AIMC Topic: Adult

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Machine learning characterization of a rare neurologic disease via electronic health records: a proof-of-principle study on stiff person syndrome.

BMC neurology
BACKGROUND: Despite the frequent diagnostic delays of rare neurologic diseases (RND), it remains difficult to study RNDs and their comorbidities due to their rarity and hence the statistical underpowering. Affecting one to two in a million annually, ...

Proteomics and machine learning in the prediction and explanation of low pectoralis muscle area.

Scientific reports
Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an individual's risk for developing low muscle mass using proteomics and machine learning. We identifie...

Comparison of three artificial intelligence algorithms for automatic cobb angle measurement using teaching data specific to three disease groups.

Scientific reports
Spinal deformities, including adolescent idiopathic scoliosis (AIS) and adult spinal deformity (ASD), affect many patients. The measurement of the Cobb angle on coronal radiographs is essential for their diagnosis and treatment planning. To enhance t...

Combining robotics and functional electrical stimulation for assist-as-needed support of leg movements in stroke patients: A feasibility study.

Medical engineering & physics
PURPOSE: Rehabilitation technology can be used to provide intensive training in the early phases after stroke. The current study aims to assess the feasibility of combining robotics and functional electrical stimulation (FES), with an assist-as-neede...

Estimating three-dimensional foot bone kinematics from skin markers using a deep learning neural network model.

Journal of biomechanics
The human foot is a complex structure comprising 26 bones, whose coordinated movements facilitate proper deformation of the foot, ensuring stable and efficient locomotion. Despite their critical role, the kinematics of foot bones during movement rema...

Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT.

Journal of computer assisted tomography
OBJECTIVE: The purpose of this study is to explore the impact of deep learning image reconstruction (DLIR) algorithm on the quantification of radiomic features in ultra-low-dose computed tomography (ULD-CT) compared with adaptive statistical iterativ...

Development and validation of a nomogram to predict impacted ureteral stones via machine learning.

Minerva urology and nephrology
BACKGROUND: To develop and evaluate a nomogram for predicting impacted ureteral stones using some simple and easily available clinical features.

A Deep Learning Approach for Accurate Discrimination Between Optic Disc Drusen and Papilledema on Fundus Photographs.

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
BACKGROUND: Optic disc drusen (ODD) represent an important differential diagnosis of papilledema caused by intracranial hypertension, but their distinction may be difficult in clinical practice. The aim of this study was to train, validate, and test ...

Automatic diagnosis for adenomyosis in ultrasound images by deep neural networks.

European journal of obstetrics, gynecology, and reproductive biology
OBJECTIVE: To present a new noninvasive technique for automatic diagnosis of adenomyosis, using a novel end-to-end unified network framework based on transformer networks.

Deep-learning based analysis of in-vivo confocal microscopy images of the subbasal corneal nerve plexus' inferior whorl in patients with neuropathic corneal pain and dry eye disease.

The ocular surface
PURPOSE: To evaluate and compare subbasal corneal nerve parameters of the inferior whorl in patients with dry eye disease (DED), neuropathic corneal pain (NCP), and controls using a novel deep-learning-based algorithm to analyze in-vivo confocal micr...