A foundation model for generalized brain MRI analysis.

Artificial intelligence (AI) applied to brain magnetic resonance imaging (MRI) has the potential to improve disease diagnosis and management but requires algorithms with generalizable knowledge that can perform well in a variety of clinical scenarios. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. Foundation models, by leveraging self-supervised learning, pretraining, and targeted adaptation, present a promising paradigm to overcome these limitations.

Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods.

The use of artificial intelligence (AI) holds great promise for radiation oncology, with many applications being reported in the literature, including some of which are already in clinical use. These are mainly in areas where AI provides benefits in efficiency (such as automatic segmentation and treatment planning). Prediction models that directly impact patient decision-making are far less mature in terms of their application in clinical practice.

Empowering Data Sharing in Neuroscience: A Deep Learning De-identification Method for Pediatric Brain MRIs.

Background And Purpose: Privacy concerns, such as identifiable facial features within brain scans, have hindered the availability of pediatric neuroimaging datasets for research. Consequently, pediatric neuroscience research lags adult counterparts, particularly in rare disease and under-represented populations. The removal of face regions (image defacing) can mitigate this, however existing defacing tools often fail with pediatric cases and diverse image types, leaving a critical gap in data accessibility.

Providers' perspectives of barriers and facilitators to scale-up of mental health care in the public health delivery system of Mozambique: a qualitative inquiry.

Background: A central challenge to closing the mental health treatment gap in low- and middle-income countries (LMICs) is determining the most effective pathway for delivering evidence-based mental health services. We are conducting a cluster-randomized, Type 2 hybrid implementation-effectiveness trial across 20 districts of Mozambique called the Partnerships in Research to Implement and Disseminate Sustainable and Scalable EBPs (PRIDE) program. Following training of nonspecialized providers in facilitation of evidence-based treatments for mental health and informed by the Consolidated Framework for Implementation Research (CFIR), we identified how PRIDE compares to care as usual and the perceived barriers and facilitators of implementation and modifications needed for widescale service delivery and scale-up.

Delta radiomics to track radiation response in lung tumors receiving stereotactic magnetic resonance-guided radiotherapy.

Background And Purpose: Lung cancer is a leading cause of cancer-related mortality, and stereotactic body radiotherapy (SBRT) has become a standard treatment for early-stage lung cancer. However, the heterogeneous response to radiation at the tumor level poses challenges. Currently, standardized dosage regimens lack adaptation based on individual patient or tumor characteristics.