Automatic Classification of Slit-Lamp Photographs by Imaging Illumination

Lu, Ming-Chen; Deng, Callie; Greenwald, Margaret; Farsiu, Sina; Prajna, Namperumalsamy Venkatesh; Nallasamy, Nambi; Pawar, Mercy; Hart, Joseph M.; R, Sumithra S; Kochar, Prabhleen; Selvaraj, Suvitha; Levine, Harry; Amescua, Guillermo; Sepulveda-Beltran, Paula A.; Niziol, Leslie M.; Woodward, Maria A.

doi:10.1097/ico.0000000000003318

Cited by 1 publication

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, previous AI machine learning-assisted systems that utilized single-modality digital facial images to determine the CAS of TAO often failed to consider swelling of the conjunctiva and of the caruncle or plica Lu et al ( 17 ), Gu et al. ( 18 ), Li et al.…”

Section: Introductionmentioning

confidence: 99%

An ensemble deep learning diagnostic system for determining Clinical Activity Scores in thyroid-associated ophthalmopathy: integrating multi-view multimodal images from anterior segment slit-lamp photographs and facial images

Yan,

Zhang,

Zhang

et al. 2024

Front. Endocrinol.

View full text Add to dashboard Cite

BackgroundThyroid-associated ophthalmopathy (TAO) is the most prevalent autoimmune orbital condition, significantly impacting patients’ appearance and quality of life. Early and accurate identification of active TAO along with timely treatment can enhance prognosis and reduce the occurrence of severe cases. Although the Clinical Activity Score (CAS) serves as an effective assessment system for TAO, it is susceptible to assessor experience bias. This study aimed to develop an ensemble deep learning system that combines anterior segment slit-lamp photographs of patients with facial images to simulate expert assessment of TAO.MethodThe study included 156 patients with TAO who underwent detailed diagnosis and treatment at Shanxi Eye Hospital Affiliated to Shanxi Medical University from May 2020 to September 2023. Anterior segment slit-lamp photographs and facial images were used as different modalities and analyzed from multiple perspectives. Two ophthalmologists with more than 10 years of clinical experience independently determined the reference CAS for each image. An ensemble deep learning model based on the residual network was constructed under supervised learning to predict five key inflammatory signs (redness of the eyelids and conjunctiva, and swelling of the eyelids, conjunctiva, and caruncle or plica) associated with TAO, and to integrate these objective signs with two subjective symptoms (spontaneous retrobulbar pain and pain on attempted upward or downward gaze) in order to assess TAO activity.ResultsThe proposed model achieved 0.906 accuracy, 0.833 specificity, 0.906 precision, 0.906 recall, and 0.906 F1-score in active TAO diagnosis, demonstrating advanced performance in predicting CAS and TAO activity signs compared to conventional single-view unimodal approaches. The integration of multiple views and modalities, encompassing both anterior segment slit-lamp photographs and facial images, significantly improved the prediction accuracy of the model for TAO activity and CAS.ConclusionThe ensemble multi-view multimodal deep learning system developed in this study can more accurately assess the clinical activity of TAO than traditional methods that solely rely on facial images. This innovative approach is intended to enhance the efficiency of TAO activity assessment, providing a novel means for its comprehensive, early, and precise evaluation.

show abstract

Section: Introductionmentioning

confidence: 99%