DISCOVER: 2-D multiview summarization of Optical Coherence Tomography Angiography for automatic diabetic retinopathy diagnosis

El Habib Daho, Mostafa; Li, Yihao; Zeghlache, Rachid; Boité, Hugo Le; Deman, Pierre; Borderie, Laurent; Ren, Hugang; Mannivanan, Niranchana; Lepicard, Capucine; Cochener, Béatrice; Couturier, Aude; Tadayoni, Ramin; Conze, Pierre-Henri; Lamard, Mathieu; Quellec, Gwenolé

doi:10.1016/j.artmed.2024.102803

Cited by 5 publications

(1 citation statement)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study aims to address these challenges by developing an AI approach capable to improve the precision and dependability of TBUT measurement from slit lamp videos with a limited number of datasets. Although a three-dimensional (3D) model stands as a straightforward solution to this task of video analysis, its efficacy is compromised when confronted with a limited dataset 24 . Consequently, there is a need for a more precise solution capable of operating effectively with a limited dataset, such as Siamese networks.…”

Section: Introductionmentioning

confidence: 99%

Automated tear film break-up time measurement for dry eye diagnosis using deep learning

El Barche,

Benyoussef,

El Habib Daho

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

In the realm of ophthalmology, precise measurement of tear film break-up time (TBUT) plays a crucial role in diagnosing dry eye disease (DED). This study aims to introduce an automated approach utilizing artificial intelligence (AI) to mitigate subjectivity and enhance the reliability of TBUT measurement. We employed a dataset of 47 slit lamp videos for development, while a test dataset of 20 slit lamp videos was used for evaluating the proposed approach. The multistep approach for TBUT estimation involves the utilization of a Dual-Task Siamese Network for classifying video frames into tear film breakup or non-breakup categories. Subsequently, a postprocessing step incorporates a Gaussian filter to smooth the instant breakup/non-breakup predictions effectively. Applying a threshold to the smoothed predictions identifies the initiation of tear film breakup. Our proposed method demonstrates on the evaluation dataset a precise breakup/non-breakup classification of video frames, achieving an Area Under the Curve of 0.870. At the video level, we observed a strong Pearson correlation coefficient (r) of 0.81 between TBUT assessments conducted using our approach and the ground truth. These findings underscore the potential of AI-based approaches in quantifying TBUT, presenting a promising avenue for advancing diagnostic methodologies in ophthalmology.

show abstract

Section: Introductionmentioning

confidence: 99%