AIMC Topic: Metadata

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Proposal for Using AI to Assess Clinical Data Integrity and Generate Metadata: Algorithm Development and Validation.

JMIR medical informatics
BACKGROUND: Evidence-based medicine combines scientific research, clinical expertise, and patient preferences to enhance the patient outcomes and improve health care quality. Clinical data are crucial in aligning medical decisions with evidence-based...

Gaussian random fields as an abstract representation of patient metadata for multimodal medical image segmentation.

Scientific reports
Growing rates of chronic wound occurrence, especially in patients with diabetes, has become a recent concerning trend. Chronic wounds are difficult and costly to treat, and have become a serious burden on health care systems worldwide. Innovative dee...

Instant prediction of scientific paper cited potential based on semantic and metadata features: Taking artificial intelligence field as an example.

PloS one
With the continuous increase in the number of academic researchers, the volume of scientific papers is also increasing rapidly. The challenge of identifying papers with greater potential academic impact from this large pool has received increasing at...

Automated mapping of electronic data capture fields to SDTM.

PloS one
OBJECTIVE: The goal of this work is to reduce the amount of manual work required to go from data capture to regulatory submission. It will be shown that the use of Siamese networks will allow for the generation of embeddings that can be used by tradi...

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 ...

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...