A cascaded FC-DenseNet and level set method (FCDL) for fully automatic segmentation of the right ventricle in cardiac MRI

Automatic Segmentation and Molecular Subtype Classification of Breast Cancer Using an MRI-based Deep Learning Framework

Purpose To build a deep learning framework using contrast-enhanced MRI for lesion segmentation and automatic molecular subtype classification in breast cancer. Materials and Methods This retrospective multicenter study included patients with biopsy-proven invasive breast cancer between January 2015 and January 2021. An automatic breast lesion segmentation model was developed using three-dimensional (3D) ResU-Net as the backbone, and its accuracy was evaluated in an internal and two external testing datasets using the Dice score. An ensemble model for classification of breast cancer into four molecular subtypes (Ensemble ResNet) was then developed by combining both two-dimensional and 3D lesion features. The performance of Ensemble ResNet was evaluated in the three testing datasets using the area under the receiver operating characteristic curve (AUC). Results A total of 687 female patients (mean age ± SD, 48.70 years ± 8.97) were included, with 289, 61, 73, and 264 patients included in the training, internal testing, and two external testing datasets, respectively. The proposed segmentation model achieved high accuracy in internal testing dataset 1, external testing dataset 2, and external testing dataset 3 (Dice scores: 0.86, 0.82, 0.85) and luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, and triple-negative breast cancer (TNBC) subtypes (Dice scores: 0.8571, 0.8323, 0.8199, 0.8481). Ensemble ResNet demonstrated high performance for the prediction of luminal A subtypes (AUC range, 0.74-0.84), luminal B subtypes (AUC range, 0.68-0.72), HER2-enriched subtypes (AUC range, 0.73-0.82), and TNBC (AUC range, 0.80-0.81) in the three testing datasets. Conclusion The proposed novel deep learning framework based on MRI achieved high, robust performance in fully automatic classification of breast cancer molecular subtypes. Keywords: MR-Imaging, Breast, Oncology, Breast Cancer, Molecular Subtype, Deep Learning Framework Supplemental material is available for this article. © RSNA, 2025.

Artificial intelligence and myocarditis-a systematic review of current applications

Myocarditis, marked by heart muscle inflammation, poses significant clinical challenges. This study, guided by PRISMA guidelines, explores the expanding role of artificial intelligence (AI) in myocarditis, aiming to consolidate current knowledge and guide future research. Following PRISMA guidelines, a systematic review was conducted across PubMed, Cochrane Reviews, Scopus, Embase, and Web of Science databases. MeSH terms including artificial intelligence, deep learning, machine learning, myocarditis, and inflammatory cardiomyopathy were used. Inclusion criteria involved original articles utilizing AI for myocarditis, while exclusion criteria eliminated reviews, editorials, and non-AI-focused studies. The search yielded 616 articles, with 42 meeting inclusion criteria after screening. The identified articles, spanning diagnostic, survival prediction, and molecular analysis aspects, were analyzed in each subsection. Diagnostic studies showcased the versatility of AI algorithms, achieving high accuracies in myocarditis detection. Survival prediction models exhibited robust discriminatory power, particularly in emergency settings and pediatric populations. Molecular analyses demonstrated AI's potential in deciphering complex immune interactions. This systematic review provides a comprehensive overview of AI applications in myocarditis, highlighting transformative potential in diagnostics, survival prediction, and molecular understanding. Collaborative efforts are crucial for overcoming limitations and realizing AI's full potential in improving myocarditis care.

Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section.