AIMC Topic: Diagnostic Techniques, Ophthalmological

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Enhancing AI-based diabetic retinopathy diagnosis through universal cross-camera image adaptation.

BMJ open ophthalmology
OBJECTIVE: To evaluate the effectiveness of a deep learning-based style adaptation strategy in improving the diagnostic accuracy and cross-camera generalisability of artificial intelligence (AI) for detecting diabetic retinopathy (DR).

StrabNet-CQ: an integrated deep learning framework for automated strabismus classification and quantification using ocular landmark detection.

BMC ophthalmology
BACKGROUND: Strabismus is a common ocular misalignment that can impair binocular vision if untreated. Conventional diagnosis and treatment rely on clinical prism diopter (PD) readings, which quantify deviation along with base direction. However, thes...

Advancements in strabismus diagnosis: A comprehensive systematic review of artificial intelligence and digital health applications.

Experimental eye research
PURPOSE: To review the accuracy of artificial intelligence (AI) and digital health applications in the screening and diagnosis of strabismus.

Effectiveness of smartphone technology for detection of paediatric ocular diseases-a systematic review.

BMC ophthalmology
BACKGROUND: Artificial intelligence has become part of healthcare with a multitude of applications being customized to roles required in clinical practice. There has been an expanding growth and development of computer technology with increasing appe...

Performance of a novel multimodal large language model in ınterpreting meibomian glands quantitatively and qualitatively.

International ophthalmology
PURPOSE: To evaluate the performance of a multimodal large language model (LLM), Claude 3.5 Sonnet, in interpreting meibography images for Meibomian gland dropout grading and morphological abnormality detection.

Functional blepharoptosis screening with generative augmented deep learning from external ocular photography.

Orbit (Amsterdam, Netherlands)
PURPOSE: To develop and validate a deep learning model for the detection of functional blepharoptosis from external ocular photographs, and to quantify the impact of augmenting the training data with synthetic images on model performance.

Advancements in artificial intelligence for the diagnosis and management of anterior segment diseases.

Current opinion in ophthalmology
PURPOSE OF REVIEW: The integration of artificial intelligence (AI) in the diagnosis and management of anterior segment diseases has rapidly expanded, demonstrating significant potential to revolutionize clinical practice.

Performance of a Deep Learning Diabetic Retinopathy Algorithm in India.

JAMA network open
IMPORTANCE: While prospective studies have investigated the accuracy of artificial intelligence (AI) for detection of diabetic retinopathy (DR) and diabetic macular edema (DME), to date, little published data exist on the clinical performance of thes...

Internal validation of a convolutional neural network pipeline for assessing meibomian gland structure from meibography.

Optometry and vision science : official publication of the American Academy of Optometry
SIGNIFICANCE: Optimal meibography utilization and interpretation are hindered due to poor lid presentation, blurry images, or image artifacts and the challenges of applying clinical grading scales. These results, using the largest image dataset analy...