AIMC Topic: Retrospective Studies

Clear Filters Showing 8351 to 8360 of 9989 articles

Deep Learning-Based Multimodal Feature Interaction-Guided Fusion: Enhancing the Evaluation of EGFR in Advanced Lung Adenocarcinoma.

Academic radiology
RATIONALE AND OBJECTIVES: The aim of this study is to develop a deep learning-based multimodal feature interaction-guided fusion (DL-MFIF) framework that integrates macroscopic information from computed tomography (CT) images with microscopic informa...

An X-ray bone age assessment method for hands and wrists of adolescents in Western China based on feature fusion deep learning models.

International journal of legal medicine
The epiphyses of the hand and wrist serve as crucial indicators for assessing skeletal maturity in adolescents. This study aimed to develop a deep learning (DL) model for bone age (BA) assessment using hand and wrist X-ray images, addressing the chal...

Utilizing Artificial Intelligence: Machine Learning Algorithms to Develop a Preoperative Endometriosis Prediction Model.

Journal of minimally invasive gynecology
OBJECTIVE: To evaluate the predictive value of clinical features in the diagnosis of endometriosis by utilizing machine learning algorithms (MLAs), aiming to develop an accurate, explainable prediction model.

Improving Deep Learning-Based Grading of Partial-thickness Supraspinatus Tendon Tears with Guided Diffusion Augmentation.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and validate a deep learning system with guided diffusion-based data augmentation for grading partial-thickness supraspinatus tendon (SST) tears and to compare its performance with experienced radiologists, includ...

Federated Learning for Renal Tumor Segmentation and Classification on Multi-Center MRI Dataset.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning (DL) models for accurate renal tumor characterization may benefit from multi-center datasets for improved generalizability; however, data-sharing constraints necessitate privacy-preserving solutions like federated learning (...

Decision support system based on ensemble models in distinguishing epilepsy types.

Epilepsy & behavior : E&B
This study aimed to classify patients' focal (frontal, temporal, parietal, occipital), multifocal, and generalized epileptiform activities based on EEG findings using artificial intelligence models. The study included 575 patients followed in the Neu...

The Data-Augmented, Technology-Assisted Medical Decision Making (DATA-MD) Curriculum: A Machine Learning and Artificial Intelligence Curriculum for Clinical Trainees.

Academic medicine : journal of the Association of American Medical Colleges
PROBLEM: Despite the rapidly expanding role of artificial intelligence (AI) and machine learning (ML) in health care, a significant knowledge gap remains among clinicians in their ability to evaluate and use AI and ML tools.

Evaluating efficacy of 0.125% atropine using a myopia progression machine learning model.

Japanese journal of ophthalmology
PURPOSE: To investigate the usefulness of a machine learning (ML) model that can predict the natural course of childhood myopia in evaluation of the inhibitory effects of 0.125% atropine on the progression of childhood myopia.