AIMC Topic: Humans

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Model-Driven Deep Learning Method for Pancreatic Cancer Segmentation Based on Spiral-Transformation.

IEEE transactions on medical imaging
Pancreatic cancer is a lethal malignant tumor with one of the worst prognoses. Accurate segmentation of pancreatic cancer is vital in clinical diagnosis and treatment. Due to the unclear boundary and small size of cancers, it is challenging to both m...

Cohort profile: Japanese human milk study, a prospective birth cohort: baseline data for lactating women, infants and human milk macronutrients.

BMJ open
PURPOSE: The Japanese Human Milk Study, a longitudinal prospective cohort study, was set up to clarify how maternal health, nutritional status, lifestyle and sociodemographic and economic factors affect breastfeeding practices and human milk composit...

Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach.

Sensors (Basel, Switzerland)
The automatic segmentation of the pancreatic cyst lesion (PCL) is essential for the automated diagnosis of pancreatic cyst lesions on endoscopic ultrasonography (EUS) images. In this study, we proposed a deep-learning approach for PCL segmentation on...

Machine learning approaches for the prediction of postoperative complication risk in liver resection patients.

BMC medical informatics and decision making
BACKGROUND: For liver cancer patients, the occurrence of postoperative complications increases the difficulty of perioperative nursing, prolongs the hospitalization time of patients, and leads to large increases in hospitalization costs. The ability ...

Document-level medical relation extraction via edge-oriented graph neural network based on document structure and external knowledge.

BMC medical informatics and decision making
OBJECTIVE: Relation extraction (RE) is a fundamental task of natural language processing, which always draws plenty of attention from researchers, especially RE at the document-level. We aim to explore an effective novel method for document-level med...

Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies.

PLoS computational biology
Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static...

Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives.

Computational intelligence and neuroscience
Chronic kidney disease (CKD) is a global health issue with a high rate of morbidity and mortality and a high rate of disease progression. Because there are no visible symptoms in the early stages of CKD, patients frequently go unnoticed. The early de...

RegVar: Tissue-specific Prioritization of Non-coding Regulatory Variants.

Genomics, proteomics & bioinformatics
Non-coding genomic variants constitute the majority of trait-associated genome variations; however, the identification of functional non-coding variants is still a challenge in human genetics, and a method for systematically assessing the impact of r...

A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory.

Clinical chemistry and laboratory medicine
OBJECTIVES: Delta check (DC) is widely used for detecting sample mix-up. Owing to the inadequate error detection and high false-positive rate, the implementation of DC in real-world settings is labor-intensive and rarely capable of absolute detection...

Big Data/AI in Neurocritical Care: Maybe/Summary.

Neurocritical care
Big data (BD) and artificial intelligence (AI) have increasingly been used in neurocritical care. "BD" can be operationally defined as extremely large datasets that are so large and complex that they cannot be analyzed by using traditional statistica...