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Deep Learning Model for the Differential Diagnosis of Nasal Polyps and Inverted Papilloma by CT Images: A Multicenter Study.

Academic radiology
RATIONALE AND OBJECTIVES: Nasal polyps (NP) and inverted papilloma (IP) are benign tumors within the nasal cavity, each necessitating distinct treatment approaches. Herein, we investigate the utility of a deep learning (DL) model for distinguishing b...

Predicting the likelihood of readmission in patients with ischemic stroke: An explainable machine learning approach using common data model data.

International journal of medical informatics
BACKGROUND: Ischemic stroke affects 15 million people worldwide, causing five million deaths annually. Despite declining mortality rates, stroke incidence and readmission risks remain high, highlighting the need for preventing readmission to improve ...

Prognostic Impact of Tumor Cell Nuclear Size Assessed by Artificial Intelligence in Esophageal Squamous Cell Carcinoma.

Laboratory investigation; a journal of technical methods and pathology
Tumor cell nuclear size (NS) indicates malignant potential in breast cancer; however, its clinical significance in esophageal squamous cell carcinoma (ESCC) is unknown. Artificial intelligence (AI) can quantitatively evaluate histopathological findin...

Artificial intelligence virtual assistants in primary eye care practice.

Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)
PURPOSE: To propose a novel artificial intelligence (AI)-based virtual assistant trained on tabular clinical data that can provide decision-making support in primary eye care practice and optometry education programmes.

Heterogeneous blood pressure treatment effects on cognitive decline in type 2 diabetes: A machine learning analysis of a randomized clinical trial.

Diabetes, obesity & metabolism
AIM: We aimed to identify the characteristics of patients with diabetes who can derive cognitive benefits from intensive blood pressure (BP) treatment using machine learning methods.

Comparison of population pharmacokinetic modeling and machine learning approaches for predicting voriconazole trough concentrations in critically ill patients.

International journal of antimicrobial agents
BACKGROUND: Despite the widespread use of voriconazole in antifungal treatment, its high pharmacokinetic and pharmacodynamic variability may lead to suboptimal efficacy, especially in intensive care unit (ICU) patients. Machine learning (ML), an arti...

A Self-supervised Deep Learning Model for Diagonal Sulcus Detection with Limited Labeled Data.

Neuroinformatics
Sulci are a fundamental part of brain morphology, closely linked to brain function, cognition, and behavior. Tertiary sulci, characterized as the shallowest and smallest subtype, pose a challenging task for detection. The diagonal sulcus (ds), locate...

Immunohistochemistry-Free Enhanced Histopathology of the Rat Spleen Using Deep Learning.

Toxicologic pathology
Enhanced histopathology of the immune system uses a precise, compartment-specific, and semi-quantitative evaluation of lymphoid organs in toxicology studies. The assessment of lymphocyte populations in tissues is subject to sampling variability and l...

The Potential of SHAP and Machine Learning for Personalized Explanations of Influencing Factors in Myopic Treatment for Children.

Medicina (Kaunas, Lithuania)
The rising prevalence of myopia is a significant global health concern. Atropine eye drops are commonly used to slow myopia progression in children, but their long-term use raises concern about intraocular pressure (IOP). This study uses SHapley Add...