Scientific Data

ObjectiveTo establish the local diagnostic reference levels (LDRLs) for trunk multi-slice CT (MSCT) examinations in the Gaza Strip, Palestine. MethodA cross-sectional study of adult oncology patients undergoing trunk MSCT at two governmental hospitals in Gaza Strip; Al Shifa Medical Complex (SMC) and Al Aqsa Hospital (AMH), using an adapted dose survey booklet. Data collected from July 2019 to March 2020 included patient characteristics, volumetric CT dose index (CTDIvol) and dose length produc...

BackgroundMuch of the worlds historical and current epidemiological data remains locked in static formats, such as PDF situation reports or image-based surveillance bulletins. Recovering this data for spatial analysis typically requires proprietary software (e.g., ArcGIS) or laborious manual entry, which is prone to transcription errors. MethodsI developed Map Liberator, an open-source application built in R and Shiny. It provides a split-screen digitisation interface that allows users to overl...

Background and Purpose: Magnetic resonance imaging (MRI) for radiation therapy treatment planning is currently being used in many anatomical sites to better visualize soft tissue landmarks, a technique known as an MRI simulation. A core component of modern MRI simulation configurations are the use of external laser positioning systems (ELPS) to help set up the patient. Though necessary for accurate and reproducible patient setup, the ELPS, if left on during imaging, may interfere negatively with...

BackgroundLattice radiotherapy (LRT) delivers heterogeneous dose distribution through a three-dimensional array of vertices within the tumor. It is typically applied in 1[~]5 fractions for patients with large tumor volumes. However, conventional LRT generally employs only a single vertex set, which may limit the biological advantages of this technique in multi-fraction treatments. PurposeThis study proposes a novel vertex arrangement strategy in LRT aimed at improving intratumoral dose homogene...

Immune-related adverse events (irAEs) affect up to 40% of patients receiving immune checkpoint inhibitors, yet their identification depends on laborious and inconsistent manual chart review. Here we developed and evaluated an agentic large language model system to extract the presence, temporality, severity grade, attribution, and certainty of six irAE types from clinical notes. Retrospectively (263 notes), the system achieved macro-averaged F1 of 0.92 for detection and 0.66 for multi-class seve...

While Americans are using herbal dietary supplements (natural products) more than ever, the consumption of natural products with prescription drugs can lead to harmful interactions. Pharmacovigilance of natural products depends on careful expert review and interpretation of a wide variety of evidence. In prior work, we demonstrated the value of knowledge graph (NP-KG) for assisting with natural product safety investigations. However, scaling the NP-KG from 33 natural products to the thousands on...

BackgroundAccurate contouring of target volumes and organs at risk is critical for radiotherapy. While deep learning (DL) models offer efficient automation, their generalizability to real-world clinical cases containing anatomical variations and artifacts requires rigorous validation. PurposeTo evaluate the clinical accuracy and robustness of RatoGuide, a novel DL-based auto-segmentation software, using a dataset derived from routine clinical practice including atypical cases. MethodsThis sing...

BackgroundRetrieval-augmented generation (RAG) frameworks such as RAPID [1] have demonstrated that staged planning and retrieval grounding improve long-form text generation. However, most implementations remain similarity-driven and open-domain, lacking the epistemic safeguards required for biomedical synthesis, where mechanistic completeness, temporal governance, traceability, and explicit gap classification are essential. ObjectiveTo develop and evaluate a topology-aware, graph-augmented retr...

ObjectivesCase reports and case series comprise a significant portion of the biomedical literature, yet unlike case reports, the National Library of Medicine does not index case series as a Publication Type. This hurts clinicians and researchers ability to retrieve, identify and analyze evidence from this type of study. Materials and MethodsPubMed articles mentioning "case series" in title or abstract were characterized to learn what are considered to be case series by the authors themselves. W...

BackgroundAlthough deep learning models have improved individual PET analysis, image processing and quantification tasks, end-to-end automation from raw DICOM to quantitative clinical reporting remains limited, particularly in heterogeneous real-world settings. MethodsAs a proof-of-concept, an autonomous large language model (LLM)-orchestrated multi-tool agent for end-to-end PET/CT interpretation was developed. A reasoning-based text LLM selected appropriate series from raw DICOM, coordinated r...

