AIMC Topic: Metadata

Clear Filters Showing 1 to 10 of 84 articles

A Framework for a Standard-Enabled FAIR Data Management Workflow for Synthetic Biology.

ACS synthetic biology
Synthetic biology laboratories generate diverse forms of data and metadata throughout a project's life cycle, such as sequences, models, protocols, images, and time-series measurements. Unfortunately, these assets are scattered across spreadsheets, p...

Identifying Biomedical Entities for Datasets in Scientific Articles: 4-Step Cache-Augmented Generation Approach Using GPT-4o and PubTator 3.0.

JMIR formative research
BACKGROUND: The accurate extraction of biomedical entities in scientific articles is essential for effective metadata annotation of research datasets, ensuring data findability, accessibility, interoperability, and reusability in collaborative resear...

A methodology for developing dermatological datasets: lessons from retrospective data collection for AI-based applications.

BMC medical research methodology
PURPOSE: The integration of artificial intelligence into dermatological research has underscored the need for robust and well-structured dermatological datasets. However, these datasets vary widely in their development processes, and there is current...

A multimodal multitask deep learning model for predicting stroke lesion and functional outcomes using 4D CTP imaging and clinical metadata.

Scientific reports
Acute ischemic stroke is a major global health challenge, leading to long-term disability or death without timely intervention. Among neuroimaging modalities, spatio-temporal (4D) computed tomography perfusion (CTP) is widely used to assess cerebral ...

A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers.

Scientific data
Machine learning classification algorithms have emerged as promising tools to support the early detection of skin cancers. Existing algorithms typically assess malignancy of skin lesions based on a single skin image. This is in contrast with how clin...

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...

Skull CT metadata for automatic bone age assessment by using three-dimensional deep learning framework.

International journal of legal medicine
Bone age assessment (BAA) means challenging tasks in forensic science especially in some extreme situations like only skulls found. This study aimed to develop an accurate three-dimensional deep learning (DL) framework at skull CT metadata for BAA an...

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...