AIMC Topic: Algorithms

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Prediction of hearing recovery with deep learning algorithm in sudden sensorineural hearing loss.

Scientific reports
This study aimed to establish a deep learning-based predictive model for the prognosis of idiopathic sudden sensorineural hearing loss (SSNHL). Data from 1108 patients with SSNHL between January 2015 and May 2023 were retrospectively analyzed. Patien...

Unveiling the potential of machine learning approaches in predicting the emergence of stroke at its onset: a predicting framework.

Scientific reports
A stroke is a dangerous, life-threatening disease that mostly affects people over 65, but an unhealthy diet is also contributing to the development of strokes at younger ages. Strokes can be treated successfully if they are identified early enough, a...

From data to decision: Machine learning determination of aerobic and anaerobic thresholds in athletes.

PloS one
Lactate analysis plays an important role in sports science and training decisions for optimising performance, endurance, and overall success in sports. Two parameters are widely used for these goals: aerobic (AeT) and anaerobic (AnT) thresholds. Howe...

The simplification of the symptom Checklist-90 scale utilizing machine learning techniques.

Journal of affective disorders
The Symptom Checklist-90 (SCL-90), widely utilized for psychological assessments, faces challenges due to its extensive nature. Streamlining the SCL-90 is essential in order to enhance its practicality without compromising its broad applicability acr...

Beyond pixel: Superpixel-based MRI segmentation through traditional machine learning and graph convolutional network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Tendon segmentation is crucial for studying tendon-related pathologies like tendinopathy, tendinosis, etc. This step further enables detailed analysis of specific tendon regions using automated or semi-automated methods. Thi...

Myo-regressor Deep Informed Neural NetwOrk (Myo-DINO) for fast MR parameters mapping in neuromuscular disorders.

Computer methods and programs in biomedicine
Magnetic Resonance (MR) parameters mapping in muscle Magnetic Resonance Imaging (mMRI) is predominantly performed using pattern recognition-based algorithms, which are characterised by high computational costs and scalability issues in the context of...

Machine learning approach to investigate pregnancy and childbirth risk factors of sleep problems in early adolescence: Evidence from two cohort studies.

Computer methods and programs in biomedicine
BACKGROUND: This study aimed to predict early adolescent sleep problems using pregnancy and childbirth risk factors through machine learning algorithms, and to evaluate model performance internally and externally.

Improved detection of small pulmonary embolism on unenhanced computed tomography using an artificial intelligence-based algorithm - a single centre retrospective study.

The international journal of cardiovascular imaging
To preliminarily verify the feasibility of a deep-learning (DL) artificial intelligence (AI) model to localize pulmonary embolism (PE) on unenhanced chest-CT by comparison with pulmonary artery (PA) CT angiography (CTA). In a monocentric study, we re...

A comparison of machine learning methods for recovering noisy and missing 4D flow MRI data.

International journal for numerical methods in biomedical engineering
Experimental blood flow measurement techniques are invaluable for a better understanding of cardiovascular disease formation, progression, and treatment. One of the emerging methods is time-resolved three-dimensional phase-contrast magnetic resonance...

Artificial intelligence-based motion tracking in cancer radiotherapy: A review.

Journal of applied clinical medical physics
Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body...