BackgroundMotor threshold (MT) estimation is fundamental to transcranial magnetic stimulation (TMS), guiding individualized stimulation intensity in research and therapy. Conventional methods such as the 5-out-of-10 rule require many stimuli, while adaptive approaches like Parameter Estimation by Sequential Testing (PEST) improve efficiency but can exhibit poor convergence under certain conditions. ObjectiveThis study introduces the Bayesian Uncertainty Dynamic Algorithm for Parameter Estimatio...

AO_SCPLOWBSTRACTC_SCPLOWMagnetic resonance imaging (MRI) is a cornerstone of modern neuroimaging, where accurate segmentation of brain structures and lesions is essential for diagnosis, treatment planning, and clinical research. However, most current foundation models are trained on mixed-organ datasets, while the anatomical structures of the brain differ substantially from those of other organs such as the lungs and kidneys. As a result, these models often struggle to adapt to the distinctive c...

ObjectiveTo address the inefficiency, subjectivity, and high expertise barrier of traditional epidemiological causal inference, this study designed, developed, and validated an AI-powered agent (EpiCausalX Agent) to automate the end-to-end workflow. It integrates cross-database literature retrieval, intelligent causal reasoning, and Directed Acyclic Graph (DAG) visualization to provide a reliable, accessible tool for researchers. Materials and MethodsBuilt on the LangChain 1.0 framework with a ...

PurposeLarge language models (LLMs) are increasingly applied in radiology, but key challenges remain, including data leakage from cloud-based systems, false outputs, and limited reasoning transparency. This study aimed to develop an open-source, offline-deployable retrieval-augmented LLM (RA-LLM) system in which local execution prevents data leakage and retrieval-augmented generation (RAG) improves output accuracy and transparency using reliable external knowledge (REK), demonstrated in pancreat...

As implantable brain-computer interfaces (iBCIs) for communication and movement transition from cutting-edge research to clinical practice, a standardized approach will be required to reliably plan neurosurgeries involving complex microelectrode arrays and other neural sensors. Here, through our BrainGate study experiences, we present a replicable methodology, using open-source tools, to create interactive, personalized, 3-dimensional, virtual and physical, functional mapping models to guide iBC...

Current standard of care imaging practices cannot reliably differentiate among certain renal tumors such as benign oncocytoma and clear cell renal cell carcinoma (RCC), and between low and high grade RCCs. Previous work has explored using deep learning, radiomics, and texture analysis to predict renal tumor subtypes and differentiate between low and high grade RCCs with mixed success. To further this work, large diverse datasets are needed to improve model performance and provide strong evaluati...

PurposeTo externally evaluate three binary classification models designed to differentiate the molecular subtype of pediatric low-grade glioma (pLGG) between BRAF Fusion, BRAF Mutation, and Wild Type on T2-weighted magnetic resonance imaging using self-supervised transfer learning, which enables effective performance in a low data setting. Materials and methodsThis retrospective study evaluates pLGG molecular subtyping models, pre-trained using data collected at Dana Farber Cancer Institute/Bos...

Deep-learning based super-resolution has shown promise for enhancing the spatial resolution of brain magnetic resonance images, which may help visualize small anatomical structures more clearly. However, when only limited training data are available, it remains uncertain which model assessment method provides the most reliable estimate of out-of-sample performance. In this study, three widely used assessment strategies (three-way holdout, k-fold cross-validation, and nested cross-validation) wer...

BackgroundDeep grey matter structures such as the thalamus and basal nuclei are implicated in numerous neurological disorders, yet accurate segmentation of these structures from standard T1-weighted MRI remains challenging due to poor intra-subcortical contrast, long preprocessing pipelines, and fragmented toolsets. MethodsWe introduce THOMASINA a deep learning pipeline for comprehensive subcortical segmentation from standard T1-weighted (T1w) as well as white-matter-nulled (WMn) MRI. The metho...

There are many alternative methods to joystick control for control of Electric Powered Wheelchairs for users with neuromuscular disabilities, such as muscular dystrophy, and spinal cord injuries, such as tetraplegia. However, these methods- which include the sip-and-puff method, head and neck movement, blinking, or tongue movement- hinder social interaction, and are therefore detrimental to user independence. In recent years, research has explored the use of Electromyography (EMG) signals from a...