AIMC Topic: ROC Curve
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The KMeansGraphMIL Model: A Weakly Supervised Multiple Instance Learning Model for Predicting Colorectal Cancer Tumor Mutational Burden.
The American journal of pathology
Jan 10, 2025
Colorectal cancer (CRC) is one of the top three most lethal malignancies worldwide, posing a significant threat to human health. Recently proposed immunotherapy checkpoint blockade treatments have proven effective for CRC, but their use depends on me...
Multimodal deep-learning model using pre-treatment endoscopic images and clinical information to predict efficacy of neoadjuvant chemotherapy in esophageal squamous cell carcinoma.
Esophagus : official journal of the Japan Esophageal Society
Jan 10, 2025
BACKGROUND: Neoadjuvant chemotherapy is standard for advanced esophageal squamous cell carcinoma, though often ineffective. Therefore, predicting the response to chemotherapy before treatment is desirable. However, there is currently no established m...
Utilizing artificial intelligence and cellular population data for timely identification of bacteremia in hospitalized patients.
International journal of medical informatics
Jan 10, 2025
BACKGROUND: Bacteremia is a critical condition with high mortality that requires prompt detection to prevent progression to life-threatening sepsis. Traditional diagnostic approaches, such as blood cultures, are time-consuming. This limitation has en...
Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.
Scientific reports
Jan 10, 2025
The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU). We collected data from two centers as the develo...
Traditional versus modern approaches to screening mammography: a comparison of computer-assisted detection for synthetic 2D mammography versus an artificial intelligence algorithm for digital breast tomosynthesis.
Breast cancer research and treatment
Jan 9, 2025
PURPOSE: Traditional computer-assisted detection (CADe) algorithms were developed for 2D mammography, while modern artificial intelligence (AI) algorithms can be applied to 2D mammography and/or digital breast tomosynthesis (DBT). The objective is to...
Deep learning model for automatic detection of different types of microaneurysms in diabetic retinopathy.
Eye (London, England)
Jan 9, 2025
PURPOSE: This study aims to develop a deep-learning-based software capable of detecting and differentiating microaneurysms (MAs) as hyporeflective or hyperreflective on structural optical coherence tomography (OCT) images in patients with non-prolife...
Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features.
BMC cancer
Jan 9, 2025
OBJECTIVES: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).
The impact of preschool children's physical fitness evaluation under self organizing maps neural network.
Scientific reports
Jan 9, 2025
To improve the scientific accuracy and precision of children's physical fitness evaluations, this study proposes a model that combines self-organizing maps (SOM) neural networks with cluster analysis. Existing evaluation methods often rely on traditi...
Using XBGoost, an interpretable machine learning model, for diagnosing prostate cancer in patients with PSA < 20 ng/ml based on the PSAMR indicator.
Scientific reports
Jan 9, 2025
To create a diagnostic tool before biopsy for patients with prostate-specific antigen (PSA) levels < 20 ng/ml to minimize prostate biopsy-related discomfort and risks. Data from 655 patients who underwent transperineal prostate biopsy at the First Af...
Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma.
Scientific reports
Jan 9, 2025
This study aimed to develop and validate machine learning (ML) models to predict the occurrence of delayed hyponatremia after transsphenoidal surgery for pituitary adenoma. We retrospectively collected clinical data on patients with pituitary adenoma...
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