AIMC Topic: Deep Learning

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Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.

Cerebral cortex (New York, N.Y. : 1991)
Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and...

Application of deep learning to pressure injury staging.

Journal of wound care
OBJECTIVE: Accurate assessment of pressure injuries (PIs) is necessary for a good outcome. Junior and non-specialist nurses have less experience with PIs and lack clinical practice, and so have difficulty staging them accurately. In this work, a deep...

UTILIZATION OF ARTIFICIAL INTELLIGENCE FOR PREDICTIVE MODELING IN DENTAL IMPLANTOLOGY.

Georgian medical news
Artificial intelligence (AI) is making waves in dentistry, with applications in predicting dental implant success. AI models analyze patient data (X-rays, medical history) to identify factors influencing implant viability. The aim is to identify exis...

Role of artificial intelligence in multiple sclerosis management.

European review for medical and pharmacological sciences
From a clinical viewpoint, there are enormous obstacles to early detection and diagnosis as well as treatment interventions for multiple sclerosis (MS). With the growing application of methods based on artificial intelligence (AI) to medical problems...

Prediction of prognosis using artificial intelligence-based histopathological image analysis in patients with soft tissue sarcomas.

Cancer medicine
BACKGROUND: Prompt histopathological diagnosis with accuracy is required for soft tissue sarcomas (STSs) which are still challenging. In addition, the advances in artificial intelligence (AI) along with the development of pathology slides digitizatio...

PallorMetrics: Software for Automatically Quantifying Optic Disc Pallor in Fundus Photographs, and Associations With Peripapillary RNFL Thickness.

Translational vision science & technology
PURPOSE: We sough to develop an automatic method of quantifying optic disc pallor in fundus photographs and determine associations with peripapillary retinal nerve fiber layer (pRNFL) thickness.

Prediction of Visual Outcome After Rhegmatogenous Retinal Detachment Surgery Using Artificial Intelligence Techniques.

Translational vision science & technology
PURPOSE: This study aimed to develop artificial intelligence models for predicting postoperative functional outcomes in patients with rhegmatogenous retinal detachment (RRD).

Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT.

Radiology
Background Accurate characterization of suspicious small renal masses is crucial for optimized management. Deep learning (DL) algorithms may assist with this effort. Purpose To develop and validate a DL algorithm for identifying benign small renal ma...