AIMC Topic: Retrospective Studies

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Opportunities for Improving Glaucoma Clinical Trials via Deep Learning-Based Identification of Patients with Low Visual Field Variability.

Ophthalmology. Glaucoma
PURPOSE: Develop and evaluate the performance of a deep learning model (DLM) that forecasts eyes with low future visual field (VF) variability, and study the impact of using this DLM on sample size requirements for neuroprotective trials.

Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome.

Scientific reports
The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photog...

The Effect of Noise on Deep Learning for Classification of Pathological Voice.

The Laryngoscope
OBJECTIVE: This study aimed to evaluate the significance of background noise in machine learning models assessing the GRBAS scale for voice disorders.

Diagnosis of skull-base invasion by nasopharyngeal tumors on CT with a deep-learning approach.

Japanese journal of radiology
PURPOSE: To develop a convolutional neural network (CNN) model to diagnose skull-base invasion by nasopharyngeal malignancies in CT images and evaluate the model's diagnostic performance.

AI-generated CT body composition biomarkers associated with increased mortality risk in socioeconomically disadvantaged individuals.

Abdominal radiology (New York)
PURPOSE: To evaluate the relationship between socioeconomic disadvantage using national area deprivation index (ADI) and CT-based body composition measures derived from fully automated artificial intelligence (AI) tools to identify body composition m...

An evaluation of the Invisalign® Aligner Technique and consideration of the force system: a systematic review.

Systematic reviews
OBJECTIVE: Since its introduction 25 years ago, the Invisalign® system has undergone multiple digital and biomechanical evolutions and its effectiveness is often compared to traditional systems without considering the many differences which character...

Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach.

Heart rhythm
BACKGROUND: Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individuals with ≥1 major risk markers are considered for primary prevention implantable cardioverter-defibrillators. Guidelines recommend cardiac magnetic r...

Deep Learning Models for the Screening of Cognitive Impairment Using Multimodal Fundus Images.

Ophthalmology. Retina
OBJECTIVE: We aimed to develop a deep learning system capable of identifying subjects with cognitive impairment quickly and easily based on multimodal ocular images.

Development of Artificial Intelligence Image Classification Models for Determination of Umbilical Cord Vascular Anomalies.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVE: The goal of this work was to develop robust techniques for the processing and identification of SUA using artificial intelligence (AI) image classification models.

Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography.

BMC medical informatics and decision making
BACKGROUND: The epiretinal membrane (ERM) is a common retinal disorder characterized by abnormal fibrocellular tissue at the vitreomacular interface. Most patients with ERM are asymptomatic at early stages. Therefore, screening for ERM will become in...