Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers.

Background: The rapid advancement of deep learning in health care presents significant opportunities for automating complex medical tasks and improving clinical workflows. However, widespread adoption is impeded by data privacy concerns and the necessity for large, diverse datasets across multiple institutions. Federated learning (FL) has emerged as a viable solution, enabling collaborative artificial intelligence model development without sharing individual patient data. To effectively implement FL in health care, robust and secure infrastructures are essential. Developing such federated deep learning frameworks is crucial to harnessing the full potential of artificial intelligence while ensuring patient data privacy and regulatory compliance. Objective: The objective is to introduce an innovative FL infrastructure called the Personal Health Train (PHT) that includes the procedural, technical, and governance components needed to implement FL on real-world health care data, including training deep learning neural networks. The study aims to apply this federated deep learning infrastructure to the use case of gross tumor volume segmentation on chest computed tomography images of patients with lung cancer and present the results from a proof-of-concept experiment. Methods: The PHT framework addresses the challenges of data privacy when sharing data, by keeping data close to the source and instead bringing the analysis to the data. Technologically, PHT requires 3 interdependent components: “tracks” (protected communication channels), “trains” (containerized software apps), and “stations” (institutional data repositories), which are supported by the open source “Vantage6” software. The study applies this federated deep learning infrastructure to the use case of gross tumor volume segmentation on chest computed tomography images of patients with lung cancer, with the introduction of an additional component called the secure aggregation server, where the model averaging is done in a trusted and inaccessible environment. Results: We demonstrated the feasibility of executing deep learning algorithms in a federated manner using PHT and presented the results from a proof-of-concept study. The infrastructure linked 12 hospitals across 8 nations, covering 4 continents, demonstrating the scalability and global reach of the proposed approach. During the execution and training of the deep learning algorithm, no data were shared outside the hospital. Conclusions: The findings of the proof-of-concept study, as well as the implications and limitations of the infrastructure and the results, are discussed. The application of federated deep learning to unstructured medical imaging data, facilitated by the PHT framework and Vantage6 platform, represents a significant advancement in the field. The proposed infrastructure addresses the challenges of data privacy and enables collaborative model development, paving the way for the widespread adoption of deep learning–based tools in the medical domain and beyond. The introduction of the secure aggregation server implied that data leakage problems in FL can be prevented by careful design decisions of the infrastructure. Trial Registration: ClinicalTrials.gov NCT05775068; https://clinicaltrials.gov/study/NCT05775068

Deep learning (DL) techniques may support localizing the epileptogenic zone (EZ) and improve surgical outcomes in drug-resistant epilepsy. This systematic review synthesizes current evidence on DL-assisted EZ localization from neuroimaging acquisitions, aiming to outline methodological trends, limitations, and future directions that bridge the gap between clinical translation and technological advances. We systematically searched PubMed, Scopus, and Embase (via Ovid) on April 15, 2025, for studies applying DL to localize the EZ using neuroimaging data. The bias and applicability of studies was assessed using the PROBAST+AI tool. We extracted methodological details, as well as key performance metrics. Thirty-six studies met the eligibility criteria, most focusing on segmenting epileptogenic lesions using structural MRI. Focal cortical dysplasia was the most commonly targeted pathology, with fully convolutional networks being the predominant DL architecture. Approximately two-thirds of the studies showed high risk of bias and clinical applicability concerns, limited by non-representative cohorts and suboptimal evaluation methods. Five studies reported promising EZ detection rate in MRI-negative cases using large multi-center cohorts, yet progress in fine-grained localization tasks, such as lesion segmentation, remained moderate. This review highlights methodological limitations hindering the clinical translation of current DL approaches for EZ localization and provides a comprehensive set of recommendations to address them. Future work should prioritize developing standardized, clinically informative evaluation frameworks and explore research avenues aligned with modern DL practices, spanning from uncertainty quantification to large-scale vision foundation models and synthetic data generation.

Focal cortical dysplasia (FCD) is among the leading causes of drug-resistant focal epilepsy and can be challenging to detect on routine MRI due to the subtle and heterogeneous appearance of lesions. Motivated by the scarcity of clinically representative cohorts, we investigate the feasibility of simulating FCD-like lesions in healthy MRI using a composition of hand-crafted image transformations. We develop Syn-thFCD , a phenomenological FCD simulator, and create 2,164 synthetic examples from healthy controls in the heterogeneous FOMO300k dataset. We then pretrain deep learning (DL) segmentation models on synthetic lesions and fine-tune them on real FCD cases from the University Hospital Bonn (UHB) cohort. Comprehensive evaluations show consistent improvements across simulator presets, with detection-rate gains of up to 18.5 percentage points over training from scratch and 7.4 points over self-supervised pretraining, while achieving state-of-the-art segmentation performance (0.366 DSC) on the challenging held-out UHB benchmark. These findings suggest that simulation-based synthetic data generation can bridge existing radiological knowledge and advances in DL to provide meaningful supervisory signals that could ultimately aid the detection of underrepresented and diagnostically challenging cases.