AIMC Topic: Photography

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Machine learning derived retinal pigment score from ophthalmic imaging shows ethnicity is not biology.

Nature communications
Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as a surrogate marker for biological variability. We derived a continuous, measured metric, the retinal pigment score (RPS),...

Evaluating the efficacy of AI systems in diabetic retinopathy detection: A comparative analysis of Mona DR and IDx-DR.

Acta ophthalmologica
PURPOSE: To compare two artificial intelligence (AI)-based Automated Diabetic Retinopathy Image Assessment (ARIA) softwares in terms of concordance with specialist human graders and referable diabetic retinopathy (DR) diagnostic capacity.

Prediction of Visual Acuity After Cataract Surgery by Deep Learning Methods Using Clinical Information and Color Fundus Photography.

Current eye research
PURPOSE: To examine the performance of deep-learning models that predicts the visual acuity after cataract surgery using preoperative clinical information and color fundus photography (CFP).

Who's that lady? - Applying open source intelligence in a history context.

Endeavour
During a network analysis of the Dutch astronomer and psychologist Rebekka Aleida Biegel (1886-1943), we stumbled upon an often investigated group photo that most likely shows two of her close friends and a third woman posing with Albert Einstein amo...

Evaluating the reproducibility of a deep learning algorithm for the prediction of retinal age.

GeroScience
Recently, a deep learning algorithm (DLA) has been developed to predict the chronological age from retinal images. The Retinal Age Gap (RAG), a deviation between predicted age from retinal images (Retinal Age, RA) and chronological age, correlates wi...

Artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs.

Scientific reports
Rapid technological advances and growing participation from amateur naturalists have made countless images of insects in their natural habitats available on global web portals. Despite advances in automated species identification, traits like develop...

Developing an AI-based application for caries index detection on intraoral photographs.

Scientific reports
This study evaluates the effectiveness of an Artificial Intelligence (AI)-based smartphone application designed for decay detection on intraoral photographs, comparing its performance to that of junior dentists. Conducted at The Aga Khan University H...

Personalized melanoma grading system: a presentation of a patient with four melanomas detected over two decades with evolving whole-body imaging and artificial intelligence systems.

Dermatology online journal
Melanoma is a life-threatening tumor that significantly impacts individuals' health and society worldwide. Therefore, its diagnostic tools must be revolutionized, representing the most remarkable human efforts toward successful management. This retro...

Rapid and noninvasive estimation of human arsenic exposure based on 4-photo-set of the hand and foot photos through artificial intelligence.

Journal of hazardous materials
Chronic exposure to arsenic is linked to the development of cancers in the skin, lungs, and bladder. Arsenic exposure manifests as variegated pigmentation and characteristic pitted keratosis on the hands and feet, which often precede the onset of int...