AIMC Topic: Photography

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Development of an artificial intelligence system to classify pathology and clinical features on retinal fundus images.

Clinical & experimental ophthalmology
IMPORTANCE: Artificial intelligence (AI) algorithms are under development for use in diabetic retinopathy photo screening pathways. To be clinically acceptable, such systems must also be able to classify other fundus abnormalities and clinical featur...

Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy.

Eye (London, England)
Remarkable advances in biomedical research have led to the generation of large amounts of data. Using artificial intelligence, it has become possible to extract meaningful information from large volumes of data, in a shorter frame of time, with very ...

The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment.

Medical & biological engineering & computing
Recently, researchers have built new deep learning (DL) models using a single image modality to diagnose age-related macular degeneration (AMD). Retinal fundus and optical coherence tomography (OCT) images in clinical settings are the most important ...

Development of a deep residual learning algorithm to screen for glaucoma from fundus photography.

Scientific reports
The Purpose of the study was to develop a deep residual learning algorithm to screen for glaucoma from fundus photography and measure its diagnostic performance compared to Residents in Ophthalmology. A training dataset consisted of 1,364 color fundu...

Multimodal skin lesion classification using deep learning.

Experimental dermatology
While convolutional neural networks (CNNs) have successfully been applied for skin lesion classification, previous studies have generally considered only a single clinical/macroscopic image and output a binary decision. In this work, we have presente...

Deep learning with cinematic rendering: fine-tuning deep neural networks using photorealistic medical images.

Physics in medicine and biology
Deep learning has emerged as a powerful artificial intelligence tool to interpret medical images for a growing variety of applications. However, the paucity of medical imaging data with high-quality annotations that is necessary for training such met...

Personalized Exposure Control Using Adaptive Metering and Reinforcement Learning.

IEEE transactions on visualization and computer graphics
We propose a reinforcement learning approach for real-time exposure control of a mobile camera that is personalizable. Our approach is based on Markov Decision Process (MDP). In the camera viewfinder or live preview mode, given the current frame, our...

Deep Learning-Based Enhanced Presentation Attack Detection for Iris Recognition by Combining Features from Local and Global Regions Based on NIR Camera Sensor.

Sensors (Basel, Switzerland)
Iris recognition systems have been used in high-security-level applications because of their high recognition rate and the distinctiveness of iris patterns. However, as reported by recent studies, an iris recognition system can be fooled by the use o...

Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age.

International journal of oral and maxillofacial surgery
This observational study aimed to use artificial intelligence to describe the impact of orthognathic treatment on facial attractiveness and age appearance. Pre- and post-treatment photographs (n=2164) of 146 consecutive orthognathic patients were col...