We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.

Research across disciplinary boundaries: the Institute for Applied Mathematics at the University of Bonn and the Clinic for Neuroradiology at the University Hospital Bonn (UKB) have received funding of around 160,000 euros for a joint project on the automated detection of brain hemorrhages using artificial intelligence. The funding is provided by the Hausdorff Center for Mathematics (HCM) Cluster of Excellence at the University of Bonn. Cerebral hemorrhages are among the clinical emergencies in which rapid intervention is essential for the further course of the disease. In this context, radiology plays a central role, because only the reliable diagnosis of brain hemorrhage by means of CT (computed tomography) enables the correct classification of the hemorrhage and the initiation of further therapeutic steps. In order to be able to automatically detect brain hemorrhages in the future using artificial intelligence, mathematicians and physicians are working closely together in their project.

The use of electronic health records in medical research is difficult because of the unstructured format. Extracting information within reports and summarising patient presentations in a way amenable to downstream analysis would be enormously beneficial for operational and clinical research. In this work we present a natural language processing pipeline for information extraction of radiological reports in neurology. Our pipeline uses a hybrid sequence of rule-based and artificial intelligence models to accurately extract and summarise neurological reports. We train and evaluate a custom language model on a corpus of 150000 radiological reports from National Hospital for Neurology and Neurosurgery, London MRI imaging. We also present results for standard NLP tasks on domain-specific neuroradiology datasets. We show our pipeline, called `neuroNLP', can reliably extract clinically relevant information from these reports, enabling downstream modelling of reports and associated imaging on a heretofore unprecedented scale.

Ryan K. Lee, MD, MBA, a member of the Applied Radiology Editorial Advisory Board, has been appointed Chair of the Department of Radiology at Einstein Healthcare Network in Pennsylvania. He succeeds Terence Matalon, MD, who will remain on staff as a radiologist and will continue to serve as a faculty member. Prior to his appointment as Chair of Radiology, Dr. Lee served as Vice Chair of Quality and Safety, Section Chief of Neuroradiology, and Magnetic Resonance Medical Director for the Department of Radiology for Einstein Healthcare Network. He is Associate Professor of Radiology at the Sidney Kimmel College at Thomas Jefferson University. Dr. Lee attended Cornell University for his undergraduate education and attained his medical degree at Drexel University College of Medicine.

FIG 1. Total number of publications from PubMed using search of “brain” AND [“artificial intelligence” OR “machine learning” OR “deep learning”] …

While neurological diseases seem sudden, striking out of nowhere, many of them are actually progressive. The brain develops such conditions over time, and the symptoms can be barely noticeable until it is too late. The secret to helping such patients in time is befriending deep learning technology. Various types of image analysis software, mostly based on deep learning (a subfield of artificial intelligence) are being increasingly adopted in radiology due to their ability to automate image processing and segmentation, reducing the time on scan interpretation. According to a recent report, the forecast for the global medical image analysis market is set to reach $4.26 billion by 2025.

Physicians at the University of California, Irvine and UCI Health System have launched the UCI Center for Artificial Intelligence in Diagnostic Medicine, which seeks to advance patient care, improve health outcomes and lower costs by leveraging machine learning technology in all areas of healthcare. Led by Peter D. Chang, MD, and Daniel S. Chow, MD, neuroradiologists in the Department of Radiological Sciences, UCI School of Medicine, the center is a cross-specialty initiative with a specific focus on developing and applying deep learning neural networks to healthcare applications, such as diagnostics, disease prediction and therapy planning. "Our goal is to empower health care providers, researchers and patients through the use of artificial intelligence in healthcare," said Chang. The Center for Artificial Intelligence in Diagnostic Medicine will provide a central research core that enables all UCI faculty, physicians and researchers, to collaborate on translating AI-based concepts into clinical tools to improve individual and population health. "The center will develop machine learning tools that can be implemented for routine clinical use today," said Chow.