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Can deep learning identify humans by automatically constructing a database with dental panoramic radiographs?

PloS one
The aim of this study was to propose a novel method to identify individuals by recognizing dentition change, along with human identification process using deep learning. Recent and past images of adults aged 20-49 years with more than two dental pano...

Racial and Ethnic Disparities in Predictive Accuracy of Machine Learning Algorithms Developed Using a National Database for 30-Day Complications Following Total Joint Arthroplasty.

The Journal of arthroplasty
BACKGROUND: While predictive capabilities of machine learning (ML) algorithms for hip and knee total joint arthroplasty (TJA) have been demonstrated in previous studies, their performance in racial and ethnic minority patients has not been investigat...

Enhancing cross-domain robustness in phonocardiogram signal classification using domain-invariant preprocessing and transfer learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Phonocardiogram (PCG) signal analysis is a non-invasive and cost-efficient approach for diagnosing cardiovascular diseases. Existing PCG-based approaches employ signal processing and machine learning (ML) for automatic disea...

Empirical investigation of multi-source cross-validation in clinical ECG classification.

Computers in biology and medicine
Traditionally, machine learning-based clinical prediction models have been trained and evaluated on patient data from a single source, such as a hospital. Cross-validation methods can be used to estimate the accuracy of such models on new patients or...

BioTRY: A Comprehensive Knowledge Base for Titer, Rate, and Yield of Biosynthesis.

ACS synthetic biology
Synthetic biology is rapidly evolving into a data-intensive science that increasingly relies on massive data sets; one of its applications is the evaluation of the economic viability of fermentation processes. However, the key economic indicators, na...

Multi-scale dual-channel feature embedding decoder for biomedical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Attaining global context along with local dependencies is of paramount importance for achieving highly accurate segmentation of objects from image frames and is challenging while developing deep learning-based biomedical ima...

Publicly Available Dental Image Datasets for Artificial Intelligence.

Journal of dental research
The development of artificial intelligence (AI) in dentistry requires large and well-annotated datasets. However, the availability of public dental imaging datasets remains unclear. This study aimed to provide a comprehensive overview of all publicly...

PLRTE: Progressive learning for biomedical relation triplet extraction using large language models.

Journal of biomedical informatics
Document-level relation triplet extraction is crucial in biomedical text mining, aiding in drug discovery and the construction of biomedical knowledge graphs. Current language models face challenges in generalizing to unseen datasets and relation typ...

USCT-UNet: Rethinking the Semantic Gap in U-Net Network From U-Shaped Skip Connections With Multichannel Fusion Transformer.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Medical image segmentation is a crucial component of computer-aided clinical diagnosis, with state-of-the-art models often being variants of U-Net. Despite their success, these models' skip connections introduce an unnecessary semantic gap between th...

Integrating color histogram analysis and convolutional neural networks for skin lesion classification.

Computers in biology and medicine
The color of skin lesions is a crucial diagnostic feature for identifying malignant melanoma and other skin diseases. Typical colors associated with melanocytic lesions include tan, brown, black, red, white, and blue-gray. This study introduces a nov...