AIMC Topic: Photography
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Enhancing deep learning pre-trained networks on diabetic retinopathy fundus photographs with SLIC-G.
Medical & biological engineering & computing
Apr 23, 2024
Diabetic retinopathy disease contains lesions (e.g., exudates, hemorrhages, and microaneurysms) that are minute to the naked eye. Determining the lesions at pixel level poses a challenge as each pixel does not reflect any semantic entities. Furthermo...
Prediction of cardiovascular risk factors from retinal fundus photographs: Validation of a deep learning algorithm in a prospective non-interventional study in Kenya.
Diabetes, obesity & metabolism
Apr 15, 2024
AIM: Hypertension and diabetes mellitus (DM) are major causes of morbidity and mortality, with growing burdens in low-income countries where they are underdiagnosed and undertreated. Advances in machine learning may provide opportunities to enhance d...
Applying Object Detection and Large Language Model to Establish a Smart Telemedicine Diagnosis System with Chatbot: A Case Study of Pressure Injuries Diagnosis System.
Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Mar 21, 2024
The scarcity of medical resources and personnel has worsened due to COVID-19. Telemedicine faces challenges in assessing wounds without physical examination. Evaluating pressure injuries is time consuming, energy intensive, and inconsistent. Most of...
Head to head comparison of diagnostic performance of three non-mydriatic cameras for diabetic retinopathy screening with artificial intelligence.
Eye (London, England)
Mar 11, 2024
BACKGROUND: Diabetic Retinopathy (DR) is a leading cause of blindness worldwide, affecting people with diabetes. The timely diagnosis and treatment of DR are essential in preventing vision loss. Non-mydriatic fundus cameras and artificial intelligenc...
Development of a deep learning model to distinguish the cause of optic disc atrophy using retinal fundus photography.
Scientific reports
Mar 1, 2024
The differential diagnosis for optic atrophy can be challenging and requires expensive, time-consuming ancillary testing to determine the cause. While Leber's hereditary optic neuropathy (LHON) and optic neuritis (ON) are both clinically significant ...
Patient and dermatologists' perspectives on augmented intelligence for melanoma screening: A prospective study.
Journal of the European Academy of Dermatology and Venereology : JEADV
Feb 27, 2024
BACKGROUND: Artificial intelligence (AI) shows promising potential to enhance human decision-making as synergistic augmented intelligence (AuI), but requires critical evaluation for skin cancer screening in a real-world setting.
Artificial Intelligence Versus Human Focus Group Rating of Facial Attractiveness.
Facial plastic surgery & aesthetic medicine
Feb 19, 2024
Many open-access artificial intelligence (AI)-based websites that rate facial attractiveness are available, but none have been compared with human focus group outcomes. To compare human and AI-based websites scoring of facial attractiveness of adul...
DeepAlienorNet: A deep learning model to extract clinical features from colour fundus photography in age-related macular degeneration.
Acta ophthalmologica
Feb 12, 2024
OBJECTIVE: This study aimed to develop a deep learning (DL) model, named 'DeepAlienorNet', to automatically extract clinical signs of age-related macular degeneration (AMD) from colour fundus photography (CFP).
AI-based diagnosis and phenotype - Genotype correlations in syndromic craniosynostoses.
Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Feb 5, 2024
Apert (AS), Crouzon (CS), Muenke (MS), Pfeiffer (PS), and Saethre Chotzen (SCS) are among the most frequently diagnosed syndromic craniosynostoses. The aims of this study were (1) to train an innovative model using artificial intelligence (AI)-based ...
The performance of an artificial intelligence-based computer vision mobile application for the image diagnosis of genital dermatoses: a prospective cross-sectional study.
International journal of dermatology
Feb 5, 2024
BACKGROUND: There is a huge demand-supply gap between the incidence of genital dermatoses (including sexually transmitted infections and non-venereal genital dermatoses) and physicians trained to manage them.
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