This study offers a comprehensive analysis of novel information for linear diophantine multi-fuzzy sets and illustrates its applications in practical scenarios. We introduce innovative similarity metrics tailored for linear diophantine multi-fuzzy se...
Synthetic data is becoming a valuable tool for computational pathologists, aiding in tasks like data augmentation and addressing data scarcity and privacy. However, its use necessitates careful planning and evaluation to prevent the creation of clini...
Machine learning has gained attention in the medical field. Continuous efforts are being made to develop robust models for early prognosis purposes. The brain is the most pivotal organ in the human body. A brain stroke is generally caused by a blocka...
Non-specific shoulder pain is a common musculoskeletal condition, especially among college students, and it can have a negative impact on the patient's life. Therapists have used scapular stabilization exercises (SSE) to enhance scapular control and ...
This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients in a hospital setting, focusing specifically on predicting the complications leading to Intensive Care Unit (ICU) admission or mortality, which are min...
OBJECTIVES: The C-shaped mandibular second molars (MSMs) may pose an endodontic challenge. The aim of this study was to develop a convolutional neural network (CNN)-based deep learning system for the diagnosis of C-shaped MSMs on panoramic radiograph...
BACKGROUND: Kawasaki disease (KD) is an acute pediatric vasculitis that can lead to coronary artery aneurysms and severe cardiovascular complications, often presenting with obvious fever in the early stages. In current clinical practice, distinguishi...
BACKGROUND: Although digital technology represents a growing field aiming to revolutionize early Alzheimer disease risk prediction and monitoring, the perspectives of older adults on an integrated digital brain health platform have not been investiga...
BACKGROUND: The presence of a blurred area, depending on its localization, in a mammogram can limit diagnostic accuracy. The goal of this study was to develop a model for automatic detection of blur in diagnostically relevant locations in digital mam...
PURPOSE OF REVIEW: In modern healthcare, the integration of artificial intelligence (AI) has revolutionized clinical practices, particularly in data management and patient visit summary creation. Manual creation of patient summary is repetitive, time...
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