney U tests, Chi-squared tests, LASSO, area under the curve (AUC), decision curve analysis (DCA), calibration curves, Kaplan-Meier analysis. The AUCs for the four models ranged from 0.778 to 0.930 in the training set. In the internal validation cohort, the AUCs of clinical, habitat radiomics, DL, and fusion models were 0.785 (95% CI 0.767-0.803), 0.827 (95% CI 0.809-0.845), 0.828 (95% CI 0.815-0.841), and 0.862 (95% CI 0.828-0.896), respectively. In the external validation cohort, the corresponding AUCs were 0.711 (95% CI 0.599-0.644), 0.817 (95% CI 0.801-0.833), 0.759 (95% CI 0.743-0.773), and 0.820 (95% CI 0.770-0.860), respectively. TDs-positive patients predicted by the fusion model had significantly poorer DFS (median: 30.7 months) than TDs-negative patients (median follow-up period: 39.9 months). A fusion model may identify TDs in rectal cancer and could allow to stratify DFS risk. 3.

This study evaluates the accuracy of an Artificial Intelligence (AI) system, specifically a convolutional neural network (CNN), in classifying elbow fractures using the detailed 2018 AO/OTA fracture classification system. A retrospective analysis of 5,367 radiograph exams visualizing the elbow from adult patients (2002-2016) was conducted using a deep neural network. Radiographs were manually categorized according to the 2018 AO/OTA system by orthopedic surgeons. A pretrained Efficientnet B4 network with squeeze and excitation layers was fine-tuned. Performance was assessed against a test set of 208 radiographs reviewed independently by four orthopedic surgeons, with disagreements resolved via consensus. The study evaluated 54 distinct fracture types, each with a minimum of 10 cases, ensuring adequate dataset representation. Overall fracture detection achieved an AUC of 0.88 (95% CI 0.83-0.93). The weighted mean AUC was 0.80 for proximal radius fractures, 0.86 for proximal ulna, and 0.85 for distal humerus. These results underscore the AI system's ability to accurately detect and classify a broad spectrum of elbow fractures. AI systems, such as CNNs, can enhance clinicians' ability to identify and classify elbow fractures, offering a complementary tool to improve diagnostic accuracy and optimize treatment decisions. The findings suggest AI can reduce the risk of undiagnosed fractures, enhancing clinical outcomes and radiologic evaluation.

Deep neural networks excel in radiological image classification but frequently suffer from poor interpretability, limiting clinical acceptance. We present MedicalPatchNet, an inherently self-explainable architecture for chest X-ray classification that transparently attributes decisions to distinct image regions. MedicalPatchNet splits images into non-overlapping patches, independently classifies each patch, and aggregates predictions, enabling intuitive visualization of each patch's diagnostic contribution without post-hoc techniques. Trained on the CheXpert dataset (223,414 images), MedicalPatchNet matches the classification performance (AUROC 0.907 vs. 0.908) of EfficientNet-B0, while substantially improving interpretability: MedicalPatchNet demonstrates substantially improved interpretability with higher pathology localization accuracy (mean hit-rate 0.485 vs. 0.376 with Grad-CAM) on the CheXlocalize dataset. By providing explicit, reliable explanations accessible even to non-AI experts, MedicalPatchNet mitigates risks associated with shortcut learning, thus improving clinical trust. Our model is publicly available with reproducible training and inference scripts and contributes to safer, explainable AI-assisted diagnostics across medical imaging domains. We make the code publicly available: https://github.com/TruhnLab/MedicalPatchNet

The choroid of the eye is a rare site for metastatic tumor spread, and as small lesions on the periphery of brain MRI studies, these choroidal metastases are often missed. To improve their detection, we aimed to use artificial intelligence to distinguish between brain MRI scans containing normal orbits and choroidal metastases. We present a novel hierarchical deep learning framework for sequential cropping and classification on brain MRI images to detect choroidal metastases. The key innovation of this approach lies in training an orbit localization network based on a YOLOv5 architecture to focus on the orbits, isolating the structures of interest and eliminating irrelevant background information. The initial sub-task of localization ensures that the input to the subsequent classification network is restricted to the precise anatomical region where choroidal metastases are likely to occur. In Step 1, we trained a localization network on 386 T2-weighted brain MRI axial slices from 97 patients. Using the localized orbit images from Step 1, in Step 2 we trained a binary classifier network with 33 normal and 33 choroidal metastasis-containing brain MRIs. To address the challenges posed by the small dataset, we employed a data-efficient evolutionary strategies approach, which has been shown to avoid both overfitting and underfitting in small training sets. Our orbit localization model identified globes with 100% accuracy and a mean Average Precision of Intersection over Union thresholds of 0.5 to 0.95 (mAP(0.5:0.95)) of 0.47 on held-out testing data. Similarly, the model generalized well to our Step 2 dataset which included orbits demonstrating pathologies, achieving 100% accuracy and mAP(0.5:0.95) of 0.44. mAP(0.5:0.95) appeared low because the model could not distinguish left and right orbits. Using the cropped orbits as inputs, our evolutionary strategies-trained convolutional neural network achieved a testing set area under the curve (AUC) of 0.93 (95% CI [0.83, 1.03]), with 100% sensitivity and 87% specificity at the optimal Youden's index. The semi-automated pipeline from brain MRI slices to choroidal metastasis classification demonstrates the utility of a sequential localization and classification approach, and clinical relevance for identifying small, "corner-of-the-image", easily overlooked lesions. AI = artificial intelligence; AUC = area under the curve; CNN = convolutional neural network; DNE = deep neuroevolution; IoU = intersection over union; mAP = mean average precision; ROC = receiver operating characteristic.

