Hematology

Large Language Models' Performances regarding logical observation identifiers names and codes mapping in laboratory medicine: A comparative analysis of ChatGPT-4.0, Gemini, and Perplexity.

OBJECTIVES: This study aimed to assess the feasibility and practical utility of using large language models (LLMs) for Logical Observation Identifiers Names and Codes (LOINC) mapping to standardise healthcare data in the field of laboratory medicine. We evaluated the accuracy and applicability of three LLMs-ChatGPT-4.0 (OpenAI), Gemini 1.5 (Google DeepMind), and Perplexity AI (Perplexity.ai)-in ma...

CRViT-YOLO: A method for multi-morphological blood cell detection using convolution-restructured vision transformer.

Complete blood cell counting plays a critical role in medical diagnostics; however, conventional manual examination is time-consuming and prone to errors due to variations in data sources, image quality, cell morphology, and staining characteristics. Deep learning has emerged as a promising solution to enhance both the accuracy and efficiency of blood cell detection. In this study, we present CRVi...

Quantitative evaluation of retinal vascular morphology based on the human visual bionic mechanism for the evaluation of diabetic retinopathy onset.

BACKGROUND AND AIMS: A limited amount of diabetic retinopathy (DR) development can be explained by traditional risk factors. This study aimed to deter...

Identifying transcriptional signatures of leukocytes in tissue and blood for multicancer diagnosis by using machine learning methods.

Investigating the transcriptional signatures of immune cells in various cancer types is crucial for understanding their roles in the tumor microenviro...

Noninvasive real-time dynamic monitoring of white blood cells based on microscopic imaging and deep learning.

BACKGROUND AND OBJECTIVE: White blood cells (WBCs) are key biomarkers of immune status, but current monitoring still relies on intermittent blood samp...

Immunomodulatory Agents and Factors in Poor Hematopoietic Stem Cell Mobilization: Current Strategies and Future Prospects.

UNLABELLED: Hematopoietic stem cell (HSC) mobilization is a critical step in bone marrow transplantation for treating hematological malignancies and o...

Reliable leukemia detection via transfer-enhanced Bayesian CNNs.

Accurate and early detection of Acute Lymphoblastic Leukemia (ALL) is critical for timely intervention and improved patient outcomes. However, the dev...

ADHTransNet-based radiomics on multimodal pituitary MRI for non-invasive hormone prediction in children.

BACKGROUND AND OBJECTIVE: Growth hormone deficiency (GHD) and idiopathic central precocious puberty (ICPP) are typically diagnosed through invasive st...

Construction and validation of a screening model for minimal hepatic encephalopathy in patients with cirrhosis: A multi-center study.

BACKGROUND: Clinical practice currently lacks objective and accurate screening tools for minimal hepatic encephalopathy (MHE). Therefore, we aimed to ...

Preoperative prediction of lymph node metastasis and prognosis in gastric cancer patients: A machine learning model based on 18F-FDG metabolic parameters.

OBJECTIVE: To construct and validate a model for predicting lymph node metastasis (LNMs) of gastric cancer (GC) based on 18F-FDG PET/CT multi-paramete...

UDV-Net: A hybrid CNN and transformer vein segmentation network with vascular prior and spatial awareness.

CNNs handling multi-scale variations and Transformers modeling long-range dependencies are crucial for vascular segmentation. The fusion of these two ...

Development and validation of machine learning models to predict PTSD at multiple time points in hospitalized trauma patients.

BACKGROUND: Posttraumatic stress disorder (PTSD) is a severe trauma-related mental disorder with high global burden. Early identification remains chal...

Beneficial effect of animal skin-derived bioactive peptides on type 2 diabetes mellitus: A review on preparation, structure-activity relationship, and blood glucose regulation mechanism.

Type 2 diabetes mellitus (T2DM) is a global disease threatening human health. Regulating blood glucose homeostasis is a key strategy for the treatment...

SERS on analyte-enriched blood for rapid, culture-free sepsis recognition and causative pathogen identification with super operational neural networks.

Sepsis remains a leading cause of morbidity and mortality, yet routine diagnostics are slow, culture-dependent, and often lack the sensitivity or spec...

Pirinixic Acid Protects Against Hemorrhagic Shock by Regulating Neutrophil-Associated Key Genes.

BACKGROUND: The uncontrolled inflammatory cascade triggered by hemorrhagic shock (HS) can exacerbate tissue damage and organ dysfunction. Neutrophils,...

The Role of Artificial Intelligence in Transfusion Medicine.

Artificial intelligence (AI) is rapidly transforming the field of transfusion medicine by enhancing precision, efficiency, and safety across the trans...

Ensemble learning-based radiomics model for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma.

PURPOSE: To develop and validate a multimodal ensemble machine learning model integrating multi-sequence magnetic resonance imaging (MRI) radiomics, c...

Risk Prediction for Delayed Bleeding After Endoscopic Submucosal Dissection in Colorectal Precancerous Lesions Using Artificial Intelligence: Retrospective Clinical Trial.

BACKGROUND: Delayed bleeding is a common complication after endoscopic submucosal dissection. OBJECTIVE: Our study aimed to assess risk factors for de...

Ensemble machine learning for early mortality risk stratification in septic orthopedic trauma: an international cohort study.

BACKGROUND: Sepsis represents a life-threatening complication in severe orthopedic trauma, significantly increasing short-term mortality risk. Despite...

Integrative analysis of single-cell and bulk transcriptomes reveals metabolic heterogeneity and identifies PFKP as a therapeutic target in cervical cancer.

Cervical cancer (CC) is still a major gynecological tumor among women globally. The heterogeneity landscape and prognostic value of metabolic reprogra...