Artificial intelligence assisted pterygium diagnosis: current status and perspectives

,; Chen, Bang; Wu, Mao-Nian; ,; Zhu, Shao-Jun; ,; Zheng, Bo; ,; Liu, Bang-Quan; ,; Wu, Tao; ,; Hong, Xiang-Qian; ,; Wang, Jian-Tao; ,; Yang, Wei-Hua; ,

doi:10.18240/ijo.2023.09.04

Cited by 2 publications

(2 citation statements)

References 54 publications

(120 reference statements)

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“…The advent of artificial intelligence (AI) and deep learning has provided new prospects for medical diagnostics, including ophthalmology Yoo and Choi ( 8 ), Benet and Pellicer-Valero ( 9 ), Chen et al. ( 10 ), Gong et al. ( 11 ).…”

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.

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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.

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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.

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“…Pterygium is a common ocular surface disease caused by overgrowth of fibro vascularity in the subconjunctival tissue, resulting in invasion of the inner eyelid and outer cornea ( 1 ). It is most prevalent in areas with high ultraviolet light; in some areas, 9.5% of the pterygium patient population is associated with prolonged exposure to high ultraviolet light ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

DBPF-net: dual-branch structural feature extraction reinforcement network for ocular surface disease image classification

Wan,

Mao,

et al. 2024

Front. Med.

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Pterygium and subconjunctival hemorrhage are two common types of ocular surface diseases that can cause distress and anxiety in patients. In this study, 2855 ocular surface images were collected in four categories: normal ocular surface, subconjunctival hemorrhage, pterygium to be observed, and pterygium requiring surgery. We propose a diagnostic classification model for ocular surface diseases, dual-branch network reinforced by PFM block (DBPF-Net), which adopts the conformer model with two-branch architectural properties as the backbone of a four-way classification model for ocular surface diseases. In addition, we propose a block composed of a patch merging layer and a FReLU layer (PFM block) for extracting spatial structure features to further strengthen the feature extraction capability of the model. In practice, only the ocular surface images need to be input into the model to discriminate automatically between the disease categories. We also trained the VGG16, ResNet50, EfficientNetB7, and Conformer models, and evaluated and analyzed the results of all models on the test set. The main evaluation indicators were sensitivity, specificity, F1-score, area under the receiver operating characteristics curve (AUC), kappa coefficient, and accuracy. The accuracy and kappa coefficient of the proposed diagnostic model in several experiments were averaged at 0.9789 and 0.9681, respectively. The sensitivity, specificity, F1-score, and AUC were, respectively, 0.9723, 0.9836, 0.9688, and 0.9869 for diagnosing pterygium to be observed, and, respectively, 0.9210, 0.9905, 0.9292, and 0.9776 for diagnosing pterygium requiring surgery. The proposed method has high clinical reference value for recognizing these four types of ocular surface images.

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Artificial intelligence assisted pterygium diagnosis: current status and perspectives

Cited by 2 publications

References 54 publications

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

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

DBPF-net: dual-branch structural feature extraction reinforcement network for ocular surface disease image classification

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