X-Factor: Quality Is a Dataset-Intrinsic Property.

In the universal quest to optimize machine-learning classifiers, three factors-model architecture, dataset size, and class balance-have been shown to influence test-time performance but do not fully account for it. Previously, evidence was presented for an additional factor that can be referred to as dataset quality, but it was unclear whether this was actually a joint property of the dataset and the model architecture, or an intrinsic property of the dataset itself. If quality is truly dataset-intrinsic and independent of model architecture, dataset size, and class balance, then the same datasets should perform better (or worse) regardless of these other factors.

Self-supervised learning for label-free segmentation in cardiac ultrasound.

Segmentation and measurement of cardiac chambers from ultrasound is critical, but laborious and poorly reproducible. Neural networks can assist, but supervised approaches require the same problematic manual annotations. We build a pipeline for self-supervised segmentation combining computer vision, clinical knowledge, and deep learning.

Detecting New Lesions Using a Large Language Model: Applications in Real-World Multiple Sclerosis Datasets.

Objective: Neuroimaging is routinely utilized to identify new inflammatory activity in multiple sclerosis (MS). A large language model to classify narrative magnetic resonance imaging reports in the electronic health record (EHR) as discrete data could provide significant benefits for MS research. The objectives of the current study were to develop such a prompt and to illustrate its research applications through a common clinical scenario: monitoring response to B-cell depleting therapy (BCDT).

RadSearch, a Semantic Search Model for Accurate Radiology Report Retrieval with Large Language Model Integration.

Background Current radiology report search tools are limited to keyword searches, which lack semantic understanding of underlying clinical conditions and are prone to false positives. Semantic search models address this issue, but their development requires scalable methods for generating radiology-specific training data. Purpose To develop a scalable method for training semantic search models for radiology reports and to evaluate a model, RadSearch, trained using this method.

Applications of artificial intelligence and advanced imaging in pediatric diffuse midline glioma.

Diffuse midline glioma (DMG) is a rare, aggressive, and fatal tumor that largely occurs in the pediatric population. To improve outcomes, it is important to characterize DMGs, which can be performed via magnetic resonance imaging (MRI) assessment. Recently, artificial intelligence (AI) and advanced imaging have demonstrated their potential to improve the evaluation of various brain tumors, gleaning more information from imaging data than is possible without these methods.

Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement.

This study forms the basis of a digital twin system of the knee joint, using advanced quantitative MRI (qMRI) and machine learning to advance precision health in osteoarthritis (OA) management and knee replacement (KR) prediction. We combined deep learning-based segmentation of knee joint structures with dimensionality reduction to create an embedded feature space of imaging biomarkers. Through cross-sectional cohort analysis and statistical modeling, we identified specific biomarkers, including variations in cartilage thickness and medial meniscus shape, that are significantly associated with OA incidence and KR outcomes.

Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning.

In deep learning, achieving high performance on image classification tasks requires diverse training sets. However, the current best practice-maximizing dataset size and class balance-does not guarantee dataset diversity. We hypothesized that, for a given model architecture, model performance can be improved by maximizing diversity more directly.

Association Between Tibiofemoral Bone Shape Features and Retears After Anterior Cruciate Ligament Reconstruction.

Background: A retear after anterior cruciate ligament (ACL) reconstruction remains a common and devastating complication. Knee bone morphology is associated with the risk of ACL injuries, ACL retears, and osteoarthritis, and a combination of tools that derive bone shape from clinical imaging, such as magnetic resonance imaging (MRI) and statistical shape modeling, could identify patients at risk of developing these joint conditions.

Purpose: To identify bone shape features before primary ACL reconstruction in patients with an eventual retear compared to those with a known intact ACL graft.

Deep learning segmentation-based bone removal from computed tomography of the brain improves subdural hematoma detection.

Purpose: Timely identification of intracranial blood products is clinically impactful, however the detection of subdural hematoma (SDH) on non-contrast CT scans of the head (NCCTH) is challenging given interference from the adjacent calvarium. This work explores the utility of a NCCTH bone removal algorithm for improving SDH detection.

Methods: A deep learning segmentation algorithm was designed/trained for bone removal using 100 NCCTH.

Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.

The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval.

Improving the Generalizability of Deep Learning for T2-Lesion Segmentation of Gliomas in the Post-Treatment Setting.

Although fully automated volumetric approaches for monitoring brain tumor response have many advantages, most available deep learning models are optimized for highly curated, multi-contrast MRI from newly diagnosed gliomas, which are not representative of post-treatment cases in the clinic. Improving segmentation for treated patients is critical to accurately tracking changes in response to therapy. We investigated mixing data from newly diagnosed ( = 208) and treated ( = 221) gliomas in training, applying transfer learning (TL) from pre- to post-treatment imaging domains, and incorporating spatial regularization for T2-lesion segmentation using only T2 FLAIR images as input to improve generalization post-treatment.

A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation.

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available.

Lessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI Challenges.

The Radiological Society of North America (RSNA) has held artificial intelligence competitions to tackle real-world medical imaging problems at least annually since 2017. This article examines the challenges and processes involved in organizing these competitions, with a specific emphasis on the creation and curation of high-quality datasets. The collection of diverse and representative medical imaging data involves dealing with issues of patient privacy and data security.

Proceedings From the 2022 ACR-RSNA Workshop on Safety, Effectiveness, Reliability, and Transparency in AI.

Despite the surge in artificial intelligence (AI) development for health care applications, particularly for medical imaging applications, there has been limited adoption of such AI tools into clinical practice. During a 1-day workshop in November 2022, co-organized by the ACR and the RSNA, participants outlined experiences and problems with implementing AI in clinical practice, defined the needs of various stakeholders in the AI ecosystem, and elicited potential solutions and strategies related to the safety, effectiveness, reliability, and transparency of AI algorithms. Participants included radiologists from academic and community radiology practices, informatics leaders responsible for AI implementation, regulatory agency employees, and specialty society representatives.