AIMC Topic: Male

Clear Filters Showing 3071 to 3080 of 27433 articles

Machine Learning-Assisted Prediction of Persistent Incomplete Occlusion in Intracranial Aneurysms From Angiographic Parametric Imaging-Derived Features.

Academic radiology
RATIONALE AND OBJECTIVES: To develop machine-learning (ML) models incorporating angiographic parametric imaging (API)-derived parameters in predicting persistent incomplete occlusion of intracranial aneurysms (IAs) after flow diverter (FD) treatment.

Decision support using machine learning for predicting adequate bladder filling in prostate radiotherapy: a feasibility study.

Radiological physics and technology
This study aimed to develop a model for predicting the bladder volume ratio between daily CBCT and CT to determine adequate bladder filling in patients undergoing treatment for prostate cancer with external beam radiation therapy (EBRT). The model wa...

Evaluating the impact of human expertise in human-centered AI: A case study on finger-tapping video analysis for dementia detection.

Computers in biology and medicine
PURPOSE: Human-centered artificial intelligence (AI) plays a crucial role in medical research. This paper evaluates the impact of human expertise in AI systems, using dementia prediction as a case study. Specifically, plasma phospho-tau181 (ptau181) ...

Artificial intelligence for early gastric cancer boundary recognition in NBI and nF-NBI endoscopic images.

Scandinavian journal of gastroenterology
OBJECTIVES: Precise delineation of early gastric cancer (EGC) margins is essential for complete resection during endoscopic submucosal dissection. This study aimed to develop deep learning-based models for EGC boundary detection in narrow-band imagin...

A Machine Learning Trauma Triage Model for Critical Care Transport.

JAMA network open
IMPORTANCE: Under austere prehospital conditions, rapid classification of injured patients for intervention or transport is essential for providing lifesaving care. Discerning which patients need care most urgently further allows for optimal allocati...

Efficiency and Quality of Generative AI-Assisted Radiograph Reporting.

JAMA network open
IMPORTANCE: Diagnostic imaging interpretation involves distilling multimodal clinical information into text form, a task well-suited to augmentation by generative artificial intelligence (AI). However, to our knowledge, impacts of AI-based draft radi...

Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models.

JAMA network open
IMPORTANCE: Clinical artificial intelligence (AI) systems are susceptible to performance degradation due to data shifts, which can lead to erroneous predictions and potential patient harm. Proactively detecting and mitigating these shifts is crucial ...

Accuracy of Artificial Intelligence for Gatekeeping in Referrals to Specialized Care.

JAMA network open
IMPORTANCE: Integrating artificial intelligence (AI) technologies into gatekeeping holds significant potential, as it efficiently handles repetitive tasks and can process large amounts of information quickly.

Unsupervised learning-based quantitative analysis of CT intratumoral subregions predicts risk stratification of bladder cancer patients.

BMC medicine
BACKGROUND: Preoperative diagnosis of muscle invasion and American Joint Committee on Cancer (AJCC) stage plays a crucial role in guiding treatment strategies for bladder cancer (BCa). Utilizing quantitative analysis of tumor subregions via CT imagin...

Integrative machine learning identifies robust inflammation-related diagnostic biomarkers and stratifies immune-heterogeneous subtypes in Kawasaki disease.

Pediatric rheumatology online journal
BACKGROUND: Kawasaki disease (KD), a pediatric systemic vasculitis, lacks reliable diagnostic biomarkers and exhibits immune heterogeneity, complicating clinical management. Current therapies face challenges in targeting specific immune pathways and ...