Myocarditis is an inflammation of heart tissue. Cardiovascular magnetic resonance imaging (CMR) has emerged as an important non-invasive imaging tool for diagnosing myocarditis, however, interpretation remains a challenge for novice physicians. Advancements in machine learning (ML) models have further improved diagnostic accuracy, demonstrating good performance. Our study aims to assess the diagnostic accuracy of ML in identifying myocarditis using CMR. A systematic search was performed using PubMed, Embase, Web of Science, Cochrane, and Scopus to identify studies reporting the diagnostic accuracy of ML in the detection of myocarditis using CMR. The included studies evaluated both image-based and report-based assessments using various ML models. Diagnostic accuracy was estimated using a Random-Effects model (R software). We found a total of 141 ML model results from a total of 12 studies, which were included in the systematic review. The best models achieved 0.93 (95% Confidence Interval (CI) 0.88-0.96) sensitivity and 0.95 (95% CI 0.89-0.97) specificity. Pooled area under the curve was 0.97 (95% CI 0.93-0.98). Comparisons with human physicians showed comparable results for diagnostic accuracy of myocarditis. Quality assessment concerns and heterogeneity were present. CMR augmented using ML models with advanced algorithms can provide high diagnostic accuracy for myocarditis, even surpassing novice CMR radiologists. However, high heterogeneity, quality assessment concerns, and lack of information on cost-effectiveness may limit the clinical implementation of ML. Future investigations should explore cost-effectiveness and minimize biases in their methodologies.

This mini-review explores the transformative potential of artificial intelligence (AI) in improving the diagnosis, management, and long-term care of congenital heart diseases (CHDs). AI offers significant advancements across the spectrum of CHD care, from prenatal screening to postnatal management and long-term monitoring. Using AI algorithms, enhanced fetal echocardiography, and genetic tests improves prenatal diagnosis and risk stratification. Postnatally, AI revolutionizes diagnostic imaging analysis, providing more accurate and efficient identification of CHD subtypes and severity. Compared with traditional methods, advanced signal processing techniques enable a more precise assessment of hemodynamic parameters. AI-driven decision support systems tailor treatment strategies, thereby optimizing therapeutic interventions and predicting patient outcomes with greater accuracy. This personalized approach leads to better clinical outcomes and reduced morbidity. Furthermore, AI-enabled remote monitoring and wearable devices facilitate ongoing surveillance, thereby enabling early detection of complications and provision of prompt interventions. This continuous monitoring is crucial in the immediate postoperative period and throughout the patient's life. Despite the immense potential of AI, challenges remain. These include the need for standardized datasets, the development of transparent and understandable AI algorithms, ethical considerations, and seamless integration into existing clinical workflows. Overcoming these obstacles through collaborative data sharing and responsible implementation will unlock the full potential of AI to improve the lives of patients with CHD, ultimately leading to better patient outcomes and improved quality of life.

Double expression lymphoma (DEL) is an independent high-risk prognostic factor for primary CNS lymphoma (PCNSL), and its diagnosis currently relies on invasive methods. This study first integrates radiomics and habitat radiomics features to enhance preoperative DEL status prediction models via intratumoral heterogeneity analysis. Clinical, pathological, and MRI imaging data of 139 PCNSL patients from two independent centers were collected. Radiomics, habitat radiomics, and combined models were constructed using machine learning classifiers, including KNN, DT, LR, and SVM. The AUC in the test set was used to evaluate the optimal predictive model. DCA curve and calibration curve were employed to evaluate the predictive performance of the models. SHAP analysis was utilized to visualize the contribution of each feature in the optimal model. For the radiomics-based models, the Combined radiomics model constructed by LR demonstrated better performance, with the AUC of 0.8779 (95% CI: 0.8171-0.9386) in the training set and 0.7166 (95% CI: 0.497-0.9361) in the test set. The Habitat radiomics model (SVM) based on T1-CE showed an AUC of 0.7446 (95% CI: 0.6503- 0.8388) in the training set and 0.7433 (95% CI: 0.5322-0.9545) in the test set. Finally, the Combined all model exhibited the highest predictive performance: LR achieved AUC values of 0.8962 (95% CI: 0.8299-0.9625) and 0.8289 (95% CI: 0.6785-0.9793) in training and test sets, respectively. The Combined all model developed in this study can provide effective reference value in predicting the DEL status of PCNSL, and habitat radiomics significantly enhances the predictive efficacy.