[HTML][HTML] Multi-modality approaches for medical support systems: A systematic review of the last decade
Healthcare traditionally relies on single-modality approaches, which limit the information
available for medical decisions. However, advancements in technology and the availability …
available for medical decisions. However, advancements in technology and the availability …
Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: A review
Background In recent years, ophthalmology has emerged as a new frontier in medical
artificial intelligence (AI) with multi-modal AI in ophthalmology garnering significant attention …
artificial intelligence (AI) with multi-modal AI in ophthalmology garnering significant attention …
Hierarchical pretraining on multimodal electronic health records
Pretraining has proven to be a powerful technique in natural language processing (NLP),
exhibiting remarkable success in various NLP downstream tasks. However, in the medical …
exhibiting remarkable success in various NLP downstream tasks. However, in the medical …
[HTML][HTML] Latest developments of generative artificial intelligence and applications in ophthalmology
The emergence of generative artificial intelligence (AI) has revolutionized various fields. In
ophthalmology, generative AI has the potential to enhance efficiency, accuracy …
ophthalmology, generative AI has the potential to enhance efficiency, accuracy …
Eye diseases diagnosis using deep learning and multimodal medical eye imaging
The present study carries out an empirical evaluation and comparison of the seven most
recent deep Convolutional Neural Network (CNN) techniques (VGG19, DenseNet121 …
recent deep Convolutional Neural Network (CNN) techniques (VGG19, DenseNet121 …
Semantic-oriented Visual Prompt Learning for Diabetic Retinopathy Grading on Fundus Images
Diabetic retinopathy (DR) is a serious ocular condition that requires effective monitoring and
treatment by ophthalmologists. However, constructing a reliable DR grading model remains …
treatment by ophthalmologists. However, constructing a reliable DR grading model remains …
Updating methods for AI-based clinical prediction models: a sco** review
Objective To give an overview of methods for updating AI-based clinical prediction models
based on new data. Study Design and Setting We comprehensively searched Scopus and …
based on new data. Study Design and Setting We comprehensively searched Scopus and …
Grading the severity of diabetic retinopathy using an ensemble of self-supervised pre-trained convolutional neural networks: ESSP-CNNs
Diabetic retinopathy (DR) is a common eye disorder that can lead to vision problems and
blindness, necessitating accurate grading for effective treatment. While various artificial …
blindness, necessitating accurate grading for effective treatment. While various artificial …
Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning
Fundus diseases are prevalent causes of visual impairment and blindness worldwide,
particularly in regions with limited access to ophthalmologists for timely diagnosis. Current …
particularly in regions with limited access to ophthalmologists for timely diagnosis. Current …
Retinal image segmentation for diabetic retinopathy detection using U-Net architecture
Diabetic retinopathy is one of the most serious eye diseases and can lead to permanent
blindness if not diagnosed early. The main cause of this is diabetes. Not every diabetic will …
blindness if not diagnosed early. The main cause of this is diabetes. Not every diabetic will …