AIMC Topic: Photography

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Addressing underestimation and explanation of retinal fundus photo-based cardiovascular disease risk score: Algorithm development and validation.

Computers in biology and medicine Mar 9, 2025
OBJECTIVE: To resolve the underestimation problem and investigate the mechanism of the AI model which employed to predict cardiovascular disease (CVD) risk scores from retinal fundus photos.
United Kingdom Female Photography Aged Adult Male Deep Learning Cardiovascular Diseases Fundus Oculi Middle Aged Retina Risk Assessment Humans Algorithms
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Point-of-interest recommender model using geo-tagged photos in accordance with imperialist Fuzzy C-means clustering.

PloS one Mar 7, 2025
Although recommender systems (RSs) strive to provide recommendations based on individuals' histories and preferences, most recommendations made by these systems do not utilize location and time-based information. This paper presents a travel recommen...
Cluster Analysis Photography Travel Algorithms Humans Models, Theoretical Fuzzy Logic
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Large-scale benchmarking and boosting transfer learning for medical image analysis.

Medical image analysis Feb 21, 2025
Transfer learning, particularly fine-tuning models pretrained on photographic images to medical images, has proven indispensable for medical image analysis. There are numerous models with distinct architectures pretrained on various datasets using di...
Benchmarking Photography Humans Neural Networks, Computer Machine Learning Deep Learning Image Processing, Computer-Assisted Diagnostic Imaging
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An Evaluation of ChatGPT for Nutrient Content Estimation from Meal Photographs.

Nutrients Feb 7, 2025
: Advances in artificial intelligence now allow combined use of large language and vision models; however, there has been limited evaluation of their potential in dietary assessment. This study aimed to evaluate the accuracy of ChatGPT-4 in estimatin...
Artificial Intelligence Reproducibility of Results Nutrition Assessment Generative Artificial Intelligence Humans Photography Nutrients Nutritive Value Diet Surveys Meals
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Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning.

Scientific reports Feb 3, 2025
In the wagyu industry worldwide, high-quality marbling beef is produced by promoting intramuscular fat deposition during cattle fattening stage through dietary vitamin A control. Thus, however, cattle become susceptible to either vitamin A deficiency...
Animals Deep Learning Cattle Vitamin A Deficiency Photography Cattle Diseases Fundus Oculi Vitamin A
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[Use of Artificial Intelligence in Diabetic Retinopathy Screening: Experience in a Health Service in Santiago, Chile].

Revista medica de Chile Feb 3, 2025
UNLABELLED: Early detection of diabetic retinopathy is critical for preventing vision loss.
Sensitivity and Specificity Female Humans Mass Screening Reproducibility of Results Male Diabetic Retinopathy Chile Aged Photography Middle Aged Artificial Intelligence Adult
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Predicting Age and Visual-Motor Integration Using Origami Photographs: Deep Learning Study.

JMIR formative research Jan 10, 2025
BACKGROUND: Origami is a popular activity among preschool children and can be used by therapists as an evaluation tool to assess children's development in clinical settings. It is easy to implement, appealing to children, and time-efficient, requirin...
Humans Child, Preschool Child Age Factors Photography Female Child Development Male Deep Learning
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Exploring the potential of machine learning models to predict nasal measurements through facial landmarks.

The Journal of prosthetic dentistry Jan 3, 2025
STATEMENT OF PROBLEM: Information on predicting the measurements of the nose from selected facial landmarks to assist in maxillofacial prosthodontics is lacking.
Humans Machine Learning Anatomic Landmarks Face Photography Nose Female Adult Male Middle Aged
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Estimating Visual Acuity With Spectacle Correction From Fundus Photos Using Artificial Intelligence.

JAMA network open Jan 2, 2025
IMPORTANCE: Determining spectacle-corrected visual acuity (VA) is essential when managing many ophthalmic diseases. If artificial intelligence (AI) evaluations of macular images estimated this VA from a fundus image, AI might provide spectacle-correc...
Aged Cross-Sectional Studies Male Middle Aged Visual Acuity Tomography, Optical Coherence Fundus Oculi Artificial Intelligence Algorithms Retrospective Studies Female Humans Diabetic Retinopathy Macular Edema Photography Eyeglasses
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Deep-learning prediction of cardiovascular outcomes from routine retinal images in individuals with type 2 diabetes.

Cardiovascular diabetology Jan 2, 2025
BACKGROUND: Prior studies have demonstrated an association between retinal vascular features and cardiovascular disease (CVD), however most studies have only evaluated a few simple parameters at a time. Our aim was to determine whether a deep-learnin...
Retinal Vessels Predictive Value of Tests Heart Disease Risk Factors Image Interpretation, Computer-Assisted Photography Female Deep Learning Time Factors Reproducibility of Results Humans Risk Factors Diabetes Mellitus, Type 2 Male Diabetic Retinopathy Aged Cardiovascular Diseases Decision Support Techniques Middle Aged Prognosis Risk Assessment
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