An Outperforming Artificial Intelligence Model to Identify Referable Blepharoptosis for General Practitioners

Application of artificial intelligence in oculoplastics

Oculoplastics is a subspecialty of ophthalmology/dermatology concerned with eyelid, orbital, and lacrimal diseases. Artificial intelligence (AI), with its powerful ability to analyze large data sets, has dramatically benefited oculoplastics. The cutting-edge AI technology is widely applied to extract ocular parameters and to use these results for further assessment, such as screening and diagnosis of blepharoptosis and predicting the progression of thyroid eye disease. AI also assists in treatment procedures, such as surgical strategy planning in blepharoptosis. High efficiency and high reliability are the most apparent advantages of AI, with promising prospects. The possibilities of AI in oculoplastics may lie in three-dimensional modeling technology and image generation. We retrospectively summarize AI applications involving eyelid, orbital, and lacrimal diseases in oculoplastics, and we also examine the strengths and weaknesses of AI technology in oculoplastics.

Development and validation of a convolutional neural network to identify blepharoptosis

Blepharoptosis is a recognized cause of reversible vision loss and a non-specific indicator of neurological issues, occasionally heralding life-threatening conditions. Currently, diagnosis relies on human expertise and eyelid examination, with most existing Artificial Intelligence algorithms focusing on eyelid positioning under specialized settings. This study introduces a deep learning model with convolutional neural networks to detect blepharoptosis in more realistic conditions. Our model was trained and tested using high quality periocular images from patients with blepharoptosis as well as those with other eyelid conditions. The model achieved an area under the receiver operating characteristic curve of 0.918. For validation, we compared the model's performance against nine medical experts-oculoplastic surgeons, general ophthalmologists, and general practitioners-with varied expertise. When tested on a new dataset with varied image quality, the model's performance remained statistically comparable to that of human graders. Our findings underscore the potential to enhance telemedicine services for blepharoptosis detection.

The Challenges and Therapeutic Prospects in Eye Disease

A number of key insights into eye disease have been revealed in the past decade, which has resulted in the development of novel, effective, targeted therapies such as teprotumumab for the treatment of thyroid eye disease (also known as Graves' orbitopathy) [...].

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.