AIMC Topic: Metadata

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A cross-attention-based deep learning approach for predicting functional stroke outcomes using 4D CTP imaging and clinical metadata.

Medical image analysis
Acute ischemic stroke (AIS) remains a global health challenge, leading to long-term functional disabilities without timely intervention. Spatio-temporal (4D) Computed Tomography Perfusion (CTP) imaging is crucial for diagnosing and treating AIS due t...

Data flow in clinical laboratories: could metadata and peridata bridge the gap to new AI-based applications?

Clinical chemistry and laboratory medicine
In the last decades, clinical laboratories have significantly advanced their technological capabilities, through the use of interconnected systems and advanced software. Laboratory Information Systems (LIS), introduced in the 1970s, have transformed ...

The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative review.

Diagnostic and interventional radiology (Ankara, Turkey)
With the ongoing revolution of artificial intelligence (AI) in medicine, the impact of AI in radiology is more pronounced than ever. An increasing number of technical and clinical AI-focused studies are published each day. As these tools inevitably a...

Metabolic phenotyping combined with transcriptomics metadata fortifies the diagnosis of early-stage Hepatocellular carcinoma.

Journal of advanced research
INTRODUCTION: The low sensitivity of alpha-fetoprotein (AFP) renders it unsuitable as a stand-alone marker for early hepatocellular carcinoma (eHCC) surveillance. Therefore, additional blood-based biomarkers with enhanced sensitivities are required.

Leveraging AI and patient metadata to develop a novel risk score for skin cancer detection.

Scientific reports
Melanoma of the skin is the 17th most common cancer worldwide. Early detection of suspicious skin lesions (melanoma) can increase 5-year survival rates by 20%. The 7-point checklist (7PCL) has been extensively used to suggest urgent referrals for pat...

Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes.

Computer methods and programs in biomedicine
OBJECTIVE: In diabetes mellitus patients, hyperuricemia may lead to the development of diabetic complications, including macrovascular and microvascular dysfunction. However, the level of blood uric acid in diabetic patients is obtained by sampling p...

Metadata-enhanced contrastive learning from retinal optical coherence tomography images.

Medical image analysis
Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-...

Patient Re-Identification Based on Deep Metric Learning in Trunk Computed Tomography Images Acquired from Devices from Different Vendors.

Journal of imaging informatics in medicine
During radiologic interpretation, radiologists read patient identifiers from the metadata of medical images to recognize the patient being examined. However, it is challenging for radiologists to identify "incorrect" metadata and patient identificati...

Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning.

Biomedical engineering online
BACKGROUND: The escalating impact of diabetes and its complications, including diabetic foot ulcers (DFUs), presents global challenges in quality of life, economics, and resources, affecting around half a billion people. DFU healing is hindered by hy...

Mass spectrometry-based proteomics data from thousands of HeLa control samples.

Scientific data
Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable am...